HW_Unit_12_13 (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fdcf9421",
   "metadata": {},
   "source": [
    "## Week 12 and 13 Assignment - DATASCI200 Introduction to Data Science Programming, UC Berkeley MIDS\n",
    "\n",
    "Write code in this Jupyter Notebook to solve the following problems. Please upload this **Notebook** with your solutions to your GitHub repository and provide a link in the last question in gradescope. \n",
    "\n",
    "Assignment due date: 11:59PM PT the night before the Week 13 Live Session. Do **NOT** push/upload the data fil"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6433c24c",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "- Explore and glean insights from a real dataset using pandas\n",
    "- Practice using pandas for exploratory analysis, information gathering, and discovery\n",
    "- Practice using matplotlib for data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71eb82ac",
   "metadata": {},
   "source": [
    "## General Guidelines:\n",
    "\n",
    "- This is a **real** dataset and so it may contain errors and other pecularities to work through\n",
    "- This dataset is ~218mb, which will take some time to load (and probably won't load in Google Sheets or Excel)\n",
    "- If you make assumptions, annotate them in your responses\n",
    "- While there is one code/markdown cell positioned after each question as a placeholder, some of your code/responses may require multiple cells\n",
    "- Double-click the markdown cells that say for example **1a answer here:** to enter your written answers. If you need more cells for your written answers, make them markdown cells (rather than code cells)\n",
    "- This homework assignment is not autograded because of the variety of responses one could give. \n",
    "  - Please upload this notebook to the autograder page and the TAs will manually grade it. \n",
    "  - Ensure that each cell is run and outputs your answer for ease of grading! \n",
    "  - Highly suggest to do a `restart & run all` before uploading your code to ensure everything runs and outputs correctly.\n",
    "  - Answers without code (or code that runs) will be given 0 points.\n",
    "- **This is meant to simulate real world data so you will have to do some external research to determine what some of the answers are!** \n",
    "- Only use the plotting libraries matplotlib or seaborn for the visualizations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9071e0f4",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "You are to analyze campaign contributions to the 2016 U.S. presidential primary races made in California. Use the csv file located here: https://drive.google.com/file/d/1ftdw7L9Z6WQJ6i0SXj030PA7ObsXfPsg/view?usp=sharing. You should download and save this file in a folder outside where this notebook is stored. This file originally came from the U.S. Federal Election Commission (https://www.fec.gov/).\n",
    "\n",
    "This file is already cleaned and ready to use - you **do not** need to filter or clean it anymore! (you may not agree with the how it was cleaned but please use this file as is).\n",
    "\n",
    "**DO NOT PUSH THIS FILE TO YOUR GITHUB REPO!**\n",
    "\n",
    "- Best practice is to not have DATA files in your code repo. As shown below, the default load is outside of the folder this notebook is in. If you change the folder where the file is stored please update the first cell!\n",
    "- If you do accidentally push the file to your github repo - follow the directions here to fix it: https://docs.google.com/document/d/15Irgb5V5G7pKPWgAerH7FPMpKeQRunbNflaW-hR2hTA/edit?usp=sharing\n",
    "\n",
    "Documentation for this data can be found here: https://drive.google.com/file/d/11o_SByceenv0NgNMstM-dxC1jL7I9fHL/view?usp=sharing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c17ae4",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Run the cell below as it will load the data into a pandas dataframe named `contrib`. Note that a custom date parser is defined to speed up loading. If Python were to guess the date format, it would take even longer to load."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de9b9512",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# These commands below set some options for pandas and to have matplotlib show the charts in the notebook\n",
    "pd.set_option('display.max_rows', 1000)\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the data\n",
    "# We have this defaulted to the folder OUTSIDE of your repo - please change it as needed\n",
    "contrib = pd.read_csv('2016_ca_primary_cleaned.csv', index_col=False, parse_dates=['contb_receipt_dt'])\n",
    "\n",
    "# Note - for now, it is okay to ignore the warning about mixed types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84db1a98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 668864 entries, 0 to 668863\n",
      "Data columns (total 18 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   cmte_id            668864 non-null  object        \n",
      " 1   cand_id            668864 non-null  object        \n",
      " 2   cand_nm            668864 non-null  object        \n",
      " 3   contbr_nm          668864 non-null  object        \n",
      " 4   contbr_city        668863 non-null  object        \n",
      " 5   contbr_st          668864 non-null  object        \n",
      " 6   contbr_zip         668860 non-null  object        \n",
      " 7   contbr_employer    619110 non-null  object        \n",
      " 8   contbr_occupation  664126 non-null  object        \n",
      " 9   contb_receipt_amt  668864 non-null  float64       \n",
      " 10  contb_receipt_dt   668864 non-null  datetime64[ns]\n",
      " 11  receipt_desc       8558 non-null    object        \n",
      " 12  memo_cd            27302 non-null   object        \n",
      " 13  memo_text          388991 non-null  object        \n",
      " 14  form_tp            668864 non-null  object        \n",
      " 15  file_num           668864 non-null  int64         \n",
      " 16  tran_id            668864 non-null  object        \n",
      " 17  election_tp        668864 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(15)\n",
      "memory usage: 91.9+ MB\n"
     ]
    }
   ],
   "source": [
    "contrib.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0d8f3e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.00     95516\n",
       "100.00    78388\n",
       "25.00     77657\n",
       "10.00     61162\n",
       "27.00     54078\n",
       "          ...  \n",
       "-20.15        1\n",
       "430.27        1\n",
       "37.05         1\n",
       "92.86         1\n",
       "45.67         1\n",
       "Name: contb_receipt_amt, Length: 6531, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contrib.contb_receipt_amt.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1ecfc014",
   "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>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "      <th>tran_id</th>\n",
       "      <th>election_tp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00575795</td>\n",
       "      <td>P00003392</td>\n",
       "      <td>Clinton, Hillary Rodham</td>\n",
       "      <td>AULL, ANNE</td>\n",
       "      <td>LARKSPUR</td>\n",
       "      <td>CA</td>\n",
       "      <td>949,391,913.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>* HILLARY VICTORY FUND</td>\n",
       "      <td>SA18</td>\n",
       "      <td>1091718</td>\n",
       "      <td>C4768722</td>\n",
       "      <td>P2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00575795</td>\n",
       "      <td>P00003392</td>\n",
       "      <td>Clinton, Hillary Rodham</td>\n",
       "      <td>CARROLL, MARYJEAN</td>\n",
       "      <td>CAMBRIA</td>\n",
       "      <td>CA</td>\n",
       "      <td>934,284,638.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2016-04-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>* HILLARY VICTORY FUND</td>\n",
       "      <td>SA18</td>\n",
       "      <td>1091718</td>\n",
       "      <td>C4747242</td>\n",
       "      <td>P2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00575795</td>\n",
       "      <td>P00003392</td>\n",
       "      <td>Clinton, Hillary Rodham</td>\n",
       "      <td>GANDARA, DESIREE</td>\n",
       "      <td>FONTANA</td>\n",
       "      <td>CA</td>\n",
       "      <td>923,371,507.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2016-04-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>* HILLARY VICTORY FUND</td>\n",
       "      <td>SA18</td>\n",
       "      <td>1091718</td>\n",
       "      <td>C4666603</td>\n",
       "      <td>P2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00577130</td>\n",
       "      <td>P60007168</td>\n",
       "      <td>Sanders, Bernard</td>\n",
       "      <td>LEE, ALAN</td>\n",
       "      <td>CAMARILLO</td>\n",
       "      <td>CA</td>\n",
       "      <td>930,111,214.00</td>\n",
       "      <td>AT&amp;T GOVERNMENT SOLUTIONS</td>\n",
       "      <td>SOFTWARE ENGINEER</td>\n",
       "      <td>40.00</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>* EARMARKED CONTRIBUTION: SEE BELOW</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>1077404</td>\n",
       "      <td>VPF7BKWA097</td>\n",
       "      <td>P2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00577130</td>\n",
       "      <td>P60007168</td>\n",
       "      <td>Sanders, Bernard</td>\n",
       "      <td>LEONELLI, ODETTE</td>\n",
       "      <td>REDONDO BEACH</td>\n",
       "      <td>CA</td>\n",
       "      <td>902,784,310.00</td>\n",
       "      <td>VERICOR ENTERPRISES INC.</td>\n",
       "      <td>PHARMACIST</td>\n",
       "      <td>35.00</td>\n",
       "      <td>2016-03-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>* EARMARKED CONTRIBUTION: SEE BELOW</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>1077404</td>\n",
       "      <td>VPF7BKX3MB3</td>\n",
       "      <td>P2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cmte_id    cand_id                  cand_nm          contbr_nm  \\\n",
       "0  C00575795  P00003392  Clinton, Hillary Rodham         AULL, ANNE   \n",
       "1  C00575795  P00003392  Clinton, Hillary Rodham  CARROLL, MARYJEAN   \n",
       "2  C00575795  P00003392  Clinton, Hillary Rodham   GANDARA, DESIREE   \n",
       "3  C00577130  P60007168         Sanders, Bernard          LEE, ALAN   \n",
       "4  C00577130  P60007168         Sanders, Bernard   LEONELLI, ODETTE   \n",
       "\n",
       "     contbr_city contbr_st     contbr_zip            contbr_employer  \\\n",
       "0       LARKSPUR        CA 949,391,913.00                        NaN   \n",
       "1        CAMBRIA        CA 934,284,638.00                        NaN   \n",
       "2        FONTANA        CA 923,371,507.00                        NaN   \n",
       "3      CAMARILLO        CA 930,111,214.00  AT&T GOVERNMENT SOLUTIONS   \n",
       "4  REDONDO BEACH        CA 902,784,310.00   VERICOR ENTERPRISES INC.   \n",
       "\n",
       "   contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc memo_cd  \\\n",
       "0            RETIRED              50.00       2016-04-26          NaN       X   \n",
       "1            RETIRED             200.00       2016-04-20          NaN       X   \n",
       "2            RETIRED               5.00       2016-04-02          NaN       X   \n",
       "3  SOFTWARE ENGINEER              40.00       2016-03-04          NaN     NaN   \n",
       "4         PHARMACIST              35.00       2016-03-05          NaN     NaN   \n",
       "\n",
       "                             memo_text form_tp  file_num      tran_id  \\\n",
       "0               * HILLARY VICTORY FUND    SA18   1091718     C4768722   \n",
       "1               * HILLARY VICTORY FUND    SA18   1091718     C4747242   \n",
       "2               * HILLARY VICTORY FUND    SA18   1091718     C4666603   \n",
       "3  * EARMARKED CONTRIBUTION: SEE BELOW   SA17A   1077404  VPF7BKWA097   \n",
       "4  * EARMARKED CONTRIBUTION: SEE BELOW   SA17A   1077404  VPF7BKX3MB3   \n",
       "\n",
       "  election_tp  \n",
       "0       P2016  \n",
       "1       P2016  \n",
       "2       P2016  \n",
       "3       P2016  \n",
       "4       P2016  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contrib.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0bf8323",
   "metadata": {},
   "source": [
    "## 1. Plotting Data (30 points)\n",
    "\n",
    "Your boss asked for you to make a few charts from the dataset to better explore the information. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9919956a",
   "metadata": {},
   "source": [
    "**1a.** Plotting a histogram of contribute amount (10 points)\n",
    "\n",
    "Make a histogram of the **full range** of values in the `contb_receipt_amt` variable. \n",
    "\n",
    "Remember to include on your histogram:\n",
    "- Include a title\n",
    "- Include axis labels\n",
    "- The correct number of bins to see the breakout of values\n",
    "\n",
    "Based on the histogram - what range of contributions have the highest count frequency?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b87fcb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   668,864.00\n",
       "mean        123.01\n",
       "std         439.35\n",
       "min      -5,700.00\n",
       "25%          15.00\n",
       "50%          27.00\n",
       "75%          75.00\n",
       "max      10,800.00\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contrib['contb_receipt_amt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d19658f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "47953f7f",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 1a YOUR CODE HERE\n",
    "# Plotting a basic histogram\n",
    "\n",
    "plt.hist(contrib['contb_receipt_amt'],bins=9,color='skyblue', edgecolor='black')\n",
    " \n",
    "# Adding labels and title\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Basic Histogram')\n",
    " \n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8458c6e",
   "metadata": {},
   "source": [
    "- **1a answer here:** In the 2016 CA elections primary contributions dataset, the range of contributions that have the highest count frequency are -10 to 2000."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324a0a3b",
   "metadata": {},
   "source": [
    "**1b.** Plotting a histogram of contribute amount (10 points)\n",
    "\n",
    "Make a 'zoomed-in' histogram of the range that had the highest count frequencies from 1a above. \n",
    "\n",
    "Remember to include on your histogram:\n",
    "- Include a title\n",
    "- Include axis labels\n",
    "- The correct number of bins to see the breakout of values\n",
    "\n",
    "What are some insights that you can see from these two histograms (1a and 1b)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "28b64ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnoAAAGHCAYAAAAnTv9WAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAuuElEQVR4nO3de7hdVX3v//eHoKhFkItguGiwoBY9NUoO0qMibTyA9ShWsUasRksPLaI/0VqNoHKxtNKeWktF+qMViRYE1FrQSjHFUtpT7hhFQCRclJAIapBykavf88ccW1a2K9l7J/uSzLxfz7OevdaYc8w55thr7Xwy5hxrpqqQJElS/2w20w2QJEnS1DDoSZIk9ZRBT5IkqacMepIkST1l0JMkSeopg54kSVJPGfSkjUSSOUkqybyZbsvGIMnTknwtyX1JNrrvkWq/64Pb8yn93Sc5PclXpmLbkmaWQU+aYWv6RzbJvPaP+5xWdBswG1g6zu3emuS9k9bQjc97gZ2AuXT9NlSSxyf5oyTfaKFwVZJLk/x+ki0mqzFJ3prk3glUmQ18ebL239qwX3tPbT9q0buA35nMfY3RjtcmeTTJGdO1z8mylj6UNkibz3QDJI1PVT0K/GCm2zFMksdX1UMz3Y5Rdgeuqqob17RCkscDFwAvAD4M/DvwE+C/A+8BbgAumuqGjm5TVT1UVdP2u66qu6drX83vAX8G/H9Jtqmqu6Z5/9ImwxE9aSMx+vRdksclOSnJiiQPJrktyUfbsouAZwB/3urUwHZem+SagTpHJ8nA8h2TnJfkp0m+l+RtSb6d5NiBdSrJEUn+Icl9wJ8kmZXkU0luaXVvTPK+JJsN1Ds9yVeSvD/JD5LcneSjSTZLcmySO1v5+8fRH7+fZFmSh9rP/z2w7FbgIOAtra2nr2EzRwIvA15eVSdV1Teq6paqOgf4H8DVbXtbJPl4kjuSPNBG/F4ysL+RUZ75SS5Lcn+SK5O8cGQ58Gngl0Z+HyP92UZej01yWpKfAGcM9PHBo9r7rCT/0drwnST7D2nD9gNlP3/PtJHhf22LfjjYL6NHldf3eNcmyS7ArwP/B7gUeNOo5SPbfkWSq9p76d+T7JLkZUm+meTe9j7abqDeZkk+1N7TD7b3+EHD+mLU/oadIn9dkiXtuK5L8j9Hlq+lD/dt/XRve19fluR5Y/WHNOWqyocPHzP4AE4HvjKkfB5QwJz2ek57Pa+9/kO607n7Ak+nCyZva8u2bcuOA54GPK2V7wU82sqfRfeP7L3AOwf2+8/AN4FfozvteSFwD3DswDoF3Ek3MvNMYDfgccDxdKNhc4DfphsdO3TUsf4X8DfAc4A3Aj9r+/zT1qY/aNvfay199lvAw8A7Wp13ttevasufCiwBzm7Hv/UatvNN4Gvj+B39FbASeCXwK8Dftn6b3Zbv19p8OV2IeQ7dSOH1QIDH050evW/k9wFs2ere2vrkfXSjkHsM9PHBo373y1u/Pgf4a+CnwM6j2rD9QLtH6s0DZgGvba/3HOwXRr0H1/d4x+jLDwHntudvBZaOWj647ZcCvwp8G/i/dO/FF7XjuQX464F67279eEh7TxxP916fO+zzM+q9PLqfvwO8CtgDWAz8GNhyTX1Id3bsLrrw+sutPw4BfmWm/7748DHjDfDhY1N/tH9kH2n/kA4+7mftQe+k9g/f0H9Y6QLEe0eVnQF8fVTZscDy9vzZbR/7DCzftf2DeexAWQ3+I7uWY/so8C+jjvU2YNZA2ZXAt8Zq+6jl/xc4bUg//sfA668Ap4/RvvuBvxpjnV8CHgLeMlA2C7gJ+OP2er/WJwcMrPPiVrZLe/1W4N41/J6+PKR8WAA5emD5ZsB3h7RhaNBb0zoDffeVyTzeNfRlgJsHjmtLuvC718A6w7b9jlb2wlHv228PvL4d+PCo/V0E/P2wvhijn39/YPnOrewla+nnbVvZyyby2ffhYzoenrqVNgwX042eDT4OGaPO6W297yY5OckrB0+TrsGv0IWkQf8B7JxkK7qRiJ/RhS8Aquo2YMWQbV05uiDJH7RTeD9MN/Hg3XSjjYOuq+56wxF3ANeMWucOYId1OI4911JnmIy9Cr9MN1r58/219l8yZH/fGng+0mdrO44Rv9CXa3DJQBt+Blw2pA3rayqPdz6wDW2SSVXdC/wj3cjwaIPbvqP9vGZU2Q4A7b27E5Pznhi97zGPq6pW0X0eL0jyT0nek2TXddivNOkMetKG4f6qWjb4oDtNt0ZVdTXdCMRRdJ/lxcCSMcJe6EYehm6S8QWfEfettuHkDcDH6f7BO4AuhH6S7rTloIeH7HdY2Vh/n4Ydx5qObU2+Sxca12akT8azv4eHLBvP39n7xl5lTD9rPwd/h49bh+1M5fH+HvAU4L4kjyR5BFgAvDHJk8badlWNLhu9r7W1+Rf6J8ma+ufn+6mqcf0eq+ptdKeVLwZeTfcfsAPWVkeaDgY9aSNWVfdU1eer6nC666l+g+46L+hOv80aVeU64CWjyl5Cd+r2HrprrDaju5YP+PnF8zuNozkvAS6rqk9U1dUtrP7yRI9pnK5n+HFcN8HtnAm8fPQF+vDzi/u3ApbR9eXgZIRZdNcwTmR/w34fE7XPQBsC7E3XFwA/bD8Hv0pm7pA2MEY7Jut4V5NkW+A1wEJWH7l+PvAgMHriybhV1X/Rjbyt7T0xnv4ZjzX2YVV9s6pOrKr96E4bL1yH7UuTyq9XkTZSSd5Dd8H8UroRiEPoLkYfGQm8FXhpkr8HHqyqHwF/AVzRZnyeSTdx4g/pRgWpqhuSXAD8TZLDgQeAP+ex6wXX5rvAW5O8gi4sLKCb0XrXJBzuaH8OfD7JVcDXgAPpJpa8doLb+Tjwm3QjocfQjcbcTfd1K+8Fjqqqi5KcAnw0yY/oJgG8G9iRbsRyvG4FntBmcH6DbhT3/gm29/Ak36U7hfl2upnVp7Rly+iufzw2ySK60d4Pjqr/Pbrf4yuTfBn4aTt9+nNVdd8kHe9ob6ab1HPGqFP3JPkHutG+z6zH9v8cOD7JjcBVdN8L+FLaf1qq6qdJLgXen+QmukkUf7oO+/mFPqSb/PP7wHl01wo+k24SySlr2og0XRzRkzZe9wB/RDc78Wq60YlXDISHD9NNpLiJNprRTve+Hngd3UzGj7bHJwa2+1a6sHgR3T9cZ9DNsH1gjPb8/8A5dAHyCrqg8RfrenBrU1X/SDfT9t10IzbvAt5eVRP6guGqehDYn64PDqW7Du1quhmwi4H/bKu+n+7YPk0XrH8VOLCqVk5gX/9JN9v4c3S/j/dNpK3NIrrv9/smXbj9rapa3rb/MF24fmZbfhwtwA+04XbgGOAEumvcBn/vg9b7eIc4FPjS6JDXfJ7uPyXPWo/tn0QX9v6M7r39W8DrqmrpwDq/235eQfd+HR2Ex7SGPryfbqbv5+n+w7OY7nNz4jochzSp8tjlB5L0i9r3sq0A3lhVX5zp9kiSxs9Tt5JWk+Q3gCfTnR7cgW7k4kd033UnSdqIGPQkjfY44I/pTgHeT/cVHvtW1WTMDJUkTSNP3UqSJPWUkzEkSZJ6yqAnSZLUU16jN8T2229fc+bMmelmSJIkjemqq676UVU9ddgyg94Qc+bM4corx3vrSUmSpJmT5HtrWuapW0mSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9NW1BL8lTknwhyXeSXJ/k15Jsm2RJkhvbz20G1v9AkmVJbkhywED5XkmuactOSpJWvkWSs1v5ZUnmDNRZ2PZxY5KF03XMkiRJM2nzadzXXwH/XFUHJ3k88CTgKODCqvpokkXAIuD9SfYEFgDPBXYC/iXJs6rqUeAU4DDgUuCrwIHA+cChwF1VtXuSBcCJwBuSbAscA8wDCrgqyXlVddf0Hbq08Ttk4dtYccedY6630447cObiT09DiyRJY5mWoJdkK2Bf4K0AVfUQ8FCSg4D92mqLgYuA9wMHAWdV1YPALUmWAXsnuRXYqqouadv9DPAauqB3EHBs29YXgE+00b4DgCVVtarVWUIXDj83Vccr9dGKO+7kiJPPHHO9k484ZBpaI0kaj+ka0Xsm8EPg00meD1wFvAvYsapWAlTVyiQ7tPV3phuxG7G8lT3cno8uH6lzW9vWI0nuBrYbLB9S5+eSHEY3Usjs2bNZunTpuh6r1Evz930ps26/YVzr+fmRpA3DdAW9zYEXAu+sqsuS/BXdado1yZCyWkv5utZ5rKDqVOBUgHnz5tXcuXPX0jxp03PkoqN5zhsOH3O9Cy8+jg8dtbaPtyRpukzXZIzlwPKquqy9/gJd8LsjyWyA9vPOgfV3Hai/C7Cile8ypHy1Okk2B7YGVq1lW5IkSb02LUGvqn4A3Jbk2a1oPnAdcB4wMgt2IXBue34esKDNpN0N2AO4vJ3mvSfJPu36u7eMqjOyrYOBr1dVARcA+yfZps3q3b+VSZIk9dp0zrp9J3BGm3F7M/A2uqB5TpJDge8DrweoqmuTnEMXBh8BjmgzbgEOB04Hnkg3CeP8Vv4p4LNt4sYqulm7VNWqJB8BrmjrHT8yMUOSJKnPpi3oVdVSuq84GW3+GtY/AThhSPmVwPOGlD9AC4pDlp0GnDaB5kqSJG30vDOGJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSemragl6SW5Nck2Rpkitb2bZJliS5sf3cZmD9DyRZluSGJAcMlO/VtrMsyUlJ0sq3SHJ2K78syZyBOgvbPm5MsnC6jlmSJGkmTfeI3q9X1dyqmtdeLwIurKo9gAvba5LsCSwAngscCHwyyaxW5xTgMGCP9jiwlR8K3FVVuwN/CZzYtrUtcAzwImBv4JjBQClJktRXM33q9iBgcXu+GHjNQPlZVfVgVd0CLAP2TjIb2KqqLqmqAj4zqs7Itr4AzG+jfQcAS6pqVVXdBSzhsXAoSZLUW9MZ9Ar4WpKrkhzWynasqpUA7ecOrXxn4LaBustb2c7t+ejy1epU1SPA3cB2a9mWJElSr20+jft6cVWtSLIDsCTJd9ayboaU1VrK17XOYzvswudhALNnz2bp0qVraZ606Zm/70uZdfsN41rPz48kbRimLehV1Yr2884kX6K7Xu6OJLOramU7LXtnW305sOtA9V2AFa18lyHlg3WWJ9kc2BpY1cr3G1XnoiHtOxU4FWDevHk1d+7cdT1UqZeOXHQ0z3nD4WOud+HFx/GhoxZNQ4skSWOZllO3SX4pyZNHngP7A98GzgNGZsEuBM5tz88DFrSZtLvRTbq4vJ3evSfJPu36u7eMqjOyrYOBr7fr+C4A9k+yTZuEsX8rkyRJ6rXpGtHbEfhS+yaUzYEzq+qfk1wBnJPkUOD7wOsBquraJOcA1wGPAEdU1aNtW4cDpwNPBM5vD4BPAZ9NsoxuJG9B29aqJB8BrmjrHV9Vq6byYCVJkjYE0xL0qupm4PlDyn8MzF9DnROAE4aUXwk8b0j5A7SgOGTZacBpE2u1JEnSxm2mv15FkiRJU8SgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ6a1qCXZFaSbyT5Snu9bZIlSW5sP7cZWPcDSZYluSHJAQPleyW5pi07KUla+RZJzm7llyWZM1BnYdvHjUkWTuMhS5IkzZjpHtF7F3D9wOtFwIVVtQdwYXtNkj2BBcBzgQOBTyaZ1eqcAhwG7NEeB7byQ4G7qmp34C+BE9u2tgWOAV4E7A0cMxgoJUmS+mragl6SXYBXAn83UHwQsLg9Xwy8ZqD8rKp6sKpuAZYBeyeZDWxVVZdUVQGfGVVnZFtfAOa30b4DgCVVtaqq7gKW8Fg4lCRJ6q3Np3FfHwfeBzx5oGzHqloJUFUrk+zQyncGLh1Yb3kre7g9H10+Uue2tq1HktwNbDdYPqTOzyU5jG6kkNmzZ7N06dIJH6DUZ/P3fSmzbr9hXOv5+ZGkDcO0BL0k/wu4s6quSrLfeKoMKau1lK9rnccKqk4FTgWYN29ezZ07dxzNlDYdRy46mue84fAx17vw4uP40FGLpqFFkqSxTNep2xcDr05yK3AW8BtJ/h64o52Opf28s62/HNh1oP4uwIpWvsuQ8tXqJNkc2BpYtZZtSZIk9dq0BL2q+kBV7VJVc+gmWXy9qn4HOA8YmQW7EDi3PT8PWNBm0u5GN+ni8naa954k+7Tr794yqs7Itg5u+yjgAmD/JNu0SRj7tzJJkqRem85r9Ib5KHBOkkOB7wOvB6iqa5OcA1wHPAIcUVWPtjqHA6cDTwTObw+ATwGfTbKMbiRvQdvWqiQfAa5o6x1fVaum+sAkSZJm2rQHvaq6CLioPf8xMH8N650AnDCk/ErgeUPKH6AFxSHLTgNOW9c2S5IkbYy8M4YkSVJPGfQkSZJ6yqAnSZLUUwY9SZKknjLoSZIk9ZRBT5IkqacMepIkST1l0JMkSeopg54kSVJPjTvoJXl1kpm+ZZokSZLGaSIjeh8BVib5RJIXTVWDJEmSNDnGHfSq6vnAy4GfAl9MckOSDyaZM1WNkyRJ0rqb0DV6VfXNqvojYFfgCOD1wE1JLk7ypiRe8ydJkrSBmPA1d0l+Gfid9vgZ8GHg+8A7gNcBr53MBkqSJGndjDvoJTkCeDOwO3AO8OaqunRg+ReBOye9hZIkSVonExnRewXwF8C5VfXQ6IVVdX8SR/MkSZI2EBMJegcDj1bVwyMFSR4HbFZVDwJU1dcmuX2SJElaRxOZPPE1YK9RZXsBF0xecyRJkjRZJhL0fhW4bFTZ5cDzJ685kiRJmiwTCXo/AXYcVbYjcN+ktUaSJEmTZiJB74vAmUmel+RJSf4b8Bm6GbiSJEnawEwk6B0NXE93uvYe4FLgBuCoKWiXJEmS1tO4Z91W1QPAEUneAWwP/KiqaspaJkmSpPUyoTtjJNkaeDawZXsNQFV9fdJbJkmSpPUykTtjvBU4GbgXuH9gUQHPnNxmSZIkaX1NZETvBODgqjp/qhojSZKkyTORyRib031psiRJkjYCEwl6JwIfTDKROpIkSZohEzl1+27gacD7kvx4cEFVPX1SWyVJkqT1NpGg9ztT1gpJkiRNuol8j96/TWVDJEmSNLnGfb1dki2SnJDk5iR3t7L92xcoS5IkaQMzkYkVfwk8D3gT3XfnAVwLHD7ZjZIkSdL6m8g1er8F7F5V9yX5GUBV3Z5k56lpmiRJktbHREb0HmJUMEzyVODHw1eXJEnSTJpI0Ps8sDjJbgBJZgOfAM6aioZJkiRp/Uwk6B0F3ApcAzwFuBFYARw36a2SJEnSepvI16s8BBwJHNlO2f6oqmrttSRJkjRTxh30kjxzVNGTkwBQVTdPZqMkSZK0/iYy63YZ3deqZKBsZERv1qS1SJIkSZNiIqduV7ueL8nTgGOAf5/sRkmSJGn9TWQyxmqq6gd01+z96aS1RpIkSZNmnYNe82zgSWOtlOQJSS5P8s0k1yY5rpVvm2RJkhvbz20G6nwgybIkNyQ5YKB8ryTXtGUnpV0o2G7RdnYrvyzJnIE6C9s+bkyycD2PWZIkaaMwkXvd/nuSiwceVwKXAR8bR/UHgd+oqucDc4EDk+wDLAIurKo9gAvba5LsCSwAngscCHwyych1gKcAhwF7tMeBrfxQ4K6q2p3udm0ntm1tS3eK+UXA3sAxg4FSkiSpryYyGePvRr2+D/hmVd04VsX2NSz3tpePa48CDgL2a+WLgYuA97fys6rqQeCWJMuAvZPcCmxVVZcAJPkM8Brg/Fbn2LatLwCfaKN9BwBLqmpVq7OELhx+btxHLkmStBGayGSMxeuzozYidxWwO3ByVV2WZMeqWtm2vzLJDm31nYFLB6ovb2UPt+ejy0fq3Na29UiSu4HtBsuH1JEkSeqtiXyP3vHjWa+qPryG8keBuUmeAnwpyfPWtrthm1hL+brWeWyHyWF0p4SZPXs2S5cuXUvzpE3P/H1fyqzbbxjXen5+JGnDMJFTt3sArwOuAL4HPJ3umrcvAg+0dca8U0ZV/STJRXSnT+9IMruN5s0G7myrLQd2Hai2C93t1pa356PLB+ssT7I5sDWwqpXvN6rORUPadSpwKsC8efNq7ty5Yx2KtEk5ctHRPOcNh4+53oUXH8eHjlo0DS2SJI1lIrNuA7yxql5cVYdU1UvoJkxQVW9rj98dWjF5ahvJI8kTgZcD3wHOA0ZmwS4Ezm3PzwMWtJm0u9GFzMvbad57kuzTrr97y6g6I9s6GPh6uzbwAmD/JNu0SRj7tzJJkqRem8iI3iuAN40qOxf49DjqzgYWt+v0NgPOqaqvJLkEOCfJocD3gdcDVNW1Sc4BrgMeAY5op34BDgdOB55INwnj/Fb+KeCzbeLGKh4LoauSfIRuJBLg+JGJGZIkSX020VugHQGcNFD2duCmsSpW1beAFwwp/zEwfw11TgBOGFJ+JfAL1/dV1QO0oDhk2WnAaWO1U5IkqU8mEvR+j24SxfuA2+lmrj4CvHYqGiZJkqT1M5GvV/lGkj2AfYCdgJXAJVX18FQ1TpIkSetufe51ezHw+CS/NIntkSRJ0iSZyC3Q/hvwXeBv6SY+ALwMr32TJEnaIE1kRO8U4MNV9Ry6O1QA/BvwkklvlSRJktbbRILec4G/b88LoKruo/uaE0mSJG1gJhL0bgX2GixIsjfd165IkiRpAzORr1f5EPBPSf6GbhLGB4A/AP73lLRMkiRJ62XcI3pV9RW6u2M8le7avGcAr62qr01R2yRJkrQexjWi125d9l1gz6p6+9Q2SZIkSZNhXCN67T6zjwJPmNrmSJIkabJM5Bq9jwPnJPkTYDlt5i1AVd08ye2SJEnSehoz6CV5WlX9APhEK3o5kIFVCpg1BW2TJEnSehjPqdvvAlTVZlW1GXDeyPP2MORJkiRtgMYT9DLq9cumoiGSJEmaXOMJejXq9ejgJ0mSpA3QeCZjbJ7k13ks4M0a9Zqq+vpUNE6SJEnrbjxB707gtIHXPx71uoBnTmajJEmStP7GDHpVNWca2iFJkqRJNu5boEmSJGnjYtCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnpiXoJdk1yb8muT7JtUne1cq3TbIkyY3t5zYDdT6QZFmSG5IcMFC+V5Jr2rKTkqSVb5Hk7FZ+WZI5A3UWtn3cmGThdByzJEnSTJuuEb1HgD+sql8B9gGOSLInsAi4sKr2AC5sr2nLFgDPBQ4EPplkVtvWKcBhwB7tcWArPxS4q6p2B/4SOLFta1vgGOBFwN7AMYOBUpIkqa+mJehV1cqquro9vwe4HtgZOAhY3FZbDLymPT8IOKuqHqyqW4BlwN5JZgNbVdUlVVXAZ0bVGdnWF4D5bbTvAGBJVa2qqruAJTwWDiVJknpr8+neYTul+gLgMmDHqloJXRhMskNbbWfg0oFqy1vZw+356PKROre1bT2S5G5gu8HyIXUG23UY3Ughs2fPZunSpet8jFIfzd/3pcy6/YZxrefnR5I2DNMa9JJsCXwROLKq/qtdXjd01SFltZbyda3zWEHVqcCpAPPmzau5c+euqW3SJunIRUfznDccPuZ6F158HB86atE0tEiSNJZpm3Wb5HF0Ie+MqvqHVnxHOx1L+3lnK18O7DpQfRdgRSvfZUj5anWSbA5sDaxay7YkSZJ6bbpm3Qb4FHB9VX1sYNF5wMgs2IXAuQPlC9pM2t3oJl1c3k7z3pNkn7bNt4yqM7Ktg4Gvt+v4LgD2T7JNm4SxfyuTJEnqtek6dfti4M3ANUmWtrKjgI8C5yQ5FPg+8HqAqro2yTnAdXQzdo+oqkdbvcOB04EnAue3B3RB8rNJltGN5C1o21qV5CPAFW2946tq1RQdpyRJ0gZjWoJeVf0Hw6+VA5i/hjonACcMKb8SeN6Q8gdoQXHIstOA08bbXkmSpD7wzhiSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqKYOeJElSTxn0JEmSesqgJ0mS1FMGPUmSpJ4y6EmSJPWUQU+SJKmnDHqSJEk9ZdCTJEnqqWkJeklOS3Jnkm8PlG2bZEmSG9vPbQaWfSDJsiQ3JDlgoHyvJNe0ZSclSSvfIsnZrfyyJHMG6ixs+7gxycLpOF5JkqQNwXSN6J0OHDiqbBFwYVXtAVzYXpNkT2AB8NxW55NJZrU6pwCHAXu0x8g2DwXuqqrdgb8ETmzb2hY4BngRsDdwzGCglCRJ6rNpCXpVdTGwalTxQcDi9nwx8JqB8rOq6sGqugVYBuydZDawVVVdUlUFfGZUnZFtfQGY30b7DgCWVNWqqroLWMIvBk5JkqRe2nwG971jVa0EqKqVSXZo5TsDlw6st7yVPdyejy4fqXNb29YjSe4GthssH1JnNUkOoxstZPbs2SxdunSdD0zqo/n7vpRZt98wrvX8/EjShmEmg96aZEhZraV8XeusXlh1KnAqwLx582ru3LljNlTalBy56Gie84bDx1zvwouP40NHLZqGFkmSxjKTs27vaKdjaT/vbOXLgV0H1tsFWNHKdxlSvlqdJJsDW9OdKl7TtiRJknpvJoPeecDILNiFwLkD5QvaTNrd6CZdXN5O896TZJ92/d1bRtUZ2dbBwNfbdXwXAPsn2aZNwti/lUmSJPXetJy6TfI5YD9g+yTL6WbCfhQ4J8mhwPeB1wNU1bVJzgGuAx4BjqiqR9umDqebwftE4Pz2APgU8Nkky+hG8ha0ba1K8hHgirbe8VU1elKIJElSL01L0KuqN65h0fw1rH8CcMKQ8iuB5w0pf4AWFIcsOw04bdyNlSRJ6gnvjCFJktRTBj1JkqSeMuhJkiT1lEFPkiSppwx6kiRJPWXQkyRJ6imDniRJUk8Z9CRJknrKoCdJktRTBj1JkqSeMuhJkiT1lEFPkiSppwx6kiRJPWXQkyRJ6imDniRJUk8Z9CRJknrKoCdJktRTBj1JkqSeMuhJkiT1lEFPkiSppwx6kiRJPWXQkyRJ6imDniRJUk8Z9CRJknrKoCdJktRTBj1JkqSeMuhJkiT11OYz3QBJ/XLTTTex34GvHNe6O+24A2cu/vQUt0iSNl0GPUmT6mdVHHHymeNa9+QjDpni1kjSps1Tt5IkST1l0JMkSeopg54kSVJPGfQkSZJ6yqAnSZLUUwY9SZKknjLoSZIk9ZRBT5IkqacMepIkST1l0JMkSeopg54kSVJPea9bSZomhyx8GyvuuHNc6+604w6cufjTU9wiSX1n0JOkabLijjs54uQzx7XuyUccMsWtkbQp2GSCXpIDgb8CZgF/V1UfneEmSRqn8Y6EOQomSavbJIJeklnAycD/BJYDVyQ5r6qum9mWSRqP8Y6EOQomSavbVCZj7A0sq6qbq+oh4CzgoBlukyRJ0pTaJEb0gJ2B2wZeLwdeNENt0Qzw1N+ajbdvbr31e5O+75tuuon9DnzlpO17vNtbsXw5O+2yy6Sttym+byRtHFJVM92GKZfk9cABVfV77fWbgb2r6p0D6xwGHNZePhu4YdobOjm2B340043YyNhnE2efTZx9NnH22cTZZxPTl/56RlU9ddiCTWVEbzmw68DrXYAVgytU1anAqdPZqKmQ5MqqmjfT7diY2GcTZ59NnH02cfbZxNlnE7Mp9Nemco3eFcAeSXZL8nhgAXDeDLdJkiRpSm0SI3pV9UiSdwAX0H29ymlVde0MN0uSJGlKbRJBD6Cqvgp8dabbMQ02+tPPM8A+mzj7bOLss4mzzybOPpuY3vfXJjEZQ5IkaVO0qVyjJ0mStMkx6G1kkhyb5PYkS9vjNweWfSDJsiQ3JDlgoHyvJNe0ZSclSSvfIsnZrfyyJHNm4JCmVJI/T/KdJN9K8qUkT2nlc5L8dKAf/2agzibbX2NJcmB7fy1Lsmim2zOTkuya5F+TXJ/k2iTvauWT9hntoyS3tmNdmuTKVrZtkiVJbmw/txlYf5PusyTPHngvLU3yX0mO9H22uiSnJbkzybcHyibtfbVR//2vKh8b0QM4FnjvkPI9gW8CWwC7ATcBs9qyy4FfAwKcD7yilb8d+Jv2fAFw9kwf3xT01/7A5u35icCJ7fkc4NtrqLPJ9tcYfTmrva+eCTy+vd/2nOl2zWB/zAZe2J4/Gfhu+xxO2me0jw/gVmD7UWV/BixqzxcNfE7ts9X7aRbwA+AZvs9+4bj3BV44+Hd9Mt9XG/Pff0f0+uMg4KyqerCqbgGWAXsnmQ1sVVWXVPcO/QzwmoE6i9vzLwDz+/Q/PICq+lpVPdJeXkr3HYprtKn31xi8leCAqlpZVVe35/cA19PdhWdN1uUzuqkY/GwtZvXPnH32mPnATVW1tlvFbJJ9VlUXA6tGFU/m+2qj/ftv0Ns4vaOdijxtYCh62G3edm6P5UPKV6vTwtDdwHZT2fAZ9rt0/0MbsVuSbyT5tyQvbWX215qt6T22yWuncV4AXNaKJusz2kcFfC3JVenuSASwY1WthC5AAzu0cvtsdQuAzw289n22dpP5vtpo//4b9DZASf4lybeHPA4CTgF+GZgLrAT+YqTakE3VWsrXVmejMkZ/jaxzNPAIcEYrWgk8vapeALwHODPJVmwC/bUeNvXjHyrJlsAXgSOr6r+Y3M9oH724ql4IvAI4Ism+a1nXPmvSfdn/q4HPtyLfZ+tuk/r3cpP5Hr2NSVW9fDzrJflb4Cvt5Zpu87ac1U9XDt7+baTO8iSbA1vzi0PfG7yx+ivJQuB/AfPbcDxV9SDwYHt+VZKbgGexCfTXehjzVoKbmiSPowt5Z1TVPwBU1R0Dy9f3M9o7VbWi/bwzyZfoLgm4I8nsqlrZTp/d2Va3zx7zCuDqkfeX77Nxmcz31Ub7998RvY1Me7OO+C1gZIbRecCCNjNoN2AP4PI2XH1Pkn3a9QRvAc4dqLOwPT8Y+PpIEOqLJAcC7wdeXVX3D5Q/Ncms9vyZdP1186beX2PwVoID2vvjU8D1VfWxgfLJ/Iz2SpJfSvLkked0k6W+zeqfrYWs/pnbpPtswBsZOG3r+2xcJvN9tfH+/Z/p2SA+JvYAPgtcA3yL7o03e2DZ0XSzh25gYDYVMI/uj8BNwCd47Iuyn0B3GmAZ3UyjZ8708U1Bfy2ju65iaXuMzJp6HXAt3cyrq4FX2V/j6s/fpJtdehNw9Ey3Z4b74iV0p26+NfD++s3J/Iz27UE3Y/ub7XHtyHuI7lqnC4Eb289t7bPV+u1JwI+BrQfKfJ+t3kefozuF/TDd6Nuhk/m+2pj//ntnDEmSpJ7y1K0kSVJPGfQkSZJ6yqAnSZLUUwY9SZKknjLoSZIk9ZRBT9ImK8m1SfZrz49N8veTuO2jkvzdZG1PktaFQU/SjElySJIrk9ybZGWS85O8ZBK2e3qSPx5rvap6blVdNAn72y/J4D0yqao/qarfW99tj7HPSvK+qdrH+kpya5Jx3elH0tQw6EmaEUneA3wc+BNgR+DpwCeBg9ZSbbL23YfbPy6kuwXTwrFWlLTpMuhJmnZJtgaOB46oqn+oqvuq6uGq+nJV/VFbZ4skH0+yoj0+nmSLtmy/JMuT/GGSO9to4NvassOANwHvayOFX27ltyZ5f5JvAfcl2XzIiNMTkpyd5J4kVyd5/kCbK8nuA69PT/LH7VZe5wM7tf3dm2Sn0aeCk7y6nSr+SZKLkvzKwLJbk7w3ybeS3N3a8IS19N+T6G7DdATdbenmDSyb09r6tiS3JbkryR8k+e9t+z9J8omB9TdL8sEk32t9+Zn2+xk6UjnYZ+0Yz2l17mnHN68t+yxdeP9y65MNduRR6jODnqSZ8Gt0txT60lrWORrYB5gLPB/YG/jgwPKn0d1YfGe62x2dnGSbqjoVOAP4s6rasqpeNVDnjcArgadU1SND9nkQ3W2OtgXOBP4xyePWdiBVdR/dDedXtP1tWVWr3Sw+ybPobtF0JPBU4Kt0AejxA6v9NnAgsBvwq8Bb17Lb1wH3trZeQHdPztFeRHcPzzfQjZweDbwceC7w20le1tZ7a3v8Ot0tyraku/XTeL0aOAt4Ct2tuD4BUFVvBr5Pd3vBLavqzyawTUmTxKAnaSZsB/xoDWFrxJuA46vqzqr6IXAc8OaB5Q+35Q9X1Vfpgs+zx9jvSVV1W1X9dA3Lr6qqL1TVw8DH6MLoPuM5oDG8AfinqlrStv1/gCcC/2NU21ZU1Srgy3QBd00WAmdX1aN0gfSNQwLpR6rqgar6GnAf8LnWl7cD/w68oK33JuBjVXVzVd0LfIDuhu/jPb39H1X11daWz9KFckkbCIOepJnwY2D7McLETsD3Bl5/r5X9fBujguL9dKNRa3PbeJdX1c/obo6+05pXH7fVjqVt+za60cgRPxh4vsZjSbIr3ejbGa3oXLpA+spRq94x8PynQ16PbH9YP29Od93keIxu9xN6cg2k1AsGPUkz4RLgAeA1a1lnBfCMgddPb2XjURMsH7HryJMkmwG7DOzzfuBJA+s+bQLbXe1YkqTt6/Yx6g3zZrq/3V9O8gPgZrqgN+z07XgM6+dH6ILhfQwcc5JZdKeex2usfpE0xQx6kqZdVd0NfJjuurrXJHlSkscleUWSkWu5Pgd8MMlTk2zf1h/v99zdQXe92UTtleS1bUTqSOBB4NK2bClwSJJZSQ4EXjZQ7w5gu5FJDEOcA7wyyfx2ivUP27b/cx3a+Ba609hzBx6va9vfbh229zng3Ul2S7Il3Szos9to6XfpRuhe2dr9QWCLCWx7XX8PkiaJQU/SjKiqjwHvoQsPP6Q7lfkO4B/bKn8MXAl8C7gGuLqVjcengD3bDNN/HGvlAefSXU93F93I2WvbNXUA7wJeBfyE7rq2n2+3qr5DF5hubvtc7XRvVd0A/A7w18CP2nZeVVUPTaBtJNkHmAOcXFU/GHicByyjm2wyUafRXVt3MXAL3UjrO1u77wbeDvwd3ejjfXSns8frT+nC+k+SvHcd2iZpPaXKkXVJkqQ+ckRPkiSppwx6kiRJPWXQkyRJ6imDniRJUk8Z9CRJknrKoCdJktRTBj1JkqSeMuhJkiT1lEFPkiSpp/4fVQCOOEa+N8gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1b YOUR CODE HERE\n",
    "# Extract contribution amounts and drop NaN values for plotting\n",
    "contribution_amounts = pd.to_numeric(contrib['contb_receipt_amt'], errors='coerce').dropna()\n",
    "\n",
    "# Plot the histogram for contribution amounts\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(contribution_amounts, bins=50, color='skyblue', edgecolor='black', alpha=0.7)\n",
    "plt.title('Histogram of Contribution Amounts', fontsize=14)\n",
    "plt.xlabel('Contribution Amount', fontsize=12)\n",
    "plt.ylabel('Frequency', fontsize=12)\n",
    "plt.grid(axis='y', alpha=0.75)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e93648e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(-90.0, 240.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recalculate the histogram data without plotting\n",
    "hist_data, bin_edges = np.histogram(contribution_amounts, bins=50)\n",
    "\n",
    "# Identify the range with the highest frequency\n",
    "max_freq_index = hist_data.argmax()\n",
    "max_range_start = bin_edges[max_freq_index]\n",
    "max_range_end = bin_edges[max_freq_index + 1]\n",
    "\n",
    "# Filter data for the zoomed-in range\n",
    "zoomed_data = contribution_amounts[\n",
    "    (contribution_amounts >= max_range_start) & (contribution_amounts < max_range_end)\n",
    "]\n",
    "\n",
    "# Plot the zoomed-in histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(zoomed_data, bins=20, color='orange', edgecolor='black', alpha=0.7)\n",
    "plt.title(f'Zoomed-in Histogram ({max_range_start:.2f} to {max_range_end:.2f})', fontsize=14)\n",
    "plt.xlabel('Contribution Amount', fontsize=12)\n",
    "plt.ylabel('Frequency', fontsize=12)\n",
    "plt.grid(axis='y', alpha=0.75)\n",
    "plt.show()\n",
    "\n",
    "# Return the range with the highest frequency\n",
    "max_range_start, max_range_end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1e763e",
   "metadata": {},
   "source": [
    "- **1b answer here:** \n",
    "We can see that maxmum number of contributions are between -90 and 240. Additional some of the contributions in the amount of -90 has been returned. We will need to investigate this further.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0012e029",
   "metadata": {},
   "source": [
    "**1c.** Present a time-series line chart with the following elements (10 points)  \n",
    "\n",
    "- The chart needs to have two lines - one for Bernie Sanders and one for Hillary Clinton.\n",
    "- Show the date on the x-axis\n",
    "- Add vertical lines for these dates of the primary debates: 17 Jan 2016, 11 Feb 2016, 9 Mar 2016\n",
    "- Show the total contribution amount for that day on the y-axis\n",
    "- Include a title\n",
    "- Include axis labels\n",
    "\n",
    "How did the debates effect fundraising / donations? Did fundraising increase or decrease after the debates for each candidate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cbafc2a2",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'numpy.ndarray'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/_8/33t6x7jj04x73mh8761v7k2c0000gn/T/ipykernel_4347/2053551262.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontributions_bernie\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Candidate X\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontributions_clinton\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Candidate Y\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdebate_dates\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3017\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0m_copy_docstring_and_deprecators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3018\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3019\u001b[0;31m     return gca().plot(\n\u001b[0m\u001b[1;32m   3020\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3021\u001b[0m         **({\"data\": data} if data is not None else {}), **kwargs)\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \"\"\"\n\u001b[1;32m   1604\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1605\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1606\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1607\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    313\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m    496\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mupdate_units\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1446\u001b[0m         \u001b[0mneednew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1447\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1448\u001b[0;31m         \u001b[0mdefault\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1449\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1450\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/category.py\u001b[0m in \u001b[0;36mdefault_units\u001b[0;34m(data, axis)\u001b[0m\n\u001b[1;32m    107\u001b[0m         \u001b[0;31m# the conversion call stack is default_units -> axis_info -> convert\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m             \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mUnitData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/category.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_counter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/category.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;31m# check if convertible to number:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m         \u001b[0mconvertible\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    219\u001b[0m             \u001b[0;31m# OrderedDict just iterates over unique values in data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0m_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_isinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAFpCAYAAAA7jJSFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAATx0lEQVR4nO3df4xld3nf8c+TXYyEIYHgBRn/CJZq4q7UQMzEgYq0ThHFpq3cX1K8iSBBbVducZX8F6MqbSpSqUFNRagN2xVxaaQWpym0Ma0Tq0V1aZvSeF0Zw2JsFofixVFZhyiFRK274ds/7nF0M5ndubN75pmd29dLGu3cc8+e+T66O3fee+6PqTFGAADo8y17vQAAgP/fCDAAgGYCDACgmQADAGgmwAAAmgkwAIBm2wZYVd1bVV+tqs+e4/qqqvdX1amqeqyqbpx/mQAA62OVM2AfTnLLea6/Ncn108fRJB+8+GUBAKyvbQNsjPHJJF87zy63Jfn5sfCpJC+tqivnWiAAwLqZ4zlgVyV5euny6WkbAABbODjDMWqLbVv+fqOqOprFw5S5/PLLX3/DDTfM8OUBAHbXI4888uwY49Bcx5sjwE4nuWbp8tVJntlqxzHG8STHk2RjY2OcOHFihi8PALC7qup/zHm8OR6CvD/JO6ZXQ74hyW+PMX5jhuMCAKylbc+AVdVHktyc5IqqOp3k7yR5QZKMMY4leSDJ25KcSvK7Sd65W4sFAFgH2wbYGOPINtePJO+abUUAAGvOO+EDADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBspQCrqluq6omqOlVVd21x/bdV1cer6tNVdbKq3jn/UgEA1sO2AVZVB5Lck+TWJIeTHKmqw5t2e1eSz40xXpvk5iQ/U1WXzbxWAIC1sMoZsJuSnBpjPDXGeC7JfUlu27TPSPKSqqokL07ytSRnZ10pAMCaWCXArkry9NLl09O2ZXcn+aNJnknymSQ/Osb45iwrBABYM6sEWG2xbWy6/NYkjyZ5VZLXJbm7qr71Dx2o6mhVnaiqE2fOnNnhUgEA1sMqAXY6yTVLl6/O4kzXsncm+dhYOJXk15PcsPlAY4zjY4yNMcbGoUOHLnTNAAD72ioB9nCS66vquumJ9bcnuX/TPl9O8uYkqapXJvnOJE/NuVAAgHVxcLsdxhhnq+rOJA8mOZDk3jHGyaq6Y7r+WJL3JPlwVX0mi4csf3yM8ewurhsAYN/aNsCSZIzxQJIHNm07tvT5M0n+9LxLAwBYT94JHwCgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKDZSgFWVbdU1RNVdaqq7jrHPjdX1aNVdbKq/uO8ywQAWB8Ht9uhqg4kuSfJW5KcTvJwVd0/xvjc0j4vTfKBJLeMMb5cVa/YpfUCAOx7q5wBuynJqTHGU2OM55Lcl+S2Tfv8YJKPjTG+nCRjjK/Ou0wAgPWxSoBdleTppcunp23LXpPkZVX1UFU9UlXv2OpAVXW0qk5U1YkzZ85c2IoBAPa5VQKsttg2Nl0+mOT1Sf5Mkrcm+Ymqes0f+ktjHB9jbIwxNg4dOrTjxQIArINtnwOWxRmva5YuX53kmS32eXaM8TtJfqeqPpnktUmenGWVAABrZJUzYA8nub6qrquqy5LcnuT+Tfv8UpLvq6qDVfWiJN+b5PF5lwoAsB62PQM2xjhbVXcmeTDJgST3jjFOVtUd0/XHxhiPV9WvJHksyTeTfGiM8dndXDgAwH5VY2x+OlePjY2NceLEiT352gAAO1FVj4wxNuY6nnfCBwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACg2UoBVlW3VNUTVXWqqu46z37fU1W/V1V/eb4lAgCsl20DrKoOJLknya1JDic5UlWHz7HfTyd5cO5FAgCsk1XOgN2U5NQY46kxxnNJ7kty2xb7/c0kH03y1RnXBwCwdlYJsKuSPL10+fS07fdV1VVJ/kKSY+c7UFUdraoTVXXizJkzO10rAMBaWCXAaottY9Pl9yX58THG753vQGOM42OMjTHGxqFDh1ZcIgDAejm4wj6nk1yzdPnqJM9s2mcjyX1VlSRXJHlbVZ0dY/zrORYJALBOVgmwh5NcX1XXJflKktuT/ODyDmOM657/vKo+nOTfiC8AgK1tG2BjjLNVdWcWr248kOTeMcbJqrpjuv68z/sCAOAPWuUMWMYYDyR5YNO2LcNrjPEjF78sAID15Z3wAQCaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBotlKAVdUtVfVEVZ2qqru2uP6Hquqx6eNXq+q18y8VAGA9bBtgVXUgyT1Jbk1yOMmRqjq8abdfT/InxxjfleQ9SY7PvVAAgHWxyhmwm5KcGmM8NcZ4Lsl9SW5b3mGM8atjjN+aLn4qydXzLhMAYH2sEmBXJXl66fLpadu5/JUkv3wxiwIAWGcHV9inttg2ttyx6vuzCLA3neP6o0mOJsm111674hIBANbLKmfATie5Zuny1Ume2bxTVX1Xkg8luW2M8ZtbHWiMcXyMsTHG2Dh06NCFrBcAYN9bJcAeTnJ9VV1XVZcluT3J/cs7VNW1ST6W5O1jjCfnXyYAwPrY9iHIMcbZqrozyYNJDiS5d4xxsqrumK4/luRvJ3l5kg9UVZKcHWNs7N6yAQD2rxpjy6dz7bqNjY1x4sSJPfnaAAA7UVWPzHlyyTvhAwA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQbKUAq6pbquqJqjpVVXdtcX1V1fun6x+rqhvnXyoAwHrYNsCq6kCSe5LcmuRwkiNVdXjTbrcmuX76OJrkgzOvEwBgbaxyBuymJKfGGE+NMZ5Lcl+S2zbtc1uSnx8Ln0ry0qq6cua1AgCshVUC7KokTy9dPj1t2+k+AAAkObjCPrXFtnEB+6SqjmbxEGWS/J+q+uwKX3+/uiLJs3u9iF1kvv1rnWdLzLffmW//WufZkuQ75zzYKgF2Osk1S5evTvLMBeyTMcbxJMeTpKpOjDE2drTafcR8+9s6z7fOsyXm2+/Mt3+t82zJYr45j7fKQ5APJ7m+qq6rqsuS3J7k/k373J/kHdOrId+Q5LfHGL8x50IBANbFtmfAxhhnq+rOJA8mOZDk3jHGyaq6Y7r+WJIHkrwtyakkv5vknbu3ZACA/W2VhyAzxnggi8ha3nZs6fOR5F07/NrHd7j/fmO+/W2d51vn2RLz7Xfm27/WebZk5vlq0U4AAHTxq4gAAJrNFmBVdU1V/YeqeryqTlbVj07bv72q/l1VfWH682VLf+fd068veqKq3rq0/fVV9ZnpuvdX1VZvc9Fq5vn+XlU9XVXf2ItZtjLXfFX1oqr6t1X1+ek4f3+vZlo28+33K1X16ek4x6bfFrFn5pxt6fr7L5W3iZn5tnto2vbo9PGKvZhp2czzXVZVx6vqyel78C/txUzLZrxvecnS7fZoVT1bVe/bo7F+38y335Fa/Ox7bLqfuWIvZlpaz5yz/cA018mqeu9ezLPZTuerqpdP+3+jqu7edKydd8sYY5aPJFcmuXH6/CVJnsziVxe9N8ld0/a7kvz09PnhJJ9O8sIk1yX5YpID03W/luSNWby/2C8nuXWudV4i871hOt439nquuedL8qIk3z/tc1mS/7SGt9+3Tn9Wko8muX1dZpuu/4tJ/nmSz+717bYLt91DSTb2eqZdnO/vJvmp6fNvSXLFOs236biPJPkT6zJfFs/J/urzt9n0939yTWZ7eZIvJzk07fdPk7x5H952lyd5U5I7kty96Vg77pbdHOyXkrwlyRNJrlwa9onp83cneffS/g9Oi78yyeeXth9J8o/3+oaaa75Nx7hkAmw35pu2/2ySv7bX8+zS7feCJB9P8gN7Pc9csyV5cZL/PN0JXRIBNvN8D+USC7CZ53s6yeV7PcNuzbe07fpp1trreeaab7o/OZPkO7L4IX4sydG9nmem2b4nyb9f2v72JB/Y63l2Ot/Sfj+SpQDLBXbLrjwHrKpeneS7k/y3JK8c03uCTX8+f8r/XL++6Krp883bLxkXOd8lb675quqlSf5ckk/s7op3Zo75qurBLP63+vUk/3L3V72aGWZ7T5KfyeLtZC45M/3b/CfTQ1g/sdLDBI0uZr7p+y1J3lNV/72qfrGqXtmy8BXNeN95JMkvjOmn3aXiYuYbY/zfJH89yWeyeCPzw0l+rmfl27vI2+5Ukhuq6tVVdTDJn88ffPP2PbfifOdyQd0ye4BV1YuzeFjmx8YY/+t8u26xbZxn+yVhhvkuaXPNN32TfSTJ+8cYT827ygs313xjjLdm8b+eFyb5U7Mu8gJd7GxV9bokf2SM8a92Y30Xa6bb7ofGGH8syfdNH2+fd5UXbob5DmbxW0j+yxjjxiT/Nck/mH2hF2jm+87bs7h/uWTM8P33giwC7LuTvCrJY1mcUdpzFzvbGOO3spjtF7J4WsqXkpyde50XagfznfMQW2zb9uf9rAE2/QP6aJJ/Nsb42LT5f1bVldP1V2Zx1iA5968vOj19vnn7nptpvkvWzPMdT/KFMcb7dnXROzD37TfG+N9Z/BaI23Zz3auYabY3Jnl9VX0pi4chX1NVD+3+6rc31203xvjK9OfXs3ie2027v/rtzTTfb2Zx5vL5gP7FJDfu8tJXMuf3XlW9NsnBMcYju77wFc003+uSZIzxxenM3r9I8sd3f/XnN+P33sfHGN87xnhjFg/xfaFj/dvZ4XznckHdMuerICuL06WPjzH+4dJV9yf54enzH87iMdbnt99eVS+squuyeEz/16bTfV+vqjdMx3zH0t/ZM3PN17XenZpzvqr6qSTfluTHGpa+krnmq6oXL31jHsziN0B8vmOGc5nxe++DY4xXjTFencUTTZ8cY9zcMcP5zHjbHazpVWXTne6fTbLnr/Sc8fYbWTwn8eZpvzcn+dwuL39bu3DfeSSX0NmvGef7SpLDVXVo2u8tSR7f7fWfz8w/F14x/fmyJH8jyYd2f4Lzu4D5tnTB3TLjk9felMUpt8eSPDp9vC2LVz98Iova/USSb1/6O38ri1dJPJGlVwwk2cjijvGLSe7OJfBEy5nne28WxfzN6c+fXJf5sij/kcUdx/PH+atrNN8rs/j9qI8lOZnkH2Xxv/F9P9umY746l8iT8Ge87S7P4pVzz992P5stXl23X+ebtn9Hkk9Ox/pEkmvXab7puqeS3LDXc+3S7XdHFvedj2UR0y9fo9k+ksV/CD6XPX7l+EXO96UkX0vyjSx+fh+etu+4W7wTPgBAM++EDwDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAs/8HEcisFh9Y4QMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1c YOUR CODE HERE\n",
    "dates = [\"15-Jan-2016\", \"16-Jan-2016\", \"17-Jan-2016\", \"18-Jan-2016\", \"10-Feb-2016\", \"11-Feb-2016\", \"12-Feb-2016\", \"08-Mar-2016\", \"09-Mar-2016\", \"10-Mar-2016\"]\n",
    "\n",
    "# df.groupby(df['date'].dt.strftime('%B'))['Revenue'].sum().sort_values()\n",
    "\n",
    "df['Ship Date'] = pd.to_datetime(df['Ship Date'], errors='coerce')sa\n",
    "    \n",
    "'''contributions_bernie = contrib[contrib[\"cand_nm\"] == \"Sanders, Bernard\"]\n",
    "contributions_bernie.groupby(contributions_bernie['contb_receipt_dt'].dt.strftime('%B'))['contb_receipt_amt'].sum().sort_values()\n",
    "contributions_clinton = contrib[contrib[\"cand_nm\"] == \"Clinton, Hillary Rodham\"]\n",
    "contributions_clinton.groupby(contributions_clinton['contb_receipt_dt'].dt.strftime('%B'))['contb_receipt_amt'].sum().sort_values()\n",
    "# Parse dates\n",
    "dates = [datetime.strptime(date, \"%d-%b-%Y\") for date in dates]\n",
    "\n",
    "debate_dates = [datetime.strptime(date, \"%d %b %Y\") for date in [\"17 Jan 2016\", \"11 Feb 2016\", \"9 Mar 2016\"]]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(dates, contributions_bernie, label=\"Candidate X\")\n",
    "plt.plot(dates, contributions_clinton, label=\"Candidate Y\")\n",
    "for date in debate_dates:\n",
    "    plt.axvline(x=date, color='r', linestyle='--', label=\"Primary Debate\")\n",
    "\n",
    "# Set labels and title\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Total Contribution Amount\")\n",
    "plt.title(\"Daily Contribution Trends \")\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cdb77f46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3         40.00\n",
       "4         35.00\n",
       "5        100.00\n",
       "6         25.00\n",
       "8         10.00\n",
       "          ...  \n",
       "666333    50.00\n",
       "666334   100.00\n",
       "666335    15.00\n",
       "666336    15.00\n",
       "666337    15.00\n",
       "Name: contb_receipt_amt, Length: 379284, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "contributions_bernie = contrib[contrib[\"cand_nm\"] == \"Sanders, Bernard\"].contb_receipt_amt\n",
    "contributions_clinton = contrib[contrib[\"cand_nm\"] == \"Clinton, Hillary Rodham\"].contb_receipt_amt\n",
    "contributions_clinton \n",
    "contributions_bernie "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23ae652",
   "metadata": {},
   "source": [
    "- **1c answer here:** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f49fe3f",
   "metadata": {},
   "source": [
    "## 2. Exploring Campaign Contributions (35 points)\n",
    "\n",
    "Let's investigate the donations to the candidates. (5 points)\n",
    "\n",
    "**2a.** Present a table that shows the number of donations to each candidate sorted by number of donations.**\n",
    "\n",
    "- When presenting data as a table, it is often best to sort the data in a meaningful way. This makes it easier for your reader to examine what you've done and to glean insights.  From now on, all tables that you present in this assignment (and course) should be sorted.\n",
    "- Hint: Use the `groupby` method. Groupby is explained in Unit 13: async 13.3 & 13.5\n",
    "- Hint: Use the `sort_values` method to sort the data so that candidates with the largest number of donations appear on top.\n",
    "\n",
    "Which candidate received the largest number/count of contributions (variable 'contb_receipt_amt')?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5724a721",
   "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>contb_receipt_amt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cand_nm</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sanders, Bernard</th>\n",
       "      <td>379284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinton, Hillary Rodham</th>\n",
       "      <td>171104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cruz, Rafael Edward 'Ted'</th>\n",
       "      <td>56355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carson, Benjamin S.</th>\n",
       "      <td>27015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rubio, Marco</th>\n",
       "      <td>13267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiorina, Carly</th>\n",
       "      <td>4679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paul, Rand</th>\n",
       "      <td>4164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trump, Donald J.</th>\n",
       "      <td>3806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bush, Jeb</th>\n",
       "      <td>3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kasich, John R.</th>\n",
       "      <td>2957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walker, Scott</th>\n",
       "      <td>666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huckabee, Mike</th>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O'Malley, Martin Joseph</th>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lessig, Lawrence</th>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christie, Christopher J.</th>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Lindsey O.</th>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stein, Jill</th>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Webb, James Henry Jr.</th>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perry, James R. (Rick)</th>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santorum, Richard J.</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson, Gary</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jindal, Bobby</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pataki, George E.</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gilmore, James S III</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           contb_receipt_amt\n",
       "cand_nm                                     \n",
       "Sanders, Bernard                      379284\n",
       "Clinton, Hillary Rodham               171104\n",
       "Cruz, Rafael Edward 'Ted'              56355\n",
       "Carson, Benjamin S.                    27015\n",
       "Rubio, Marco                           13267\n",
       "Fiorina, Carly                          4679\n",
       "Paul, Rand                              4164\n",
       "Trump, Donald J.                        3806\n",
       "Bush, Jeb                               3056\n",
       "Kasich, John R.                         2957\n",
       "Walker, Scott                            666\n",
       "Huckabee, Mike                           531\n",
       "O'Malley, Martin Joseph                  392\n",
       "Lessig, Lawrence                         353\n",
       "Christie, Christopher J.                 333\n",
       "Graham, Lindsey O.                       305\n",
       "Stein, Jill                              180\n",
       "Webb, James Henry Jr.                    106\n",
       "Perry, James R. (Rick)                   104\n",
       "Santorum, Richard J.                      85\n",
       "Johnson, Gary                             68\n",
       "Jindal, Bobby                             31\n",
       "Pataki, George E.                         20\n",
       "Gilmore, James S III                       3"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2a YOUR CODE HERE\n",
    "table1=contrib.groupby(['cand_nm']).agg({'contb_receipt_amt':len})\n",
    "table1.sort_values('contb_receipt_amt', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a9027b",
   "metadata": {},
   "source": [
    "- **2a answer here:** Sanders, Bernard received the largest number/count of contributions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8501abbf",
   "metadata": {},
   "source": [
    "**2b.** Now, present a table that shows the total value of donations to each candidate sorted by total value of the donations. (5 points)\n",
    "\n",
    "Which candidate raised the most money in California?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e5d104ad",
   "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>contb_receipt_amt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cand_nm</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clinton, Hillary Rodham</th>\n",
       "      <td>38,969,122.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sanders, Bernard</th>\n",
       "      <td>19,062,998.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cruz, Rafael Edward 'Ted'</th>\n",
       "      <td>5,926,568.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rubio, Marco</th>\n",
       "      <td>4,998,700.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bush, Jeb</th>\n",
       "      <td>3,316,836.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carson, Benjamin S.</th>\n",
       "      <td>3,022,105.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiorina, Carly</th>\n",
       "      <td>1,473,989.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kasich, John R.</th>\n",
       "      <td>1,471,417.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trump, Donald J.</th>\n",
       "      <td>868,672.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paul, Rand</th>\n",
       "      <td>721,563.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walker, Scott</th>\n",
       "      <td>492,706.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christie, Christopher J.</th>\n",
       "      <td>456,066.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Lindsey O.</th>\n",
       "      <td>379,495.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O'Malley, Martin Joseph</th>\n",
       "      <td>297,334.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huckabee, Mike</th>\n",
       "      <td>230,890.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perry, James R. (Rick)</th>\n",
       "      <td>208,400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lessig, Lawrence</th>\n",
       "      <td>148,032.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Webb, James Henry Jr.</th>\n",
       "      <td>76,568.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santorum, Richard J.</th>\n",
       "      <td>36,329.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson, Gary</th>\n",
       "      <td>31,787.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pataki, George E.</th>\n",
       "      <td>30,450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stein, Jill</th>\n",
       "      <td>25,841.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jindal, Bobby</th>\n",
       "      <td>23,231.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gilmore, James S III</th>\n",
       "      <td>8,100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           contb_receipt_amt\n",
       "cand_nm                                     \n",
       "Clinton, Hillary Rodham        38,969,122.68\n",
       "Sanders, Bernard               19,062,998.80\n",
       "Cruz, Rafael Edward 'Ted'       5,926,568.27\n",
       "Rubio, Marco                    4,998,700.92\n",
       "Bush, Jeb                       3,316,836.83\n",
       "Carson, Benjamin S.             3,022,105.15\n",
       "Fiorina, Carly                  1,473,989.42\n",
       "Kasich, John R.                 1,471,417.54\n",
       "Trump, Donald J.                  868,672.16\n",
       "Paul, Rand                        721,563.14\n",
       "Walker, Scott                     492,706.90\n",
       "Christie, Christopher J.          456,066.00\n",
       "Graham, Lindsey O.                379,495.00\n",
       "O'Malley, Martin Joseph           297,334.26\n",
       "Huckabee, Mike                    230,890.60\n",
       "Perry, James R. (Rick)            208,400.00\n",
       "Lessig, Lawrence                  148,032.52\n",
       "Webb, James Henry Jr.              76,568.16\n",
       "Santorum, Richard J.               36,329.88\n",
       "Johnson, Gary                      31,787.80\n",
       "Pataki, George E.                  30,450.00\n",
       "Stein, Jill                        25,841.00\n",
       "Jindal, Bobby                      23,231.26\n",
       "Gilmore, James S III                8,100.00"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2b YOUR CODE HERE\n",
    "table2=contrib.groupby(['cand_nm']).agg({'contb_receipt_amt':sum})\n",
    "table2.sort_values('contb_receipt_amt', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b970851",
   "metadata": {},
   "source": [
    "- **2b answer here:**  Clinton, Hillary Rodham raised the most money in California. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f71c2a1",
   "metadata": {},
   "source": [
    "**2c.** Combine the tables (sorted by either a or b above). (5 points)\n",
    "\n",
    "- Looking at the two tables you presented above - if those tables are Series convert them to DataFrames.\n",
    "- Rename the variable (column) names to accurately describe what is presented.\n",
    "- Merge together your tables to show the *count* and the *value* of donations to each candidate in one table.\n",
    "- Hint: Use the `merge` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "899afccb",
   "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>contb_receipt_amt_x</th>\n",
       "      <th>contb_receipt_amt_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cand_nm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bush, Jeb</th>\n",
       "      <td>3056</td>\n",
       "      <td>3,316,836.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carson, Benjamin S.</th>\n",
       "      <td>27015</td>\n",
       "      <td>3,022,105.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christie, Christopher J.</th>\n",
       "      <td>333</td>\n",
       "      <td>456,066.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinton, Hillary Rodham</th>\n",
       "      <td>171104</td>\n",
       "      <td>38,969,122.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cruz, Rafael Edward 'Ted'</th>\n",
       "      <td>56355</td>\n",
       "      <td>5,926,568.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           contb_receipt_amt_x  contb_receipt_amt_y\n",
       "cand_nm                                                            \n",
       "Bush, Jeb                                 3056         3,316,836.83\n",
       "Carson, Benjamin S.                      27015         3,022,105.15\n",
       "Christie, Christopher J.                   333           456,066.00\n",
       "Clinton, Hillary Rodham                 171104        38,969,122.68\n",
       "Cruz, Rafael Edward 'Ted'                56355         5,926,568.27"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2c YOUR CODE HERE\n",
    "\n",
    "#table3=contrib.groupby(['cand_nm']).agg(Donations_Count=('contb_receipt_amt',len),Donations_Amount=('contb_receipt_amt',sum))\n",
    "#table3\n",
    "table_chk = pd.merge(table1, table2, how=\"left\", on=\"cand_nm\")\n",
    "table_chk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccea1960",
   "metadata": {},
   "source": [
    "**2d. Calculate and add a new variable to the table from 2c that shows the average \\$ per donation. Print this table sorted by the average donation** (5 points)\n",
    "\n",
    "Which candidate had the highest average donation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e76bd4ed",
   "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>Donations_Count</th>\n",
       "      <th>Donations_Amount</th>\n",
       "      <th>AverageDonation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cand_nm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gilmore, James S III</th>\n",
       "      <td>3</td>\n",
       "      <td>8,100.00</td>\n",
       "      <td>2,700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perry, James R. (Rick)</th>\n",
       "      <td>104</td>\n",
       "      <td>208,400.00</td>\n",
       "      <td>2,003.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pataki, George E.</th>\n",
       "      <td>20</td>\n",
       "      <td>30,450.00</td>\n",
       "      <td>1,522.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christie, Christopher J.</th>\n",
       "      <td>333</td>\n",
       "      <td>456,066.00</td>\n",
       "      <td>1,369.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Lindsey O.</th>\n",
       "      <td>305</td>\n",
       "      <td>379,495.00</td>\n",
       "      <td>1,244.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bush, Jeb</th>\n",
       "      <td>3056</td>\n",
       "      <td>3,316,836.83</td>\n",
       "      <td>1,085.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O'Malley, Martin Joseph</th>\n",
       "      <td>392</td>\n",
       "      <td>297,334.26</td>\n",
       "      <td>758.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jindal, Bobby</th>\n",
       "      <td>31</td>\n",
       "      <td>23,231.26</td>\n",
       "      <td>749.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walker, Scott</th>\n",
       "      <td>666</td>\n",
       "      <td>492,706.90</td>\n",
       "      <td>739.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Webb, James Henry Jr.</th>\n",
       "      <td>106</td>\n",
       "      <td>76,568.16</td>\n",
       "      <td>722.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kasich, John R.</th>\n",
       "      <td>2957</td>\n",
       "      <td>1,471,417.54</td>\n",
       "      <td>497.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson, Gary</th>\n",
       "      <td>68</td>\n",
       "      <td>31,787.80</td>\n",
       "      <td>467.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huckabee, Mike</th>\n",
       "      <td>531</td>\n",
       "      <td>230,890.60</td>\n",
       "      <td>434.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santorum, Richard J.</th>\n",
       "      <td>85</td>\n",
       "      <td>36,329.88</td>\n",
       "      <td>427.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lessig, Lawrence</th>\n",
       "      <td>353</td>\n",
       "      <td>148,032.52</td>\n",
       "      <td>419.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rubio, Marco</th>\n",
       "      <td>13267</td>\n",
       "      <td>4,998,700.92</td>\n",
       "      <td>376.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiorina, Carly</th>\n",
       "      <td>4679</td>\n",
       "      <td>1,473,989.42</td>\n",
       "      <td>315.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trump, Donald J.</th>\n",
       "      <td>3806</td>\n",
       "      <td>868,672.16</td>\n",
       "      <td>228.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinton, Hillary Rodham</th>\n",
       "      <td>171104</td>\n",
       "      <td>38,969,122.68</td>\n",
       "      <td>227.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paul, Rand</th>\n",
       "      <td>4164</td>\n",
       "      <td>721,563.14</td>\n",
       "      <td>173.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stein, Jill</th>\n",
       "      <td>180</td>\n",
       "      <td>25,841.00</td>\n",
       "      <td>143.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carson, Benjamin S.</th>\n",
       "      <td>27015</td>\n",
       "      <td>3,022,105.15</td>\n",
       "      <td>111.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cruz, Rafael Edward 'Ted'</th>\n",
       "      <td>56355</td>\n",
       "      <td>5,926,568.27</td>\n",
       "      <td>105.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sanders, Bernard</th>\n",
       "      <td>379284</td>\n",
       "      <td>19,062,998.80</td>\n",
       "      <td>50.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Donations_Count  Donations_Amount  AverageDonation\n",
       "cand_nm                                                                      \n",
       "Gilmore, James S III                     3          8,100.00         2,700.00\n",
       "Perry, James R. (Rick)                 104        208,400.00         2,003.85\n",
       "Pataki, George E.                       20         30,450.00         1,522.50\n",
       "Christie, Christopher J.               333        456,066.00         1,369.57\n",
       "Graham, Lindsey O.                     305        379,495.00         1,244.25\n",
       "Bush, Jeb                             3056      3,316,836.83         1,085.35\n",
       "O'Malley, Martin Joseph                392        297,334.26           758.51\n",
       "Jindal, Bobby                           31         23,231.26           749.40\n",
       "Walker, Scott                          666        492,706.90           739.80\n",
       "Webb, James Henry Jr.                  106         76,568.16           722.34\n",
       "Kasich, John R.                       2957      1,471,417.54           497.60\n",
       "Johnson, Gary                           68         31,787.80           467.47\n",
       "Huckabee, Mike                         531        230,890.60           434.82\n",
       "Santorum, Richard J.                    85         36,329.88           427.41\n",
       "Lessig, Lawrence                       353        148,032.52           419.36\n",
       "Rubio, Marco                         13267      4,998,700.92           376.78\n",
       "Fiorina, Carly                        4679      1,473,989.42           315.02\n",
       "Trump, Donald J.                      3806        868,672.16           228.24\n",
       "Clinton, Hillary Rodham             171104     38,969,122.68           227.75\n",
       "Paul, Rand                            4164        721,563.14           173.29\n",
       "Stein, Jill                            180         25,841.00           143.56\n",
       "Carson, Benjamin S.                  27015      3,022,105.15           111.87\n",
       "Cruz, Rafael Edward 'Ted'            56355      5,926,568.27           105.16\n",
       "Sanders, Bernard                    379284     19,062,998.80            50.26"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2d YOUR CODE HERE\n",
    "table4=contrib.groupby(['cand_nm']).agg(Donations_Count=('contb_receipt_amt',len),Donations_Amount=('contb_receipt_amt',sum),AverageDonation=('contb_receipt_amt',np.mean))\n",
    "table4.sort_values('AverageDonation', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff4ec099",
   "metadata": {},
   "source": [
    "- **2d answer here:** Gilmore, James S III candidate had the highest average donation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6faff47",
   "metadata": {},
   "source": [
    "**2e. Plotting a Bar Chart** (10 points)\n",
    "\n",
    "Make a single bar chart that shows two different bars per candidate with one bar as the total value of the donations and the other as average $ per donation. \n",
    "- Show the Candidates Name on the x-axis\n",
    "- Show the amount on the y-axis\n",
    "- Include a title\n",
    "- Include axis labels\n",
    "- Sort by total value of the donations\n",
    "- Make the y-axis a log-scale to show both numbers! (matplotlib docs: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.yscale.html )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cb0d4a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We further need to manipulate the data\n",
    "\n",
    "#converting donation column to a series \n",
    "cand=contrib['cand_nm'].values.tolist()\n",
    "\n",
    "#converting AverageDonation column to a series \n",
    "avg_don=table4['AverageDonation'].values.tolist()\n",
    "\n",
    "#converting total donation amount column to a series \n",
    "tot_don=table4['Donations_Amount'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "352360eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "candidates_l=contrib.cand_nm.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "924956e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(len(candidates_l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "a5162502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2e YOUR CODE HERE\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "\n",
    "candidates_l=contrib.cand_nm.unique()\n",
    "\n",
    "X_axis = np.arange(len(candidates_l)) \n",
    "\n",
    "\n",
    "plt.bar(X_axis-0.2, avg_don,color ='maroon',width = 0.5, label = 'AverageDonation')  \n",
    "plt.bar(X_axis + 0.2, tot_don,color='blue',width= 0.5, label = 'Donations_Amount') \n",
    "plt.rcParams[\"figure.figsize\"] = [7.00, 10.00]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "plt.xticks(X_axis,candidates_l,rotation=90) \n",
    "ax2.set_yscale('log')\n",
    "plt.xlabel(\"Candidates\",ha='right') \n",
    "plt.ylabel(\"Donations\",ha='right') \n",
    "plt.title(\"Donations per Candidates\") \n",
    "plt.legend() \n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8df977",
   "metadata": {},
   "source": [
    "**2f. Comment on the results of your data analysis in a short paragraph.** (5 points)\n",
    "\n",
    "- There are several interesting conclusions you can draw from the table you have created.\n",
    "- What have you learned about campaign contributions in California?\n",
    "- We are looking for data insights here rather than comments on the code!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b79826",
   "metadata": {},
   "source": [
    "- **2f answer here:** Sanders, Bernard has the highest count of donations at 379284 followed by Hillary Clinton at 171104. Hillary Clinton has tthe highest total amount of donation at 38,969,122.68 followed by Sanders, Bernard at 19,062,998.80"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653b0dde",
   "metadata": {},
   "source": [
    "## 3. Exploring Donor Occupations (35 points)\n",
    "\n",
    "Above in part 2, we saw that some simple data analysis can give us insights into the campaigns of our candidates. Now let's quickly look to see what *kind* of person is donating to each campaign using the `contbr_occupation` variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2227ba0d",
   "metadata": {},
   "source": [
    "**3a.** Show the top 5 occupations of individuals that contributed to Hillary Clinton. (5 points)\n",
    "\n",
    "- Subset your data to create a dataframe with only donations for Hillary Clinton.\n",
    "- Then use the `value_counts` and `head` methods to present the top 5 occupations (`contbr_occupation`) for her donors.\n",
    "- Note: we are just interested in the count of donations, not the value of those donations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ba110522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RETIRED                  35767\n",
       "ATTORNEY                  7514\n",
       "INFORMATION REQUESTED     4991\n",
       "TEACHER                   3848\n",
       "HOMEMAKER                 3522\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3a YOUR CODE HERE\n",
    "#contrib.info()\n",
    "filtered_df=contrib[contrib[\"cand_nm\"]=='Clinton, Hillary Rodham']\n",
    "\n",
    "filtered_df2=filtered_df.contbr_occupation.value_counts()\n",
    "top5_clinton=filtered_df2.squeeze(axis=0)[:5]\n",
    "top5_clinton"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0f057f",
   "metadata": {},
   "source": [
    "The top 5 occupations of individuals that contributed to Hillary Clinton are \n",
    "1. Retired\n",
    "2. Attorney\n",
    "3. Information Requested \n",
    "4. Teacher\n",
    "5. Homemaker"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eecfacae",
   "metadata": {},
   "source": [
    "**3b.** Write a function called `get_donors`. (5 points)\n",
    " \n",
    "Imagine that you want to do the previous operation on several candidates.  To keep your work neat, you want to take the work you did on the Clinton-subset and wrap it in a function that you can apply to other subsets of the data.\n",
    "\n",
    "- The function should take a DataFrame as a parameter, and return a Series containing the counts for the top 5 occupations contained in that DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "07a3a233",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_donors(df):\n",
    "    \"\"\"This function takes a dataframe that contains a variable named contbr_occupation.\n",
    "    It outputs a Series containing the counts for the 5 most common values of that\n",
    "    variable.\"\"\"\n",
    "    \n",
    "    # 3b YOUR CODE HERE\n",
    "\n",
    "    df=df.contbr_occupation.value_counts()\n",
    "    top5=df.squeeze(axis=0)[:5]\n",
    "    return top5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5307681b",
   "metadata": {},
   "source": [
    "**3c.** Now run the `get_donors` function from 3b on subsets of the dataframe corresponding to three candidates. Show each of the three candidates below. (5 points)\n",
    "\n",
    "- Hillary Clinton\n",
    "- Bernie Sanders\n",
    "- Donald Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "4625a08e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RETIRED                  35767\n",
       "ATTORNEY                  7514\n",
       "INFORMATION REQUESTED     4991\n",
       "TEACHER                   3848\n",
       "HOMEMAKER                 3522\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3c YOUR CODE HERE\n",
    "#Hillary Clinton\n",
    "clinton_df=contrib[contrib[\"cand_nm\"]=='Clinton, Hillary Rodham']\n",
    "get_donors(clinton_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "30ab8fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NOT EMPLOYED         98217\n",
       "RETIRED              15352\n",
       "TEACHER              10390\n",
       "SOFTWARE ENGINEER     7957\n",
       "ENGINEER              7237\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bernie Sanders\n",
    "brnie_df=contrib[contrib[\"cand_nm\"]=='Sanders, Bernard']\n",
    "get_donors(brnie_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "d7fb1750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RETIRED                  1719\n",
       "INFORMATION REQUESTED      88\n",
       "CEO                        62\n",
       "SALES                      58\n",
       "OWNER                      57\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Donald Trump\n",
    "trump_df=contrib[contrib[\"cand_nm\"]=='Trump, Donald J.']\n",
    "get_donors(trump_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6523d39",
   "metadata": {},
   "source": [
    "**3d.** Finally, use `groupby` to separate the entire dataset by candidate. (10 points)\n",
    "\n",
    "- Call .apply(get_donors) on your groupby object, which will apply the function you wrote to each subset of your data.\n",
    "- Look at your output and marvel at what pandas can do in just one line!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "fb66b0d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm                                                          \n",
       "Bush, Jeb                  RETIRED                                     992\n",
       "                           HOMEMAKER                                   177\n",
       "                           ATTORNEY                                     90\n",
       "                           Q.A.                                         89\n",
       "                           PRESIDENT                                    83\n",
       "Carson, Benjamin S.        RETIRED                                   13635\n",
       "                           INFORMATION REQUESTED PER BEST EFFORTS     1610\n",
       "                           HOMEMAKER                                  1028\n",
       "                           PHYSICIAN                                   321\n",
       "                           R.N.                                        232\n",
       "Christie, Christopher J.   RETIRED                                      38\n",
       "                           NONE                                         22\n",
       "                           CONSULTANT                                   18\n",
       "                           CEO                                          16\n",
       "                           HOMEMAKER                                    15\n",
       "Clinton, Hillary Rodham    RETIRED                                   35767\n",
       "                           ATTORNEY                                   7514\n",
       "                           INFORMATION REQUESTED                      4991\n",
       "                           TEACHER                                    3848\n",
       "                           HOMEMAKER                                  3522\n",
       "Cruz, Rafael Edward 'Ted'  RETIRED                                   22617\n",
       "                           INFORMATION REQUESTED PER BEST EFFORTS     1633\n",
       "                           HOMEMAKER                                  1237\n",
       "                           ENGINEER                                   1078\n",
       "                           TEACHER                                     827\n",
       "Fiorina, Carly             RETIRED                                    1969\n",
       "                           HOMEMAKER                                   166\n",
       "                           ATTORNEY                                    140\n",
       "                           INFORMATION REQUESTED PER BEST EFFORTS       95\n",
       "                           INVESTOR                                     64\n",
       "Gilmore, James S III       FINANCIAL ADVISOR                             1\n",
       "                           COB                                           1\n",
       "                           INVESTMENT MANAGEMENT                         1\n",
       "Graham, Lindsey O.         RETIRED                                      73\n",
       "                           INFORMATION REQUESTED PER BEST EFFORTS       19\n",
       "                           P/T ADJUNCT                                  13\n",
       "                           EXECUTIVE                                    11\n",
       "                           REAL ESTATE                                  11\n",
       "Huckabee, Mike             RETIRED                                     206\n",
       "                           REQUESTED PER BEST EFFORTS                   36\n",
       "                           HOMEMAKER                                    22\n",
       "                           OWNER                                        13\n",
       "                           FUNERAL DIRECTOR                             12\n",
       "Jindal, Bobby              RETIRED                                       5\n",
       "                           ENGINEER                                      4\n",
       "                           PROFESSOR                                     3\n",
       "                           REALTOR                                       2\n",
       "                           PRESIDENT                                     2\n",
       "Johnson, Gary              RETIRED                                      16\n",
       "                           TWICE REQUESTED, NOT RECEIVED                11\n",
       "                           SOFTWARE ENGINEER                             5\n",
       "                           ELECTRONIC TECHNICIAN                         3\n",
       "                           ENGINEER                                      2\n",
       "Kasich, John R.            RETIRED                                     719\n",
       "                           ATTORNEY                                    124\n",
       "                           CEO                                          76\n",
       "                           HOMEMAKER                                    65\n",
       "                           PRESIDENT                                    63\n",
       "Lessig, Lawrence           SOFTWARE ENGINEER                            56\n",
       "                           DEVELOPER                                    42\n",
       "                           RETIRED                                      26\n",
       "                           ATTORNEY                                     18\n",
       "                           ENGINEER                                     17\n",
       "O'Malley, Martin Joseph    RETIRED                                      52\n",
       "                           ATTORNEY                                     26\n",
       "                           LECTURER                                     23\n",
       "                           CONSULTANT                                   20\n",
       "                           CEO                                          14\n",
       "Pataki, George E.          ATTORNEY                                      3\n",
       "                           INFORMATION REQUESTED                         2\n",
       "                           HOMEMAKER                                     2\n",
       "                           PRESIDENT                                     2\n",
       "                           CONSULTANT                                    1\n",
       "Paul, Rand                 RETIRED                                     866\n",
       "                           ENGINEER                                    329\n",
       "                           INFORMATION REQUESTED                       176\n",
       "                           CONSULTANT                                   95\n",
       "                           HOMEMAKER                                    79\n",
       "Perry, James R. (Rick)     HOMEMAKER                                    10\n",
       "                           PRESIDENT                                    10\n",
       "                           ATTORNEY                                      7\n",
       "                           EXECUTIVE                                     6\n",
       "                           OWNER                                         5\n",
       "Rubio, Marco               RETIRED                                    5504\n",
       "                           HOMEMAKER                                   577\n",
       "                           INFORMATION REQUESTED PER BEST EFFORTS      482\n",
       "                           ATTORNEY                                    340\n",
       "                           PHYSICIAN                                   181\n",
       "Sanders, Bernard           NOT EMPLOYED                              98217\n",
       "                           RETIRED                                   15352\n",
       "                           TEACHER                                   10390\n",
       "                           SOFTWARE ENGINEER                          7957\n",
       "                           ENGINEER                                   7237\n",
       "Santorum, Richard J.       RETIRED                                      45\n",
       "                           ENGINEER                                      5\n",
       "                           MACHINIST                                     4\n",
       "                           PSYCHOLOGIST                                  3\n",
       "                           GENERAL CONTRACTOR                            3\n",
       "Stein, Jill                RETIRED                                      30\n",
       "                           RESEARCHER                                   20\n",
       "                           WRITER                                       19\n",
       "                           SOFTWARE ENGINEER                             8\n",
       "                           FORMAL FINANCIAL SERVICES EMPLOYEE            8\n",
       "Trump, Donald J.           RETIRED                                    1719\n",
       "                           INFORMATION REQUESTED                        88\n",
       "                           CEO                                          62\n",
       "                           SALES                                        58\n",
       "                           OWNER                                        57\n",
       "Walker, Scott              RETIRED                                     228\n",
       "                           INFORMATION REQUESTED                        67\n",
       "                           PRESIDENT                                    33\n",
       "                           HOMEMAKER                                    31\n",
       "                           ATTORNEY                                     27\n",
       "Webb, James Henry Jr.      RETIRED                                      14\n",
       "                           ATTORNEY                                      8\n",
       "                           DEPUTY DISTRICT ATTORNEY                      7\n",
       "                           NOT EMPLOYED                                  5\n",
       "                           MATH EDUCATOR                                 5\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3d YOUR CODE HERE\n",
    "contrib.groupby('cand_nm').apply(get_donors)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e05fcd7",
   "metadata": {},
   "source": [
    "**3e.** Comment on your data insights & findings in a short paragraph. (5 points)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e808a24",
   "metadata": {},
   "source": [
    "- **3e answer here:** It is interesting to note that the Retired community has the most number of donations amongs all candidates. Bernie Sanders has the most number of donations at folowed by Hillary Cinton. It is interesting to note that 'Not Employed' donor category was the highest for Bernie Sanders, who had overall highest number of donors.   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964388c9",
   "metadata": {},
   "source": [
    "**3f.** Think about your findings in section 3 vs. your findings in section 2 of this assignment. (5 points)\n",
    "\n",
    "Do you have any new data insights into the results you saw in section 2 now that you see the top occupations for each candidate?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377713a0",
   "metadata": {},
   "source": [
    "- **3f answer here:** It is interesting to note that maximum number of donors are RETIRED and NOT EMPLOYED.    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3252182f",
   "metadata": {},
   "source": [
    "## If you have feedback for this homework, please submit it using the link below:\n",
    "\n",
    "http://goo.gl/forms/74yCiQTf6k"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}