data_gen.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['industry', 'store', 'age', 'gender', 'revisit', 'emotion', 'time_spent']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "jewellery : jewel\n",
    "toys : toys\n",
    "electronics : elec\n",
    "apparel : app\n",
    "cosmetics : cos\n",
    "automotive : auto\n",
    "'''\n",
    "data_dict_1 = {'jewel':\n",
    "               [63,\n",
    "                {\n",
    "                    'age':{\n",
    "                        (0,15):5,\n",
    "                        (15,25):13,\n",
    "                        (25,35):22,\n",
    "                        (35,45):35,\n",
    "                        (45,55):18,\n",
    "                        (55,60):7\n",
    "                    }, \n",
    "                    'gender':{\n",
    "                        'm':32,\n",
    "                        'f':68\n",
    "                    }, \n",
    "                    'revisit': 19, \n",
    "                    'emotion':{\n",
    "                        'sad':5,\n",
    "                        'happy':15,\n",
    "                        'disgust':5,\n",
    "                        'anger':10,\n",
    "                        'surprise':15,\n",
    "                        'neutral':50\n",
    "                    }, \n",
    "                    'time_spent':{\n",
    "                        (0,10):5,\n",
    "                        (10,20):22,\n",
    "                        (20,40):40,\n",
    "                        (40,50):30,\n",
    "                        (50,250):3\n",
    "                    }\n",
    "                }\n",
    "               ],\n",
    "               'toys':\n",
    "               [93,\n",
    "                {\n",
    "                    'age':{\n",
    "                        (0,5):3,\n",
    "                        (5,10):40,\n",
    "                        (10,15):30,\n",
    "                        (15,20):17,\n",
    "                        (20,60):10\n",
    "                    }, \n",
    "                    'gender':{\n",
    "                        'm':50,\n",
    "                        'f':50\n",
    "                    }, \n",
    "                    'revisit': 30, \n",
    "                    'emotion':{\n",
    "                        'sad':5,\n",
    "                        'happy':27,\n",
    "                        'disgust':3,\n",
    "                        'anger':15,\n",
    "                        'surprise':10,\n",
    "                        'neutral':40\n",
    "                    }, \n",
    "                    'time_spent':{\n",
    "                        (0,10):10,\n",
    "                        (10,30):40,\n",
    "                        (30,40):40,\n",
    "                        (50,250):10\n",
    "                    }\n",
    "                }\n",
    "               ],\n",
    "               'elec':\n",
    "               [142,\n",
    "                {\n",
    "                    'age':{\n",
    "                        (0,5):0,\n",
    "                        (5,15):10,\n",
    "                        (15,25):50,\n",
    "                        (25,35):20,\n",
    "                        (35,50):20,\n",
    "                        (50,60):0\n",
    "                    }, \n",
    "                    'gender':{\n",
    "                        'm':60,\n",
    "                        'f':40\n",
    "                    }, \n",
    "                    'revisit': 30, \n",
    "                    'emotion':{\n",
    "                        'sad':10,\n",
    "                        'happy':10,\n",
    "                        'disgust':5,\n",
    "                        'anger':5,\n",
    "                        'surprise':15,\n",
    "                        'neutral':55\n",
    "                    }, \n",
    "                    'time_spent':{\n",
    "                        (0,10):10,\n",
    "                        (10,20):20,\n",
    "                        (20,60):60,\n",
    "                        (60,250):10\n",
    "                    }\n",
    "                }\n",
    "               ],\n",
    "               'app':\n",
    "               [167,\n",
    "                {\n",
    "                    'age':{\n",
    "                        (0,15):0,\n",
    "                        (15,20):20,\n",
    "                        (20,30):40,\n",
    "                        (30,40):30,\n",
    "                        (40,50):5,\n",
    "                        (50,60):5\n",
    "                    }, \n",
    "                    'gender':{\n",
    "                        'm':20,\n",
    "                        'f':80\n",
    "                    }, \n",
    "                    'revisit': 40, \n",
    "                    'emotion':{\n",
    "                        'sad':10,\n",
    "                        'happy':10,\n",
    "                        'disgust':10,\n",
    "                        'anger':10,\n",
    "                        'surprise':10,\n",
    "                        'neutral':50\n",
    "                    }, \n",
    "                    'time_spent':{\n",
    "                        (0,10):20,\n",
    "                        (10,30):40,\n",
    "                        (30,40):30,\n",
    "                        (40,250):10\n",
    "                    }\n",
    "                }\n",
    "               ],\n",
    "               'cos':\n",
    "               [55,\n",
    "                {\n",
    "                    'age':{\n",
    "                        (0,15):5,\n",
    "                        (15,20):10,\n",
    "                        (20,30):40,\n",
    "                        (30,40):30,\n",
    "                        (40,50):10,\n",
    "                        (50,60):5\n",
    "                    }, \n",
    "                    'gender':{\n",
    "                        'm':5,\n",
    "                        'f':95\n",
    "                    }, \n",
    "                    'revisit': 30, \n",
    "                    'emotion':{\n",
    "                        'sad':5,\n",
    "                        'happy':15,\n",
    "                        'disgust':5,\n",
    "                        'anger':10,\n",
    "                        'surprise':15,\n",
    "                        'neutral':50\n",
    "                    }, \n",
    "                    'time_spent':{\n",
    "                        (0,10):5,\n",
    "                        (10,20):50,\n",
    "                        (20,30):40,\n",
    "                        (30,250):5\n",
    "                    }\n",
    "                }\n",
    "               ],\n",
    "               'auto':\n",
    "               [37,\n",
    "                {\n",
    "                    'age':{\n",
    "                        (0,10):10,\n",
    "                        (10,20):20,\n",
    "                        (20,30):30,\n",
    "                        (30,50):30,\n",
    "                        (50,60):10\n",
    "                    }, \n",
    "                    'gender':{\n",
    "                        'm':70,\n",
    "                        'f':30\n",
    "                    }, \n",
    "                    'revisit': 20, \n",
    "                    'emotion':{\n",
    "                        'sad':10,\n",
    "                        'happy':20,\n",
    "                        'disgust':5,\n",
    "                        'anger':5,\n",
    "                        'surprise':10,\n",
    "                        'neutral':50\n",
    "                    }, \n",
    "                    'time_spent':{\n",
    "                        (0,10):10,\n",
    "                        (10,30):30,\n",
    "                        (30,40):40,\n",
    "                        (40,50):10,\n",
    "                        (50,250):10\n",
    "                    }\n",
    "                }\n",
    "               ]\n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['industry', 'store', 'age', 'gender', 'revisit', 'emotion', 'time_spent']\n",
    "main_data = {column:list() for column in columns}\n",
    "for industry in data_dict_1:\n",
    "    entries = data_dict_1[industry][0]\n",
    "    for column in data_dict_1[industry][1]:\n",
    "        col_dic = data_dict_1[industry][1][column]\n",
    "        data = []\n",
    "        if column=='age' or column=='time_spent':\n",
    "            for start,end in col_dic:\n",
    "                countval = int((col_dic[(start,end)]/100.0)*entries)\n",
    "                data.extend([random.randint(start,end) for _ in range(countval)])\n",
    "        if column=='gender' or column=='emotion':\n",
    "            for key,count in col_dic.items():\n",
    "                countval = int((count/100.0)*entries)\n",
    "                data.extend([key for _ in range(countval)])\n",
    "            for _ in range(100):\n",
    "                random.shuffle(data)\n",
    "        if column=='revisit':\n",
    "            countval = int((col_dic/100.0)*entries)\n",
    "            data.extend([1 for _ in range(countval)])\n",
    "            data.extend([0 for _ in range(entries-countval)])\n",
    "            for _ in range(100):\n",
    "                random.shuffle(data)\n",
    "        if(len(data)!=0):\n",
    "            data.extend([random.choice(data) for _ in range(entries-len(data))])\n",
    "        main_data[column].extend(data)\n",
    "    main_data['industry'].extend([industry for _ in range(entries)])\n",
    "    main_data['store'].extend([1 for _ in range(entries)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.from_dict(main_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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