Gitcoin_analysis_and_visualization.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Adebolaorogun/Data-analysis/blob/main/Gitcoin_analysis_and_visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> This project involved exploration and analysis of the data with the goal of finding trends related to grabnt approval. At this time, focus was placed on grant results and grant contributions following data cleaning. More work will be required to refine analysis and create a dynamic open source tool that allows users to define and test their own hypothesis against the data collected.\n",
        "\n",
        "> In the meantime, I've created a first draft using the tool Akuko - https://app.akuko.io/post/2b107419-ac30-4376-b172-5f82fd3880bf\n",
        "\n",
        "> From review of trends, it seems that grants with more complete submissions have a slight edge in terms of approval. This is specifically shown with github url and website inclusion which could be related to evidence of work and activity. Additionally, there does seem to be a trend regarding constribution amount and distinct count of contributors. There were quite a few denied grants that had a lot more contributions to a small number of distinct contributors."
      ],
      "metadata": {
        "id": "Otg0R_B2m5cu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8-t6OussWN-h"
      },
      "outputs": [],
      "source": [
        "# Import necessary packages\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Links to base tables\n",
        "results = pd.read_json('/content/drive/MyDrive/Gitcoin_assignment/Copy of grants_applications_gr15.json', orient='index')\n",
        "applications = pd.read_json('/content/drive/MyDrive/Gitcoin_assignment/Copy of gr15_grants.json', orient='index')\n",
        "contributions = pd.read_csv('/content/drive/MyDrive/Gitcoin_assignment/Copy of hackathon-contributions-dataset.csv')\n",
        "contributions_v2 = pd.read_csv('/content/drive/MyDrive/Gitcoin_assignment/Copy of hackathon-contributions-dataset_v2.csv')\n",
        "\n",
        "total_contributions = pd.concat([contributions , contributions_v2], ignore_index=True, axis=0)"
      ],
      "metadata": {
        "id": "Iw42VLMzWy1g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Review and print of base tables\n",
        "print('Results')\n",
        "print(len(results))\n",
        "print(results.head())\n",
        "results.to_csv('results.csv', index=False)\n",
        "\n",
        "print('Applications')\n",
        "print(len(applications))\n",
        "print(applications.head())\n",
        "applications.to_csv('applications.csv', index=False)\n",
        "\n",
        "print('Contributions')\n",
        "print(len(total_contributions))\n",
        "print(total_contributions.head())\n",
        "total_contributions.to_csv('contributions.csv', index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJCgNk8bXn_I",
        "outputId": "04ee577f-5416-4678-cf14-41ac4e2d1e71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results\n",
            "808\n",
            "   grant_id  active  approved                                     address  \\\n",
            "0      6841   False     False  0xDc4C1D5D42f829dfd4b772d3a678CfB178ee6476   \n",
            "1      6842    True      True  0xCE41b49d28800De3Ba9925f68D82129dFc4b870B   \n",
            "2      6843   False     False  0x8871b4405018e75f08a0bbc9f1fb0241f8b188ae   \n",
            "3      6844   False     False  0x4C3477e9756610fcB7Dac007C03aE0D666fFc54E   \n",
            "4      6845   False     False  0x9486C5CFE392763A25dC30C9F62f6D21f73d220f   \n",
            "\n",
            "                                               title  \\\n",
            "0                                            4C Labs   \n",
            "1    C# library for accessing NFTs on mobile devices   \n",
            "2                                VDAO - First VC DAO   \n",
            "3  Unisound: Web3.0 sample pack marketplace for m...   \n",
            "4    Univerz.io decentralized - marketplace platform   \n",
            "\n",
            "                                                 url  \\\n",
            "0             https://gitcoin.co/grants/6841/4c-labs   \n",
            "1  https://gitcoin.co/grants/6842/c-library-for-a...   \n",
            "2   https://gitcoin.co/grants/6843/vdao-first-vc-dao   \n",
            "3  https://gitcoin.co/grants/6844/unisound-web30-...   \n",
            "4  https://gitcoin.co/grants/6845/univerzio-decen...   \n",
            "\n",
            "                                         description  \\\n",
            "0  Project Core:\\r\\n\\t4C hopes to become the Brid...   \n",
            "1  This is a lightweight open source C# library t...   \n",
            "2  The project is a fund investing (early stage i...   \n",
            "3  Unisound enables professional, yet undiscovere...   \n",
            "4  Hello Fam we are looking to raise little funds...   \n",
            "\n",
            "                      created_on  \n",
            "0  2022-06-24T03:39:54.292-06:00  \n",
            "1  2022-06-24T07:41:41.000-06:00  \n",
            "2  2022-06-24T09:37:40.087-06:00  \n",
            "3  2022-06-24T11:00:32.560-06:00  \n",
            "4  2022-06-24T12:33:04.737-06:00  \n",
            "Applications\n",
            "1503\n",
            "   grant_id  active                                              title  \\\n",
            "0      8017    True  Alphabetting, the first hedge fund based on fa...   \n",
            "1      7979    True  Ikhala Impact - a one stop data shop for small...   \n",
            "2      7977    True                             DOM – unstoppable bank   \n",
            "3      7976    True  Aquarian Creative: Artist-In-Residence, Schola...   \n",
            "4      7974    True                SolDAO — Building Solidity as a DAO   \n",
            "\n",
            "                                      address  amount_received  \\\n",
            "0  0xE7048aB8dFA7F7a80d79fD6c5AcF8B0C1d174fdF          15.2506   \n",
            "1  0xcC17a95B3f5861BC2cfD28ca81d611077ef36ED1          13.2079   \n",
            "2  0x715465a41a34Fb1Af53328708171c0FBf2B88825          31.1689   \n",
            "3  0x7ABDd18D37571e1BA884A8fA9E07a3612b584661           6.5128   \n",
            "4  0xF77494B20F2DF03a7663465a001486F834739A78         237.7176   \n",
            "\n",
            "   amount_received_in_round  contribution_count  contributor_count  \\\n",
            "0                   15.2506                  13                 13   \n",
            "1                   13.2079                   7                  7   \n",
            "2                   31.1689                   7                  7   \n",
            "3                    6.5128                   5                  5   \n",
            "4                  237.7176                 167                148   \n",
            "\n",
            "                                         description  \\\n",
            "0  Alpha Fund returns are not correlated to the p...   \n",
            "1  👋About ADTech👋🏾\\r\\n\\r\\nAfrican Data Technologi...   \n",
            "2  DOM – unstoppable bank\\r\\nturn on, tune in, dr...   \n",
            "3  The Aquarian Creative mission promotes the edu...   \n",
            "4  Solidity smart contracts are more secure and e...   \n",
            "\n",
            "                                         website  \\\n",
            "0                https://www.alphabetting.com.br   \n",
            "1                  https://www.africandata.tech/   \n",
            "2                             https://dom.money/   \n",
            "3  http://m0niquesullivan.com/aquarian-creative/   \n",
            "4                       https://solgov.eth.limo/   \n",
            "\n",
            "                           github_project_url twitter_handle_2  \\\n",
            "0                                        None     leanderdulac   \n",
            "1  https://github.com/SambaGod/IkhalaPlatform     bekker_wihan   \n",
            "2                                        None       GringamorH   \n",
            "3                                        None  M0niquesullivan   \n",
            "4                  https://github.com/Sol-DAO          _SolDAO   \n",
            "\n",
            "  twitter_handle_1  twitter_verified                 created_on  \\\n",
            "0     leanderdulac              True  2022-09-16 00:08:35+00:00   \n",
            "1     IkhalaImpact              True  2022-09-15 14:39:42+00:00   \n",
            "2   DOMUNSTOPPABLE              True  2022-09-15 12:57:56+00:00   \n",
            "3  M0niquesullivan              True  2022-09-15 12:03:10+00:00   \n",
            "4          _SolDAO             False  2022-09-15 09:33:47+00:00   \n",
            "\n",
            "                        last_update  \n",
            "0  2022-09-28 19:53:25.215202+00:00  \n",
            "1  2022-09-22 15:23:39.957873+00:00  \n",
            "2         2022-09-15 12:57:56+00:00  \n",
            "3         2022-09-15 12:03:10+00:00  \n",
            "4         2022-09-15 09:33:47+00:00  \n",
            "Contributions\n",
            "950053\n",
            "                                              txn_id  \\\n",
            "0  9a7955760b945121d7ac2e3ccf8c53bb19389939d83a78...   \n",
            "1  8d656b874ef8b642bbab30e2e0164922287d8156182e61...   \n",
            "2  fe14bb2c777595fcc407de7ec454e26ddaa48df2c11a6e...   \n",
            "3  1d5435fae1a9942df47eb4a30c49acf418888fc7bbd233...   \n",
            "4  e3aa9456a2fac0019086e42b9a065ac0e40d108e436d1b...   \n",
            "\n",
            "                                             user_id  \\\n",
            "0  205fdbe0783c1725849a6c243e23156d3c6679e2403beb...   \n",
            "1  9872683fceb5011d980cbdce560d5c3c1dd4007ee936be...   \n",
            "2  67d35e9ecb471310efe99c9d12ffc6a9816db2cf6e9ac0...   \n",
            "3  9872683fceb5011d980cbdce560d5c3c1dd4007ee936be...   \n",
            "4  9872683fceb5011d980cbdce560d5c3c1dd4007ee936be...   \n",
            "\n",
            "                                      address  grant_id        chain  token  \\\n",
            "0  0x76f69dcddd0593b0aff5fd3280c3433ddb68e0d2      6972      eth_std    ETH   \n",
            "1  0x1bc5ebee4738fd95bd96751c45a90889f772e0f3      2400   eth_zksync    ETH   \n",
            "2  0x3812801cbf0e41413db4835a5e36228ad45e32bf      7243  eth_polygon  MATIC   \n",
            "3  0x1bc5ebee4738fd95bd96751c45a90889f772e0f3      3023   eth_zksync    ETH   \n",
            "4  0x1bc5ebee4738fd95bd96751c45a90889f772e0f3      4811   eth_zksync    ETH   \n",
            "\n",
            "   amount_in_usdt                         timestamp txn_hash network  \n",
            "0        1.283650  2022-09-22 23:59:55.773190+00:00      NaN     NaN  \n",
            "1        1.001247  2022-09-22 23:59:53.052568+00:00      NaN     NaN  \n",
            "2        1.001943  2022-09-22 23:59:52.331798+00:00      NaN     NaN  \n",
            "3        1.001247  2022-09-22 23:59:52.133974+00:00      NaN     NaN  \n",
            "4        1.001247  2022-09-22 23:59:51.260699+00:00      NaN     NaN  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset with grant approval results as well as data from the application\n",
        "results_extended = results.merge(applications, on='grant_id', how='left')\n",
        "results_extended.to_csv('results_extended.csv', index=False)\n",
        "\n",
        "results_extended.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "mFrqLm5NYfCV",
        "outputId": "de0aa21f-4f8e-454f-9904-9524f6b97f2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   grant_id  active_x  approved                                   address_x  \\\n",
              "0      6841     False     False  0xDc4C1D5D42f829dfd4b772d3a678CfB178ee6476   \n",
              "1      6842      True      True  0xCE41b49d28800De3Ba9925f68D82129dFc4b870B   \n",
              "2      6843     False     False  0x8871b4405018e75f08a0bbc9f1fb0241f8b188ae   \n",
              "3      6844     False     False  0x4C3477e9756610fcB7Dac007C03aE0D666fFc54E   \n",
              "4      6845     False     False  0x9486C5CFE392763A25dC30C9F62f6D21f73d220f   \n",
              "\n",
              "                                             title_x  \\\n",
              "0                                            4C Labs   \n",
              "1    C# library for accessing NFTs on mobile devices   \n",
              "2                                VDAO - First VC DAO   \n",
              "3  Unisound: Web3.0 sample pack marketplace for m...   \n",
              "4    Univerz.io decentralized - marketplace platform   \n",
              "\n",
              "                                                 url  \\\n",
              "0             https://gitcoin.co/grants/6841/4c-labs   \n",
              "1  https://gitcoin.co/grants/6842/c-library-for-a...   \n",
              "2   https://gitcoin.co/grants/6843/vdao-first-vc-dao   \n",
              "3  https://gitcoin.co/grants/6844/unisound-web30-...   \n",
              "4  https://gitcoin.co/grants/6845/univerzio-decen...   \n",
              "\n",
              "                                       description_x  \\\n",
              "0  Project Core:\\r\\n\\t4C hopes to become the Brid...   \n",
              "1  This is a lightweight open source C# library t...   \n",
              "2  The project is a fund investing (early stage i...   \n",
              "3  Unisound enables professional, yet undiscovere...   \n",
              "4  Hello Fam we are looking to raise little funds...   \n",
              "\n",
              "                    created_on_x active_y title_y  ... contribution_count  \\\n",
              "0  2022-06-24T03:39:54.292-06:00      NaN     NaN  ...                NaN   \n",
              "1  2022-06-24T07:41:41.000-06:00      NaN     NaN  ...                NaN   \n",
              "2  2022-06-24T09:37:40.087-06:00      NaN     NaN  ...                NaN   \n",
              "3  2022-06-24T11:00:32.560-06:00      NaN     NaN  ...                NaN   \n",
              "4  2022-06-24T12:33:04.737-06:00      NaN     NaN  ...                NaN   \n",
              "\n",
              "   contributor_count  description_y  website  github_project_url  \\\n",
              "0                NaN            NaN      NaN                 NaN   \n",
              "1                NaN            NaN      NaN                 NaN   \n",
              "2                NaN            NaN      NaN                 NaN   \n",
              "3                NaN            NaN      NaN                 NaN   \n",
              "4                NaN            NaN      NaN                 NaN   \n",
              "\n",
              "  twitter_handle_2 twitter_handle_1 twitter_verified created_on_y last_update  \n",
              "0              NaN              NaN              NaN          NaN         NaN  \n",
              "1              NaN              NaN              NaN          NaN         NaN  \n",
              "2              NaN              NaN              NaN          NaN         NaN  \n",
              "3              NaN              NaN              NaN          NaN         NaN  \n",
              "4              NaN              NaN              NaN          NaN         NaN  \n",
              "\n",
              "[5 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29bdd4ee-90b1-4a37-8388-b91f68247d28\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>grant_id</th>\n",
              "      <th>active_x</th>\n",
              "      <th>approved</th>\n",
              "      <th>address_x</th>\n",
              "      <th>title_x</th>\n",
              "      <th>url</th>\n",
              "      <th>description_x</th>\n",
              "      <th>created_on_x</th>\n",
              "      <th>active_y</th>\n",
              "      <th>title_y</th>\n",
              "      <th>...</th>\n",
              "      <th>contribution_count</th>\n",
              "      <th>contributor_count</th>\n",
              "      <th>description_y</th>\n",
              "      <th>website</th>\n",
              "      <th>github_project_url</th>\n",
              "      <th>twitter_handle_2</th>\n",
              "      <th>twitter_handle_1</th>\n",
              "      <th>twitter_verified</th>\n",
              "      <th>created_on_y</th>\n",
              "      <th>last_update</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6841</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0xDc4C1D5D42f829dfd4b772d3a678CfB178ee6476</td>\n",
              "      <td>4C Labs</td>\n",
              "      <td>https://gitcoin.co/grants/6841/4c-labs</td>\n",
              "      <td>Project Core:\\r\\n\\t4C hopes to become the Brid...</td>\n",
              "      <td>2022-06-24T03:39:54.292-06:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6842</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>0xCE41b49d28800De3Ba9925f68D82129dFc4b870B</td>\n",
              "      <td>C# library for accessing NFTs on mobile devices</td>\n",
              "      <td>https://gitcoin.co/grants/6842/c-library-for-a...</td>\n",
              "      <td>This is a lightweight open source C# library t...</td>\n",
              "      <td>2022-06-24T07:41:41.000-06:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6843</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0x8871b4405018e75f08a0bbc9f1fb0241f8b188ae</td>\n",
              "      <td>VDAO - First VC DAO</td>\n",
              "      <td>https://gitcoin.co/grants/6843/vdao-first-vc-dao</td>\n",
              "      <td>The project is a fund investing (early stage i...</td>\n",
              "      <td>2022-06-24T09:37:40.087-06:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6844</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0x4C3477e9756610fcB7Dac007C03aE0D666fFc54E</td>\n",
              "      <td>Unisound: Web3.0 sample pack marketplace for m...</td>\n",
              "      <td>https://gitcoin.co/grants/6844/unisound-web30-...</td>\n",
              "      <td>Unisound enables professional, yet undiscovere...</td>\n",
              "      <td>2022-06-24T11:00:32.560-06:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6845</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0x9486C5CFE392763A25dC30C9F62f6D21f73d220f</td>\n",
              "      <td>Univerz.io decentralized - marketplace platform</td>\n",
              "      <td>https://gitcoin.co/grants/6845/univerzio-decen...</td>\n",
              "      <td>Hello Fam we are looking to raise little funds...</td>\n",
              "      <td>2022-06-24T12:33:04.737-06:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29bdd4ee-90b1-4a37-8388-b91f68247d28')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-29bdd4ee-90b1-4a37-8388-b91f68247d28 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-29bdd4ee-90b1-4a37-8388-b91f68247d28');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset of contributions extended with join on grant_id\n",
        "contributions_extended = total_contributions.merge(results, on='grant_id', how='left')\n",
        "contributions_extended.rename(columns = {'address_x':'address', 'address_y':'result_address'}, inplace = True)\n",
        "\n",
        "contributions_extended.to_csv('contributions_result_extended_.csv', index=False)\n",
        "contributions_extended.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "mhfzw0iDY4vc",
        "outputId": "3700cec1-f054-4ac0-963d-bc69ed1006e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              txn_id  \\\n",
              "0  9a7955760b945121d7ac2e3ccf8c53bb19389939d83a78...   \n",
              "1  8d656b874ef8b642bbab30e2e0164922287d8156182e61...   \n",
              "2  fe14bb2c777595fcc407de7ec454e26ddaa48df2c11a6e...   \n",
              "3  1d5435fae1a9942df47eb4a30c49acf418888fc7bbd233...   \n",
              "4  e3aa9456a2fac0019086e42b9a065ac0e40d108e436d1b...   \n",
              "\n",
              "                                             user_id  \\\n",
              "0  205fdbe0783c1725849a6c243e23156d3c6679e2403beb...   \n",
              "1  9872683fceb5011d980cbdce560d5c3c1dd4007ee936be...   \n",
              "2  67d35e9ecb471310efe99c9d12ffc6a9816db2cf6e9ac0...   \n",
              "3  9872683fceb5011d980cbdce560d5c3c1dd4007ee936be...   \n",
              "4  9872683fceb5011d980cbdce560d5c3c1dd4007ee936be...   \n",
              "\n",
              "                                      address  grant_id        chain  token  \\\n",
              "0  0x76f69dcddd0593b0aff5fd3280c3433ddb68e0d2      6972      eth_std    ETH   \n",
              "1  0x1bc5ebee4738fd95bd96751c45a90889f772e0f3      2400   eth_zksync    ETH   \n",
              "2  0x3812801cbf0e41413db4835a5e36228ad45e32bf      7243  eth_polygon  MATIC   \n",
              "3  0x1bc5ebee4738fd95bd96751c45a90889f772e0f3      3023   eth_zksync    ETH   \n",
              "4  0x1bc5ebee4738fd95bd96751c45a90889f772e0f3      4811   eth_zksync    ETH   \n",
              "\n",
              "   amount_in_usdt                         timestamp txn_hash network active  \\\n",
              "0        1.283650  2022-09-22 23:59:55.773190+00:00      NaN     NaN   True   \n",
              "1        1.001247  2022-09-22 23:59:53.052568+00:00      NaN     NaN    NaN   \n",
              "2        1.001943  2022-09-22 23:59:52.331798+00:00      NaN     NaN  False   \n",
              "3        1.001247  2022-09-22 23:59:52.133974+00:00      NaN     NaN    NaN   \n",
              "4        1.001247  2022-09-22 23:59:51.260699+00:00      NaN     NaN    NaN   \n",
              "\n",
              "  approved                              result_address     title  \\\n",
              "0     True  0x01d79BcEaEaaDfb8fD2F2f53005289CFcF483464  Lenstube   \n",
              "1      NaN                                         NaN       NaN   \n",
              "2     True  0xc323b7F24C2fD032780fC285917a24Ca9e70E366      Pyme   \n",
              "3      NaN                                         NaN       NaN   \n",
              "4      NaN                                         NaN       NaN   \n",
              "\n",
              "                                       url  \\\n",
              "0  https://gitcoin.co/grants/6972/lenstube   \n",
              "1                                      NaN   \n",
              "2      https://gitcoin.co/grants/7243/pyme   \n",
              "3                                      NaN   \n",
              "4                                      NaN   \n",
              "\n",
              "                                         description  \\\n",
              "0  Lenstube, an open-source web3 video-sharing so...   \n",
              "1                                                NaN   \n",
              "2  Pyme is a web3 marketing platform allowing use...   \n",
              "3                                                NaN   \n",
              "4                                                NaN   \n",
              "\n",
              "                      created_on  \n",
              "0  2022-07-22T23:57:26.000-06:00  \n",
              "1                            NaN  \n",
              "2  2022-08-28T08:55:20.000-06:00  \n",
              "3                            NaN  \n",
              "4                            NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c539824b-109a-4787-88a6-725305b05bfe\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>txn_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>address</th>\n",
              "      <th>grant_id</th>\n",
              "      <th>chain</th>\n",
              "      <th>token</th>\n",
              "      <th>amount_in_usdt</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>txn_hash</th>\n",
              "      <th>network</th>\n",
              "      <th>active</th>\n",
              "      <th>approved</th>\n",
              "      <th>result_address</th>\n",
              "      <th>title</th>\n",
              "      <th>url</th>\n",
              "      <th>description</th>\n",
              "      <th>created_on</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9a7955760b945121d7ac2e3ccf8c53bb19389939d83a78...</td>\n",
              "      <td>205fdbe0783c1725849a6c243e23156d3c6679e2403beb...</td>\n",
              "      <td>0x76f69dcddd0593b0aff5fd3280c3433ddb68e0d2</td>\n",
              "      <td>6972</td>\n",
              "      <td>eth_std</td>\n",
              "      <td>ETH</td>\n",
              "      <td>1.283650</td>\n",
              "      <td>2022-09-22 23:59:55.773190+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>0x01d79BcEaEaaDfb8fD2F2f53005289CFcF483464</td>\n",
              "      <td>Lenstube</td>\n",
              "      <td>https://gitcoin.co/grants/6972/lenstube</td>\n",
              "      <td>Lenstube, an open-source web3 video-sharing so...</td>\n",
              "      <td>2022-07-22T23:57:26.000-06:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8d656b874ef8b642bbab30e2e0164922287d8156182e61...</td>\n",
              "      <td>9872683fceb5011d980cbdce560d5c3c1dd4007ee936be...</td>\n",
              "      <td>0x1bc5ebee4738fd95bd96751c45a90889f772e0f3</td>\n",
              "      <td>2400</td>\n",
              "      <td>eth_zksync</td>\n",
              "      <td>ETH</td>\n",
              "      <td>1.001247</td>\n",
              "      <td>2022-09-22 23:59:53.052568+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fe14bb2c777595fcc407de7ec454e26ddaa48df2c11a6e...</td>\n",
              "      <td>67d35e9ecb471310efe99c9d12ffc6a9816db2cf6e9ac0...</td>\n",
              "      <td>0x3812801cbf0e41413db4835a5e36228ad45e32bf</td>\n",
              "      <td>7243</td>\n",
              "      <td>eth_polygon</td>\n",
              "      <td>MATIC</td>\n",
              "      <td>1.001943</td>\n",
              "      <td>2022-09-22 23:59:52.331798+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>0xc323b7F24C2fD032780fC285917a24Ca9e70E366</td>\n",
              "      <td>Pyme</td>\n",
              "      <td>https://gitcoin.co/grants/7243/pyme</td>\n",
              "      <td>Pyme is a web3 marketing platform allowing use...</td>\n",
              "      <td>2022-08-28T08:55:20.000-06:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1d5435fae1a9942df47eb4a30c49acf418888fc7bbd233...</td>\n",
              "      <td>9872683fceb5011d980cbdce560d5c3c1dd4007ee936be...</td>\n",
              "      <td>0x1bc5ebee4738fd95bd96751c45a90889f772e0f3</td>\n",
              "      <td>3023</td>\n",
              "      <td>eth_zksync</td>\n",
              "      <td>ETH</td>\n",
              "      <td>1.001247</td>\n",
              "      <td>2022-09-22 23:59:52.133974+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e3aa9456a2fac0019086e42b9a065ac0e40d108e436d1b...</td>\n",
              "      <td>9872683fceb5011d980cbdce560d5c3c1dd4007ee936be...</td>\n",
              "      <td>0x1bc5ebee4738fd95bd96751c45a90889f772e0f3</td>\n",
              "      <td>4811</td>\n",
              "      <td>eth_zksync</td>\n",
              "      <td>ETH</td>\n",
              "      <td>1.001247</td>\n",
              "      <td>2022-09-22 23:59:51.260699+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c539824b-109a-4787-88a6-725305b05bfe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c539824b-109a-4787-88a6-725305b05bfe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c539824b-109a-4787-88a6-725305b05bfe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create similar dataframe names and remove of records with no approval details\n",
        "grant_results = results_extended[pd.notnull(results_extended['active_y'])]\n",
        "contributions = contributions_extended[pd.notnull(contributions_extended['active'])]"
      ],
      "metadata": {
        "id": "dMNrXNmlTewM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #set seaborn plotting aesthetics\n",
        "sns.set(style='white')\n",
        "\n",
        "\n",
        "dataset_1 = pd.DataFrame({'True': [len(grant_results.loc[(grant_results['active_x'] == True) & (grant_results['approved'] == True)]), len(grant_results.loc[(grant_results['active_x'] == True) & (grant_results['approved'] == False)])]  ,\n",
        "                         'False': [len(grant_results.loc[(grant_results['active_x'] == False) & (grant_results['approved'] == True)]), len(grant_results.loc[(grant_results['active_x'] == False) & (grant_results['approved'] == False)])] , },\n",
        "                        index=['True', 'False'])\n",
        "\n",
        "#create stacked bar chart\n",
        "dataset_1.plot(kind='bar', stacked=True, color=['steelblue', 'red'])\n",
        "\n",
        "#add overall title\n",
        "plt.title('Grant approval by active status', fontsize=16)\n",
        "\n",
        "#add axis titles\n",
        "plt.xlabel('Grant approval')\n",
        "plt.ylabel('Total grants')\n",
        "\n",
        "#rotate x-axis labels\n",
        "plt.xticks(rotation=45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "ObcZZJvbVJbz",
        "outputId": "54b59db7-96d0-4bbb-d661-d00fbfe8ed98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #set seaborn plotting aesthetics\n",
        "sns.set(style='white')\n",
        "\n",
        "\n",
        "dataset_1 = pd.DataFrame({'Github': [len(grant_results.loc[(grant_results['github_project_url'].notnull() ) & (grant_results['approved'] == True)]), len(grant_results.loc[(grant_results['github_project_url'].notnull() ) & (grant_results['approved'] == False)])]  ,\n",
        "                         'No Github': [len(grant_results.loc[(grant_results['github_project_url'].isnull()) & (grant_results['approved'] == True)]), len(grant_results.loc[(grant_results['github_project_url'].isnull()) & (grant_results['approved'] == False)])] , },\n",
        "                        index=['True', 'False'])\n",
        "\n",
        "#create stacked bar chart\n",
        "dataset_1.plot(kind='bar', stacked=True, color=['steelblue', 'red'])\n",
        "\n",
        "#add overall title\n",
        "plt.title('Grant approval by github repo inclusion', fontsize=16)\n",
        "\n",
        "#add axis titles\n",
        "plt.xlabel('Grant approval')\n",
        "plt.ylabel('Total grants')\n",
        "\n",
        "#rotate x-axis labels\n",
        "plt.xticks(rotation=45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "0RUToIAPbdmM",
        "outputId": "13675b5e-955b-43d2-ea00-cc2d58fc2f4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #set seaborn plotting aesthetics\n",
        "sns.set(style='white')\n",
        "\n",
        "\n",
        "dataset_1 = pd.DataFrame({'Website': [len(grant_results.loc[(grant_results['website'].notnull() ) & (grant_results['approved'] == True)]), len(grant_results.loc[(grant_results['website'].notnull() ) & (grant_results['approved'] == False)])]  ,\n",
        "                         'No Website': [len(grant_results.loc[(grant_results['website'].isnull()) & (grant_results['approved'] == True)]), len(grant_results.loc[(grant_results['website'].isnull()) & (grant_results['approved'] == False)])] , },\n",
        "                        index=['True', 'False'])\n",
        "\n",
        "#create stacked bar chart\n",
        "dataset_1.plot(kind='bar', stacked=True, color=['steelblue', 'red'])\n",
        "\n",
        "#add overall title\n",
        "plt.title('Grant approval by website inclusion', fontsize=16)\n",
        "\n",
        "#add axis titles\n",
        "plt.xlabel('Grant approval')\n",
        "plt.ylabel('Total grants')\n",
        "\n",
        "#rotate x-axis labels\n",
        "plt.xticks(rotation=45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "JnlgUPqxdVH4",
        "outputId": "c0f0a4fc-b2fe-4efd-8883-adcd655af735"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #set seaborn plotting aesthetics\n",
        "sns.set(style='white')\n",
        "\n",
        "\n",
        "dataset_1 = pd.DataFrame({'Twitter Verfication': [len(grant_results.loc[(grant_results['twitter_verified'].notnull() ) & (grant_results['approved'] == True)]), len(grant_results.loc[(grant_results['twitter_verified'].notnull() ) & (grant_results['approved'] == False)])]  ,\n",
        "                         'No Twitter Verification': [len(grant_results.loc[(grant_results['twitter_verified'].isnull()) & (grant_results['approved'] == True)]), len(grant_results.loc[(grant_results['twitter_verified'].isnull()) & (grant_results['approved'] == False)])] , },\n",
        "                        index=['True', 'False'])\n",
        "\n",
        "#create stacked bar chart\n",
        "dataset_1.plot(kind='bar', stacked=True, color=['steelblue', 'red'])\n",
        "\n",
        "#add overall title\n",
        "plt.title('Grant approval by twitter verification', fontsize=16)\n",
        "\n",
        "#add axis titles\n",
        "plt.xlabel('Grant approval')\n",
        "plt.ylabel('Total grants')\n",
        "\n",
        "#rotate x-axis labels\n",
        "plt.xticks(rotation=45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "3l8j0m_IdzXg",
        "outputId": "9ffb3110-4882-4237-eda1-b94ace5add78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #set seaborn plotting aesthetics\n",
        "sns.set(style='white')\n",
        "\n",
        "\n",
        "dataset_1 = pd.DataFrame({'Avg Contributions': [(contributions['amount_in_usdt'].loc[contributions['approved'] == True]).mean(), (contributions['amount_in_usdt'].loc[contributions['approved'] == False]).mean()] },\n",
        "                        index=['True', 'False'])\n",
        "\n",
        "#create stacked bar chart\n",
        "dataset_1.plot(kind='bar', color=['steelblue'])\n",
        "\n",
        "#add overall title\n",
        "plt.title('Avg contributions by approval status', fontsize=16)\n",
        "\n",
        "#add axis titles\n",
        "plt.xlabel('Grant approval')\n",
        "plt.ylabel('Contributions ($)')\n",
        "\n",
        "#rotate x-axis labels\n",
        "plt.xticks(rotation=45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "lyPN4-fHeafv",
        "outputId": "6cec3d0f-99e7-4132-f604-3667e19fa376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}