wrangle_act_new2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gathering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import requests\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Downloading the image_prediction.tsv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = ' https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response = requests.get(url)\n",
    "file_name = url.split('/')[-1]\n",
    "if not os.path.exists(file_name):\n",
    "    with open(file_name, mode ='wb')as file:\n",
    "        file.write(response.content)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- testing the url downloaded file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = pd.read_csv(file_name,sep = '\\t')\n",
    "twitter_arch = pd.read_csv ('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- importing tweepy and json to save the content in .txt file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "from tweepy import OAuthHandler\n",
    "import json\n",
    "\n",
    "consumer_key = '*****'\n",
    "consumer_secret = '****'\n",
    "access_token = '*****'\n",
    "access_secret = '*****'\n",
    "\n",
    "auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True)\n",
    "\n",
    "tweet_ids = df_1.tweet_id.values\n",
    "\n",
    "count = 0\n",
    "# fails_dict = {}\n",
    "# Save each tweet's returned JSON as a new line in a .txt file\n",
    "with open('tweet_json.txt', 'w') as file:\n",
    "    # This loop will likely take 20-30 minutes to run because of Twitter's rate limit\n",
    "    for tweet_id in tweet_ids:\n",
    "        count += 1\n",
    "        print(str(count) + \": \" + str(tweet_id))\n",
    "        try:\n",
    "            tweet = api.get_status(tweet_id, tweet_mode='extended')\n",
    "            print(\"Success\")\n",
    "            json.dump(tweet._json, file)\n",
    "            file.write('\\n')\n",
    "        except tweepy.TweepError as e:\n",
    "            print(\"Fail\")\n",
    "            fails_dict[tweet_id] = e\n",
    "            pass\n",
    "\n",
    "# fails_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- extrcating the info from twitter txt file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import re\n",
    "# import numpy as np\n",
    "table = []\n",
    "with open ('tweet-json.txt') as f:\n",
    "    for line in f:\n",
    "        r=json.loads(line)\n",
    "        tweet_id = r['id']\n",
    "        retweet_count = r['retweet_count']\n",
    "        favorite_count = r['favorite_count']\n",
    "        rating_Numeratoe =re.findall('\\d+/\\d+',r['full_text'])[0].split('/')[0]\n",
    "        rating_Denomerator = re.findall('\\d+/\\d+',r['full_text'])[0].split('/')[1]\n",
    "        table.append({'id': tweet_id,'retweet_counts': retweet_count,'favorite_counts':favorite_count,'Num_rate':int(rating_Numeratoe),'Deno_rate':int(rating_Denomerator)})\n",
    "        \n",
    "tweet_df = pd.DataFrame(table,columns=['id','retweet_counts','favorite_counts','Num_rate','Deno_rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- visual Assess with the external csv files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- program Assess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype\n",
      "---  ------           --------------  -----\n",
      " 0   id               2354 non-null   int64\n",
      " 1   retweet_counts   2354 non-null   int64\n",
      " 2   favorite_counts  2354 non-null   int64\n",
      " 3   Num_rate         2354 non-null   int64\n",
      " 4   Deno_rate        2354 non-null   int64\n",
      "dtypes: int64(5)\n",
      "memory usage: 92.1 KB\n"
     ]
    }
   ],
   "source": [
    "tweet_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I read line by line as shown but don't know why it's 2354 not 2354"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_arch.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "image.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
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       "      <th>retweet_counts</th>\n",
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       "      <th>Num_rate</th>\n",
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      "text/plain": [
       "                   id  retweet_counts  favorite_counts  Num_rate  Deno_rate\n",
       "0  892420643555336193            8853            39467        13         10\n",
       "1  892177421306343426            6514            33819        13         10\n",
       "2  891815181378084864            4328            25461        12         10\n",
       "3  891689557279858688            8964            42908        13         10\n",
       "4  891327558926688256            9774            41048        12         10"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
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       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
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       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  \n",
       "0                  10  Phineas  None    None   None  None  \n",
       "1                  10    Tilly  None    None   None  None  \n",
       "2                  10   Archie  None    None   None  None  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2175"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['retweeted_status_timestamp'].isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2175"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['retweeted_status_user_id'].isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2175"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['retweeted_status_id'].isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None         745\n",
       "a             55\n",
       "Charlie       12\n",
       "Oliver        11\n",
       "Lucy          11\n",
       "            ... \n",
       "Edmund         1\n",
       "Horace         1\n",
       "Jebberson      1\n",
       "Dixie          1\n",
       "Marty          1\n",
       "Name: name, Length: 957, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>881536004380872706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-02 15:32:16 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a pupper approaching maximum borkdrive...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/881536004...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>792913359805018113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-31 02:17:31 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a perfect example of someone who has t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/792913359...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>772581559778025472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-04 23:46:12 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Guys this is getting so out of hand. We only r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/772581559...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>747885874273214464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-28 20:14:22 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a mighty rare blue-tailed hammer sherk...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/747885874...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>747816857231626240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-28 15:40:07 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Viewer discretion is advised. This is a terrib...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/747816857...</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>746872823977771008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-26 01:08:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a carrot. We only rate dogs. Please on...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/746872823...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>743222593470234624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-15 23:24:09 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a very rare Great Alaskan Bush Pupper....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/743222593...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>717537687239008257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-06 02:21:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>People please. This is a Deadly Mediterranean ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/717537687...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>715733265223708672</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-01 02:51:22 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a taco. We only rate dogs. Please only...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/715733265...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>704859558691414016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-02 02:43:09 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a heartbreaking scene of an incredible...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/704859558...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>704054845121142784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-28 21:25:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/704054845...</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>703079050210877440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-26 04:48:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Butternut Cumberfloof. It's not wind...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/703079050...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>702539513671897089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-24 17:04:07 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Wild Tuscan Poofwiggle. Careful not ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/702539513...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>700864154249383937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-20 02:06:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>\"Pupper is a present to world. Here is a bow f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/700864154...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>692187005137076224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-27 03:26:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a rare Arctic Wubberfloof. Unamused by...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/692187005...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>679530280114372609</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-23 05:13:38 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Guys this really needs to stop. We've been ove...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/679530280...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td>677644091929329666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-18 00:18:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a dog swinging. I really enjoyed it so...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/677644091...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>675706639471788032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-12 15:59:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Sizzlin Menorah spaniel from Brookly...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/675706639...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>675534494439489536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-12 04:35:48 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Seriously guys?! Only send in dogs. I only rat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/675534494...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>675109292475830276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-11 00:26:12 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>C'mon guys. We've been over this. We only rate...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/675109292...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>675047298674663426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-10 20:19:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a fluffy albino Bacardi Columbia mix. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/675047298...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>674082852460433408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-08 04:27:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Sagitariot Baklava mix. Loves her ne...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/674082852...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>673715861853720576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-07 04:09:13 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a heavily opinionated dog. Loves walls...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/673715861...</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>673636718965334016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-06 22:54:44 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Lofted Aphrodisiac Terrier named Kip...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/673636718...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>672604026190569472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-04 02:31:10 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a baby Rand Paul. Curls for days. 11/1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/672604026...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>671743150407421952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-01 17:30:22 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Tuscaloosa Alcatraz named Jacob (Yac...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/671743150...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>671147085991960577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-30 02:01:49 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Helvetica Listerine named Rufus. Thi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/671147085...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>670427002554466305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-28 02:20:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Deciduous Trimester mix named Spork....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670427002...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>670361874861563904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-27 22:01:40 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Rich Mahogany Seltzer named Cherokee...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670361874...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>670303360680108032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-27 18:09:09 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Speckled Cauliflower Yosemite named ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670303360...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>669923323644657664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-26 16:59:01 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a spotted Lipitor Rumpelstiltskin name...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/669923323...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>669661792646373376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-25 23:39:47 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a brave dog. Excellent free climber. T...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/669661792...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>669564461267722241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-25 17:13:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Coriander Baton Rouge named Alfredo....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/669564461...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>668955713004314625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-24 00:54:05 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Slovakian Helter Skelter Feta named ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/668955713...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>668815180734689280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-23 15:35:39 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a wild Toblerone from Papua New Guinea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/668815180...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>668614819948453888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-23 02:19:29 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a horned dog. Much grace. Can jump ove...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/668614819...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>668507509523615744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-22 19:13:05 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Birmingham Quagmire named Chuk. Love...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/668507509...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>668466899341221888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-22 16:31:42 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a mother dog caring for her pups. Snaz...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/668466899...</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>668171859951755264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-21 20:59:20 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Trans Siberian Kellogg named Alfonso...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/668171859...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>667861340749471744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-21 00:25:26 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Shotokon Macadamia mix named Cheryl....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667861340...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>667773195014021121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 18:35:10 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>This is a rare Hungarian Pinot named Jessiga. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667773195...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>667538891197542400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 03:04:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>This is a southwest Coriander named Klint. Hat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667538891...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>667470559035432960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-19 22:32:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>This is a northern Wahoo named Kohl. He runs t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667470559...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>667177989038297088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-19 03:10:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a Dasani Kingfisher from Maine. His na...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667177989...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>666983947667116034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-18 14:18:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a curly Ticonderoga named Pepe. No fee...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666983947...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2311</th>\n",
       "      <td>666781792255496192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-18 00:55:42 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Bacardi named Octaviath. Ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666781792...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>666701168228331520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-17 19:35:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a golden Buckminsterfullerene named Jo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666701168...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>666407126856765440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-17 00:06:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a southern Vesuvius bumblegruff. Can d...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666407126...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 16:37:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a funny dog. Weird toes. Won't come do...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666293911...</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:55:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>My oh my. This is a rare blond Canadian terrie...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666057090...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:49:46 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a Siberian heavily armored polar bear ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666055525...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:30:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a truly beautiful English Wilson Staff...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666050758...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "56    881536004380872706                    NaN                  NaN   \n",
       "649   792913359805018113                    NaN                  NaN   \n",
       "801   772581559778025472                    NaN                  NaN   \n",
       "1002  747885874273214464                    NaN                  NaN   \n",
       "1004  747816857231626240                    NaN                  NaN   \n",
       "1017  746872823977771008                    NaN                  NaN   \n",
       "1049  743222593470234624                    NaN                  NaN   \n",
       "1193  717537687239008257                    NaN                  NaN   \n",
       "1207  715733265223708672                    NaN                  NaN   \n",
       "1340  704859558691414016                    NaN                  NaN   \n",
       "1351  704054845121142784                    NaN                  NaN   \n",
       "1361  703079050210877440                    NaN                  NaN   \n",
       "1368  702539513671897089                    NaN                  NaN   \n",
       "1382  700864154249383937                    NaN                  NaN   \n",
       "1499  692187005137076224                    NaN                  NaN   \n",
       "1737  679530280114372609                    NaN                  NaN   \n",
       "1785  677644091929329666                    NaN                  NaN   \n",
       "1853  675706639471788032                    NaN                  NaN   \n",
       "1854  675534494439489536                    NaN                  NaN   \n",
       "1877  675109292475830276                    NaN                  NaN   \n",
       "1878  675047298674663426                    NaN                  NaN   \n",
       "1923  674082852460433408                    NaN                  NaN   \n",
       "1941  673715861853720576                    NaN                  NaN   \n",
       "1955  673636718965334016                    NaN                  NaN   \n",
       "1994  672604026190569472                    NaN                  NaN   \n",
       "2034  671743150407421952                    NaN                  NaN   \n",
       "2066  671147085991960577                    NaN                  NaN   \n",
       "2116  670427002554466305                    NaN                  NaN   \n",
       "2125  670361874861563904                    NaN                  NaN   \n",
       "2128  670303360680108032                    NaN                  NaN   \n",
       "2146  669923323644657664                    NaN                  NaN   \n",
       "2153  669661792646373376                    NaN                  NaN   \n",
       "2161  669564461267722241                    NaN                  NaN   \n",
       "2191  668955713004314625                    NaN                  NaN   \n",
       "2198  668815180734689280                    NaN                  NaN   \n",
       "2211  668614819948453888                    NaN                  NaN   \n",
       "2218  668507509523615744                    NaN                  NaN   \n",
       "2222  668466899341221888                    NaN                  NaN   \n",
       "2235  668171859951755264                    NaN                  NaN   \n",
       "2249  667861340749471744                    NaN                  NaN   \n",
       "2255  667773195014021121                    NaN                  NaN   \n",
       "2264  667538891197542400                    NaN                  NaN   \n",
       "2273  667470559035432960                    NaN                  NaN   \n",
       "2287  667177989038297088                    NaN                  NaN   \n",
       "2304  666983947667116034                    NaN                  NaN   \n",
       "2311  666781792255496192                    NaN                  NaN   \n",
       "2314  666701168228331520                    NaN                  NaN   \n",
       "2327  666407126856765440                    NaN                  NaN   \n",
       "2334  666293911632134144                    NaN                  NaN   \n",
       "2347  666057090499244032                    NaN                  NaN   \n",
       "2348  666055525042405380                    NaN                  NaN   \n",
       "2350  666050758794694657                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "56    2017-07-02 15:32:16 +0000   \n",
       "649   2016-10-31 02:17:31 +0000   \n",
       "801   2016-09-04 23:46:12 +0000   \n",
       "1002  2016-06-28 20:14:22 +0000   \n",
       "1004  2016-06-28 15:40:07 +0000   \n",
       "1017  2016-06-26 01:08:52 +0000   \n",
       "1049  2016-06-15 23:24:09 +0000   \n",
       "1193  2016-04-06 02:21:30 +0000   \n",
       "1207  2016-04-01 02:51:22 +0000   \n",
       "1340  2016-03-02 02:43:09 +0000   \n",
       "1351  2016-02-28 21:25:30 +0000   \n",
       "1361  2016-02-26 04:48:02 +0000   \n",
       "1368  2016-02-24 17:04:07 +0000   \n",
       "1382  2016-02-20 02:06:50 +0000   \n",
       "1499  2016-01-27 03:26:56 +0000   \n",
       "1737  2015-12-23 05:13:38 +0000   \n",
       "1785  2015-12-18 00:18:36 +0000   \n",
       "1853  2015-12-12 15:59:51 +0000   \n",
       "1854  2015-12-12 04:35:48 +0000   \n",
       "1877  2015-12-11 00:26:12 +0000   \n",
       "1878  2015-12-10 20:19:52 +0000   \n",
       "1923  2015-12-08 04:27:30 +0000   \n",
       "1941  2015-12-07 04:09:13 +0000   \n",
       "1955  2015-12-06 22:54:44 +0000   \n",
       "1994  2015-12-04 02:31:10 +0000   \n",
       "2034  2015-12-01 17:30:22 +0000   \n",
       "2066  2015-11-30 02:01:49 +0000   \n",
       "2116  2015-11-28 02:20:27 +0000   \n",
       "2125  2015-11-27 22:01:40 +0000   \n",
       "2128  2015-11-27 18:09:09 +0000   \n",
       "2146  2015-11-26 16:59:01 +0000   \n",
       "2153  2015-11-25 23:39:47 +0000   \n",
       "2161  2015-11-25 17:13:02 +0000   \n",
       "2191  2015-11-24 00:54:05 +0000   \n",
       "2198  2015-11-23 15:35:39 +0000   \n",
       "2211  2015-11-23 02:19:29 +0000   \n",
       "2218  2015-11-22 19:13:05 +0000   \n",
       "2222  2015-11-22 16:31:42 +0000   \n",
       "2235  2015-11-21 20:59:20 +0000   \n",
       "2249  2015-11-21 00:25:26 +0000   \n",
       "2255  2015-11-20 18:35:10 +0000   \n",
       "2264  2015-11-20 03:04:08 +0000   \n",
       "2273  2015-11-19 22:32:36 +0000   \n",
       "2287  2015-11-19 03:10:02 +0000   \n",
       "2304  2015-11-18 14:18:59 +0000   \n",
       "2311  2015-11-18 00:55:42 +0000   \n",
       "2314  2015-11-17 19:35:19 +0000   \n",
       "2327  2015-11-17 00:06:54 +0000   \n",
       "2334  2015-11-16 16:37:02 +0000   \n",
       "2347  2015-11-16 00:55:59 +0000   \n",
       "2348  2015-11-16 00:49:46 +0000   \n",
       "2350  2015-11-16 00:30:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "56    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "649   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "801   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1002  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1004  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1017  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1049  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1193  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1207  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1340  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1361  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1368  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1382  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1499  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1737  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1785  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1853  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1854  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1877  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1878  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1923  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1941  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1955  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1994  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2034  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2066  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2116  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2125  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2128  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2146  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2153  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2161  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2191  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2198  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2211  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2218  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2222  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2235  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2249  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2255  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2264  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2273  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2287  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2304  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2311  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2314  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2327  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2334  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2347  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2348  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2350  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "56    Here is a pupper approaching maximum borkdrive...                  NaN   \n",
       "649   Here is a perfect example of someone who has t...                  NaN   \n",
       "801   Guys this is getting so out of hand. We only r...                  NaN   \n",
       "1002  This is a mighty rare blue-tailed hammer sherk...                  NaN   \n",
       "1004  Viewer discretion is advised. This is a terrib...                  NaN   \n",
       "1017  This is a carrot. We only rate dogs. Please on...                  NaN   \n",
       "1049  This is a very rare Great Alaskan Bush Pupper....                  NaN   \n",
       "1193  People please. This is a Deadly Mediterranean ...                  NaN   \n",
       "1207  This is a taco. We only rate dogs. Please only...                  NaN   \n",
       "1340  Here is a heartbreaking scene of an incredible...                  NaN   \n",
       "1351  Here is a whole flock of puppers.  60/50 I'll ...                  NaN   \n",
       "1361  This is a Butternut Cumberfloof. It's not wind...                  NaN   \n",
       "1368  This is a Wild Tuscan Poofwiggle. Careful not ...                  NaN   \n",
       "1382  \"Pupper is a present to world. Here is a bow f...                  NaN   \n",
       "1499  This is a rare Arctic Wubberfloof. Unamused by...                  NaN   \n",
       "1737  Guys this really needs to stop. We've been ove...                  NaN   \n",
       "1785  This is a dog swinging. I really enjoyed it so...                  NaN   \n",
       "1853  This is a Sizzlin Menorah spaniel from Brookly...                  NaN   \n",
       "1854  Seriously guys?! Only send in dogs. I only rat...                  NaN   \n",
       "1877  C'mon guys. We've been over this. We only rate...                  NaN   \n",
       "1878  This is a fluffy albino Bacardi Columbia mix. ...                  NaN   \n",
       "1923  This is a Sagitariot Baklava mix. Loves her ne...                  NaN   \n",
       "1941  This is a heavily opinionated dog. Loves walls...                  NaN   \n",
       "1955  This is a Lofted Aphrodisiac Terrier named Kip...                  NaN   \n",
       "1994  This is a baby Rand Paul. Curls for days. 11/1...                  NaN   \n",
       "2034  This is a Tuscaloosa Alcatraz named Jacob (Yac...                  NaN   \n",
       "2066  This is a Helvetica Listerine named Rufus. Thi...                  NaN   \n",
       "2116  This is a Deciduous Trimester mix named Spork....                  NaN   \n",
       "2125  This is a Rich Mahogany Seltzer named Cherokee...                  NaN   \n",
       "2128  This is a Speckled Cauliflower Yosemite named ...                  NaN   \n",
       "2146  This is a spotted Lipitor Rumpelstiltskin name...                  NaN   \n",
       "2153  This is a brave dog. Excellent free climber. T...                  NaN   \n",
       "2161  This is a Coriander Baton Rouge named Alfredo....                  NaN   \n",
       "2191  This is a Slovakian Helter Skelter Feta named ...                  NaN   \n",
       "2198  This is a wild Toblerone from Papua New Guinea...                  NaN   \n",
       "2211  Here is a horned dog. Much grace. Can jump ove...                  NaN   \n",
       "2218  This is a Birmingham Quagmire named Chuk. Love...                  NaN   \n",
       "2222  Here is a mother dog caring for her pups. Snaz...                  NaN   \n",
       "2235  This is a Trans Siberian Kellogg named Alfonso...                  NaN   \n",
       "2249  This is a Shotokon Macadamia mix named Cheryl....                  NaN   \n",
       "2255  This is a rare Hungarian Pinot named Jessiga. ...                  NaN   \n",
       "2264  This is a southwest Coriander named Klint. Hat...                  NaN   \n",
       "2273  This is a northern Wahoo named Kohl. He runs t...                  NaN   \n",
       "2287  This is a Dasani Kingfisher from Maine. His na...                  NaN   \n",
       "2304  This is a curly Ticonderoga named Pepe. No fee...                  NaN   \n",
       "2311  This is a purebred Bacardi named Octaviath. Ca...                  NaN   \n",
       "2314  This is a golden Buckminsterfullerene named Jo...                  NaN   \n",
       "2327  This is a southern Vesuvius bumblegruff. Can d...                  NaN   \n",
       "2334  This is a funny dog. Weird toes. Won't come do...                  NaN   \n",
       "2347  My oh my. This is a rare blond Canadian terrie...                  NaN   \n",
       "2348  Here is a Siberian heavily armored polar bear ...                  NaN   \n",
       "2350  This is a truly beautiful English Wilson Staff...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "56                         NaN                        NaN   \n",
       "649                        NaN                        NaN   \n",
       "801                        NaN                        NaN   \n",
       "1002                       NaN                        NaN   \n",
       "1004                       NaN                        NaN   \n",
       "1017                       NaN                        NaN   \n",
       "1049                       NaN                        NaN   \n",
       "1193                       NaN                        NaN   \n",
       "1207                       NaN                        NaN   \n",
       "1340                       NaN                        NaN   \n",
       "1351                       NaN                        NaN   \n",
       "1361                       NaN                        NaN   \n",
       "1368                       NaN                        NaN   \n",
       "1382                       NaN                        NaN   \n",
       "1499                       NaN                        NaN   \n",
       "1737                       NaN                        NaN   \n",
       "1785                       NaN                        NaN   \n",
       "1853                       NaN                        NaN   \n",
       "1854                       NaN                        NaN   \n",
       "1877                       NaN                        NaN   \n",
       "1878                       NaN                        NaN   \n",
       "1923                       NaN                        NaN   \n",
       "1941                       NaN                        NaN   \n",
       "1955                       NaN                        NaN   \n",
       "1994                       NaN                        NaN   \n",
       "2034                       NaN                        NaN   \n",
       "2066                       NaN                        NaN   \n",
       "2116                       NaN                        NaN   \n",
       "2125                       NaN                        NaN   \n",
       "2128                       NaN                        NaN   \n",
       "2146                       NaN                        NaN   \n",
       "2153                       NaN                        NaN   \n",
       "2161                       NaN                        NaN   \n",
       "2191                       NaN                        NaN   \n",
       "2198                       NaN                        NaN   \n",
       "2211                       NaN                        NaN   \n",
       "2218                       NaN                        NaN   \n",
       "2222                       NaN                        NaN   \n",
       "2235                       NaN                        NaN   \n",
       "2249                       NaN                        NaN   \n",
       "2255                       NaN                        NaN   \n",
       "2264                       NaN                        NaN   \n",
       "2273                       NaN                        NaN   \n",
       "2287                       NaN                        NaN   \n",
       "2304                       NaN                        NaN   \n",
       "2311                       NaN                        NaN   \n",
       "2314                       NaN                        NaN   \n",
       "2327                       NaN                        NaN   \n",
       "2334                       NaN                        NaN   \n",
       "2347                       NaN                        NaN   \n",
       "2348                       NaN                        NaN   \n",
       "2350                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "56    https://twitter.com/dog_rates/status/881536004...                14   \n",
       "649   https://twitter.com/dog_rates/status/792913359...                13   \n",
       "801   https://twitter.com/dog_rates/status/772581559...                10   \n",
       "1002  https://twitter.com/dog_rates/status/747885874...                 8   \n",
       "1004  https://twitter.com/dog_rates/status/747816857...                 4   \n",
       "1017  https://twitter.com/dog_rates/status/746872823...                11   \n",
       "1049  https://twitter.com/dog_rates/status/743222593...                12   \n",
       "1193  https://twitter.com/dog_rates/status/717537687...                11   \n",
       "1207  https://twitter.com/dog_rates/status/715733265...                10   \n",
       "1340  https://twitter.com/dog_rates/status/704859558...                10   \n",
       "1351  https://twitter.com/dog_rates/status/704054845...                60   \n",
       "1361  https://twitter.com/dog_rates/status/703079050...                11   \n",
       "1368  https://twitter.com/dog_rates/status/702539513...                12   \n",
       "1382  https://twitter.com/dog_rates/status/700864154...                12   \n",
       "1499  https://twitter.com/dog_rates/status/692187005...                12   \n",
       "1737  https://twitter.com/dog_rates/status/679530280...                 7   \n",
       "1785  https://twitter.com/dog_rates/status/677644091...                11   \n",
       "1853  https://twitter.com/dog_rates/status/675706639...                10   \n",
       "1854  https://twitter.com/dog_rates/status/675534494...                11   \n",
       "1877  https://twitter.com/dog_rates/status/675109292...                 9   \n",
       "1878  https://twitter.com/dog_rates/status/675047298...                11   \n",
       "1923  https://twitter.com/dog_rates/status/674082852...                11   \n",
       "1941  https://twitter.com/dog_rates/status/673715861...                 4   \n",
       "1955  https://twitter.com/dog_rates/status/673636718...                10   \n",
       "1994  https://twitter.com/dog_rates/status/672604026...                11   \n",
       "2034  https://twitter.com/dog_rates/status/671743150...                11   \n",
       "2066  https://twitter.com/dog_rates/status/671147085...                 9   \n",
       "2116  https://twitter.com/dog_rates/status/670427002...                 9   \n",
       "2125  https://twitter.com/dog_rates/status/670361874...                 9   \n",
       "2128  https://twitter.com/dog_rates/status/670303360...                 9   \n",
       "2146  https://twitter.com/dog_rates/status/669923323...                10   \n",
       "2153  https://twitter.com/dog_rates/status/669661792...                 5   \n",
       "2161  https://twitter.com/dog_rates/status/669564461...                10   \n",
       "2191  https://twitter.com/dog_rates/status/668955713...                10   \n",
       "2198  https://twitter.com/dog_rates/status/668815180...                 7   \n",
       "2211  https://twitter.com/dog_rates/status/668614819...                 7   \n",
       "2218  https://twitter.com/dog_rates/status/668507509...                10   \n",
       "2222  https://twitter.com/dog_rates/status/668466899...                 4   \n",
       "2235  https://twitter.com/dog_rates/status/668171859...                 7   \n",
       "2249  https://twitter.com/dog_rates/status/667861340...                 9   \n",
       "2255  https://twitter.com/dog_rates/status/667773195...                 8   \n",
       "2264  https://twitter.com/dog_rates/status/667538891...                 9   \n",
       "2273  https://twitter.com/dog_rates/status/667470559...                11   \n",
       "2287  https://twitter.com/dog_rates/status/667177989...                 8   \n",
       "2304  https://twitter.com/dog_rates/status/666983947...                11   \n",
       "2311  https://twitter.com/dog_rates/status/666781792...                10   \n",
       "2314  https://twitter.com/dog_rates/status/666701168...                 8   \n",
       "2327  https://twitter.com/dog_rates/status/666407126...                 7   \n",
       "2334  https://twitter.com/dog_rates/status/666293911...                 3   \n",
       "2347  https://twitter.com/dog_rates/status/666057090...                 9   \n",
       "2348  https://twitter.com/dog_rates/status/666055525...                10   \n",
       "2350  https://twitter.com/dog_rates/status/666050758...                10   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "\n",
       "      rating_denominator name doggo floofer  pupper puppo  \n",
       "56                    10    a  None    None  pupper  None  \n",
       "649                   10    a  None    None    None  None  \n",
       "801                   10    a  None    None    None  None  \n",
       "1002                  10    a  None    None    None  None  \n",
       "1004                  10    a  None    None    None  None  \n",
       "1017                  10    a  None    None    None  None  \n",
       "1049                  10    a  None    None  pupper  None  \n",
       "1193                  10    a  None    None    None  None  \n",
       "1207                  10    a  None    None    None  None  \n",
       "1340                  10    a  None    None  pupper  None  \n",
       "1351                  50    a  None    None    None  None  \n",
       "1361                  10    a  None    None    None  None  \n",
       "1368                  10    a  None    None    None  None  \n",
       "1382                  10    a  None    None  pupper  None  \n",
       "1499                  10    a  None    None    None  None  \n",
       "1737                  10    a  None    None    None  None  \n",
       "1785                  10    a  None    None    None  None  \n",
       "1853                  10    a  None    None    None  None  \n",
       "1854                  10    a  None    None    None  None  \n",
       "1877                  10    a  None    None    None  None  \n",
       "1878                  10    a  None    None    None  None  \n",
       "1923                  10    a  None    None    None  None  \n",
       "1941                  10    a  None    None    None  None  \n",
       "1955                  10    a  None    None    None  None  \n",
       "1994                  10    a  None    None    None  None  \n",
       "2034                  10    a  None    None    None  None  \n",
       "2066                  10    a  None    None    None  None  \n",
       "2116                  10    a  None    None    None  None  \n",
       "2125                  10    a  None    None    None  None  \n",
       "2128                  10    a  None    None    None  None  \n",
       "2146                  10    a  None    None    None  None  \n",
       "2153                  10    a  None    None    None  None  \n",
       "2161                  10    a  None    None    None  None  \n",
       "2191                  10    a  None    None    None  None  \n",
       "2198                  10    a  None    None    None  None  \n",
       "2211                  10    a  None    None    None  None  \n",
       "2218                  10    a  None    None    None  None  \n",
       "2222                  10    a  None    None    None  None  \n",
       "2235                  10    a  None    None    None  None  \n",
       "2249                  10    a  None    None    None  None  \n",
       "2255                  10    a  None    None    None  None  \n",
       "2264                  10    a  None    None    None  None  \n",
       "2273                  10    a  None    None    None  None  \n",
       "2287                  10    a  None    None    None  None  \n",
       "2304                  10    a  None    None    None  None  \n",
       "2311                  10    a  None    None    None  None  \n",
       "2314                  10    a  None    None    None  None  \n",
       "2327                  10    a  None    None    None  None  \n",
       "2334                  10    a  None    None    None  None  \n",
       "2347                  10    a  None    None    None  None  \n",
       "2348                  10    a  None    None    None  None  \n",
       "2350                  10    a  None    None    None  None  \n",
       "2352                  10    a  None    None    None  None  \n",
       "2353                  10    a  None    None    None  None  \n",
       "2354                  10    a  None    None    None  None  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.query('name==\"a\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None      2099\n",
       "pupper     257\n",
       "Name: pupper, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['pupper'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2333\n",
       "11        3\n",
       "50        3\n",
       "80        2\n",
       "20        2\n",
       "2         1\n",
       "16        1\n",
       "40        1\n",
       "70        1\n",
       "15        1\n",
       "90        1\n",
       "110       1\n",
       "120       1\n",
       "130       1\n",
       "150       1\n",
       "170       1\n",
       "7         1\n",
       "0         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['rating_denominator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tweet_id',\n",
       " 'in_reply_to_status_id',\n",
       " 'in_reply_to_user_id',\n",
       " 'timestamp',\n",
       " 'source',\n",
       " 'text',\n",
       " 'retweeted_status_id',\n",
       " 'retweeted_status_user_id',\n",
       " 'retweeted_status_timestamp',\n",
       " 'expanded_urls',\n",
       " 'rating_numerator',\n",
       " 'rating_denominator',\n",
       " 'name',\n",
       " 'doggo',\n",
       " 'floofer',\n",
       " 'pupper',\n",
       " 'puppo']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(twitter_arch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch[twitter_arch['tweet_id'].duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_counts</th>\n",
       "      <th>favorite_counts</th>\n",
       "      <th>Num_rate</th>\n",
       "      <th>Deno_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, retweet_counts, favorite_counts, Num_rate, Deno_rate]\n",
       "Index: []"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_df[tweet_df['id'].duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>831650051525054464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-02-14 23:43:18 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>I usually only share these on Friday's, but th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.gofundme.com/bluethewhitehusky,http...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>852936405516943360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-14 17:27:40 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: I usually only share these on F...</td>\n",
       "      <td>8.316501e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-02-14 23:43:18 +0000</td>\n",
       "      <td>http://www.gofundme.com/bluethewhitehusky,http...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2338</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 04:02:55 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Not familiar with this breed. No tail (weird)....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666104133...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>798576900688019456</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-15 17:22:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Not familiar with this breed. N...</td>\n",
       "      <td>6.661041e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-16 04:02:55 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666104133...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2294</th>\n",
       "      <td>667138269671505920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-19 00:32:12 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Extremely intelligent dog here. Has learned to...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667138269...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>673716320723169284</td>\n",
       "      <td>6.737159e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-07 04:11:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>The millennials have spoken and we've decided ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2038</th>\n",
       "      <td>671550332464455680</td>\n",
       "      <td>6.715449e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-01 04:44:10 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After 22 minutes of careful deliberation this ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>669684865554620416</td>\n",
       "      <td>6.693544e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-26 01:11:28 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After countless hours of research and hundreds...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>668967877119254528</td>\n",
       "      <td>6.689207e+17</td>\n",
       "      <td>2.143566e+07</td>\n",
       "      <td>2015-11-24 01:42:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>12/10 good shit Bubka\\n@wane15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>667070482143944705</td>\n",
       "      <td>6.670655e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-18 20:02:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After much debate this dog is being upgraded t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "349   831650051525054464                    NaN                  NaN   \n",
       "204   852936405516943360                    NaN                  NaN   \n",
       "2338  666104133288665088                    NaN                  NaN   \n",
       "605   798576900688019456                    NaN                  NaN   \n",
       "2294  667138269671505920                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "1940  673716320723169284           6.737159e+17         4.196984e+09   \n",
       "2038  671550332464455680           6.715449e+17         4.196984e+09   \n",
       "2149  669684865554620416           6.693544e+17         4.196984e+09   \n",
       "2189  668967877119254528           6.689207e+17         2.143566e+07   \n",
       "2298  667070482143944705           6.670655e+17         4.196984e+09   \n",
       "\n",
       "                      timestamp  \\\n",
       "349   2017-02-14 23:43:18 +0000   \n",
       "204   2017-04-14 17:27:40 +0000   \n",
       "2338  2015-11-16 04:02:55 +0000   \n",
       "605   2016-11-15 17:22:24 +0000   \n",
       "2294  2015-11-19 00:32:12 +0000   \n",
       "...                         ...   \n",
       "1940  2015-12-07 04:11:02 +0000   \n",
       "2038  2015-12-01 04:44:10 +0000   \n",
       "2149  2015-11-26 01:11:28 +0000   \n",
       "2189  2015-11-24 01:42:25 +0000   \n",
       "2298  2015-11-18 20:02:51 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "349   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "204   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2338  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "605   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2294  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "1940  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2038  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2149  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2189  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2298  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "349   I usually only share these on Friday's, but th...                  NaN   \n",
       "204   RT @dog_rates: I usually only share these on F...         8.316501e+17   \n",
       "2338  Not familiar with this breed. No tail (weird)....                  NaN   \n",
       "605   RT @dog_rates: Not familiar with this breed. N...         6.661041e+17   \n",
       "2294  Extremely intelligent dog here. Has learned to...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "1940  The millennials have spoken and we've decided ...                  NaN   \n",
       "2038  After 22 minutes of careful deliberation this ...                  NaN   \n",
       "2149  After countless hours of research and hundreds...                  NaN   \n",
       "2189                     12/10 good shit Bubka\\n@wane15                  NaN   \n",
       "2298  After much debate this dog is being upgraded t...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "349                        NaN                        NaN   \n",
       "204               4.196984e+09  2017-02-14 23:43:18 +0000   \n",
       "2338                       NaN                        NaN   \n",
       "605               4.196984e+09  2015-11-16 04:02:55 +0000   \n",
       "2294                       NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "1940                       NaN                        NaN   \n",
       "2038                       NaN                        NaN   \n",
       "2149                       NaN                        NaN   \n",
       "2189                       NaN                        NaN   \n",
       "2298                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "349   http://www.gofundme.com/bluethewhitehusky,http...                13   \n",
       "204   http://www.gofundme.com/bluethewhitehusky,http...                13   \n",
       "2338  https://twitter.com/dog_rates/status/666104133...                 1   \n",
       "605   https://twitter.com/dog_rates/status/666104133...                 1   \n",
       "2294  https://twitter.com/dog_rates/status/667138269...                10   \n",
       "...                                                 ...               ...   \n",
       "1940                                                NaN                 1   \n",
       "2038                                                NaN                 1   \n",
       "2149                                                NaN                11   \n",
       "2189                                                NaN                12   \n",
       "2298                                                NaN                10   \n",
       "\n",
       "      rating_denominator  name doggo floofer pupper puppo  \n",
       "349                   10  None  None    None   None  None  \n",
       "204                   10  None  None    None   None  None  \n",
       "2338                  10  None  None    None   None  None  \n",
       "605                   10  None  None    None   None  None  \n",
       "2294                  10  None  None    None   None  None  \n",
       "...                  ...   ...   ...     ...    ...   ...  \n",
       "1940                  10  None  None    None   None  None  \n",
       "2038                  10  None  None    None   None  None  \n",
       "2149                  10  None  None    None   None  None  \n",
       "2189                  10  None  None    None   None  None  \n",
       "2298                  10  None  None    None   None  None  \n",
       "\n",
       "[217 rows x 17 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch[twitter_arch['expanded_urls'].duplicated(keep=False)].sort_values(by='expanded_urls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PROOO\\anaconda3\\lib\\site-packages\\pandas\\core\\strings.py:1954: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n",
      "  return func(self, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>45</th>\n",
       "      <td>This is Bella. She hopes her smile made you sm...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>RT @dog_rates: This is Logan, the Chow who liv...</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>This is Logan, the Chow who lived. He solemnly...</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>This is Sophie. She's a Jubilant Bush Pupper. ...</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>I've been told there's a slight possibility he...</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>Here we have uncovered an entire battalion of ...</td>\n",
       "      <td>26</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  rating_numerator\n",
       "45    This is Bella. She hopes her smile made you sm...                 5\n",
       "340   RT @dog_rates: This is Logan, the Chow who liv...                75\n",
       "695   This is Logan, the Chow who lived. He solemnly...                75\n",
       "763   This is Sophie. She's a Jubilant Bush Pupper. ...                27\n",
       "1689  I've been told there's a slight possibility he...                 5\n",
       "1712  Here we have uncovered an entire battalion of ...                26"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch[twitter_arch.text.str.contains(r\"(\\d+\\.\\d*\\/\\d+)\")][['text', 'rating_numerator']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>retweeted_status_id</th>\n",
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       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.356000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.427716e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>7.720400e+17</td>\n",
       "      <td>1.241698e+16</td>\n",
       "      <td>13.126486</td>\n",
       "      <td>10.455433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.856705e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>6.236928e+16</td>\n",
       "      <td>9.599254e+16</td>\n",
       "      <td>45.876648</td>\n",
       "      <td>6.745237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>6.661041e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783989e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>7.186315e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.196279e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>7.804657e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993373e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>8.203146e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>7.874618e+17</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "count  2.356000e+03           7.800000e+01         7.800000e+01   \n",
       "mean   7.427716e+17           7.455079e+17         2.014171e+16   \n",
       "std    6.856705e+16           7.582492e+16         1.252797e+17   \n",
       "min    6.660209e+17           6.658147e+17         1.185634e+07   \n",
       "25%    6.783989e+17           6.757419e+17         3.086374e+08   \n",
       "50%    7.196279e+17           7.038708e+17         4.196984e+09   \n",
       "75%    7.993373e+17           8.257804e+17         4.196984e+09   \n",
       "max    8.924206e+17           8.862664e+17         8.405479e+17   \n",
       "\n",
       "       retweeted_status_id  retweeted_status_user_id  rating_numerator  \\\n",
       "count         1.810000e+02              1.810000e+02       2356.000000   \n",
       "mean          7.720400e+17              1.241698e+16         13.126486   \n",
       "std           6.236928e+16              9.599254e+16         45.876648   \n",
       "min           6.661041e+17              7.832140e+05          0.000000   \n",
       "25%           7.186315e+17              4.196984e+09         10.000000   \n",
       "50%           7.804657e+17              4.196984e+09         11.000000   \n",
       "75%           8.203146e+17              4.196984e+09         12.000000   \n",
       "max           8.874740e+17              7.874618e+17       1776.000000   \n",
       "\n",
       "       rating_denominator  \n",
       "count         2356.000000  \n",
       "mean            10.455433  \n",
       "std              6.745237  \n",
       "min              0.000000  \n",
       "25%             10.000000  \n",
       "50%             10.000000  \n",
       "75%             10.000000  \n",
       "max            170.000000  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_counts</th>\n",
       "      <th>favorite_counts</th>\n",
       "      <th>Num_rate</th>\n",
       "      <th>Deno_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.354000e+03</td>\n",
       "      <td>2354.000000</td>\n",
       "      <td>2354.000000</td>\n",
       "      <td>2354.000000</td>\n",
       "      <td>2354.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.426978e+17</td>\n",
       "      <td>3164.797366</td>\n",
       "      <td>8080.968564</td>\n",
       "      <td>13.127018</td>\n",
       "      <td>10.45582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.852812e+16</td>\n",
       "      <td>5284.770364</td>\n",
       "      <td>11814.771334</td>\n",
       "      <td>45.896135</td>\n",
       "      <td>6.74809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783975e+17</td>\n",
       "      <td>624.500000</td>\n",
       "      <td>1415.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.194596e+17</td>\n",
       "      <td>1473.500000</td>\n",
       "      <td>3603.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993058e+17</td>\n",
       "      <td>3652.000000</td>\n",
       "      <td>10122.250000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>79515.000000</td>\n",
       "      <td>132810.000000</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  retweet_counts  favorite_counts     Num_rate   Deno_rate\n",
       "count  2.354000e+03     2354.000000      2354.000000  2354.000000  2354.00000\n",
       "mean   7.426978e+17     3164.797366      8080.968564    13.127018    10.45582\n",
       "std    6.852812e+16     5284.770364     11814.771334    45.896135     6.74809\n",
       "min    6.660209e+17        0.000000         0.000000     0.000000     0.00000\n",
       "25%    6.783975e+17      624.500000      1415.000000    10.000000    10.00000\n",
       "50%    7.194596e+17     1473.500000      3603.500000    11.000000    10.00000\n",
       "75%    7.993058e+17     3652.000000     10122.250000    12.000000    10.00000\n",
       "max    8.924206e+17    79515.000000    132810.000000  1776.000000   170.00000"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      558\n",
       "11      464\n",
       "10      461\n",
       "13      351\n",
       "9       158\n",
       "8       102\n",
       "7        55\n",
       "14       54\n",
       "5        37\n",
       "6        32\n",
       "3        19\n",
       "4        17\n",
       "1         9\n",
       "2         9\n",
       "420       2\n",
       "0         2\n",
       "15        2\n",
       "75        2\n",
       "80        1\n",
       "20        1\n",
       "24        1\n",
       "26        1\n",
       "44        1\n",
       "50        1\n",
       "60        1\n",
       "165       1\n",
       "84        1\n",
       "88        1\n",
       "144       1\n",
       "182       1\n",
       "143       1\n",
       "666       1\n",
       "960       1\n",
       "1776      1\n",
       "17        1\n",
       "27        1\n",
       "45        1\n",
       "99        1\n",
       "121       1\n",
       "204       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['rating_numerator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2333\n",
       "11        3\n",
       "50        3\n",
       "80        2\n",
       "20        2\n",
       "2         1\n",
       "16        1\n",
       "40        1\n",
       "70        1\n",
       "15        1\n",
       "90        1\n",
       "110       1\n",
       "120       1\n",
       "130       1\n",
       "150       1\n",
       "170       1\n",
       "7         1\n",
       "0         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['rating_denominator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "749075273010798592    1\n",
       "741099773336379392    1\n",
       "798644042770751489    1\n",
       "825120256414846976    1\n",
       "769212283578875904    1\n",
       "                     ..\n",
       "715360349751484417    1\n",
       "666817836334096384    1\n",
       "794926597468000259    1\n",
       "673705679337693185    1\n",
       "700151421916807169    1\n",
       "Name: tweet_id, Length: 2356, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['tweet_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "https://www.gofundme.com/3ti3nps,https://twitter.com/dog_rates/status/868552278524837888/photo/1,https://twitter.com/dog_rates/status/868552278524837888/photo/1                                   2\n",
       "https://twitter.com/dog_rates/status/680055455951884288/photo/1                                                                                                                                    2\n",
       "https://www.gofundme.com/surgeryforjacktheminpin,https://twitter.com/dog_rates/status/815990720817401858/photo/1                                                                                   2\n",
       "https://twitter.com/dog_rates/status/683391852557561860/photo/1                                                                                                                                    2\n",
       "https://twitter.com/dog_rates/status/839549326359670784/photo/1                                                                                                                                    2\n",
       "                                                                                                                                                                                                  ..\n",
       "https://twitter.com/dog_rates/status/828650029636317184/photo/1,https://twitter.com/dog_rates/status/828650029636317184/photo/1,https://twitter.com/dog_rates/status/828650029636317184/photo/1    1\n",
       "https://twitter.com/dog_rates/status/823581115634085888/photo/1                                                                                                                                    1\n",
       "https://twitter.com/dog_rates/status/684195085588783105/photo/1                                                                                                                                    1\n",
       "https://twitter.com/dog_rates/status/747461612269887489/photo/1                                                                                                                                    1\n",
       "https://twitter.com/dog_rates/status/749774190421639168/photo/1                                                                                                                                    1\n",
       "Name: expanded_urls, Length: 2218, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch['expanded_urls'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22    tweet_id\n",
       "dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_columns = pd.Series(list(tweet_df) + list(twitter_arch) + list(image))\n",
    "all_columns[all_columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden_retriever      150\n",
       "Labrador_retriever    100\n",
       "Pembroke               89\n",
       "Chihuahua              83\n",
       "pug                    57\n",
       "                     ... \n",
       "handkerchief            1\n",
       "silky_terrier           1\n",
       "traffic_light           1\n",
       "studio_couch            1\n",
       "bighorn                 1\n",
       "Name: p1, Length: 378, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image['p1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quality\n",
    "- columns that have \"None\" instead of Nan values \n",
    "- Denomerator not always 10\n",
    "- name has \"a\" values\n",
    "- inaccurate values in denomenator \n",
    "- inaccurate values in numerator\n",
    "- source columns has \" \\\\<a \"href = \"\n",
    "- duplicated expanded_url values \n",
    "- columns to datetime type \n",
    "- Non dog kinds in image_prediction table\n",
    "- many value that is not any kind of dogs in image_prediction table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tidness\n",
    "- multiple variable for dog stages\n",
    "- missed decimel vaLues for numerator_rate\n",
    "- the API (tweet_df) should be in twitter_archive table\n",
    "- different name for tweet_df and twitter_archive tables\n",
    "- drop some unuseful columns in twitter_archive table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_clean = twitter_arch.copy()\n",
    "api_clean= tweet_df.copy()\n",
    "image_clean = image.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- change api_clean columns name to merge with tw_clean table and merg api and tw tables\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_clean = api_clean.rename(columns={'id':'tweet_id','Num_rate':'rating_numerator','Deno_rate':'rating_denominator'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "common = ['tweet_id','rating_numerator','rating_denominator']\n",
    "tw_clean = pd.merge(tw_clean, api_clean,on=common, how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>retweet_counts</th>\n",
       "      <th>favorite_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>8853.0</td>\n",
       "      <td>39467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>33819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4328.0</td>\n",
       "      <td>25461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>8964.0</td>\n",
       "      <td>42908.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>9774.0</td>\n",
       "      <td>41048.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  retweet_counts  \\\n",
       "0                  10   Phineas  None    None   None  None          8853.0   \n",
       "1                  10     Tilly  None    None   None  None          6514.0   \n",
       "2                  10    Archie  None    None   None  None          4328.0   \n",
       "3                  10     Darla  None    None   None  None          8964.0   \n",
       "4                  10  Franklin  None    None   None  None          9774.0   \n",
       "\n",
       "   favorite_counts  \n",
       "0          39467.0  \n",
       "1          33819.0  \n",
       "2          25461.0  \n",
       "3          42908.0  \n",
       "4          41048.0  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tw_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- melt multiple variable for dog_stage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "tw_clean.doggo.replace('None', '', inplace=True)\n",
    "tw_clean.floofer.replace('None', '', inplace=True)\n",
    "tw_clean.pupper.replace('None', '', inplace=True)\n",
    "tw_clean.puppo.replace('None', '', inplace=True)\n",
    "\n",
    "# merge into column\n",
    "tw_clean['dog_stage'] = tw_clean.doggo + tw_clean.floofer + tw_clean.pupper + tw_clean.puppo\n",
    "\n",
    "# handle multiple stages\n",
    "tw_clean.loc[tw_clean.dog_stage == 'doggopupper', 'dog_stage'] = 'doggo, pupper'\n",
    "tw_clean.loc[tw_clean.dog_stage == 'doggopuppo', 'dog_stage'] = 'doggo, puppo'\n",
    "tw_clean.loc[tw_clean.dog_stage == 'doggofloofer', 'dog_stage'] = 'doggo, floofer'\n",
    "\n",
    "# handle missing values\n",
    "tw_clean.loc[tw_clean.dog_stage == '', 'dog_stage'] = np.nan\n",
    "tw_clean.drop(['doggo','floofer','pupper','puppo'],axis = 1,inplace = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>retweet_counts</th>\n",
       "      <th>favorite_counts</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1787</th>\n",
       "      <td>677565715327688705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-17 19:07:09 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Contortionist pup here. Inside pentagram. Clea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/677565715...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>513.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>818307523543449600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-09 04:04:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Meet Moose. He doesn't want his...</td>\n",
       "      <td>8.164506e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-01-04 01:05:59 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/816450570...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Moose</td>\n",
       "      <td>9366.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>832636094638288896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-02-17 17:01:29 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Orion. He just got back from the denti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/832636094...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Orion</td>\n",
       "      <td>3220.0</td>\n",
       "      <td>17379.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>817171292965273600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-06 00:49:53 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tebow. He kindly requests that you put...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/817171292...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tebow</td>\n",
       "      <td>2326.0</td>\n",
       "      <td>9690.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>832645525019123713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-02-17 17:38:57 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>There's going to be a dog terminal at JFK Airp...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.blastingnews.com/news/2017/02/jfk-an...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>not</td>\n",
       "      <td>594.0</td>\n",
       "      <td>3195.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "1787  677565715327688705                    NaN                  NaN   \n",
       "455   818307523543449600                    NaN                  NaN   \n",
       "336   832636094638288896                    NaN                  NaN   \n",
       "466   817171292965273600                    NaN                  NaN   \n",
       "335   832645525019123713                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1787  2015-12-17 19:07:09 +0000   \n",
       "455   2017-01-09 04:04:51 +0000   \n",
       "336   2017-02-17 17:01:29 +0000   \n",
       "466   2017-01-06 00:49:53 +0000   \n",
       "335   2017-02-17 17:38:57 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "1787  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "455   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "336   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "466   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "335   <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "1787  Contortionist pup here. Inside pentagram. Clea...                  NaN   \n",
       "455   RT @dog_rates: Meet Moose. He doesn't want his...         8.164506e+17   \n",
       "336   This is Orion. He just got back from the denti...                  NaN   \n",
       "466   This is Tebow. He kindly requests that you put...                  NaN   \n",
       "335   There's going to be a dog terminal at JFK Airp...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "1787                       NaN                        NaN   \n",
       "455               4.196984e+09  2017-01-04 01:05:59 +0000   \n",
       "336                        NaN                        NaN   \n",
       "466                        NaN                        NaN   \n",
       "335                        NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "1787  https://twitter.com/dog_rates/status/677565715...                 6   \n",
       "455   https://twitter.com/dog_rates/status/816450570...                13   \n",
       "336   https://twitter.com/dog_rates/status/832636094...                12   \n",
       "466   https://twitter.com/dog_rates/status/817171292...                13   \n",
       "335   http://us.blastingnews.com/news/2017/02/jfk-an...                10   \n",
       "\n",
       "      rating_denominator   name  retweet_counts  favorite_counts dog_stage  \n",
       "1787                  10   None           513.0           1409.0       NaN  \n",
       "455                   10  Moose          9366.0              0.0       NaN  \n",
       "336                   10  Orion          3220.0          17379.0       NaN  \n",
       "466                   10  Tebow          2326.0           9690.0       NaN  \n",
       "335                   10    not           594.0           3195.0       NaN  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_clean.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper            245\n",
       "doggo              83\n",
       "puppo              29\n",
       "doggo, pupper      12\n",
       "floofer             9\n",
       "doggo, puppo        1\n",
       "doggo, floofer      1\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_clean['dog_stage'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- melt many variables to specific columns in image_clean table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result = image_clean.copy()\n",
    "image_clean = pd.melt(image_clean, id_vars=['tweet_id', 'jpg_url', 'img_num','p1_conf','p1_dog','p2_conf','p2_dog','p3_conf','p3_dog'],\n",
    "                           var_name='Algorithm', value_name='result')\n",
    "image_clean = pd.melt(image_clean, id_vars=['tweet_id', 'jpg_url', 'img_num','p1_dog','Algorithm','p2_dog','result','p3_dog'],\n",
    "                           var_name='conf', value_name='Algorithm_conf')\n",
    "image_clean = pd.melt(image_clean, id_vars=['tweet_id','jpg_url','img_num','Algorithm','result','conf','Algorithm_conf'],\n",
    "                           var_name='pred', value_name='dog_prediction')\n",
    "image_clean.drop(['conf','pred',],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>result</th>\n",
       "      <th>Algorithm_conf</th>\n",
       "      <th>dog_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>56020</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>p3</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56021</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56022</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>56023</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>56024</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>56025 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tweet_id                                          jpg_url  \\\n",
       "0      666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1      666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2      666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3      666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4      666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "...                   ...                                              ...   \n",
       "56020  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "56021  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "56022  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "56023  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "56024  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "       img_num Algorithm                       result  Algorithm_conf  \\\n",
       "0            1        p1       Welsh_springer_spaniel        0.465074   \n",
       "1            1        p1                      redbone        0.506826   \n",
       "2            1        p1              German_shepherd        0.596461   \n",
       "3            1        p1          Rhodesian_ridgeback        0.408143   \n",
       "4            1        p1           miniature_pinscher        0.560311   \n",
       "...        ...       ...                          ...             ...   \n",
       "56020        2        p3  German_short-haired_pointer        0.175219   \n",
       "56021        1        p3                      spatula        0.040836   \n",
       "56022        1        p3                       kelpie        0.031379   \n",
       "56023        1        p3                     papillon        0.068957   \n",
       "56024        1        p3                       banana        0.076110   \n",
       "\n",
       "       dog_prediction  \n",
       "0                True  \n",
       "1                True  \n",
       "2                True  \n",
       "3                True  \n",
       "4                True  \n",
       "...               ...  \n",
       "56020            True  \n",
       "56021           False  \n",
       "56022            True  \n",
       "56023            True  \n",
       "56024           False  \n",
       "\n",
       "[56025 rows x 7 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- drop some unuseful columns in tw_clean table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ['text','in_reply_to_status_id','in_reply_to_user_id']\n",
    "tw_clean = tw_clean.drop(col,axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>retweet_counts</th>\n",
       "      <th>favorite_counts</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
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       "      <td>39467.0</td>\n",
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       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>33819.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>4328.0</td>\n",
       "      <td>25461.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>8964.0</td>\n",
       "      <td>42908.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>9774.0</td>\n",
       "      <td>41048.0</td>\n",
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       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>41.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>6</td>\n",
       "      <td>10</td>\n",
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       "      <td>311.0</td>\n",
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       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>47.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>48.0</td>\n",
       "      <td>132.0</td>\n",
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       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2356 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                  timestamp  \\\n",
       "0     892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "1     892177421306343426  2017-08-01 00:17:27 +0000   \n",
       "2     891815181378084864  2017-07-31 00:18:03 +0000   \n",
       "3     891689557279858688  2017-07-30 15:58:51 +0000   \n",
       "4     891327558926688256  2017-07-29 16:00:24 +0000   \n",
       "...                  ...                        ...   \n",
       "2351  666049248165822465  2015-11-16 00:24:50 +0000   \n",
       "2352  666044226329800704  2015-11-16 00:04:52 +0000   \n",
       "2353  666033412701032449  2015-11-15 23:21:54 +0000   \n",
       "2354  666029285002620928  2015-11-15 23:05:30 +0000   \n",
       "2355  666020888022790149  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  retweeted_status_id  \\\n",
       "0     <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "1     <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "2     <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "3     <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "4     <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                          NaN                        NaN   \n",
       "1                          NaN                        NaN   \n",
       "2                          NaN                        NaN   \n",
       "3                          NaN                        NaN   \n",
       "4                          NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "0     https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1     https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2     https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3     https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4     https://twitter.com/dog_rates/status/891327558...                12   \n",
       "...                                                 ...               ...   \n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator      name  retweet_counts  favorite_counts dog_stage  \n",
       "0                     10   Phineas          8853.0          39467.0       NaN  \n",
       "1                     10     Tilly          6514.0          33819.0       NaN  \n",
       "2                     10    Archie          4328.0          25461.0       NaN  \n",
       "3                     10     Darla          8964.0          42908.0       NaN  \n",
       "4                     10  Franklin          9774.0          41048.0       NaN  \n",
       "...                  ...       ...             ...              ...       ...  \n",
       "2351                  10      None            41.0            111.0       NaN  \n",
       "2352                  10         a           147.0            311.0       NaN  \n",
       "2353                  10         a            47.0            128.0       NaN  \n",
       "2354                  10         a            48.0            132.0       NaN  \n",
       "2355                  10      None           532.0           2535.0       NaN  \n",
       "\n",
       "[2356 rows x 13 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- merge datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "common2=['tweet_id']\n",
    "tweet_clean = pd.merge(tw_clean, image_clean,on=common2, how='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>retweet_counts</th>\n",
       "      <th>favorite_counts</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>result</th>\n",
       "      <th>Algorithm_conf</th>\n",
       "      <th>dog_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>33819.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>33819.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>33819.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>33819.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>33819.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>56020</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56021</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56022</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56023</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56024</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41256 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tweet_id                 timestamp  \\\n",
       "27     892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "28     892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "29     892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "30     892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "31     892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "...                   ...                       ...   \n",
       "56020  666020888022790149 2015-11-15 22:32:08+00:00   \n",
       "56021  666020888022790149 2015-11-15 22:32:08+00:00   \n",
       "56022  666020888022790149 2015-11-15 22:32:08+00:00   \n",
       "56023  666020888022790149 2015-11-15 22:32:08+00:00   \n",
       "56024  666020888022790149 2015-11-15 22:32:08+00:00   \n",
       "\n",
       "                                   source  retweeted_status_id  \\\n",
       "27     http://twitter.com/download/iphone                  NaN   \n",
       "28     http://twitter.com/download/iphone                  NaN   \n",
       "29     http://twitter.com/download/iphone                  NaN   \n",
       "30     http://twitter.com/download/iphone                  NaN   \n",
       "31     http://twitter.com/download/iphone                  NaN   \n",
       "...                                   ...                  ...   \n",
       "56020  http://twitter.com/download/iphone                  NaN   \n",
       "56021  http://twitter.com/download/iphone                  NaN   \n",
       "56022  http://twitter.com/download/iphone                  NaN   \n",
       "56023  http://twitter.com/download/iphone                  NaN   \n",
       "56024  http://twitter.com/download/iphone                  NaN   \n",
       "\n",
       "       retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "27                          NaN                        NaT   \n",
       "28                          NaN                        NaT   \n",
       "29                          NaN                        NaT   \n",
       "30                          NaN                        NaT   \n",
       "31                          NaN                        NaT   \n",
       "...                         ...                        ...   \n",
       "56020                       NaN                        NaT   \n",
       "56021                       NaN                        NaT   \n",
       "56022                       NaN                        NaT   \n",
       "56023                       NaN                        NaT   \n",
       "56024                       NaN                        NaT   \n",
       "\n",
       "                                           expanded_urls rating_numerator  \\\n",
       "27     https://twitter.com/dog_rates/status/892177421...               13   \n",
       "28     https://twitter.com/dog_rates/status/892177421...               13   \n",
       "29     https://twitter.com/dog_rates/status/892177421...               13   \n",
       "30     https://twitter.com/dog_rates/status/892177421...               13   \n",
       "31     https://twitter.com/dog_rates/status/892177421...               13   \n",
       "...                                                  ...              ...   \n",
       "56020  https://twitter.com/dog_rates/status/666020888...               18   \n",
       "56021  https://twitter.com/dog_rates/status/666020888...               18   \n",
       "56022  https://twitter.com/dog_rates/status/666020888...               18   \n",
       "56023  https://twitter.com/dog_rates/status/666020888...               18   \n",
       "56024  https://twitter.com/dog_rates/status/666020888...               18   \n",
       "\n",
       "       rating_denominator   name  retweet_counts  favorite_counts dog_stage  \\\n",
       "27                     10  Tilly          6514.0          33819.0       NaN   \n",
       "28                     10  Tilly          6514.0          33819.0       NaN   \n",
       "29                     10  Tilly          6514.0          33819.0       NaN   \n",
       "30                     10  Tilly          6514.0          33819.0       NaN   \n",
       "31                     10  Tilly          6514.0          33819.0       NaN   \n",
       "...                   ...    ...             ...              ...       ...   \n",
       "56020                  10    NaN           532.0           2535.0       NaN   \n",
       "56021                  10    NaN           532.0           2535.0       NaN   \n",
       "56022                  10    NaN           532.0           2535.0       NaN   \n",
       "56023                  10    NaN           532.0           2535.0       NaN   \n",
       "56024                  10    NaN           532.0           2535.0       NaN   \n",
       "\n",
       "                                               jpg_url  img_num Algorithm  \\\n",
       "27     https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg        1        p1   \n",
       "28     https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg        1        p2   \n",
       "29     https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg        1        p3   \n",
       "30     https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg        1        p1   \n",
       "31     https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg        1        p2   \n",
       "...                                                ...      ...       ...   \n",
       "56020  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p2   \n",
       "56021  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p3   \n",
       "56022  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p1   \n",
       "56023  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p2   \n",
       "56024  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p3   \n",
       "\n",
       "                       result  Algorithm_conf  dog_prediction  \n",
       "27                  Chihuahua        0.323581            True  \n",
       "28                   Pekinese        0.323581            True  \n",
       "29                   papillon        0.323581            True  \n",
       "30                  Chihuahua        0.090647            True  \n",
       "31                   Pekinese        0.090647            True  \n",
       "...                       ...             ...             ...  \n",
       "56020                  collie        0.156665            True  \n",
       "56021       Shetland_sheepdog        0.156665            True  \n",
       "56022  Welsh_springer_spaniel        0.061428            True  \n",
       "56023                  collie        0.061428            True  \n",
       "56024       Shetland_sheepdog        0.061428            True  \n",
       "\n",
       "[41256 rows x 19 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- replace None to Nan value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "tweet_clean = tweet_clean.replace('None',np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>retweet_counts</th>\n",
       "      <th>favorite_counts</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>result</th>\n",
       "      <th>Algorithm_conf</th>\n",
       "      <th>dog_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>8853.0</td>\n",
       "      <td>39467.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>8853.0</td>\n",
       "      <td>39467.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>8853.0</td>\n",
       "      <td>39467.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>8853.0</td>\n",
       "      <td>39467.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>8853.0</td>\n",
       "      <td>39467.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56020</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56021</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56022</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56023</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56024</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>56025 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tweet_id                  timestamp  \\\n",
       "0      892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "1      892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "2      892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "3      892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "4      892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "...                   ...                        ...   \n",
       "56020  666020888022790149  2015-11-15 22:32:08 +0000   \n",
       "56021  666020888022790149  2015-11-15 22:32:08 +0000   \n",
       "56022  666020888022790149  2015-11-15 22:32:08 +0000   \n",
       "56023  666020888022790149  2015-11-15 22:32:08 +0000   \n",
       "56024  666020888022790149  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                  source  retweeted_status_id  \\\n",
       "0      <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "1      <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "2      <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "3      <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "4      <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "...                                                  ...                  ...   \n",
       "56020  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "56021  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "56022  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "56023  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "56024  <a href=\"http://twitter.com/download/iphone\" r...                  NaN   \n",
       "\n",
       "       retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                           NaN                        NaN   \n",
       "1                           NaN                        NaN   \n",
       "2                           NaN                        NaN   \n",
       "3                           NaN                        NaN   \n",
       "4                           NaN                        NaN   \n",
       "...                         ...                        ...   \n",
       "56020                       NaN                        NaN   \n",
       "56021                       NaN                        NaN   \n",
       "56022                       NaN                        NaN   \n",
       "56023                       NaN                        NaN   \n",
       "56024                       NaN                        NaN   \n",
       "\n",
       "                                           expanded_urls  rating_numerator  \\\n",
       "0      https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1      https://twitter.com/dog_rates/status/892420643...                13   \n",
       "2      https://twitter.com/dog_rates/status/892420643...                13   \n",
       "3      https://twitter.com/dog_rates/status/892420643...                13   \n",
       "4      https://twitter.com/dog_rates/status/892420643...                13   \n",
       "...                                                  ...               ...   \n",
       "56020  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "56021  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "56022  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "56023  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "56024  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "       rating_denominator     name  retweet_counts  favorite_counts dog_stage  \\\n",
       "0                      10  Phineas          8853.0          39467.0       NaN   \n",
       "1                      10  Phineas          8853.0          39467.0       NaN   \n",
       "2                      10  Phineas          8853.0          39467.0       NaN   \n",
       "3                      10  Phineas          8853.0          39467.0       NaN   \n",
       "4                      10  Phineas          8853.0          39467.0       NaN   \n",
       "...                   ...      ...             ...              ...       ...   \n",
       "56020                  10      NaN           532.0           2535.0       NaN   \n",
       "56021                  10      NaN           532.0           2535.0       NaN   \n",
       "56022                  10      NaN           532.0           2535.0       NaN   \n",
       "56023                  10      NaN           532.0           2535.0       NaN   \n",
       "56024                  10      NaN           532.0           2535.0       NaN   \n",
       "\n",
       "                                               jpg_url  img_num Algorithm  \\\n",
       "0      https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg        1        p1   \n",
       "1      https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg        1        p2   \n",
       "2      https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg        1        p3   \n",
       "3      https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg        1        p1   \n",
       "4      https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg        1        p2   \n",
       "...                                                ...      ...       ...   \n",
       "56020  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p2   \n",
       "56021  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p3   \n",
       "56022  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p1   \n",
       "56023  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p2   \n",
       "56024  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg        1        p3   \n",
       "\n",
       "                       result  Algorithm_conf  dog_prediction  \n",
       "0                      orange        0.097049           False  \n",
       "1                       bagel        0.097049           False  \n",
       "2                      banana        0.097049           False  \n",
       "3                      orange        0.085851           False  \n",
       "4                       bagel        0.085851           False  \n",
       "...                       ...             ...             ...  \n",
       "56020                  collie        0.156665            True  \n",
       "56021       Shetland_sheepdog        0.156665            True  \n",
       "56022  Welsh_springer_spaniel        0.061428            True  \n",
       "56023                  collie        0.061428            True  \n",
       "56024       Shetland_sheepdog        0.061428            True  \n",
       "\n",
       "[56025 rows x 19 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- remove (\\\\<a href) from source column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_clean['source'] = tweet_clean['source'].str.extract('(https?.+iphone)',expand = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        http://twitter.com/download/iphone\n",
       "1        http://twitter.com/download/iphone\n",
       "2        http://twitter.com/download/iphone\n",
       "3        http://twitter.com/download/iphone\n",
       "4        http://twitter.com/download/iphone\n",
       "                        ...                \n",
       "56020    http://twitter.com/download/iphone\n",
       "56021    http://twitter.com/download/iphone\n",
       "56022    http://twitter.com/download/iphone\n",
       "56023    http://twitter.com/download/iphone\n",
       "56024    http://twitter.com/download/iphone\n",
       "Name: source, Length: 56025, dtype: object"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_clean['source']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- replace \"a\" values in name column to Nan value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_clean['name'].replace('a',np.nan,inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Phineas\n",
       "1        Phineas\n",
       "2        Phineas\n",
       "3        Phineas\n",
       "4        Phineas\n",
       "          ...   \n",
       "56020        NaN\n",
       "56021        NaN\n",
       "56022        NaN\n",
       "56023        NaN\n",
       "56024        NaN\n",
       "Name: name, Length: 56025, dtype: object"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_clean['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- replace value != 10 to =10 for Denomenator column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "deno_list = tweet_clean['rating_denominator'].unique()\n",
    "for i in deno_list:\n",
    "    tweet_clean['rating_denominator'] = tweet_clean['rating_denominator'].replace(i,10)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    56025\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_clean['rating_denominator'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Slice Numerator values ,the first digit to be equal 1 to handle values below 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_clean.rating_numerator = tweet_clean.rating_numerator.astype(str).str.pad(2, fillchar='1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      13041\n",
       "10      11637\n",
       "11      11286\n",
       "13       8181\n",
       "19       4077\n",
       "18       2565\n",
       "14       1512\n",
       "17       1404\n",
       "15        945\n",
       "16        864\n",
       "27         27\n",
       "75         27\n",
       "60         27\n",
       "121        27\n",
       "45         27\n",
       "88         27\n",
       "204        27\n",
       "420        27\n",
       "24         27\n",
       "44         27\n",
       "26         27\n",
       "80         27\n",
       "165        27\n",
       "50         27\n",
       "1776       27\n",
       "84         27\n",
       "99         27\n",
       "144        27\n",
       "143        27\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_clean['rating_numerator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 56025 entries, 0 to 56024\n",
      "Data columns (total 19 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    56025 non-null  int64  \n",
      " 1   timestamp                   56025 non-null  object \n",
      " 2   source                      54918 non-null  object \n",
      " 3   retweeted_status_id         2187 non-null   float64\n",
      " 4   retweeted_status_user_id    2187 non-null   float64\n",
      " 5   retweeted_status_timestamp  2187 non-null   object \n",
      " 6   expanded_urls               56025 non-null  object \n",
      " 7   rating_numerator            56025 non-null  object \n",
      " 8   rating_denominator          56025 non-null  int64  \n",
      " 9   name                        38934 non-null  object \n",
      " 10  retweet_counts              55971 non-null  float64\n",
      " 11  favorite_counts             55971 non-null  float64\n",
      " 12  dog_stage                   8667 non-null   object \n",
      " 13  jpg_url                     56025 non-null  object \n",
      " 14  img_num                     56025 non-null  int64  \n",
      " 15  Algorithm                   56025 non-null  object \n",
      " 16  result                      56025 non-null  object \n",
      " 17  Algorithm_conf              56025 non-null  float64\n",
      " 18  dog_prediction              56025 non-null  bool   \n",
      "dtypes: bool(1), float64(5), int64(3), object(10)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "tweet_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- change timestamp dtype to date_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_clean['timestamp'] = pd.to_datetime(tweet_clean['timestamp'])\n",
    "tweet_clean['retweeted_status_timestamp'] = pd.to_datetime(tweet_clean['retweeted_status_timestamp'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 56025 entries, 0 to 56024\n",
      "Data columns (total 19 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    56025 non-null  int64              \n",
      " 1   timestamp                   56025 non-null  datetime64[ns, UTC]\n",
      " 2   source                      54918 non-null  object             \n",
      " 3   retweeted_status_id         2187 non-null   float64            \n",
      " 4   retweeted_status_user_id    2187 non-null   float64            \n",
      " 5   retweeted_status_timestamp  2187 non-null   datetime64[ns, UTC]\n",
      " 6   expanded_urls               56025 non-null  object             \n",
      " 7   rating_numerator            56025 non-null  object             \n",
      " 8   rating_denominator          56025 non-null  int64              \n",
      " 9   name                        38934 non-null  object             \n",
      " 10  retweet_counts              55971 non-null  float64            \n",
      " 11  favorite_counts             55971 non-null  float64            \n",
      " 12  dog_stage                   8667 non-null   object             \n",
      " 13  jpg_url                     56025 non-null  object             \n",
      " 14  img_num                     56025 non-null  int64              \n",
      " 15  Algorithm                   56025 non-null  object             \n",
      " 16  result                      56025 non-null  object             \n",
      " 17  Algorithm_conf              56025 non-null  float64            \n",
      " 18  dog_prediction              56025 non-null  bool               \n",
      "dtypes: bool(1), datetime64[ns, UTC](2), float64(5), int64(3), object(8)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "tweet_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = tw_clean.copy\n",
    "\n",
    "\n",
    "# def add(a, b, c , d): \n",
    "#     return a + b + c + d\n",
    "\n",
    "\n",
    "# df = tw_clean.copy()\n",
    "# df['doggo'] = df['doggo'].replace('doggo',1.0).astype(float)\n",
    "# df['doggo'] = df['doggo'].replace(np.nan,0.0)\n",
    "# df['floofer'] = df['floofer'].replace('floofer',2.0).astype(float)\n",
    "# df['floofer'] = df['floofer'].replace(np.nan,0.0)\n",
    "# df['puppo'] = df['puppo'].replace('puppo',3.0).astype(float)\n",
    "# df['puppo'] = df['puppo'].replace(np.nan,0.0)\n",
    "# df['pupper'] = df['pupper'].replace('pupper',4.0).astype(float)\n",
    "# df['pupper'] = df['pupper'].replace(np.nan,0.0)\n",
    "# df.head(10)\n",
    "\n",
    "# df['add'] = df.apply(lambda row : add(row['doggo'], row['floofer'], row['puppo'],row['pupper']), axis = 1) \n",
    "\n",
    "# df.head(1)\n",
    "\n",
    "# items = {0.0:np.nan,1.0 :'doggo',2.0:'floofer',3.0:'pupper',4.0:'puppo'}\n",
    "# for key , value in items.items():\n",
    "#     df['add'] = df['add'].replace(key, value)\n",
    "# df['add'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- cleaning iteration. Reshape the tw_clean table "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def add(a, b, c , d): \n",
    "#     return a + b + c + d\n",
    "\n",
    "# data = tw_clean.copy()\n",
    "# items = {1.0 :'doggo',2.0:'floofer',3.0:'pupper',4.0:'puppo'}\n",
    "# for key , value in items.items():\n",
    "#     tw_clean[value] = tw_clean[value].replace(value,key).astype(float)\n",
    "#     tw_clean[value] = tw_clean[value].replace(np.nan,0.0)\n",
    "\n",
    "# tw_clean['add'] = tw_clean.apply(lambda row : add(row['doggo'], row['floofer'], row['puppo'],row['pupper']), axis = 1) \n",
    "\n",
    "# items_new = {0.0:np.nan,1.0 :'doggo',2.0:'floofer',3.0:'pupper',4.0:'puppo'}\n",
    "# for key , value in items_new.items():\n",
    "#     tw_clean['add'] = tw_clean['add'].replace(key, value)\n",
    "# tw_clean.drop(['doggo','floofer','pupper','puppo',],axis = 1, inplace = True)\n",
    "# tw_clean = tw_clean.rename(columns={'add':'kind'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tw_clean.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- there's a tweet that have two kind of dogs, I will remove this row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# kind_5 = tw_clean.query('kind==5')\n",
    "# tw_clean.drop(kind_5.index,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tw_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- remove all false value in dog_prediction column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     41256\n",
       "False    14769\n",
       "Name: dog_prediction, dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#before modification\n",
    "tweet_clean['dog_prediction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_clean = tweet_clean.query('dog_prediction == True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    41256\n",
       "Name: dog_prediction, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_clean['dog_prediction'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### storing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# image_clean.to_csv('image_master.csv',index= False)\n",
    "tweet_clean.to_csv('twitter_archive_master_new2.csv',index= False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('twitter_archive_master_new2.csv')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper            4032\n",
       "doggo             1350\n",
       "puppo              558\n",
       "doggo, pupper      234\n",
       "floofer            180\n",
       "doggo, floofer      27\n",
       "doggo, puppo        27\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_clean['dog_stage'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most kind of dogs that retweeted by people "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dog_stage\n",
       "doggo              7810.306667\n",
       "doggo, floofer     3433.000000\n",
       "doggo, pupper      4931.576923\n",
       "doggo, puppo      19196.000000\n",
       "floofer            5091.700000\n",
       "pupper             2608.683036\n",
       "puppo              6475.693548\n",
       "Name: retweet_counts, dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group1 = df[df['dog']]\n",
    "group1 = df.groupby('dog_stage')['retweet_counts'].mean()\n",
    "group1.plot(kind='bar')\n",
    "group1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most kind of dogs that loved by people reaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dog_stage\n",
       "doggo             18370.126667\n",
       "doggo, floofer    17169.000000\n",
       "doggo, pupper      9918.153846\n",
       "doggo, puppo      47844.000000\n",
       "floofer           13562.500000\n",
       "pupper             7490.187500\n",
       "puppo             21282.403226\n",
       "Name: favorite_counts, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group2 = df.groupby('dog_stage')['favorite_counts'].mean()\n",
    "group2.plot(kind='pie')\n",
    "group2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- most rated dog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dog_stage\n",
       "doggo             12.366667\n",
       "doggo, floofer    11.000000\n",
       "doggo, pupper     12.115385\n",
       "doggo, puppo      13.000000\n",
       "floofer           12.000000\n",
       "pupper            12.412946\n",
       "puppo             12.467742\n",
       "Name: rating_numerator, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group3 = df.groupby('dog_stage')['rating_numerator'].mean()\n",
    "group3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As the rate is not modified as all values are above 10 but still must be modified. The qustion is : in which way should be modified ?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_image = image_clean.copy()\n",
    "# df_image['dog_prediction'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- worst kind of dog/animal to predicit by P1\n",
    "\n",
    "- the hardest 20 kind of dogs/animal could be predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>Algorithm_conf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Egyptian_cat</td>\n",
       "      <td>0.090508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>comic_book</td>\n",
       "      <td>0.105171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>earthstar</td>\n",
       "      <td>0.127701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>weasel</td>\n",
       "      <td>0.137832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>space_heater</td>\n",
       "      <td>0.137871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>syringe</td>\n",
       "      <td>0.144712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>bonnet</td>\n",
       "      <td>0.146440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>laptop</td>\n",
       "      <td>0.153718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>koala</td>\n",
       "      <td>0.162935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>wallaby</td>\n",
       "      <td>0.164215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>piggy_bank</td>\n",
       "      <td>0.176320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>polecat</td>\n",
       "      <td>0.185382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Arctic_fox</td>\n",
       "      <td>0.188174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>seashore</td>\n",
       "      <td>0.201659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Loafer</td>\n",
       "      <td>0.202999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>bighorn</td>\n",
       "      <td>0.215438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>tabby</td>\n",
       "      <td>0.218303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Angora</td>\n",
       "      <td>0.224218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>shower_curtain</td>\n",
       "      <td>0.226309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   result  Algorithm_conf\n",
       "Algorithm                                \n",
       "p1           Egyptian_cat        0.090508\n",
       "p1             comic_book        0.105171\n",
       "p1              earthstar        0.127701\n",
       "p1                 weasel        0.137832\n",
       "p1           space_heater        0.137871\n",
       "p1                syringe        0.144712\n",
       "p1                 bonnet        0.146440\n",
       "p1                 laptop        0.153718\n",
       "p1                  koala        0.162935\n",
       "p1                wallaby        0.164215\n",
       "p1            paper_towel        0.170278\n",
       "p1             piggy_bank        0.176320\n",
       "p1                polecat        0.185382\n",
       "p1             Arctic_fox        0.188174\n",
       "p1               seashore        0.201659\n",
       "p1                 Loafer        0.202999\n",
       "p1                bighorn        0.215438\n",
       "p1                  tabby        0.218303\n",
       "p1                 Angora        0.224218\n",
       "p1         shower_curtain        0.226309"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo_worst = df.groupby(['Algorithm','result'])[['Algorithm_conf']].max().sort_values(by='Algorithm_conf')\n",
    "\n",
    "algo_worst = algo_worst.reset_index(level =['result']).loc['p1']\n",
    "algo_worst.head(20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- best 20 kind of dog/animal can be predicted by p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>Algorithm_conf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>swing</td>\n",
       "      <td>0.999984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>barrow</td>\n",
       "      <td>0.999962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>komondor</td>\n",
       "      <td>0.999956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>chow</td>\n",
       "      <td>0.999953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>web_site</td>\n",
       "      <td>0.999916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.999885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>tennis_ball</td>\n",
       "      <td>0.999834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.999828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Old_English_sheepdog</td>\n",
       "      <td>0.999715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>pug</td>\n",
       "      <td>0.999365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>jack-o'-lantern</td>\n",
       "      <td>0.999306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Great_Dane</td>\n",
       "      <td>0.999223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.999201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.999091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>refrigerator</td>\n",
       "      <td>0.998886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Saint_Bernard</td>\n",
       "      <td>0.998830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Blenheim_spaniel</td>\n",
       "      <td>0.998335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.998275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.998201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>schipperke</td>\n",
       "      <td>0.997953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         result  Algorithm_conf\n",
       "Algorithm                                      \n",
       "p1                        swing        0.999984\n",
       "p1                       barrow        0.999962\n",
       "p1                     komondor        0.999956\n",
       "p1                         chow        0.999953\n",
       "p1                     web_site        0.999916\n",
       "p1           Labrador_retriever        0.999885\n",
       "p1                  tennis_ball        0.999834\n",
       "p1                    dalmatian        0.999828\n",
       "p1         Old_English_sheepdog        0.999715\n",
       "p1                          pug        0.999365\n",
       "p1              jack-o'-lantern        0.999306\n",
       "p1                   Great_Dane        0.999223\n",
       "p1               French_bulldog        0.999201\n",
       "p1                   Rottweiler        0.999091\n",
       "p1                 refrigerator        0.998886\n",
       "p1                Saint_Bernard        0.998830\n",
       "p1             Blenheim_spaniel        0.998335\n",
       "p1                   Pomeranian        0.998275\n",
       "p1                      Samoyed        0.998201\n",
       "p1                   schipperke        0.997953"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo_best = df.groupby(['Algorithm','result'])[['Algorithm_conf']].max().sort_values(by='Algorithm_conf',ascending = False)\n",
    "\n",
    "algo_best = algo_best.reset_index(level =['result']).loc['p1']\n",
    "algo_best.head(20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Using Matplot Library to visualize between worst and best 5 kind of dog/animal for each Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def draw(algorithm):\n",
    "    algo1 = df.groupby(['Algorithm','result'])[['Algorithm_conf']].max().sort_values(by='Algorithm_conf',ascending = False)\n",
    "    algo1 = algo1.reset_index(level =['result']).loc[algorithm] \n",
    "    algo1 = algo1.head().groupby('result')['Algorithm_conf'].max()\n",
    "    algo1 = algo1.reset_index(level =['result'])\n",
    "    algo = df.groupby(['Algorithm','result'])[['Algorithm_conf']].max().sort_values(by='Algorithm_conf',ascending = False)\n",
    "    algo = algo.reset_index(level =['result']).loc[algorithm] \n",
    "    algo = algo.tail().groupby('result')['Algorithm_conf'].max()\n",
    "    algo = algo.reset_index(level =['result'])\n",
    "    algo_new= algo.append(algo1)\n",
    "    algo_new.set_index('result',inplace = True)\n",
    "    return algo_new.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- change between p1,p2 and p3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207049d73a0>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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URIwaOHDge6nXzMzWopZAnw0MkzRUUl/gaGBq+QGSBgN3AsdFxK/rX6aZmXWn2yaXiFgh6QzgPqANuCEiFkk6NX38auBrwADgSkkAKyJiVOPKNjOzSrW0oRMR04BpFfuuLvv+FOCU+pZmZmbrwiNFzcwKwoFuZlYQDnQzs4JwoJuZFYQD3cysIBzoZmYFUVO3RcvfkEk/Wq+fX3rxJ+pUiZk1K1+hm5kVhAPdzKwg3OTSjfVt6gA3d5hZNhzoVjO/uZk1Nze5mJkVhK/QraX4U4LZ2vkK3cysIJr6Ct19r83MaucrdDOzgnCgm5kVhAPdzKwgHOhmZgXhQDczKwgHuplZQTjQzcwKwoFuZlYQDnQzs4JwoJuZFYQD3cysIBzoZmYF4UA3MysIB7qZWUE40M3MCsKBbmZWEA50M7OCcKCbmRVETYEuabykpyQtkTSpyuOSdHn6+HxJu9e/VDMz60q3gS6pDbgCOAgYDhwjaXjFYQcBw9KvicBVda7TzMy6UcsV+l7Akoh4JiLeAW4FJlQcMwG4ORIPA5tJ2qrOtZqZWRcUEV0fIB0BjI+IU9Lt44DREXFG2TH3AhdHxMx0+37g7IiYU/FcE0mu4AF2AJ5az/q3BF5Zz+dYX81QAzRHHc1QAzRHHc1QAzRHHc1QAzRHHfWo4YMRMbDaA71r+GFV2Vf5LlDLMUTEtcC1NZyzJpLmRMSoej1fq9bQLHU0Qw3NUkcz1NAsdTRDDc1SR6NrqKXJpQPYtmx7EPDCezjGzMwaqJZAnw0MkzRUUl/gaGBqxTFTgePT3i57A69FxIt1rtXMzLrQbZNLRKyQdAZwH9AG3BARiySdmj5+NTANOBhYArwJnNS4kldTt+ab9dAMNUBz1NEMNUBz1NEMNUBz1NEMNUBz1NHQGrq9KWpmZq3BI0XNzArCgW5mVhAOdDOzgnCgtzBJG9Syr6eQtKGkHfKuI29+HXquWgYWNRVJ34iIs7vb1+AaBBwLbBcR50kaDHwgIh7NqobULKByIrRq+xpG0nnAL4GHIuKNrM5bpY6/A74F9AWGStoVOC8iDs24jsur7H4NmBMR92Rw/mZ5HW6k+uDCk7Oso0TSJpTlXUS8muG5D4qIH1fsOzXtIVhXrXiFfkCVfQdlXMOVwIeBY9Lt10kmMMuEpA9I2gPYUNJuknZPv8YC78uqjtRSktdhjqRHJV0iqXKunyz8G8m8Q38CiIh5wJAc6ugH7Ar8Jv1qB7YAPivpPzM4/7/RHK/DvcCP0q/7gU2Av2RdhKR/kPQHYD4wN/2a0/VP1d25kj5WVtPZrDkfVl20zBW6pM8DpwHbSZpf9lB/4MGMyxkdEbtLehwgIv6YDrrKyt8CJ5KMyL20bP+fga9kWAcRcQNwg6QPAEcCXyKZr6d/lnUAKyLiteTDU67+H/CxiFgBIOkq4KckFyILMjh/U7wOEXFH+bakKcDPcijlS8CIiMhzDpdDgXslnQWMB3ZM99VdywQ68N/Aj4GLgPI52V/P8uNTank6rXAASBoIvJvVySPiu8B3JR1e+R8na5KuI5lW+Q8kTS9HAI/lUMpCSZ8B2iQNA84EHsqhjm2AjUiaWUi/3zoiVkr6awbnb5bXodIwYHAO532aZLBjbiLiFUmHkryhzQWOiAYNAGqZQI+I10j+kxwjaQwwLCJulLSlpKER8dsMy7kcuAt4v6QLSELsnAzPX/KgpOtJAuOgdJ76D0fE9RnWMIBkBPGfgFeBV0pXpxn7R+CrwF9J3vzvA/49hzq+CcyT9HOSSev2BS6UtBHZXKGWvw5TSF6H8zM472okvU5ywaP0z98Dmd3nKvMvwEOSHiF5TQCIiDMbfeKy16CkL7AdcISkiIhN6n7OVhspKulfgVHADhGxvaStgf+JiI9kXMeOwDiSX9j7I2JxludPa/gxcCPw1YjYRVJv4PGI2DmHWnYiaQr6AtAWEYMyPv9uEfF4ludcm3QtgL1IfjcejQhPVJcTSY8CM0mauzo/RaefcgunZa7Qy3wK2I30Y31EvCApk/ZaSVuUbb5EcgXU+VgOTT9bRsRtkv4FOufdWZllAZIOAT5KciW6OTCdpOkla5emQfo/wK0RsSiHGkr2JHlNAFaSwcyjkn5IlV4lJVn3cgGQtA3wQVbvXTIj4zJWRMQXMz7nGtIml33TzZ9HxL2NOE8rBvo7ERGSSu3XG2V47rms+hhZKUg+TmXpDUkDWNWWvzer2m6zchAwA7gszyvRiNi/7MbstWk3tR9ERKbNLpIuJgn0W9JdZ0raJyL+pcGn/laDn3+dSPoGcBTwJMmbGiS/p1kH+gPpwjo/ZPUmlyy7LVb+TvyTpDERscb6zOt9rhZscvkSyQ2WA0hukJ4MTImIav1/C03JYtz/BYwEFgIDSW64zO/yB+tfx9+Q/MJC0sTwUpbnr1LPzsCXgaMiIsveR6Q9sHaNiHfT7TaSZrD2DGvoC2yfbj4VEcuzOndZDU8B7RGRxY3gruqodm8tIiKzi68sfyda7go9Ir4l6QCSLno7AF8j+3d9JB0GjCG56vhlRNyddQ0R8Zik/UheB5HDf15Jnya5Ovx5WsN/STorIm7PuI6dSK4IjwCWkax9+89Z1lBmM5IbxACbZnnidCzCd0nGBwjYVtIJOTR1PAP0oeyqOA8RMTTP85fJ5Hei5QJd0g3paLP/Tbc3JpmPfVyGNVxJ0t+41IZ+qqQDIuL0rGoosxfJwJHewO6SiIibMzz/OcCepavytAvnz4BMA53k5vAU4MCcb0JeBDwu6QFW9XJpdHNLuUtIXoOnACRtT/K67JFhDZB0FZynZH3hTHuXVJI0kqRrbb+yOrL8P3Ih8FhFz6eG/E60YpPL+SQ3Az8vaXOSkWjfiYgbM6xhETCy1JdUUi9gQUSMyKqG9LzfAz4EzKOsnTLL/zSSFpT3qklfiyfy6GnTLNKbs3uS/Od9JCJ+n+G551d+lK+2L4M6Tqi2P+veJWmvuLEkgT6N5J7PzIg4IsMavkcyaviPwHM08Hei5QIdOm+4bEpy1XFx1oNrJN0JfCEink23P5jWcUzXP1n3OhYDwxs1SKHGGiaTDG8vfVo5Cpif1dw6km6LiCMlLWD1Xh4ieXPLJMjS+xlrFRGZDLaSdAPJ6/C9dNexQO+IyGoVsfJaNgQGlz4t5CH9vdiFpM16l/R+z3UR8XcZ1vAxkubZj5J0nJgHzIiIy+p+rlYJ9LTNunMTOBd4FPgJQETcmUENpa5hm5JcgZUm49qLZHKqjze6hop6/gc4M3Jev1XS4cBHSP5dZkTEXRmee6uIeDF9U11D6U03gzoeSL/tRzJO4gmS16Od5IpsTEZ1bACcThIgIrm/dGXWNydVNklYRAxVfpOEzY6IPSXNBfYnmXdpYQ6fpttIMmN/4FTgrYjYse7naaFA76pJJSKDWdzSG5BdFfGLRteQ1lF6Y+lPMhHUo6zeTpl5n+O8qQlm4UzPeStwQUQsSLdHAl+KiBOzrCNvaYB+jKTP9W7pvtWa5zKoQcB1JDfHj07//AswL8tPLOl9hI1IZkL9JUmTT0N6grVMoDebtM/zXiTBOjvjdtLc31iqDGvufCgpof7Dmrup57GI2L1iXx5tx/MiYtfu9jXw/B8hmXGxckBPpmMkJD0SEaMlPV4W6Hn8e8yNiD3S74cAm+TQrfc/SJqH/0oykeAMYFZEvFXvc7ViL5fvAv8UEX9KtzcHLsniCr2shlNIuktOZ1VXvfMimXmw4UqBLWko8GJEvJ1ubwj8TUY1ZD2bYlVqrlk4ARYrmbDs+yRveH8PZDktxPUk0y/MZdWN8jw0yyRhD0vaMyJmR8TSHM5PRHwBOnvknUTSI+sDQN0Xo2m5K/Tyd/yu9jW4hqeAfSJiWbo9gKQNPdNVYiTNSet4J93uCzwYEXt2/ZN1OfcWXT2e1Ug8SZuSTDnQDLNwIqkf8HlWDfOeAVxVetPN4PyPRMToLM7VTR3vI5kk7MB0133A+Tm05T9JMsjqWeANMr5ZntZwBskN0T3SOmaQjF2ZXu9ztdwVOtBL0uYR8UfoDJas/x4dJDdXSl4Hns+4Bkh6L7xT2oiId5TdvOxNMQ1CrD4L5y6smkPll6wayJGZNLj/I/1ag6Q7IuLwep+3rJfNA2nPoztZ/b5K1lMaD0+/eqdfE0jmAM+0yYXsF7+pZkOSdQvmRoNnIm3FQL+EZDrM0sCVTwMXZHFiSaVJfn4HPCLpHpLwmsCqHi9ZelnSoRExNa1vApDJRP5NNAIPAElnkiysUert9H1J10bEf+VYVjWNeqO7hNXfYEsDiUrT136s2g810C0ki0ssJMO1Aipl1cupmxomZ3WulmtyAZA0gqT7T2nq2iczOu+/dvV4RHw9izpKJH2I5D/O1umuDuC4iHg6g3PvGBG/Wlv/66yvCNP28w9Huq6pkknbZmV9E6471W7e1ul5y6c5qPzkFBFxKRmSNDOrrpq2SiteoRMRiyS9TDqUV9LgiHgug/NmGtg1eDci9k5vtigiXk9vlGbhiyRXxKUrw5K8rgjF6jcBV1K9OaioNk7/3IGkv/M9JH//vyOHuY6Af01vDlcO/W/4eJGerOWu0JXMK3wJyVXpSyTdsxZnOVAgHURSbUXzTENsLV31OrtpZVTDhiS9TDonKiPDm4BldXwROIFkJSmATwI3RUQWCzPXrNE38CX9FDg8Il5Pt/uTLAAzvlHnXEsd3ydZO3MRq5pcMhkv0pO14hX6+cDewM8iYjdJ+5OsOp+lL5V93w84HMhs2TUlqyWNADatGEG7CWUTEGXkuyQzX5amLz4GuJlkXvLMRMSlSiY/Ko2QPClyWsFIXU9f2+iBToOBd8q23yGZvC1ru2Q5iMgSrRjoyyNimaReknpFxAPp3C6ZiYi5FbselJTJKNHUDsAhJFNyls9J8TrwuQzrgGQpwF3Kth+Q9ETGNZT8luSNtTfJQMHdc2jLH0sX09dGxE8bXML3gEcl3UXyielTaT1Ze1jS8Kzub1miFQP9T2mb8QzgFkkvkeHVMazRB7sXydwdH8jq/BFxD3CPpA9HxKyszrsWj0vaOyIeBpA0mhwG9CiZhfNEklXeS81hebTl5zp9bURcoGSt2VL3zbw+qYwBTlCywMRfyaH/d0/Uim3oGwFvk/yCHEsyUdYtpUE+GdXwW1aFxgqSq7HzImJmVjWkdfQDPkvS/FI+13OWo2YXk3xiKN2UHkwyMvJdsp3t8Clg5/J++XmoNrw9jyHveVPOk6X1VC13hV7qlpbKa+Xu4ax5I3BODnV8D/gV8LfAeSRvcFkOMwfI9GZbFxaSNEHluvwdMEfS9aw+fW1lE13hObjz0TJX6KV+rWuZFCpIRgVOjogrM6jlNpIbgaVFX48BNo+ITzf63BV1PJ7eGJ4fEe2S+gD3Zd3bphlIGkXSVW8hOc48qSaZvtZ6ppYJ9O6U5lMBRpcm7mrguZ6ouBFYdV+jSXo0IvaSNIPkE8PvSRZpznRmvWagZBWpa4AFlI1MjIymNDZrBi3X5LI2ac+X/UlmQKz7SLwKTXEjELg2nW3yHGAqyeCSc3Oooxm8EhGXd39YY0k6hKRrbWn62lymE7aeqTBX6CWNHLihVcuc9WHVjcAg+c/7ZESMbMR5u6hnA5I+8EPSmiAJj/OyrKMZSLqUpKllKjlOSiVpCXAYyRqzxfrPZU2vMFfoZRr5n+iQBj73e3EPyUyDcykLsR6q9Ca+d9m+PLotPk+yxJnD3DJXxCv0hkx+1IwkLcz6U4F1TdKeJE0uv2D1TwqZTo5lPVOvvAtogJ40IdNDkjy8mmShC0mXSpqTfl2SLn6RtQuAN0nGBfQv+zJruJa7Qpf02Yi4vmLfxRExKf1+izxWqslSWVt+b2AY8Aw9fDSepDtIuiyWxiYcRzKfyGFr/6mG1DEnIkZleU6zklZsQz9C0tsRcQuApCspW5uv6GGeara2/GbwoYqVgL4uaV4OdfxM0oEZzNlitoZWDPTDgKmS3iVZXurViDgt55oy5VF4Vb0laUxp+gVJHwHqvqp6DU4Hvizpr8By3G3RMtQyTS4VE2L1B+4m6fv9NegxV+a2FpJ2JWluKbWb/xE4MSLymvnRLHOtFOilCbFU9mdJ9GfXP4kAAAarSURBVMTRkbYmSZsARMSfMz5vUy3JZz1TywS6WVckXQh8szTtQzqC9p8j4pyMzn9tRExMV7OqFD1xfh3LXssFejoB1eeBfdNdPweuqVgVxnqYaiOE8xiTIEmVg4ok9ct6ST7rmVqxH/pVJIsFXJl+7ZHus56tLZ0KAehc63SDLo5vlMoutRsBP8qhDuuBWrGXy54VsxpOz3HJM2se3wful3QjyT2Wk8lnvvzfSboqIj6fNvv8CPhODnVYD9SKTS6PAZ+OiKfT7e2A23vKcH9bO0njgY+nm/8bEfflVMc3SHrb7AFcHBF35FGH9TyteIV+FslCxM+Q9HT5IHBSviVZk3icZNbJSL/PjKTyEamPkkxj/CgQkg6LiDuzrMd6ppa7QofOaWN3IAn0X3k1GJN0JDCZ5Ca5SBZJPisibs/o/Dd28XBkuc6r9VwtF+ju5WLVpPdRDoiIl9LtgcDPsl5FyixP7uViRdGrFOapZeTw+y3pm5I2kdRH0v2SXpH091nXYT1TK7ahu5eLVfMTSfcBU9Lto4BpOdRxYER8WdKngA7g08ADJL1wzBqqFQN9paQPVfRyWZlzTZaziDhL0uHAR0ja0K+NiLtyKKW0FODBwJSIeFXqSVP0W55aMdDLe7lAsp6me7kYaffAvLsI/lDSr0hmejwtbcv3KFHLRCveFO0H/DMwLt31v8B/eGh1zyTpdaqvI5vbtLXpgKI/R8TKdKRo/4j4fdZ1WM/TijdFbwaGkqzbeH76/fdyrchyExH9I2KTKl/9y8M8DdmGkfTlss2PR8TKtL43gDMbeW6zkla8Qn+isitatX1m5Ro9UVf581eeqyctXG75asUr9Mcl7V3akDSaZKELs640+s6k1vJ9Fuc2A1ropmjZwsh9gOMlPZdufxB4Ms/arCU0+qNorOX7LM5tBrRQoOOFka257SLpzyRX4xum35Nu98uvLOtJWibQvTCyraeGNntERFsjn9+sFi13U9RsbdKeLNtSdqFSWstT0hZeSNyKzoFuhSDpfOBE4GlWtVl7LU/rURzoVgiSngJ2joh38q7FLC+t2G3RrJqFwGZ5F2GWJ1+hWyFIGgXcQxLsnQueRMShuRVllrGW6eVi1o3vAt8AFgDv5lyLWS4c6FYUr0TE5XkXYZYnN7lYIUi6lKSpZSqrN7k8lltRZhlzoFshSHqgym53W7QexYFuZlYQbkO3wpD0CWAEZXOnRMR5+VVkli33Q7dCkHQ1ycLQ/0gyb8unSWbiNOsx3ORihSBpfkS0l/25MXBnRByYd21mWfEVuhXFW+mfb0raGlhOsjyhWY/hNnQrinslbQZMBh4jmaDrunxLMsuWm1yscCRtAPSLiNfyrsUsS25ysUKQ9D5J50r6TkT8FXi/JK9yZT2KA92K4kaSEaIfTrc7gH/Prxyz7DnQrSg+FBHfJLkZSkS8RYOXnTNrNg50K4p3JG1IulqRpA9RNqeLWU/gXi5WFP8G/ATYVtItwEeAk3KtyCxj7uVihSFpALA3SVPLwxHxSs4lmWXKgW6FIOn+iBjX3T6zInOTi7U0Sf2A9wFbStqcVTdCNwG2zq0wsxw40K3V/QPw/0nCey5JoAfwOvDtHOsyy5x7uVhLi4jLImIocAGwa/r9jcAzwKxcizPLmAPdiuKIiPizpDHAAcBNwFX5lmSWLQe6FcXK9M9PAFdHxD1A3xzrMcucA92K4neSrgGOBKalE3T599t6FHdbtEKQ9D5gPLAgIn4jaStg54j4ac6lmWXGgW5mVhD+SGpmVhAOdDOzgnCgm60jSSdK+nb6/SclDc+7JjNwoFsPokS9f+c/CTjQrSk40K3QJA2RtFjSlSSLR58rabak+ZK+nh6zkaQfSXpC0kJJR6X7l0raMv1+lKSfVzz3PsChwGRJ89I52M1y47lcrCfYgWRu9LuBI4C9SOZ8mSppX2Ag8EJEfAJA0qa1PGlEPCRpKnBvRNzekMrN1oGv0K0neDYiHgYOTL8eJ7la3xEYBiwAPi7pG5I+GhGv5Veq2XvnK3TrCd5I/xRwUURcU3mApD2Ag4GLJP00Is4DVrDqoqdfJpWarQdfoVtPch9wsqSNASRtI+n9krYG3oyI7wPfAnZPj18K7JF+f/hanvN1oH/jSjarnQPdeox0GoD/BmZJWgDcThLGOwOPSpoHfBX49/RHvg5cJumXrJr8q9KtwFmSHvdNUcubh/6bmRWEr9DNzArCgW5mVhAOdDOzgnCgm5kVhAPdzKwgHOhmZgXhQDczKwgHuplZQfwfubI/FfmMZyIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw('p3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- the hardest 20 kind of dogs,animal, or stuff could be predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- most named dog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cooper      261\n",
       "Charlie     252\n",
       "Tucker      234\n",
       "Oliver      216\n",
       "Lucy        207\n",
       "           ... \n",
       "Meera         9\n",
       "Jangle        9\n",
       "Storkson      9\n",
       "Darla         9\n",
       "Carll         9\n",
       "Name: name, Length: 850, dtype: int64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_name = df['name'].value_counts()\n",
    "labels=np.full(len(most_name),\"\")\n",
    "most_name.plot(kind = 'pie',figsize = (10,10), labels = labels)\n",
    "most_name\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}