ADM_hw2.ipynb

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      "source": [
        "# libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zSU9bDtyNhFa"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
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      "cell_type": "code",
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        "colab": {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        }
      ],
      "source": [
        "#--------------------------------\n",
        "# Device configuration\n",
        "#--------------------------------\n",
        "import torch\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print('Using device: %s'%device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "9edf35c3-ebc6-4ea0-e89a-979a7a5143e6"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "# https://www.aboutdatablog.com/post/how-to-successfully-add-large-data-sets-to-google-drive-and-use-them-in-google-colab\n",
        "# mounting google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05VUERuCZyDp",
        "outputId": "617280c1-90ce-49a7-d6da-a95b00372d02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  gdrive/My Drive/ADM-HM2/archive.zip\n",
            "  inflating: instagram_locations.csv  \n",
            "  inflating: instagram_posts.csv     \n",
            "  inflating: instagram_profiles.csv  \n"
          ]
        }
      ],
      "source": [
        "# unzip files\n",
        "#!unzip gdrive/My\\ Drive/ADM-HW2/datahw1.zip\n",
        "!unzip gdrive/My\\ Drive/ADM-HM2/archive.zip\n",
        "#!unzip gdrive/My\\ Drive/SapienzaColab/ADM_Ass1/datahw1.zip\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nulpal7Ig7C5"
      },
      "source": [
        "## *Research Questions*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VEdOLrYW44YQ"
      },
      "source": [
        "### Research question 1: EDA\n",
        "We use the Instagram dataset provided by Kaggle (https://www.kaggle.com/datasets/shmalex/instagram-dataset?select=instagram_profiles.csv). As Kaggle shows, the dataset is collected data from 2019. It provides information of 1 204 583 locations over the world, 4 509 586 different people and business, and 42 710 197 different posts with information about their location, user, date, number of likes, etc.\n",
        "This information is given in 3 different comma separated files.\n",
        "\n",
        "* The locations file provides 23 different parameters;\n",
        "*   The posts file provides 10 different parameters;\n",
        "*   The profiles file provides 11 different parameters.\n",
        "\n",
        "\n",
        "In this first research question, we will look into the provided datasets, so we will understand with what data we are working with. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the datasets\n",
        "df_locations = pd.read_csv(\"instagram_locations.csv\", delimiter = '\\t')\n",
        "df_locations.head(5)"
      ],
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              "      sid               id                                               name  \\\n",
              "0  719981  110296492939207                 Playa de Daimuz - Valencia, España   \n",
              "1  719983        274391278                                        Nová Vieska   \n",
              "2  719985  148885595789195                                      Everest Today   \n",
              "3  719987        263258277  BULAC - Bibliothèque universitaire des langues...   \n",
              "4  326443  406147529857708                           ABC Cable Networks Group   \n",
              "\n",
              "                       street    zip                 city region   cd  \\\n",
              "0                         NaN    NaN                  NaN    NaN  NaN   \n",
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              "2                   Himalayas   +977     Kathmandu, Nepal    NaN   NP   \n",
              "3  65, rue des Grands-Moulins  75013        Paris, France    NaN   FR   \n",
              "4          3800 W Alameda Ave  91505  Burbank, California    NaN   US   \n",
              "\n",
              "            phone aj_exact_city_match  ...               dir_city_name  \\\n",
              "0             NaN               False  ...                         NaN   \n",
              "1             NaN                True  ...                  Kis-Újfalu   \n",
              "2             NaN               False  ...                    Pasupati   \n",
              "3  01 81 69 18 00               False  ...  13ème Arrondissement Paris   \n",
              "4  (818) 569-7500               False  ...                         NaN   \n",
              "\n",
              "                dir_city_slug dir_country_id dir_country_name         lat  \\\n",
              "0                         NaN            NaN              NaN   -0.139475   \n",
              "1                  kis-ujfalu             SK         Slovakia   18.466667   \n",
              "2                    pasupati             NP            Nepal   85.330150   \n",
              "3  13eme-arrondissement-paris             FR           France    2.375995   \n",
              "4                         NaN            NaN              NaN -118.341864   \n",
              "\n",
              "         lng primary_alias_on_fb  \\\n",
              "0  38.974391         daimuzplaya   \n",
              "1  47.866667                 NaN   \n",
              "2  27.701960        EverestToday   \n",
              "3  48.827240         BULAC.Paris   \n",
              "4  34.153265                 NaN   \n",
              "\n",
              "                                                slug  \\\n",
              "0                    playa-de-daimuz-valencia-espana   \n",
              "1                                        nova-vieska   \n",
              "2                                      everest-today   \n",
              "3  bulac-bibliotheque-universitaire-des-langues-e...   \n",
              "4                           abc-cable-networks-group   \n",
              "\n",
              "                                website                      cts  \n",
              "0  https://es.wikipedia.org/wiki/Daimuz  2019-05-29 01:21:29.987  \n",
              "1                                   NaN  2019-05-29 01:21:38.037  \n",
              "2                                   NaN  2019-05-29 01:21:46.295  \n",
              "3                          www.bulac.fr  2019-05-29 01:21:54.355  \n",
              "4                                   NaN  2019-04-02 15:22:55.703  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <td>NaN</td>\n",
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          "metadata": {},
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      "source": [
        "df_profiles = pd.read_csv(\"instagram_profiles.csv\", delimiter = '\\t')\n",
        "df_profiles.head(5)"
      ],
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              "       sid    profile_id      profile_name firstname_lastname  \\\n",
              "0  4184446  4.721050e+09       jphillip033        John Pierce   \n",
              "1  4184457  5.905830e+08         mama_haas             Deanna   \n",
              "2  4184460  1.074147e+09  yellowlightbreen         Eliza Gray   \n",
              "3  4184461  1.472039e+09           tec1025       Thomas Clark   \n",
              "4  4184462  3.531421e+09        luckyfluff                NaN   \n",
              "\n",
              "                                         description  following  followers  \\\n",
              "0                       \"Document Everything Always\"      250.0      146.0   \n",
              "1  Trying to enjoy the simple things in life. Kni...      534.0     1145.0   \n",
              "2                                Maine is for Lovers      469.0      324.0   \n",
              "3                                                NaN        NaN        NaN   \n",
              "4                                                NaN        NaN        NaN   \n",
              "\n",
              "   n_posts                           url                            cts  \\\n",
              "0    170.0                           NaN  2019-08-01 14:38:55.394 -0400   \n",
              "1   2878.0  www.etsy.com/shop/MamaHaas69  2019-08-01 14:39:36.526 -0400   \n",
              "2    431.0             elizajanegray.com  2019-08-01 14:39:54.407 -0400   \n",
              "3      NaN                           NaN  2019-08-01 14:40:06.472 -0400   \n",
              "4      NaN                           NaN  2019-08-01 14:40:07.806 -0400   \n",
              "\n",
              "  is_business_account  \n",
              "0               False  \n",
              "1               False  \n",
              "2               False  \n",
              "3                 NaN  \n",
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              "      <th>0</th>\n",
              "      <td>4184446</td>\n",
              "      <td>4.721050e+09</td>\n",
              "      <td>jphillip033</td>\n",
              "      <td>John Pierce</td>\n",
              "      <td>\"Document Everything Always\"</td>\n",
              "      <td>250.0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-08-01 14:38:55.394 -0400</td>\n",
              "      <td>False</td>\n",
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              "      <th>1</th>\n",
              "      <td>4184457</td>\n",
              "      <td>5.905830e+08</td>\n",
              "      <td>mama_haas</td>\n",
              "      <td>Deanna</td>\n",
              "      <td>Trying to enjoy the simple things in life. Kni...</td>\n",
              "      <td>534.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>2878.0</td>\n",
              "      <td>www.etsy.com/shop/MamaHaas69</td>\n",
              "      <td>2019-08-01 14:39:36.526 -0400</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4184460</td>\n",
              "      <td>1.074147e+09</td>\n",
              "      <td>yellowlightbreen</td>\n",
              "      <td>Eliza Gray</td>\n",
              "      <td>Maine is for Lovers</td>\n",
              "      <td>469.0</td>\n",
              "      <td>324.0</td>\n",
              "      <td>431.0</td>\n",
              "      <td>elizajanegray.com</td>\n",
              "      <td>2019-08-01 14:39:54.407 -0400</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4184461</td>\n",
              "      <td>1.472039e+09</td>\n",
              "      <td>tec1025</td>\n",
              "      <td>Thomas Clark</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-08-01 14:40:06.472 -0400</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4184462</td>\n",
              "      <td>3.531421e+09</td>\n",
              "      <td>luckyfluff</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>2019-08-01 14:40:07.806 -0400</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-5040264c-80b4-4777-9eb2-77460af34d52');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_posts = pd.read_csv(\"instagram_posts.csv\", delimiter = '\\t', nrows = 5000000)\n",
        "df_posts.head(5)"
      ],
      "metadata": {
        "id": "eTCG5xhI5SAR",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "90314160-aa29-4c9f-9ec0-9c78288d3682"
      },
      "execution_count": null,
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              "\n",
              "                       cts  post_type  \\\n",
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              "          const element = document.querySelector('#df-41bfd520-d03f-40e7-a954-c27c029a90c1');\n",
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              "            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",
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              "  "
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          "metadata": {},
          "execution_count": 8
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DwvBWVgF-f1U",
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "88e3c453-a52f-479a-8e5d-8664d2a0c87f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              sid  sid_profile      post_id    profile_id   location_id  \\\n",
              "0        28370919      3496776  BXdjjUlgcgq  2.237948e+09  1.022366e+15   \n",
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              "\n",
              "                             cts  post_type  \\\n",
              "0        2017-08-06 20:06:57.000          2   \n",
              "1        2017-06-19 09:31:16.000          1   \n",
              "2        2017-03-11 20:05:03.000          1   \n",
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              "\n",
              "                                               description  numbr_likes  \\\n",
              "0        Wreckloose! Deevalley bike park laps on the @i...           80   \n",
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              "2999995  Beating hard on the F1 in 25kts and one crew s...           34   \n",
              "2999996  Cracking day sailing Sunday past, some great s...           41   \n",
              "2999997  Having a drifting downwind dual with the Starf...           33   \n",
              "2999998  Back sailing last Sunday for the 2nd half of A...           24   \n",
              "2999999               Lovely brunch with Jen at Cafe Rouge           10   \n",
              "\n",
              "         number_comments  \n",
              "0                      0  \n",
              "1                      1  \n",
              "2                      0  \n",
              "3                      0  \n",
              "4                      0  \n",
              "...                  ...  \n",
              "2999995               10  \n",
              "2999996                3  \n",
              "2999997                1  \n",
              "2999998                0  \n",
              "2999999                0  \n",
              "\n",
              "[3000000 rows x 10 columns]"
            ],
            "text/html": [
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              "      <th>sid</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28370919</td>\n",
              "      <td>3496776</td>\n",
              "      <td>BXdjjUlgcgq</td>\n",
              "      <td>2.237948e+09</td>\n",
              "      <td>1.022366e+15</td>\n",
              "      <td>2017-08-06 20:06:57.000</td>\n",
              "      <td>2</td>\n",
              "      <td>Wreckloose! Deevalley bike park laps on the @i...</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
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              "      <td>5.579335e+09</td>\n",
              "      <td>4.574268e+14</td>\n",
              "      <td>2017-06-19 09:31:16.000</td>\n",
              "      <td>1</td>\n",
              "      <td>🙌🏼 believe in ya dreams 🙌🏼 just like I believe...</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
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              "      <td>3.134296e+08</td>\n",
              "      <td>4.574268e+14</td>\n",
              "      <td>2017-03-11 20:05:03.000</td>\n",
              "      <td>1</td>\n",
              "      <td>#meraviglia #incensi #the #candele #profumo #a...</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28370934</td>\n",
              "      <td>-1</td>\n",
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              "      <td>1.837593e+09</td>\n",
              "      <td>4.574268e+14</td>\n",
              "      <td>2016-09-13 16:27:16.000</td>\n",
              "      <td>1</td>\n",
              "      <td>#teatime #scorpion #friends #love #mountains #...</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28370935</td>\n",
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              "      <td>8-NQrvoYLX</td>\n",
              "      <td>1.131527e+09</td>\n",
              "      <td>4.574268e+14</td>\n",
              "      <td>2015-10-18 10:19:27.000</td>\n",
              "      <td>1</td>\n",
              "      <td>thE sky gavE mE a #constEllation</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>5.944980e+08</td>\n",
              "      <td>2019-02-19 20:02:46.000</td>\n",
              "      <td>1</td>\n",
              "      <td>Beating hard on the F1 in 25kts and one crew s...</td>\n",
              "      <td>34</td>\n",
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              "      <th>2999996</th>\n",
              "      <td>31414320</td>\n",
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              "      <td>2019-02-19 20:00:49.000</td>\n",
              "      <td>1</td>\n",
              "      <td>Cracking day sailing Sunday past, some great s...</td>\n",
              "      <td>41</td>\n",
              "      <td>3</td>\n",
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              "      <th>2999997</th>\n",
              "      <td>31414321</td>\n",
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              "      <td>BtqwTa9hG-G</td>\n",
              "      <td>2.289467e+08</td>\n",
              "      <td>5.944980e+08</td>\n",
              "      <td>2019-02-09 17:39:10.000</td>\n",
              "      <td>1</td>\n",
              "      <td>Having a drifting downwind dual with the Starf...</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
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              "      <th>2999998</th>\n",
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              "      <td>2019-02-09 17:37:20.000</td>\n",
              "      <td>1</td>\n",
              "      <td>Back sailing last Sunday for the 2nd half of A...</td>\n",
              "      <td>24</td>\n",
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              "      <th>2999999</th>\n",
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              "      <td>2.367284e+08</td>\n",
              "      <td>1.850074e+06</td>\n",
              "      <td>2019-01-19 13:26:09.000</td>\n",
              "      <td>1</td>\n",
              "      <td>Lovely brunch with Jen at Cafe Rouge</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
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              "<p>3000000 rows × 10 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# first we check and remove any duplicates in the dataset\n",
        "df_locations.drop_duplicates()\n",
        "df_profiles.drop_duplicates()\n",
        "df_posts.drop_duplicates()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MhSt5wopEwuS"
      },
      "source": [
        "First, we will determine the datatypes of the parameters that are given to us. In our opinion, the datatypes match the description of the given data on Kaggle, so we will not change any of the datatypes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5nNbxSJKE3XK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "559f5886-81e9-4cab-a37e-b93a47cdc560"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sid                         int64\n",
            "id                          int64\n",
            "name                       object\n",
            "street                     object\n",
            "zip                        object\n",
            "city                       object\n",
            "region                     object\n",
            "cd                         object\n",
            "phone                      object\n",
            "aj_exact_city_match        object\n",
            "aj_exact_country_match     object\n",
            "blurb                      object\n",
            "dir_city_id                object\n",
            "dir_city_name              object\n",
            "dir_city_slug              object\n",
            "dir_country_id             object\n",
            "dir_country_name           object\n",
            "lat                       float64\n",
            "lng                       float64\n",
            "primary_alias_on_fb        object\n",
            "slug                       object\n",
            "website                    object\n",
            "cts                        object\n",
            "dtype: object\n",
            "sid                  int64\n",
            "sid_profile          int64\n",
            "post_id             object\n",
            "profile_id         float64\n",
            "location_id        float64\n",
            "cts                 object\n",
            "post_type            int64\n",
            "description         object\n",
            "numbr_likes          int64\n",
            "number_comments      int64\n",
            "dtype: object\n",
            "sid                      int64\n",
            "profile_id             float64\n",
            "profile_name            object\n",
            "firstname_lastname      object\n",
            "description             object\n",
            "following              float64\n",
            "followers              float64\n",
            "n_posts                float64\n",
            "url                     object\n",
            "cts                     object\n",
            "is_business_account     object\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(df_locations.dtypes)\n",
        "print(df_posts.dtypes)\n",
        "print(df_profiles.dtypes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdki2RoIE5d1"
      },
      "source": [
        "We can get data summaries of the three different datasets by using the describe function. \n",
        "This function only gives information based on the numerical data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ZkudXY0_XfU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "246ed41a-8410-44ff-9127-cb7ba593257d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                sid            id           lat           lng\n",
              "count  1.022658e+06  1.022658e+06  1.016495e+06  1.016495e+06\n",
              "mean   5.274856e+05  3.152851e+14 -1.529215e+01  3.421718e+01\n",
              "std    2.994567e+05  5.795031e+14  7.615085e+01  2.201081e+01\n",
              "min    1.000000e+00  7.000000e+00 -1.799830e+02 -9.000000e+01\n",
              "25%    2.688392e+05  2.522377e+08 -8.015575e+01  2.997555e+01\n",
              "50%    5.300405e+05  8.573350e+08 -2.450000e+00  4.067820e+01\n",
              "75%    7.866598e+05  3.304662e+14  3.071740e+01  4.884722e+01\n",
              "max    1.043657e+06  2.402148e+16  1.799941e+02  8.999870e+01"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>sid</th>\n",
              "      <th>id</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.022658e+06</td>\n",
              "      <td>1.022658e+06</td>\n",
              "      <td>1.016495e+06</td>\n",
              "      <td>1.016495e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.274856e+05</td>\n",
              "      <td>3.152851e+14</td>\n",
              "      <td>-1.529215e+01</td>\n",
              "      <td>3.421718e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.994567e+05</td>\n",
              "      <td>5.795031e+14</td>\n",
              "      <td>7.615085e+01</td>\n",
              "      <td>2.201081e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>-1.799830e+02</td>\n",
              "      <td>-9.000000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.688392e+05</td>\n",
              "      <td>2.522377e+08</td>\n",
              "      <td>-8.015575e+01</td>\n",
              "      <td>2.997555e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.300405e+05</td>\n",
              "      <td>8.573350e+08</td>\n",
              "      <td>-2.450000e+00</td>\n",
              "      <td>4.067820e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.866598e+05</td>\n",
              "      <td>3.304662e+14</td>\n",
              "      <td>3.071740e+01</td>\n",
              "      <td>4.884722e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.043657e+06</td>\n",
              "      <td>2.402148e+16</td>\n",
              "      <td>1.799941e+02</td>\n",
              "      <td>8.999870e+01</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "       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",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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-35b232c1-e15c-4f51-86c8-29596ae8ffb3 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-35b232c1-e15c-4f51-86c8-29596ae8ffb3');\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": 32
        }
      ],
      "source": [
        "df_locations.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L7A75tOsC-LZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "229e6d5d-86b0-42a0-c071-115296c36ac9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                sid   sid_profile    profile_id   location_id     post_type  \\\n",
              "count  3.000000e+06  3.000000e+06  2.999863e+06  2.996873e+06  3.000000e+06   \n",
              "mean   2.944016e+07  2.333161e+05  3.195472e+09  3.075259e+14  1.061249e+00   \n",
              "std    2.594281e+06  8.105454e+05  7.357614e+10  5.715731e+14  2.398066e-01   \n",
              "min    6.025000e+03 -1.000000e+00  3.000000e+00  7.670000e+02  1.000000e+00   \n",
              "25%    2.903071e+07 -1.000000e+00  2.963938e+08  2.742526e+08  1.000000e+00   \n",
              "50%    2.983005e+07 -1.000000e+00  1.610783e+09  8.805117e+08  1.000000e+00   \n",
              "75%    3.062258e+07 -1.000000e+00  5.428964e+09  3.171835e+14  1.000000e+00   \n",
              "max    3.144308e+07  4.510762e+06  9.001013e+13  1.015016e+16  3.000000e+00   \n",
              "\n",
              "        numbr_likes  number_comments  \n",
              "count  3.000000e+06     3.000000e+06  \n",
              "mean   2.869894e+02     7.160539e+00  \n",
              "std    5.560712e+03     1.579671e+02  \n",
              "min    0.000000e+00     0.000000e+00  \n",
              "25%    1.900000e+01     0.000000e+00  \n",
              "50%    4.600000e+01     1.000000e+00  \n",
              "75%    1.230000e+02     5.000000e+00  \n",
              "max    2.212264e+06     1.352210e+05  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4232e6cd-aac8-4c92-84df-ecb0f1576f0d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sid</th>\n",
              "      <th>sid_profile</th>\n",
              "      <th>profile_id</th>\n",
              "      <th>location_id</th>\n",
              "      <th>post_type</th>\n",
              "      <th>numbr_likes</th>\n",
              "      <th>number_comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.000000e+06</td>\n",
              "      <td>3.000000e+06</td>\n",
              "      <td>2.999863e+06</td>\n",
              "      <td>2.996873e+06</td>\n",
              "      <td>3.000000e+06</td>\n",
              "      <td>3.000000e+06</td>\n",
              "      <td>3.000000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.944016e+07</td>\n",
              "      <td>2.333161e+05</td>\n",
              "      <td>3.195472e+09</td>\n",
              "      <td>3.075259e+14</td>\n",
              "      <td>1.061249e+00</td>\n",
              "      <td>2.869894e+02</td>\n",
              "      <td>7.160539e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.594281e+06</td>\n",
              "      <td>8.105454e+05</td>\n",
              "      <td>7.357614e+10</td>\n",
              "      <td>5.715731e+14</td>\n",
              "      <td>2.398066e-01</td>\n",
              "      <td>5.560712e+03</td>\n",
              "      <td>1.579671e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.025000e+03</td>\n",
              "      <td>-1.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>7.670000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.903071e+07</td>\n",
              "      <td>-1.000000e+00</td>\n",
              "      <td>2.963938e+08</td>\n",
              "      <td>2.742526e+08</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.900000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.983005e+07</td>\n",
              "      <td>-1.000000e+00</td>\n",
              "      <td>1.610783e+09</td>\n",
              "      <td>8.805117e+08</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>4.600000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.062258e+07</td>\n",
              "      <td>-1.000000e+00</td>\n",
              "      <td>5.428964e+09</td>\n",
              "      <td>3.171835e+14</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.230000e+02</td>\n",
              "      <td>5.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.144308e+07</td>\n",
              "      <td>4.510762e+06</td>\n",
              "      <td>9.001013e+13</td>\n",
              "      <td>1.015016e+16</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.212264e+06</td>\n",
              "      <td>1.352210e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4232e6cd-aac8-4c92-84df-ecb0f1576f0d')\"\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",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      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-4232e6cd-aac8-4c92-84df-ecb0f1576f0d 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-4232e6cd-aac8-4c92-84df-ecb0f1576f0d');\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": 31
        }
      ],
      "source": [
        "df_posts.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "niWSrHMJDATr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "bcfcebf2-7f84-4189-fc5c-49352888d099"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                sid    profile_id     following     followers       n_posts\n",
              "count  4.509586e+06  4.477139e+06  3.452771e+06  3.452771e+06  3.452771e+06\n",
              "mean   2.255992e+06  3.083302e+09  1.405641e+03  4.739899e+03  4.095414e+02\n",
              "std    1.301807e+06  4.266575e+10  8.113473e+03  1.939087e+05  1.041940e+03\n",
              "min    1.186000e+03  4.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00\n",
              "25%    1.128596e+06  2.735177e+08  3.560000e+02  1.590000e+02  2.200000e+01\n",
              "50%    2.255992e+06  1.649248e+09  7.540000e+02  4.060000e+02  1.200000e+02\n",
              "75%    3.383389e+06  5.514691e+09  1.573000e+03  9.740000e+02  4.200000e+02\n",
              "max    4.510785e+06  9.001013e+13  4.191080e+06  2.854576e+08  1.837300e+05"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a38eac4-ea58-4c3c-bd97-37e1fbf1bda8\">\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>sid</th>\n",
              "      <th>profile_id</th>\n",
              "      <th>following</th>\n",
              "      <th>followers</th>\n",
              "      <th>n_posts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.509586e+06</td>\n",
              "      <td>4.477139e+06</td>\n",
              "      <td>3.452771e+06</td>\n",
              "      <td>3.452771e+06</td>\n",
              "      <td>3.452771e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.255992e+06</td>\n",
              "      <td>3.083302e+09</td>\n",
              "      <td>1.405641e+03</td>\n",
              "      <td>4.739899e+03</td>\n",
              "      <td>4.095414e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.301807e+06</td>\n",
              "      <td>4.266575e+10</td>\n",
              "      <td>8.113473e+03</td>\n",
              "      <td>1.939087e+05</td>\n",
              "      <td>1.041940e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.186000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.128596e+06</td>\n",
              "      <td>2.735177e+08</td>\n",
              "      <td>3.560000e+02</td>\n",
              "      <td>1.590000e+02</td>\n",
              "      <td>2.200000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.255992e+06</td>\n",
              "      <td>1.649248e+09</td>\n",
              "      <td>7.540000e+02</td>\n",
              "      <td>4.060000e+02</td>\n",
              "      <td>1.200000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.383389e+06</td>\n",
              "      <td>5.514691e+09</td>\n",
              "      <td>1.573000e+03</td>\n",
              "      <td>9.740000e+02</td>\n",
              "      <td>4.200000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.510785e+06</td>\n",
              "      <td>9.001013e+13</td>\n",
              "      <td>4.191080e+06</td>\n",
              "      <td>2.854576e+08</td>\n",
              "      <td>1.837300e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a38eac4-ea58-4c3c-bd97-37e1fbf1bda8')\"\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-9a38eac4-ea58-4c3c-bd97-37e1fbf1bda8 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-9a38eac4-ea58-4c3c-bd97-37e1fbf1bda8');\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": 30
        }
      ],
      "source": [
        "df_profiles.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpOhCA2vD_l0"
      },
      "source": [
        "Next, we can see how many null values there are present for each dataset per parameter. As visible, there are a lot of null values per parameter. These values do not provide us with any information. Therefore, we have to be careful and remove these values, depending on the type of question."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N0ETRTghDypv"
      },
      "outputs": [],
      "source": [
        "df_locations.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fkWfGaVlD63K"
      },
      "outputs": [],
      "source": [
        "df_posts.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FM-_JkN-D8qf"
      },
      "outputs": [],
      "source": [
        "df_profiles.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ComzPYUHFhAP"
      },
      "source": [
        "Furthermore, we can make correlation plots to see if there is a strong correlation between the parameters in one dataset. \n",
        "\n",
        "As shown by the three heatmaps, there is no strong correlation between any parameters within one dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DpIWZ9DbFojK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "ef625c16-0b14-4853-987b-314da5199169"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.heatmap(df_locations.corr())\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CdnSrVEGHw5X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "af4b1d50-08bd-499b-b95e-043d5dcbac54"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.heatmap(df_posts.corr())\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qqMxRxfOHxRl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "6674b30a-88d8-4750-82c0-617f595aab89"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.heatmap(df_profiles.corr())\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3hXGkDi-STT"
      },
      "source": [
        "### Research question 2: Explore dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOlXTLYZ5cJk"
      },
      "source": [
        "##### **Plot the number of posts for each profile in descending order**\n",
        "\n",
        "We first removed the rows that contained NaNs so we only use data that is actually available.\n",
        "The first subplot shows the trend of the top 150, lowest 150, and the total data. As can be seen, there are some profiles with a lot of posts, but it decreases very fast. The lowest 150 profiles have never posted anything.\n",
        "\n",
        "Afterwards, we decided to also look into the data if we would remove the profiles that do not have any likes to see what influence that would have on the data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nXJiiOyC46lW"
      },
      "outputs": [],
      "source": [
        "# remove the rows with nan in n_posts\n",
        "df_profiles_nposts = df_profiles.dropna( how='any',\n",
        "                    subset=['n_posts'])\n",
        "\n",
        "# sort the dataframe based on the nposts\n",
        "df_profiles_sort = df_profiles_nposts.sort_values(by='n_posts', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eJtWQ0sK5wTy"
      },
      "outputs": [],
      "source": [
        "#define subplot layout\n",
        "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 6))\n",
        "\n",
        "# plot the highest 150 number of posts\n",
        "ax1 = df_profiles_sort.iloc[0:150].plot(x='profile_name', y='n_posts', ax=axes[0])\n",
        "ax1.get_xaxis().set_visible(False)\n",
        "ax1.title.set_text('150 highest number of posts')\n",
        "\n",
        "# plot the lowest 150 number of posts\n",
        "ax2 = df_profiles_sort.iloc[-150:].plot(x='profile_name', y='n_posts', ax=axes[1])\n",
        "ax2.get_xaxis().set_visible(False)\n",
        "ax2.title.set_text('150 lowest number of posts')\n",
        "\n",
        "# plot for every profile\n",
        "ax3 = df_profiles_sort.plot(x='profile_name', y='n_posts', ax=axes[2])\n",
        "ax3.get_xaxis().set_visible(False)\n",
        "ax3.title.set_text('All profiles')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "sBSo6EG97ZvA",
        "outputId": "8478e474-bb1e-42ad-dcbe-1d49e927549a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# remove profiles with 0 posts\n",
        "df_profiles_sort = df_profiles_sort.loc[~((df_profiles_sort['n_posts'] == 0))]\n",
        "\n",
        "#define subplot layout\n",
        "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 6))\n",
        "\n",
        "# plot the highest 150 number of posts\n",
        "ax1 = df_profiles_sort.iloc[0:150].plot(x='profile_name', y='n_posts', ax=axes[0])\n",
        "ax1.get_xaxis().set_visible(False)\n",
        "ax1.title.set_text('150 highest number of posts')\n",
        "\n",
        "# plot the lowest 150 number of posts\n",
        "ax2 = df_profiles_sort.iloc[-150:].plot(x='profile_name', y='n_posts', ax=axes[1])\n",
        "ax2.get_xaxis().set_visible(False)\n",
        "ax2.title.set_text('150 lowest number of posts')\n",
        "\n",
        "# plot for every profile\n",
        "ax3 = df_profiles_sort.plot(x='profile_name', y='n_posts', ax=axes[2])\n",
        "ax3.get_xaxis().set_visible(False)\n",
        "ax3.title.set_text('All profiles')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3e1Uef3-ma3"
      },
      "source": [
        "##### **What posts have the highest number of \"likes\"?**\n",
        "To answer this question, we only load in certain columns of the posts data set, namely the post id, the number of likes, and the number of comments. The id post can be tracked back to the specific post. As we can see, the highest number of likes also has a lot of comments, but it does not necessarily mean the highest number of comments. It does make sense that it also has a lot of comments, since a lot of people have seen the post (which can be concluded from the fact that it has so many likes). \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_small_posts = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['post_id','numbr_likes', 'number_comments'], low_memory = True)\n"
      ],
      "metadata": {
        "id": "P7kOA9sC55nb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fAr-zncI5zQm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "f832dbd7-0635-43e6-b5bc-454ced2e6410"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "These are the first 10 post with the highest number of likes:   \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              post_id  numbr_likes  number_comments\n",
              "19624085  Bt3yaXmAM0d    8822952.0         165886.0\n",
              "19623988  BuwffB7g8cK    5447066.0          25303.0\n",
              "19624082  Buc1gyZAaMQ    5116398.0          40608.0\n",
              "19623986  BvcukE8AMuG    4271466.0          30770.0\n",
              "39701024  BqSLFVBFLfq    4264745.0          18691.0\n",
              "6293907   BwTSXBKg7gN    3586546.0          15405.0\n",
              "19624086  Btw8pFXAvQm    3558599.0          15715.0\n",
              "19802016  BvC3n6_gqin    3533326.0          59475.0\n",
              "30850564  Bw7vqLrFkHi    3340297.0         149489.0\n",
              "39812576  Bw2gTH1AiDA    3311246.0          64318.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2408daa3-f640-46e6-9b0b-18242d779861\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>post_id</th>\n",
              "      <th>numbr_likes</th>\n",
              "      <th>number_comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>19624085</th>\n",
              "      <td>Bt3yaXmAM0d</td>\n",
              "      <td>8822952.0</td>\n",
              "      <td>165886.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19623988</th>\n",
              "      <td>BuwffB7g8cK</td>\n",
              "      <td>5447066.0</td>\n",
              "      <td>25303.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19624082</th>\n",
              "      <td>Buc1gyZAaMQ</td>\n",
              "      <td>5116398.0</td>\n",
              "      <td>40608.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19623986</th>\n",
              "      <td>BvcukE8AMuG</td>\n",
              "      <td>4271466.0</td>\n",
              "      <td>30770.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39701024</th>\n",
              "      <td>BqSLFVBFLfq</td>\n",
              "      <td>4264745.0</td>\n",
              "      <td>18691.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6293907</th>\n",
              "      <td>BwTSXBKg7gN</td>\n",
              "      <td>3586546.0</td>\n",
              "      <td>15405.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19624086</th>\n",
              "      <td>Btw8pFXAvQm</td>\n",
              "      <td>3558599.0</td>\n",
              "      <td>15715.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19802016</th>\n",
              "      <td>BvC3n6_gqin</td>\n",
              "      <td>3533326.0</td>\n",
              "      <td>59475.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30850564</th>\n",
              "      <td>Bw7vqLrFkHi</td>\n",
              "      <td>3340297.0</td>\n",
              "      <td>149489.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39812576</th>\n",
              "      <td>Bw2gTH1AiDA</td>\n",
              "      <td>3311246.0</td>\n",
              "      <td>64318.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2408daa3-f640-46e6-9b0b-18242d779861')\"\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",
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              "\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-2408daa3-f640-46e6-9b0b-18242d779861 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-2408daa3-f640-46e6-9b0b-18242d779861');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Sort the posts_dataframe based on the number of likes\n",
        "df_posts_sorted = df_small_posts.sort_values(by='numbr_likes', ascending=False)\n",
        "\n",
        "# Show top 10\n",
        "print(\"These are the first 10 post with the highest number of likes:   \")\n",
        "df_posts_sorted.head(10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwcubDgjECSu"
      },
      "source": [
        "##### **What posts have the most and the least number of comments?**\n",
        "For this question we use the same dataset as used above. As we can see, the post with the highest number of comments, has way less likes than comments. This could be due to the fact that some people have responded multiple times or that it was a controversial post where people felt the need to comment, but not like. However, we are not sure if it is either of these conclusions, since we do not have any data about if people have responded multiple times or not. \n",
        "\n",
        "If we look at the 10 posts with the least amount of comments, we see that these are posts where no one has placed a comment. They still received likes, however, so people did see the post. They just did not feel the need to leave a comment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lI96RHCFZ_s-"
      },
      "outputs": [],
      "source": [
        "# remove the NAN\n",
        "df_small_posts = df_small_posts.dropna( how='any',\n",
        "                    subset=['number_comments'])\n",
        "\n",
        "# sort the dataframe based on the number of comments\n",
        "df_posts_sorted = df_small_posts.sort_values(by='number_comments', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3jRLKBS2Z00h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "4d42c9c6-d32a-42de-a748-570dd9d3a39a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "These are the first 10 posts with the most comments:   \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              post_id  numbr_likes  number_comments\n",
              "41873060  Bx2sPAjHviO      38871.0         488953.0\n",
              "12164781  BuGjhWAhAW5      81523.0         313997.0\n",
              "33261751  BJ-rkpTgn1N      54507.0         257488.0\n",
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              "5427268   BoQoM-JFkPA     129258.0         180635.0\n",
              "25257169  Bwarws1lCok     684861.0         173988.0\n",
              "6220011   Bt4qlVDAZMH    2224495.0         170836.0\n",
              "31689573  Bs9NhQkAcJA     226336.0         166919.0\n",
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              "\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Show first 10\n",
        "print(\"These are the first 10 posts with the most comments:   \")\n",
        "df_posts_sorted.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 380
        },
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "These are the least 10 posts with the least comments:   \n"
          ]
        },
        {
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              "              post_id  numbr_likes  number_comments\n",
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              "      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-377ff282-694e-4f9d-9818-b54cd8274f16 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-377ff282-694e-4f9d-9818-b54cd8274f16');\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
        }
      ],
      "source": [
        "# Show least 10\n",
        "print(\"These are the least 10 posts with the least comments:   \")\n",
        "df_posts_sorted.tail(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OBcgar43EGLm"
      },
      "source": [
        "##### **How many posts include tagged locations, and how many do not? Show it using an appropriate chart and comment your results.**\n",
        "To answer this question, we only need the column with the location id, since this column shows if (and what) location is tagged in a post. To visualise how many posts have a tagged location, we have chosen a pie chart, where the percentages of both categories are also displayed.\n",
        "\n",
        "As we can see, approximately 70% of the posts have a tagged location. It makes sense that the number of posts with a tagged location is higher than without a tagged location, because you can engage your followers more with what you do. Especially if you are a business."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "posts = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['location_id'], low_memory = True)\n",
        "\n",
        "#Counts all the posts without location\n",
        "n_no_location = posts['location_id'].isnull().sum()\n",
        "#Counts all the post with location\n",
        "n_location = posts['location_id'].count()\n"
      ],
      "metadata": {
        "id": "oAAlo72LGUTr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The number of posts with location is:   \", n_location)\n",
        "print(\"The number of posts without location is:   \", n_no_location)\n",
        "\n",
        "# Create data\n",
        "size_of_groups=[n_location, n_no_location]\n",
        "\n",
        "# Create a pieplot\n",
        "plt.pie(size_of_groups, labels=['Location', 'No location'], colors=['yellow', 'blue'], autopct='%1.2f%%')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "rYkZ20ZfCez8",
        "outputId": "1ed987ea-f28b-4724-aacc-f7b0acefcbe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of posts with location is:    29737425\n",
            "The number of posts without location is:    12972772\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2r27Pa_DEKGS"
      },
      "source": [
        "##### **How many posts include only photos? How many also have videos?**\n",
        "For this question, we use the parameter 'post_type' of the posts dataset. A pie chart is created to visualize the number of posts with photos, videos or both. \n",
        "\n",
        "The pie chart shows that more than 95% of the posts contain only a picture, approximately 3% shows a video, and 0.1% of the posts have both. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "__nF7FIPWlOV"
      },
      "outputs": [],
      "source": [
        "# load in the data\n",
        "posts_type = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['post_type'], low_memory = True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We decided the name \"test\" with \"posts\" because it's more understandable\n",
        "post_photo = posts_type[\"post_type\"].value_counts()[1]\n",
        "post_video = posts_type[\"post_type\"].value_counts()[2]\n",
        "post_both = posts_type[\"post_type\"].value_counts()[3]\n",
        "\n",
        "print(\"Number of posts with only photos:  \", post_photo)\n",
        "print(\"Number of posts with only videos:  \", post_video)\n",
        "print(\"Number of posts with photos and videos:  \", post_both)\n",
        "\n",
        "# Create data\n",
        "size_of_groups=[post_photo, post_video,post_both]\n",
        "\n",
        "# Create a pieplot\n",
        "plt.pie(size_of_groups, labels=['Photos', 'Videos', 'Both'], colors=['yellow', 'orange', 'green'], autopct='%1.2f%%')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "y3X_G7d_Dl9m",
        "outputId": "08a654b0-3a47-46ef-84b4-5cbbcb308575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of posts with only photos:   41534486\n",
            "Number of posts with only videos:   1134768\n",
            "Number of posts with photos and videos:   40943\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODgiSZdxEOuw"
      },
      "source": [
        "##### **What's the percentage of business accounts vs non-business? What can you interpret regarding that percentage?**\n",
        "\n",
        "From the code, we can see that approximately 75% of the accounts are non-business accounts. We can interpret from this that most instagram users use it for private use instead for a business."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I5Su0HKUERzz"
      },
      "outputs": [],
      "source": [
        "# first remove the rows without any info\n",
        "df_profiles_business = df_profiles.dropna( how='any',\n",
        "                    subset=['is_business_account'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CO4AeCuAFcng",
        "outputId": "54483eb8-91bd-4896-ec1f-00b3f7854158"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False    0.751764\n",
              "True     0.248236\n",
              "Name: is_business_account, dtype: float64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# calculate the percentages\n",
        "df_profiles_business['is_business_account'].value_counts(normalize=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "th85T5UHyvQR"
      },
      "source": [
        "### Research question 3:  most common time in which users publish their posts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-hnSldMcTv4"
      },
      "source": [
        "##### **What is the most common time in which users publish their posts?**\n",
        "For this question, we will need to look at the cts which represents the time a post is published. To answer this question, we will convert the cts to hours, minutes, and seconds so we can see what is the most populair time to post a post. If we look at does three aspects, we see that the most common time to post is 21:00:09."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load in data\n",
        "posts = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['cts'], low_memory = True)\n"
      ],
      "metadata": {
        "id": "nQevqnmJFa7H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MeIBq89-zIgo",
        "outputId": "1603f710-6b75-4dec-f52e-4caaf89459d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is the most common time in which users publish their posts:  \n",
            "21:00:09\n"
          ]
        }
      ],
      "source": [
        "\n",
        "#Takes only the date_time column and extrapolate only hours_minutes_seconds\n",
        "posts['cts'] = pd.to_datetime(posts['cts'], unit='ns').dt.time\n",
        "\n",
        "best_time = posts['cts'].value_counts()[0]\n",
        "c=0\n",
        "\n",
        "for values in posts['cts'].value_counts():\n",
        "  if values == best_time:\n",
        "    c+=1\n",
        "\n",
        "best_times = posts['cts'].value_counts().head(c).index.tolist()\n",
        "if c==1:\n",
        "  print(\"This is the most common time in which users publish their posts:  \")\n",
        "else:\n",
        "  print(\"Those are the most common times in which users publish their posts:  \")\n",
        "\n",
        "for x in best_times:\n",
        "  print (x)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtrVBBfGceN2"
      },
      "source": [
        "#### Create a function that receives a time intervals list as a parameter and returns a plot with the number of posts for each given interval."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "jeboXJ7iqsau"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def post_per_interval(interval, posts):\n",
        "\n",
        "  times = pd.to_datetime(posts['cts'], unit='ns')\n",
        "\n",
        "  times = pd.DataFrame(times)\n",
        "  times.set_index(pd.DatetimeIndex(times),inplace=True)\n",
        "\n",
        "  value_counts=pd.DataFrame()\n",
        "  \n",
        "  for i in interval:\n",
        "    start,end = i\n",
        "    total = times.between_time(start,end).count()\n",
        "    total = int(total[0])\n",
        "    results = pd.DataFrame({'Time interval' : [i], 'Total posts' : total})\n",
        "    value_counts = value_counts.append(results)\n",
        "\n",
        "  value_counts.reset_index(drop = True, inplace = True)\n",
        "  print(value_counts)\n",
        "\n",
        "  f = plt.figure()\n",
        "  ax = value_counts.plot(figsize=(15,8), kind='bar', color = \"green\", zorder=3)\n",
        "  plt.setp(ax, xlabel='Time Intervals', ylabel='Number of posts', title = 'Number of posts for each time interval')\n",
        "  plt.show()\n",
        "  return"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nhdtKoj8qUeM"
      },
      "source": [
        "#### Use the function that you created in the previous literal to plot the number of posts between the following time intervals:\n",
        "\n",
        "(Initial time, \tFinal time) :\n",
        "(06:00:00, 10:59:59)\n",
        "(11:00:00, 13:59:59)\n",
        "(14:00:00, 16:59:59)\n",
        "(17:00:00, 19:59:59)\n",
        "(20:00:00, 23:59:59)\n",
        "(00:00:00, 02:59:59)\n",
        "(03:00:00, 05:59:59)\n",
        "\n",
        "For this question, we will load in the cts and convert this using the function. After that, we will check what post falls in which interval.\n",
        "\n",
        "As we can see, most posts are posted between 20 and 24. In our opinion this makes sense, since most accounts are non-business accounts and this is after office hours. That is the moment people have time to look at instagram and post new posts. The least 'popular' time interval is between 03 - 06, which can be explained by the fact that most people are sleeping in this time interval. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load in data\n",
        "posts = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['profile_id','cts'], low_memory = True)"
      ],
      "metadata": {
        "id": "AyyPox6OKG9T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interval = [('06:00:00', '10:59:59'),\n",
        "('11:00:00', '13:59:59'),\n",
        "('14:00:00', '16:59:59'),\n",
        "('17:00:00', '19:59:59'),\n",
        "('20:00:00', '23:59:59'),\n",
        "('00:00:00', '02:59:59'),\n",
        "('03:00:00', '05:59:59')]"
      ],
      "metadata": {
        "id": "4Lauv2eeRZ8K"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "id": "vmFDN1yhcfOr",
        "outputId": "24510545-6ee6-43ed-a148-f1c76b7f8b45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          Time interval  Total posts\n",
            "0  (06:00:00, 10:59:59)        29067\n",
            "1  (11:00:00, 13:59:59)        25720\n",
            "2  (14:00:00, 16:59:59)        31735\n",
            "3  (17:00:00, 19:59:59)        35306\n",
            "4  (20:00:00, 23:59:59)        41501\n",
            "5  (00:00:00, 02:59:59)        19897\n",
            "6  (03:00:00, 05:59:59)        16774\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "posts = pd.read_csv(\"instagram_posts.csv\", delimiter='\\t', nrows = 200000)\n",
        "\n",
        "post_per_interval(interval, posts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQzlqNiEpucl"
      },
      "source": [
        "### Research question 4: in most cases, we will not have a consistent dataset, and the one we are dealing with is not an exception (ex. in the given datasets, you may not find the information of the profiles for some of the posts). So let’s enhance our analysis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUWm4DbdqKVv"
      },
      "source": [
        "##### **Write a function that, given a profile_id, will be able to return the posts that belong to the given profile_id.**\n",
        "For this question, we need the post id, the profile id, the number of likes, and the number of comments. The profile id is needed to filter on a certain profile. The post id is necessary so the post can be traced back. The number of likes and comments give an idea of the post and its popularity to give a quick view. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "posts = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['post_id', 'profile_id', 'numbr_likes', 'number_comments','cts'], low_memory = True)"
      ],
      "metadata": {
        "id": "YhkFamO1hbYa"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_post_by_profileID(profileID):  \n",
        "  return(posts.loc[posts['profile_id'] == profileID])\n"
      ],
      "metadata": {
        "id": "g6PKkJkzdXYa"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OB3mVnlwqGJl",
        "outputId": "1a19119c-8349-4d4e-c814-629471552eb3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please insert a profile_id:  2237947779\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              post_id    profile_id  numbr_likes  number_comments\n",
              "0         BXdjjUlgcgq  2.237948e+09         80.0              0.0\n",
              "773482    BeyPed5hKj9  2.237948e+09         86.0              2.0\n",
              "2868573   Bunhd1DFVAG  2.237948e+09        168.0              3.0\n",
              "2868706   Bppi85gliQK  2.237948e+09        102.0              2.0\n",
              "3730200   BuDfIyslzfw  2.237948e+09        145.0              2.0\n",
              "29715661  BxJsMDpA2yH  2.237948e+09        124.0              2.0\n",
              "29715667  Bt5LFpZlm3z  2.237948e+09        150.0              3.0\n",
              "29715668  BxZIzaQhS-o  2.237948e+09        166.0              2.0\n",
              "29715669  BxRZvS0gPIA  2.237948e+09        220.0              9.0\n",
              "29715769  BwygkNCAShv  2.237948e+09        198.0              2.0\n",
              "29715770  Bwm7hEKHBzs  2.237948e+09        217.0              1.0\n",
              "29715771  BwYuE0cghyB  2.237948e+09        153.0              1.0\n",
              "29715772  BwGuhAoFSWj  2.237948e+09        178.0              4.0\n",
              "29715779  BwBj2vhFuGH  2.237948e+09        119.0              1.0\n",
              "29715780  Bw_XgfXA9YT  2.237948e+09        140.0              4.0\n",
              "39961173  Bw3nuV6ACyY  2.237948e+09        171.0              7.0\n",
              "40304341  BwJS1H9lfiJ  2.237948e+09        190.0              4.0\n",
              "41441812  Bf6avRfheG1  2.237948e+09        113.0              2.0\n",
              "41878656  BxMRhd2Ajgm  2.237948e+09        123.0              0.0\n",
              "42710139  Be93_r7Bnhq  2.237948e+09        129.0              1.0\n",
              "42710140  BtNaOjolj_n  2.237948e+09        114.0              4.0\n",
              "42710141  BkMhROUhNbu  2.237948e+09        119.0              8.0\n",
              "42710142  BeOMICahv0d  2.237948e+09        153.0              2.0\n",
              "42710143  BeUrXsoB0ou  2.237948e+09        125.0              1.0\n",
              "42710145  BvLnQ5QFHAs  2.237948e+09        253.0             22.0\n",
              "42710146  BrZjxHllTd9  2.237948e+09        130.0              5.0\n",
              "42710147  BeCp_INhyoh  2.237948e+09        122.0              1.0\n",
              "42710161  BqjeI31lw-y  2.237948e+09        103.0              0.0\n",
              "42710174  BoQpcWQB-gC  2.237948e+09         76.0              0.0\n",
              "42710175  Bn1k1odAXa5  2.237948e+09         73.0              0.0\n",
              "42710176  BmaTjjpBTG9  2.237948e+09         50.0              0.0\n",
              "42710177  BmNSg2ih5ex  2.237948e+09         77.0              1.0\n",
              "42710178  BkjvPM1Bpxa  2.237948e+09         60.0              2.0\n",
              "42710179  Bg22N6AhebK  2.237948e+09         47.0              0.0\n",
              "42710180  BgS2rd4BozM  2.237948e+09        113.0              1.0\n",
              "42710181  Bc6jrZIHttD  2.237948e+09        107.0              1.0\n",
              "42710182  BgDZmg1D0If  2.237948e+09         69.0              0.0\n",
              "42710183  Bf-QTObhSmW  2.237948e+09        133.0              2.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8f1e9ac-01df-4843-82aa-682f41e69159\">\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>post_id</th>\n",
              "      <th>profile_id</th>\n",
              "      <th>numbr_likes</th>\n",
              "      <th>number_comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>50.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>42710177</th>\n",
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              "      <td>77.0</td>\n",
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              "      <th>42710178</th>\n",
              "      <td>BkjvPM1Bpxa</td>\n",
              "      <td>2.237948e+09</td>\n",
              "      <td>60.0</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
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              "      <td>Bg22N6AhebK</td>\n",
              "      <td>2.237948e+09</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>42710180</th>\n",
              "      <td>BgS2rd4BozM</td>\n",
              "      <td>2.237948e+09</td>\n",
              "      <td>113.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>42710181</th>\n",
              "      <td>Bc6jrZIHttD</td>\n",
              "      <td>2.237948e+09</td>\n",
              "      <td>107.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
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              "      <th>42710182</th>\n",
              "      <td>BgDZmg1D0If</td>\n",
              "      <td>2.237948e+09</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>42710183</th>\n",
              "      <td>Bf-QTObhSmW</td>\n",
              "      <td>2.237948e+09</td>\n",
              "      <td>133.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8f1e9ac-01df-4843-82aa-682f41e69159')\"\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "#Example : 2237947779\n",
        "#The profile_id in the table shown below is written in a compact form,\n",
        "#the real form is the one shown in the row up this\n",
        "profileID = float(input(\"Please insert a profile_id:  \"))\n",
        "find_post_by_profileID(profileID)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZbJ4EAGqPLE"
      },
      "source": [
        "##### **Write another function that, given an input n (an integer), will return the posts that belong to the n top posted profiles (top n profiles that have posted the highest number of posts) that their data is available in the profile.csv using the previously written function.**\n",
        "First, we will sort the profiles on the number of posts. After that, we will loop over the given number and run the 'find_post_by_profileID' function that is programmed in the previous question. This will give the posts of the top n popular profiles based on their number of posts. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJ-yY2jyqTUT",
        "outputId": "3e648b08-cb74-42c5-f2d2-dd5ccc62fadc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Insert an integer number:  3\n"
          ]
        }
      ],
      "source": [
        "profiles = pd.read_csv('instagram_profiles.csv', delimiter='\\t')\n",
        "df_profiles_sorted_by_n_posts = profiles.sort_values(by='n_posts', ascending=False)\n",
        "\n",
        "def posts_of_topN(n):\n",
        "  top_n = df_profiles_sorted_by_n_posts.head(n)\n",
        "  for i in range(n):\n",
        "    find_post_by_profileID(top_n.iloc[i].profile_id)\n",
        "  return\n",
        "\n",
        "n = int(input(\"Insert an integer number:  \"))\n",
        "posts_of_topN(n)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEFNhniNF2Bk"
      },
      "source": [
        "##### **What is the average number of \"likes\" and comments of the top 10 profiles with the highest number of posts which their information is available in profile.csv?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ewopSxvDF6xX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e73f98c7-9044-4c81-eb3b-9da7abd061d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The average of likes of user  jasem33312  is:  7.666666666666667\n",
            "The average of comments of user  jasem33312  is:  0.0\n",
            "\n",
            "\n",
            "The average of likes of user  koenigpic  is:  0.0\n",
            "The average of comments of user  koenigpic  is:  0.0\n",
            "\n",
            "\n",
            "The average of likes of user  rudyanto62  is:  2.96\n",
            "The average of comments of user  rudyanto62  is:  0.04\n",
            "\n",
            "\n",
            "The average of likes of user  hometown_ni  is:  0.2962962962962963\n",
            "The average of comments of user  hometown_ni  is:  0.0\n",
            "\n",
            "\n",
            "The average of likes of user  dwfitfiber_thailand  is:  1.5405405405405406\n",
            "The average of comments of user  dwfitfiber_thailand  is:  0.0\n",
            "\n",
            "\n",
            "The average of likes of user  waxking911  is:  2.026315789473684\n",
            "The average of comments of user  waxking911  is:  0.18421052631578946\n",
            "\n",
            "\n",
            "The average of likes of user  gamt_official  is:  6.477611940298507\n",
            "The average of comments of user  gamt_official  is:  0.04477611940298507\n",
            "\n",
            "\n",
            "The average of likes of user  collarosedw_official  is:  0.4\n",
            "The average of comments of user  collarosedw_official  is:  0.04\n",
            "\n",
            "\n",
            "The average of likes of user  gamt_authentic  is:  3.921875\n",
            "The average of comments of user  gamt_authentic  is:  0.0\n",
            "\n",
            "\n",
            "The average of likes of user  ivankkkka.trmp  is:  nan\n",
            "The average of comments of user  ivankkkka.trmp  is:  nan\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# load in the data\n",
        "profiles = pd.read_csv('instagram_profiles.csv', delimiter='\\t')\n",
        "# sort on the number of posts\n",
        "df_profiles_sorted_by_n_posts = profiles.sort_values(by='n_posts', ascending=False)\n",
        "# print the top 10\n",
        "top_10 = df_profiles_sorted_by_n_posts.head(10)\n",
        "\n",
        "# loop over the top 10\n",
        "for i in range(10):\n",
        "  post_i = find_post_by_profileID(top_10.iloc[i].profile_id)\n",
        "  user = top_10.iloc[i].profile_name\n",
        "  # determine average likes\n",
        "  avg_likes_i = post_i['numbr_likes'].mean()\n",
        "  # determine average number of comments\n",
        "  avg_comments_i = post_i['number_comments'].mean()\n",
        "  print(\"The average of likes of user \",user, \" is: \", avg_likes_i)\n",
        "  print(\"The average of comments of user \",user, \" is: \", avg_comments_i)\n",
        "  print(\"\\n\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xpXFzTHsHIwR"
      },
      "source": [
        "##### **Plot the number of posts that these top 10 profiles have sent on Instagram in the given interval in question RQ3. Interpret the resulting chart.**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "XrXipQVEHJ7p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "outputId": "c990fb48-de3e-4300-8a5d-ab9fb8f580bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          Time interval  Total posts\n",
            "0  (06:00:00, 10:59:59)           44\n",
            "1  (11:00:00, 13:59:59)           14\n",
            "2  (14:00:00, 16:59:59)           17\n",
            "3  (17:00:00, 19:59:59)           11\n",
            "4  (20:00:00, 23:59:59)           10\n",
            "5  (00:00:00, 02:59:59)           25\n",
            "6  (03:00:00, 05:59:59)           41\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "profiles = pd.read_csv('instagram_profiles.csv', delimiter='\\t')\n",
        "df_profiles_sorted_by_n_posts = profiles.sort_values(by='n_posts', ascending=False)\n",
        "top_10 = df_profiles_sorted_by_n_posts.head(10)\n",
        "\n",
        "posts_top10 = pd.DataFrame()\n",
        "\n",
        "#We know that the problem requires the plot of 10 profiles,\n",
        "#but our hardware does not allow us to do this becasue we constantly ran out of memory. \n",
        "for x in range(5):\n",
        "  post_x = find_post_by_profileID(top_10.iloc[x].profile_id)\n",
        "  posts_top10=posts_top10.append(post_x)\n",
        "\n",
        "post_per_interval(interval, posts_top10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drmW5loHHfKQ"
      },
      "source": [
        "### Research question 5: most influencial users"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUfgYqPnHtv7"
      },
      "source": [
        "##### **Plot the top 10 most popular users in terms of followers and their number of posts.**\n",
        "\n",
        "First the rows with no information for both the followers and the nr of posts are deleted. After the data cleaning, the data is sorted based on the followers or on the number of posts, and two bar plots are created.\n",
        "\n",
        "As can be seen, instagram has the most followers, while jasem33312 has the most posts. Furthermore, it is also visible that there is no overlap between the users with the most followers and the users with the most posts. \n",
        "\n",
        "Lastly, we have also created a plot for the users with the most followers, where the number of followers are displayed on the y-axis and the number of posts are displayed on the x-axis. It shows that there is no real correlation between the number of posts and number of followers. This is only based on 10 datapoints, so we cannot conclude this for sure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        },
        "id": "JiMHH6nlHlC3",
        "outputId": "51bfb67b-471d-463f-8b92-5dab0643784a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             sid    profile_id     profile_name      firstname_lastname  \\\n",
              "2880218   336492  2.502532e+07        instagram               Instagram   \n",
              "2582507   371414  3.416684e+06  victoriassecret       Victoria's Secret   \n",
              "3820939   337097  2.082449e+07              nba                     NBA   \n",
              "701965    330376  1.876191e+08     louisvuitton  Louis Vuitton Official   \n",
              "2414951   292359  2.125507e+09    buzzfeedtasty                   Tasty   \n",
              "2785241  3437294  4.968651e+08   andresiniesta8          Andres Iniesta   \n",
              "333193    158690  2.341008e+07               hm                     H&M   \n",
              "3934581  3615154  2.423993e+07  princessyahrini                Syahrini   \n",
              "1311970   508140  5.041706e+07     danbilzerian           Dan Bilzerian   \n",
              "1703764  3513205  9.070254e+08      akshaykumar            Akshay Kumar   \n",
              "\n",
              "                                               description  following  \\\n",
              "2880218  Bringing you closer to the people and things y...      226.0   \n",
              "2582507                   Confidence is sexy. #wearitdaily      549.0   \n",
              "3820939  30 teams, 1 🏆\\n👟: @nbakicks \\n📽: @nbahistory \\...      817.0   \n",
              "701965    The official Instagram account of Louis Vuitton.        5.0   \n",
              "2414951  Recipes that will make you say \"oh yes!\"\\n👇 Cl...      298.0   \n",
              "2785241  Andres Iniesta Official Instagram Account, \\nB...      151.0   \n",
              "333193   Welcome to #HM! Follow us at @HM_Man @HM_Kids ...      403.0   \n",
              "3934581                                                NaN      835.0   \n",
              "1311970                               Snapchat: dbilzerian      252.0   \n",
              "1703764                                       #AkshayKumar        5.0   \n",
              "\n",
              "           followers  n_posts                                   url  \\\n",
              "2880218  285457645.0   5760.0                    help.instagram.com   \n",
              "2582507   65269440.0   7355.0             like2b.uy/victoriassecret   \n",
              "3820939   35339708.0  28816.0               nba.app.link/js6X7SVaxT   \n",
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          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# delete the rows if there is nothing now about followers\n",
        "df_profiles_followers = df_profiles.dropna( how='any',\n",
        "                    subset=['followers'])\n",
        "\n",
        "# sort the dataframe based on the nposts\n",
        "df_profiles_foll_sort = df_profiles_followers.sort_values(by='followers', ascending=False)\n",
        "\n",
        "# Show first 10\n",
        "df_profiles_foll_sort.head(10)\n",
        "\n",
        "# Print the number of followers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 739
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        "id": "cxjlNeMhIbCl",
        "outputId": "02c5cd5c-5941-4716-8478-8aa2486554fc"
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              "      <td>9.813508e+06</td>\n",
              "      <td>hometown_ni</td>\n",
              "      <td>AngelsCowboysLakers</td>\n",
              "      <td>All about live &amp; let live. Peace &amp; Harmony. La...</td>\n",
              "      <td>7488.0</td>\n",
              "      <td>1755.0</td>\n",
              "      <td>125220.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-06-27 02:49:38.140 -0400</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4219642</th>\n",
              "      <td>3851869</td>\n",
              "      <td>1.607309e+09</td>\n",
              "      <td>dwfitfiber_thailand</td>\n",
              "      <td>เห็นผลตั้งแต่เซ็ตแรกท้าลอง</td>\n",
              "      <td>✿รับตัวแทนรายได้ดีมาก\\n🏧 KTB l SCB l KBANK | B...</td>\n",
              "      <td>7490.0</td>\n",
              "      <td>44951.0</td>\n",
              "      <td>117262.0</td>\n",
              "      <td>bit.ly/2GdIYJl</td>\n",
              "      <td>2019-07-13 02:10:07.317 -0400</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2207897</th>\n",
              "      <td>345591</td>\n",
              "      <td>1.087614e+09</td>\n",
              "      <td>waxking911</td>\n",
              "      <td>Frank Hernandez</td>\n",
              "      <td>NaN</td>\n",
              "      <td>134.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>117099.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4313908</th>\n",
              "      <td>3944512</td>\n",
              "      <td>1.603890e+09</td>\n",
              "      <td>gamt_official</td>\n",
              "      <td>Official IG Of GAMT Thailand</td>\n",
              "      <td>❥ แว่น gamt พร้อมส่งของแท้💯 มีโปร1290 พร้อมซอง...</td>\n",
              "      <td>7470.0</td>\n",
              "      <td>36001.0</td>\n",
              "      <td>113780.0</td>\n",
              "      <td>bit.ly/2GdIYJl</td>\n",
              "      <td>2019-07-14 21:10:30.785 -0400</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4219476</th>\n",
              "      <td>3851808</td>\n",
              "      <td>2.181730e+08</td>\n",
              "      <td>collarosedw_official</td>\n",
              "      <td>grapeseed+collarose</td>\n",
              "      <td>🍇 สินค้าแบรนด์ dwplus\\n💓 collarose+grapeseed+f...</td>\n",
              "      <td>7496.0</td>\n",
              "      <td>18021.0</td>\n",
              "      <td>113182.0</td>\n",
              "      <td>bit.ly/2asesw7</td>\n",
              "      <td>2019-07-13 02:08:17.407 -0400</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4236057</th>\n",
              "      <td>3868432</td>\n",
              "      <td>2.855497e+08</td>\n",
              "      <td>gamt_authentic</td>\n",
              "      <td>Official IG Of GAMT Thailand</td>\n",
              "      <td>❥ แว่น gamt พร้อมส่ง มีโปร1290 พร้อมซองแว่น+ผ้...</td>\n",
              "      <td>7480.0</td>\n",
              "      <td>27511.0</td>\n",
              "      <td>112777.0</td>\n",
              "      <td>bit.ly/2T2RIqG</td>\n",
              "      <td>2019-07-13 09:28:30.025 -0400</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1631526</th>\n",
              "      <td>1312725</td>\n",
              "      <td>8.312603e+09</td>\n",
              "      <td>ivankkkka.trmp</td>\n",
              "      <td>IVANKA DARCEY K. TRUMP</td>\n",
              "      <td>DORA. 👄 #Mango</td>\n",
              "      <td>7497.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>112723.0</td>\n",
              "      <td>www.flightclub.com/yeezy</td>\n",
              "      <td>2019-04-01 02:41:26.980 -0400</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b687d41d-0064-4bdf-aaca-868a5a37dce2')\"\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-b687d41d-0064-4bdf-aaca-868a5a37dce2 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-b687d41d-0064-4bdf-aaca-868a5a37dce2');\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": 5
        }
      ],
      "source": [
        "# delete the rows if there is nothing now about n_posts\n",
        "df_profiles_posts = df_profiles.dropna( how='any',\n",
        "                    subset=['n_posts'])\n",
        "\n",
        "# sort the dataframe based on the nposts\n",
        "df_profiles_posts_sort = df_profiles_followers.sort_values(by='n_posts', ascending=False)\n",
        "\n",
        "# Show first 10\n",
        "df_profiles_posts_sort.head(10) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "T1ZFEDuKIlLd",
        "outputId": "03636a39-0bb6-453f-b0fc-b29b26126fad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 0, '')]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plot the top 10 of followers and nr posts\n",
        "#define subplot layout\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
        "\n",
        "# plot the highest top 10 followers\n",
        "ax1 = df_profiles_foll_sort.iloc[0:10].plot.bar(x='profile_name', y='followers', ax=axes[0])\n",
        "ax1.title.set_text('10 users with most followers')\n",
        "plt.sca(axes[0])\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "ax1.set(xlabel=None)\n",
        "\n",
        "\n",
        "# plot the top 10 posts\n",
        "ax2 = df_profiles_posts_sort.iloc[0:10].plot.bar(x='profile_name', y='n_posts', ax=axes[1])\n",
        "ax2.title.set_text('10 users with most posts')\n",
        "plt.sca(axes[1])\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "ax2.set(xlabel=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "iaJQ7TlUMglj",
        "outputId": "032da2ff-1f95-42f9-c2ff-ecad8f7c1bb9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plot the highest top 10 followers\n",
        "fig = plt.figure()\n",
        "ax1 = df_profiles_foll_sort.iloc[0:10].plot(x='n_posts', y='followers')\n",
        "ax1.title.set_text('10 users with most followers against their number of followers')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRBaUd4uONON"
      },
      "source": [
        "##### **Who is the most influential user?**\n",
        "It depends on the definition of influential. If the definition of influential means who will reach the most people, the most influential user is Instagram."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCq6mlZsORSq"
      },
      "source": [
        "##### **Have they posted anything with tagged locations? Extract the most frequent areas on their posts and plot the number of times each city has been visited.**\n",
        "\n",
        "Based on the data, 95.6% of their posts have a tagged location. If we look at the locations of these posts (see the bar chart), we see that only one city is tagged twice, while all the other cities are tagged only once.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_instagram = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['sid_profile','location_id','post_type'], low_memory = True)\n"
      ],
      "metadata": {
        "id": "48QLr5c-ISJw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sid nr of instgram\n",
        "profileNr = 336492\n",
        "\n",
        "# make a new datagram based on the posts of instagram\n",
        "df_instagram = df_instagram[df_instagram.sid_profile == profileNr]\n"
      ],
      "metadata": {
        "id": "aP4ixl31IPlw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# determine how big percentage they have posted with tagged locations\n",
        "perc = (len(df_instagram)-df_instagram['location_id'].isna().sum())/len(df_instagram)*100\n",
        "print('Percentage of posts with tagged location:', perc, '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdrOlPuuvZiE",
        "outputId": "c6c654c9-eb05-48bd-d1b8-3e2c1d04aeaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of posts with tagged location: 95.65217391304348 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove the posts without a tagged location\n",
        "df_instagram = df_instagram.dropna( how='any', subset = ['location_id'])\n"
      ],
      "metadata": {
        "id": "IDIBRnXfxXcV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load information of locations\n",
        "df_locations_small = pd.read_csv('instagram_locations.csv', delimiter=\"\\t\", usecols = ['id','city', 'dir_country_name'], low_memory = True)\n"
      ],
      "metadata": {
        "id": "HrAwD_CUHNx4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# combine the instagram and location df based on sid number\n",
        "df_combined = pd.merge(df_instagram, df_locations_small, left_on='location_id', right_on = 'id', how=\"left\")"
      ],
      "metadata": {
        "id": "t2g_j-Ckwb5T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check if dataset makes sense\n",
        "print(df_combined.shape)\n",
        "df_combined.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "waYVTsPVHsQs",
        "outputId": "372bbc57-0794-4dc9-ec47-b333b026ae1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(44, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sid_profile   location_id  post_type               id  \\\n",
              "0       336492  5.332856e+06          1          5332856   \n",
              "1       336492  5.873874e+08          1        587387369   \n",
              "2       336492  8.770667e+14          1  877066659067597   \n",
              "3       336492  3.500213e+08          1        350021298   \n",
              "4       336492  5.243120e+08          1        524311966   \n",
              "\n",
              "                   city dir_country_name  \n",
              "0         Dallas, Texas    United States  \n",
              "1                   NaN              NaN  \n",
              "2           Cusco, Peru             Peru  \n",
              "3  Stony Plain, Alberta              NaN  \n",
              "4                Forres              NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-01e3952e-ca04-46c7-8e1b-3c6b9cc5a5aa\">\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>sid_profile</th>\n",
              "      <th>location_id</th>\n",
              "      <th>post_type</th>\n",
              "      <th>id</th>\n",
              "      <th>city</th>\n",
              "      <th>dir_country_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>336492</td>\n",
              "      <td>5.332856e+06</td>\n",
              "      <td>1</td>\n",
              "      <td>5332856</td>\n",
              "      <td>Dallas, Texas</td>\n",
              "      <td>United States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>336492</td>\n",
              "      <td>5.873874e+08</td>\n",
              "      <td>1</td>\n",
              "      <td>587387369</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>336492</td>\n",
              "      <td>8.770667e+14</td>\n",
              "      <td>1</td>\n",
              "      <td>877066659067597</td>\n",
              "      <td>Cusco, Peru</td>\n",
              "      <td>Peru</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>336492</td>\n",
              "      <td>3.500213e+08</td>\n",
              "      <td>1</td>\n",
              "      <td>350021298</td>\n",
              "      <td>Stony Plain, Alberta</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>336492</td>\n",
              "      <td>5.243120e+08</td>\n",
              "      <td>1</td>\n",
              "      <td>524311966</td>\n",
              "      <td>Forres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01e3952e-ca04-46c7-8e1b-3c6b9cc5a5aa')\"\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-01e3952e-ca04-46c7-8e1b-3c6b9cc5a5aa 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-01e3952e-ca04-46c7-8e1b-3c6b9cc5a5aa');\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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5, 10))\n",
        "df_combined['city'].value_counts(sort=False).plot.barh()\n",
        "plt.title('Number of tagged posts per city of instagram account')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "D-VZcegFy1mX",
        "outputId": "7e207877-b4f2-4ae4-f0fc-79c1de0c303b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **How many pictures-only posts have they published? How many reels? (only videos) and how many with both contents? Provide the number as percentages and interpret those figures.**\n",
        "\n",
        "93% of their posts contain a picture. 7% of their posts contain a video. No posts contain both. It looks like instagram mainly likes to post posts with only pictures. "
      ],
      "metadata": {
        "id": "TgbKZ_aXjDaG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by post_type\n",
        "type1 = (df_instagram['post_type']==1).sum()\n",
        "type2 = (df_instagram['post_type']==2).sum()\n",
        "type3 = (df_instagram['post_type']==3).sum()\n",
        "\n",
        "\n",
        "# Create data\n",
        "size_of_groups=[type1, type2, type3]\n",
        "\n",
        "# Create a pieplot\n",
        "plt.pie(size_of_groups, labels=['Photo', 'Video', 'Both'], colors=['yellow', 'orange', 'red'], autopct='%1.2f%%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "ShR-xFfwf84H",
        "outputId": "b14885df-5a3d-41ef-a9f5-53e38a5b5191"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7fa8517b8d50>,\n",
              "  <matplotlib.patches.Wedge at 0x7fa8517c6550>,\n",
              "  <matplotlib.patches.Wedge at 0x7fa8517c6dd0>],\n",
              " [Text(-1.0769925073829814, 0.2238015617481677, 'Photo'),\n",
              "  Text(1.0769925178598874, -0.22380151133050968, 'Video'),\n",
              "  Text(1.0999999999999954, 1.0298943244593887e-07, 'Both')],\n",
              " [Text(-0.5874504585725352, 0.1220735791353642, '93%'),\n",
              "  Text(0.5874504642872113, -0.12207355163482345, '7%'),\n",
              "  Text(0.5999999999999974, 5.6176054061421195e-08, '0%')])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **How many \"likes\" and comments did posts with only pictures receive? How about videos and mixed posts? Try to provide the average numbers and confront them with their followers amount, explaining what you can say from that comparison.**\n",
        "\n",
        "If we only look at the average number of likes and comments, we can see that likes are a 100 times more placed than comments for the profile. This can be explained by the fact that a like is a very easy action to perform in comparison with leaving a comment. If we compare the number of likes and comments for pictures vs videos, we can see that pictures receive both more likes and comments. This can be explained by the fact that a lot of people do not take the time to completely watch a video when they scroll through instagram. \n",
        "\n",
        "If we compare the numbers with the number of followers (285 457 645), we see that less than 1% of the followers leave a like, and even a smaller amount leaves a comment. Even though the number of likes on itself seems a lot, compared to the number of followers it is very low."
      ],
      "metadata": {
        "id": "pCpdzHk6jGtx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nrfollowers = df_profiles.loc[df_profiles['profile_id'] == 25025320.0, 'followers'].iloc[0]\n",
        "\n",
        "print('number of followers of instagram:', nrfollowers)\n",
        "\n",
        "df_inst_photos = df_instagram[df_instagram.post_type == 1]\n",
        "df_inst_videos = df_instagram[df_instagram.post_type == 2]\n",
        "\n",
        "photolikes = df_inst_photos['numbr_likes'].mean()\n",
        "photocomments = df_inst_photos['number_comments'].mean()\n",
        "videolikes = df_inst_videos['numbr_likes'].mean()\n",
        "videocomments = df_inst_videos['number_comments'].mean()\n",
        "\n",
        "\n",
        "dataset = {'medium': ['pictures', 'videos', 'mixed'], \n",
        "        'nr likes': [photolikes, videolikes , '-'],\n",
        "        'likes/followers': [photolikes/nrfollowers, videolikes/nrfollowers , '-'], \n",
        "        'nr comments': [photocomments , videocomments , '-'],\n",
        "        ' comments/followers': [photocomments/nrfollowers, videocomments/nrfollowers , '-']}\n",
        "df_likes = pd.DataFrame(data=dataset)\n",
        "df_likes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "kvyQ923xxTIH",
        "outputId": "387d3198-2b01-4656-fc89-17a37107e728"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of followers of instagram: 285457645.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     medium       nr likes likes/followers  nr comments  comments/followers\n",
              "0  pictures  905933.451613        0.003174  6703.290323            0.000023\n",
              "1    videos  593409.666667        0.002079  5749.333333             0.00002\n",
              "2     mixed              -               -            -                   -"
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>medium</th>\n",
              "      <th>nr likes</th>\n",
              "      <th>likes/followers</th>\n",
              "      <th>nr comments</th>\n",
              "      <th>comments/followers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pictures</td>\n",
              "      <td>905933.451613</td>\n",
              "      <td>0.003174</td>\n",
              "      <td>6703.290323</td>\n",
              "      <td>0.000023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>videos</td>\n",
              "      <td>593409.666667</td>\n",
              "      <td>0.002079</td>\n",
              "      <td>5749.333333</td>\n",
              "      <td>0.00002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mixed</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-738ca81c-264d-4f40-9be2-47ed686e75c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Research question 6: user posting effectiveness"
      ],
      "metadata": {
        "id": "sf66iM8W7XUS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **What is the average time (days and minutes) a user lets pass before publishing another post? Plot the top 3 users that publish posts more frequently (calculate the average time that passes between posts), including their amount of followers and following. Provide insights from that chart.**\n",
        "\n",
        "The average time that a user waits before posting a new post is 32 days 07:23:38.337708570.\n",
        "\n",
        "If we look at the three accounts that have the smallest time between posts, we see that two of the three accounts follow more people than they have followers."
      ],
      "metadata": {
        "id": "Et8WpEFZ7dXw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load in data\n",
        "df_posts_small = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['sid_profile','cts'], low_memory = True)\n"
      ],
      "metadata": {
        "id": "DLGPVo1-9PpA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# change the cts to times\n",
        "df_posts_small['cts'] = pd.to_datetime(df_posts_small['cts'], unit='ns')\n",
        "\n"
      ],
      "metadata": {
        "id": "_SdpnUYQWq7b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sort the values both on the sid and the cts so that the sids are grouped together and the cts are in order\n",
        "df_sorted_posts = df_posts_small.sort_values(['sid_profile','cts'])\n",
        "# remove the users that have only posted once\n",
        "df_sorted_posts = df_sorted_posts[df_sorted_posts.groupby('sid_profile').sid_profile.transform('count') > 1]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRbyopT4XIYT",
        "outputId": "59718a63-19b0-4265-d390-46710e623e32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "42710197\n",
            "42610137\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determine the interval between the times\n",
        "df_sorted_posts['Time_diff'] = df_sorted_posts['cts'].diff()\n",
        "df_sorted_posts.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QWqYEQlaY7CA",
        "outputId": "871c1717-b928-4b73-8676-0c3be92deb39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          sid_profile                 cts        Time_diff\n",
              "35149671           -1 2010-07-19 01:12:23              NaT\n",
              "4225968            -1 2010-08-21 06:40:27 33 days 05:28:04\n",
              "1939650            -1 2010-09-05 21:48:24 15 days 15:07:57\n",
              "39517938           -1 2010-09-06 18:36:05  0 days 20:47:41\n",
              "35207772           -1 2010-10-15 00:48:15 38 days 06:12:10"
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# Remove the first row of every new profile so we only keep the differences for the same profile\n",
        "df_cleaned = df_sorted_posts[df_sorted_posts.Time_diff > pd.Timedelta(0)]\n"
      ],
      "metadata": {
        "id": "r5YIVt4aab9b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# determine the mean of the time difference\n",
        "df_time_diff = df_cleaned.groupby('sid_profile', as_index=False)['Time_diff'].mean()\n",
        "\n",
        "print('The average time that a user waits before posting a new post is', df_time_diff['Time_diff'].mean())\n",
        "df_time_diff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "nA7s3zGIYrmz",
        "outputId": "284e9398-5304-49ff-a5b1-e6e9467ac233"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The average time that a user waits before posting a new post is 29 days 03:21:47.309517514\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         sid_profile                  Time_diff\n",
              "0                 -1  0 days 00:00:27.289843250\n",
              "1               1186 18 days 04:34:02.628571428\n",
              "2               1187    14 days 15:43:24.950000\n",
              "3               1188 34 days 20:52:37.285714285\n",
              "4               1189            3 days 19:54:01\n",
              "...              ...                        ...\n",
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              "2178041      4510782           74 days 15:48:54\n",
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              "2178043      4510784  5 days 14:12:25.047619047\n",
              "2178044      4510785           14 days 23:04:27\n",
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              "          document.querySelector('#df-f616f95f-f10e-414b-814c-612dcb700d9b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f616f95f-f10e-414b-814c-612dcb700d9b');\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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find the three users that post the most frequently\n",
        "df_time_diff = df_time_diff.sort_values(by=['Time_diff'], ascending = True)"
      ],
      "metadata": {
        "id": "xNiGS-FReodd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# only keep three top rows\n",
        "df_top3 = df_time_diff.iloc[:3]"
      ],
      "metadata": {
        "id": "N5blvjpfgyys"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load in the number of followers and following\n",
        "df_profiles_small = pd.read_csv('instagram_profiles.csv', delimiter=\"\\t\", usecols = ['sid','followers', 'following'], low_memory = True)\n"
      ],
      "metadata": {
        "id": "S4VhPJoLc7Ew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# find the number of followers and the following or the top three and plot the dataset\n",
        "df_top3_combined = df_top3.merge(df_profiles_small, how = 'left',  left_on='sid_profile', right_on='sid')\n",
        "df_top3_combined.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "aYGEE8nbgr3h",
        "outputId": "6450fd0b-ee9e-461d-8941-563e00cf5a4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sid_profile       Time_diff      sid  following  followers\n",
              "0      3015220 0 days 00:00:01  3015220       35.0        4.0\n",
              "1       130281 0 days 00:00:01   130281       25.0       47.0\n",
              "2      2838569 0 days 00:00:01  2838569      395.0       82.0"
            ],
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              "  <thead>\n",
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              "      <th>sid_profile</th>\n",
              "      <th>Time_diff</th>\n",
              "      <th>sid</th>\n",
              "      <th>following</th>\n",
              "      <th>followers</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>3015220</td>\n",
              "      <td>0 days 00:00:01</td>\n",
              "      <td>3015220</td>\n",
              "      <td>35.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>130281</td>\n",
              "      <td>0 days 00:00:01</td>\n",
              "      <td>130281</td>\n",
              "      <td>25.0</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2838569</td>\n",
              "      <td>0 days 00:00:01</td>\n",
              "      <td>2838569</td>\n",
              "      <td>395.0</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-42cd961e-d9e8-4cac-93c5-13303db9af9d 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-42cd961e-d9e8-4cac-93c5-13303db9af9d');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the following and followers in a chart\n",
        "# plotting graph\n",
        "df_top3_combined.plot(x=\"sid\", y=[\"following\", \"followers\"], kind=\"bar\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "3wXPcxdLhnc3",
        "outputId": "1c7e7089-3eeb-4229-99db-b02cea5f490e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff9196c92d0>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Using the function you previously coded, plot the time intervals with the highest average number of “likes” and the ones with the highest average number of comments on posts.**\n",
        "The highest average number of likes  is from the profile, with profile sid 531404. They have posted several posts with in general not so much time in between.\n",
        "\n",
        "The profile with on average the highest number of comments is user 468168, which has posted 2 posts in total with approximately 151 days in between the two posts."
      ],
      "metadata": {
        "id": "k4qbn27i7hTk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load in data\n",
        "df_posts_small = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['sid_profile','cts', 'numbr_likes', 'number_comments'], low_memory = True)\n"
      ],
      "metadata": {
        "id": "cul3IjUZjByy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# change the cts to times\n",
        "df_posts_small['cts'] = pd.to_datetime(df_posts_small['cts'], unit='ns')\n",
        "\n",
        "# sort the values both on the sid and the cts so that the sids are grouped together and the cts are in order\n",
        "df_sorted_posts = df_posts_small.sort_values(['sid_profile','cts'])\n",
        "# remove the users that have only posted once\n",
        "df_sorted_posts = df_sorted_posts[df_sorted_posts.groupby('sid_profile').sid_profile.transform('count') > 1]\n",
        "\n",
        "# determine the interval between the times\n",
        "df_sorted_posts['Time_diff'] = df_sorted_posts['cts'].diff()\n",
        "\n",
        "# Remove the first row of every new profile so we only keep the differences for the same profile\n",
        "df_cleaned = df_sorted_posts[df_sorted_posts.Time_diff > pd.Timedelta(0)]\n"
      ],
      "metadata": {
        "id": "VT1hWye7jcYs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# determine the sid number with highest number of likes and comments\n",
        "sid_nrlikes = df_cleaned.groupby('sid_profile', as_index=False)['numbr_likes'].mean()\n",
        "sid_nrcomments = df_cleaned.groupby('sid_profile', as_index=False)['number_comments'].mean()\n"
      ],
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        "id": "Q4iG0YHjkI03"
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      "execution_count": null,
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    {
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        "sid_nrlikes.loc[sid_nrlikes['numbr_likes'] == sid_nrlikes['numbr_likes'].max()]\n"
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      "execution_count": null,
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              "        sid_profile  numbr_likes\n",
              "243730       531404    3528630.5"
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        "sid_nrcomments.loc[sid_nrcomments['number_comments'] == sid_nrcomments['number_comments'].max()]\n"
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      "execution_count": null,
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        {
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            "text/plain": [
              "        sid_profile  number_comments\n",
              "230742       468168         170836.0"
            ],
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      "cell_type": "code",
      "source": [
        "# make df only for sid_nrlikes and sid_nrcomments\n",
        "df_likes = df_cleaned[df_cleaned.sid_profile == 531404]\n",
        "df_comments = df_cleaned[df_cleaned.sid_profile == 468168]"
      ],
      "metadata": {
        "id": "wLnzVv-cl8ID"
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        "df_comments"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "         sid_profile                 cts  numbr_likes  number_comments  \\\n",
              "6220011       468168 2019-02-15 03:18:33    2224495.0         170836.0   \n",
              "\n",
              "                Time_diff  \n",
              "6220011 151 days 02:58:28  "
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    {
      "cell_type": "code",
      "source": [
        "# plot the difference in times for profile with the highest number of likes and highest number of comments\n",
        "#define subplot layout\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6, 10))\n",
        "\n",
        "# plot the highest 150 number of posts\n",
        "ax1 = df_likes.iloc[0:150].plot( y='Time_diff', ax=axes[0])\n",
        "ax1.get_xaxis().set_visible(False)\n",
        "ax1.title.set_text('150 highest number of posts')\n",
        "\n",
        "# plot the lowest 150 number of posts\n",
        "ax2 = df_comments.iloc[-150:].plot(y='Time_diff', ax=axes[1])\n",
        "ax2.get_xaxis().set_visible(False)\n",
        "ax2.title.set_text('150 lowest number of posts')\n"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 594
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        "id": "hB7qPDZ1mTcz",
        "outputId": "ef5a8541-6fb9-4add-f736-53f6e588954e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Research question 7: calculating probabilities"
      ],
      "metadata": {
        "id": "ub42grWEfCgk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **What's the probability that a post receives more than 20% \"likes\" of the number of followers a user has?**\n",
        "The probability is approximately 6%."
      ],
      "metadata": {
        "id": "AdWp399bfKO6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# first half of set\n",
        "df_small_posts1 = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['profile_id','numbr_likes'], low_memory = True, nrows = 8500000)\n",
        "df_small_profiles = pd.read_csv('instagram_profiles.csv', delimiter=\"\\t\", usecols = ['profile_id','followers'], low_memory = True)"
      ],
      "metadata": {
        "id": "lLaOYzLTfQDT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# combine the posts and followers into one dataframe\n",
        "df_combined_small = pd.merge(df_small_posts1, df_small_profiles, on=\"profile_id\", how=\"left\")\n",
        "\n",
        "# remove any nans\n",
        "df_combined_small = df_combined_small.dropna( how='any')\n"
      ],
      "metadata": {
        "id": "yWXk1tom9rG3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# add extra column (nrlikes/ followers)\n",
        "df_combined_small[\"perc_likes\"] = (df_combined_small[\"numbr_likes\"] / df_combined_small[\"followers\"])*100"
      ],
      "metadata": {
        "id": "YcbW72hpEHAM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# see how many posts have more than 20% of the number of followers\n",
        "count = df_combined_small[\"perc_likes\"][df_combined_small[\"perc_likes\"] > 20].count()\n",
        "\n",
        "# determine probability\n",
        "print('The probability that a post receives more than 20% likes of the number of followers is', (count/len(df_combined_small))*100,'%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pgz7qYCxEKGd",
        "outputId": "0ec69e61-dcc9-463b-f9d2-1f53cec6ab69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The probability that a post receives more than 20% likes of the number of followers is 6.308404401687083 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Do users usually return to locations? Extract the probability that a user returns to a site after having posted it in the past. Does that probability make sense to you? Explain why or why not.**\n",
        "A user returns to a site when the location is tagged more than once. To determine the probability that a user will return to a previous location, we will first group the posts on the user and on the location and count how many times a certain user has visited a location. Since the change of returning will be different for every user, we will determine an average probability of return with a 95% confidence interval. This is done by looping through the list of users and their locations and determining the probability of return per user.\n",
        "\n",
        "This results in a probability of return of 24.63 % with a 95% confidence interval of 24.6% - 24.67%.\n",
        "In our opinion this makes sense, since a lot of the accounts are non-business accounts. These are people that want to show what they are doing and were they are going, especially if they are going to new places. We did expect to have a bigger confidence interval for the locations, since there are both business and non-business account. However, there are so many accounts that the confidence interval will be lower. "
      ],
      "metadata": {
        "id": "4uGUn5urfNAH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load in data\n",
        "df_posts_small = pd.read_csv('instagram_posts.csv', delimiter=\"\\t\", usecols = ['sid_profile','location_id'], low_memory = True)\n"
      ],
      "metadata": {
        "id": "uj6IsVoy3q8n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# group by the profile id and the location id to see how many users go to what location\n",
        "df_sorted = df_posts_small.groupby(['sid_profile', 'location_id']).size().reset_index(name='count') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RxbbqxfmiGJd",
        "outputId": "bdd795aa-2b80-4a27-c4dd-6239812a4c40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          sid_profile   location_id  count\n",
              "0                  -1  1.000000e+01     28\n",
              "1                  -1  4.600000e+01     29\n",
              "2                  -1  5.400000e+01     26\n",
              "3                  -1  9.000000e+01     29\n",
              "4                  -1  1.200000e+02     27\n",
              "...               ...           ...    ...\n",
              "10335980      4510784  5.830319e+08     22\n",
              "10335981      4510785  2.283802e+08      2\n",
              "10335982      4510785  5.830319e+08      1\n",
              "10335983      4510785  1.780794e+14      2\n",
              "10335984      4510785  9.386501e+14      1\n",
              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make a new df to save the percentage of returning per user\n",
        "df_prob = pd.DataFrame(columns = ['sid_profile', 'Probability'])\n",
        "\n",
        "# initialize counter\n",
        "counter = 0\n",
        "lencounter = 0\n",
        "# determine the probability that someone will return to a location for every user separately\n",
        "for i in range(len(df_sorted)):\n",
        "\n",
        "  if i == 0:\n",
        "    counter += df_sorted['count'][i]\n",
        "    lencounter += 1\n",
        "  elif df_sorted['sid_profile'][i] == df_sorted['sid_profile'][i-1]:\n",
        "    counter += df_sorted['count'][i]\n",
        "    lencounter += 1\n",
        "  # looped through one profile\n",
        "  else:\n",
        "    df_prob = df_prob.append({'sid_profile' : df_sorted['sid_profile'][i-1], 'Probability' : (1-(lencounter/counter))},ignore_index = True)\n",
        "    # initialize counter and lencounter again\n",
        "    counter = df_sorted['count'][i]\n",
        "    lencounter = 1"
      ],
      "metadata": {
        "id": "lidle6PrN5-P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate mean and standard deviation\n",
        "mean = df_prob['Probability'].mean()\n",
        "sd = df_prob['Probability'].std()\n",
        "n = len(df_prob)\n",
        "# Z value for 95% confidence interval\n",
        "Zstar=1.65\n",
        "\n",
        "# Determine lower and upper bound\n",
        "se = sd/np.sqrt(n)\n",
        "lcb = (mean - Zstar * se)*100\n",
        "ucb = (mean + Zstar * se)*100\n",
        "\n",
        "# Print mean and CI\n",
        "print('The probability that a user returns to a site is', mean*100,'%')\n",
        "print(\" with CI (95%):\", (round(lcb,2),round(ucb,2)))\n"
      ],
      "metadata": {
        "id": "LGsH5m3aeNLe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87e86ef1-914c-4f55-a5bd-eb26962a62e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The probability that a user returns to a site is 24.631076010598125 %\n",
            " with CI (95%): (24.6, 24.67)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Research question 8: reinforcement with charts, statistical tests and analysis methods"
      ],
      "metadata": {
        "id": "e1pdMSEmjLrq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Does more “likes” also mean more comments? Plot a scatter plot of “likes” vs comments for posts.**\n",
        "Based on the scatter plot, it does not look like that more comments also mean more likes. To be sure that there is no strong correlation, we also determined the correlation number. This number is 0.41, which is considered moderate and not strong."
      ],
      "metadata": {
        "id": "CaoeSg-Ojb9q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = df_posts.plot.scatter('numbr_likes', 'number_comments')\n",
        "ax.set_title(\"Number of likes vs number of comments\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "efK34HGUjejl",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff33afaa0d0>"
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          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr = df_posts['numbr_likes'].corr(df_posts[\"number_comments\"])\n",
        "print(\"Correlation between the number of likes and the number of comments is: \", round(corr, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQkL77XQlCri",
        "outputId": "e62ffdfa-de50-4423-91c8-add9ff65f8ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation between the number of likes and the number of comments is:  0.41\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Can you find any significant relationship between the time a user publishes a post and the number of comments and “likes”? Use an appropriate statistical test or technique and support your choice.**\n",
        "\n",
        "To define if there is any significant relationship between two or more parameters, we will look at the correlation between parameters, which looks for a linear relationship between two parameters. We interpret the correlation as follows:\n",
        "\n",
        "Complete correlation between two variables is a value of either + 1 or -1. Complete absence of correlation is represented by a value of 0. A moderate correlation is a correlation between 0.4-0.7. A higher correlation is a strong correlation.\n",
        "\n",
        "First, we will convert the given dates to the hour in which a post is posted, since the changes are very slim that multiple posts are posted in the same minute. \n",
        "Next we will make a heatplot based on the correlation to see if there is any correlation between the parameters.\n",
        "\n",
        "\n",
        "As we can see from the heatmap, there is no correlation between the number of likes and the hour in which a post is posted (0.0018), and there is also no correlation between the number of comments and the hour in which a post is posted (0.0011).\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "OMwKx_MYlh8U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make a dataframe with only the needed columns\n",
        "posts_corr = df_posts[[\"cts\", \"numbr_likes\", \"number_comments\"]]\n",
        "\n",
        "#Takes only the date_time column and extrapolate only hours \n",
        "posts_corr['cts'] = pd.to_datetime(posts_corr['cts'], unit='ns').dt.hour\n",
        "\n",
        "sns.heatmap(posts_corr.corr(), annot = True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "NY6mtP4Slktr",
        "outputId": "ada30bfd-2ad5-4616-9e4e-d4b48e3a936c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **What’s the distribution of followers? Plot the empirical distribution of followers amongst all users and extract the mean, mode, and quantiles. Interpret those figures.**\n",
        "\n",
        "First, we removed the rows that do not have any information regarding the number of followers. Second, we removed any outliers so we can plot the distribution. As can be seen in the plot, most of the users fall in the same bin, namely the first bin, that goes from 0 to approximately 300. \n",
        "\n",
        "If we look at the mean, we see that this falls in the first bin, which also explains why the first bin is so big. Even if we make the number of bins higher, we still see this phenomenon (unless you would make it really high, but this is not the idea of a histogram). If we look at the mode, we see a mode of 50, which is also really low and explains the same phenomenon. The reason why the mean is way higher than the mode, can be explained by the fact that there are some users with a large number of followers. You also see this back in the quantiles."
      ],
      "metadata": {
        "id": "z5n8PC6bmGi0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "\n",
        "# remove the rows with nan in followers\n",
        "df_profiles_nfollowers = df_profiles.dropna( how='any',\n",
        "                    subset=['followers'])\n",
        "\n",
        "# remove outliers\n",
        "q_low = df_profiles_nfollowers[\"followers\"].quantile(0.01)\n",
        "q_hi  = df_profiles_nfollowers[\"followers\"].quantile(0.99)\n",
        "df_filtered = df_profiles_nfollowers[(df_profiles_nfollowers[\"followers\"] < q_hi) & (df_profiles_nfollowers[\"followers\"] > q_low)]\n",
        "\n",
        "df_filtered.hist(column='followers', bins=30, grid=False, figsize=(12,8), color='#86bf91', zorder=2, rwidth=0.9)\n",
        "\n",
        "print(\"mode:\", df_profiles_nfollowers['followers'].mode())\n",
        "df_profiles_nfollowers['followers'].describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "qRpRxG0KmIDQ",
        "outputId": "0e1a6689-3224-4e8f-8907-977288e36beb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mode: 0    50.0\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    3.380715e+06\n",
              "mean     1.333294e+03\n",
              "std      3.654137e+03\n",
              "min      3.000000e+00\n",
              "25%      1.630000e+02\n",
              "50%      4.060000e+02\n",
              "75%      9.560000e+02\n",
              "max      4.531600e+04\n",
              "Name: followers, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **What are histograms, bar plots, scatterplots and pie charts used for?**\n",
        "\n",
        "**Histograms:**\n",
        "A histogram shows the frequency of occurences of a parameter based on bins. You can use it for discrete or continuous data.\n",
        "\n",
        "**Bar plots:**\n",
        "A bar plot shows the relationship between numeric and categorical data. You can group different parameters together based on a certain category and show a comparison based on their values.\n",
        "\n",
        "**Scatter plots:**\n",
        "Scatter plots are used to show the relationship between two numerical/continuous parameters. The independent variable is plotted on the x-axis, while the dependenent variable is plotted on the y-axis. A scatter plot visualizes any correlation between the two parameters.\n",
        "\n",
        "**Pie charts:**\n",
        "A pie chart is used to show numerical proportions based on categories. Most of the time the percentage/ fraction of a proportion is displayed as well. "
      ],
      "metadata": {
        "id": "CvTlzYiMtoxi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **What insights can you extract from a Box Plot?**\n",
        "\n",
        "With a boxplot, the variation of a certain parameter is visualised. a boxplot typically shows five main characteristics of the data:\n",
        "\n",
        "*   The minimum\n",
        "*   The maximum\n",
        "*   The lower quartile\n",
        "*   The upper quartile\n",
        "*   The median\n",
        "\n",
        "Sometimes it also displayes any outliers. Between the lower and upper quartile, the inter quartile range (IQR) is visible. \n",
        "Furthermore, the position of the median in the IQR also tells a lot about the skewness of the data. If the median is in the middle of the IQR, the data is symmetric. Otherwise, it is either left or right skewed. \n",
        "\n"
      ],
      "metadata": {
        "id": "6TS-1Ey7tssq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Algorithmic question**"
      ],
      "metadata": {
        "id": "y_iIqjacNRR2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Algorithmic Question 1***"
      ],
      "metadata": {
        "id": "UkkkVBtHSH5G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###What is the output of this algorithm? Describe the mechanism of the algorithm in detail . We do not want to know only its final result."
      ],
      "metadata": {
        "id": "nO637-xkNigo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The algorithm scrolls the sequence starting from the 0 position to the end.\n",
        "it fixes the 0 position and start to the 1 and so on, practically it starts from the end;\n",
        "Fixed the all sequence's position since the third last, it reverses the last two, and print the the entire sequence with the input order and the sequence with the two last position swapped.\n",
        "Now fixes all the positions since the fourth last, the third last position is swapped with the second last and do the same as before, but now it works with the last two values, that are the original third last and last.\n",
        "Therefore the algorithm is doing all the possible disposition given a sequence, for this reason there will be *len(sequence) factorial* steps."
      ],
      "metadata": {
        "id": "jm5sI8LFN4At"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###What is asymptotically (i.e., we are asking for big-O complexity) the algorithm's running time as a function of N?"
      ],
      "metadata": {
        "id": "UMEF8Hf3PuK5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The algorithm doesn't care about the content of the sequence but only about its length, it is exactly a function of ***(N!)***"
      ],
      "metadata": {
        "id": "icQ-M7ZjP0dg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Is this algorithm the optimal one to produce this output? If not, can you suggest a better algorithm to perform the same task?"
      ],
      "metadata": {
        "id": "4L5S7uXIQb-z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If I understand correctly the aim of the algorithm, which is to create a set of all the possible dispositions, it means that the length of the set is exactly ***N!*** so it can't be better."
      ],
      "metadata": {
        "id": "ZoGZD7xpQj7k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***Algorithmic question 2***"
      ],
      "metadata": {
        "id": "U3ac77I4SO6Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Implement a recursive algorithm to help Alex find the maximum possible value after multiplying the length of the pieces. Explain your algorithm in detail."
      ],
      "metadata": {
        "id": "ZE3ZwlxQSV0N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tagliaN(N):\n",
        "    if  N <= 1 :                                            #  1\n",
        "        return 0                                            # <= 1 \n",
        "    elif N == 2:                                            #  1\n",
        "        return 1                                            # <= 1\n",
        "    elif N == 3:                                            #  1\n",
        "        return 2                                            # <= 1\n",
        "    else:                                                   #  1\n",
        "        mas=0                                               # 1\n",
        "        for n in range(2,N):                                # N-3\n",
        "            mas=  max(mas, max(n*(N-n), n*tagliaN(N-n) ) )  # <= (N-2)* T(N-2) \n",
        "                \n",
        "    return mas                                              # 1\n",
        "tagliaN(int(input()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gEKr1hmdSow6",
        "outputId": "8466ed2b-0be1-41e4-aec1-ec4c93caf6ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Show that the algorithm has exponential running time."
      ],
      "metadata": {
        "id": "NKM7SOHiS6-Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can write the running time of this function in this way:\n",
        "\n",
        "\n",
        "$T(N)=\n",
        "\\begin{cases}\n",
        "C_1, & \\text{if $N=0,1,2,3$,} \\\\\n",
        "\\sum_{i=2}^{N-1}T(N-i), & \\text{Otherwise}\n",
        "\\end{cases}\n",
        "$\n",
        "\n",
        "$T(N)=\\sum_{i=2}^{N-1}T(N-i)=T(N-2)+T(N-3)+ ... +T(N-(N-1))=$\n",
        "$=T(N-2)+T(N-3)+ ... +T(1)$\n",
        "\n",
        "$=T(N-1)+T(N-2)+T(N-3)+ ... +T(1)-T(N-1)$\n",
        "\n",
        "If we look at the singol terms of the sum:\n",
        "\n",
        "$T(N-1)=T(N-2)+T(N-3)+ ... T(1)- T(N-2)$\n",
        "\n",
        "So we can write $T(N)$ as:\n",
        "\n",
        "$T(N)=T(N-1)+[T(N-1)+T(N-2)]-T(N-1) $\n",
        "\n",
        "$=T(N-1)+T(N-2)$\n",
        "\n",
        "$=T(N-2)+ 2T(N-3)+T(n-4)$\n",
        "\n",
        "$=T(N-3)+ 2^2T(N-4)+2T(n-5)$\n",
        "\n",
        "$=T(N-4)+ 2^3T(N-5)+2^2T(n-6)+ 2T(N-7)$\n",
        "\n",
        "$ ... $\n",
        "\n",
        "$=T(3)+2^{N-1}T(2)+2^{N-2}T(1)+2^{N-3}T(0)+O(1)$\n",
        "\n",
        "Where $T(0),T(1),T(2),T(3)$ are the base case so asymptotically to $O(1)$\n",
        "\n",
        "$T(N)=O(1)+2^{N-1}O(1)+2^{N-2}O(1)+2^{N-3}O(1)=O(2^{N-1})$"
      ],
      "metadata": {
        "id": "U3jobSQ6Tf1s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Now provide a polynomial solution, based on dynamic programming, and compute its running-time complexity."
      ],
      "metadata": {
        "id": "Qx5BHNtgTGoU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def taglio_polinomial(N):\n",
        "    P = np.zeros(N+1)\n",
        "    for i in range(1,N+1):\n",
        "        mas = 0\n",
        "        for j in range(1, i):\n",
        "            mas = max(mas, (i-j)*j,  P[i-j]*j)\n",
        "            P[i] = mas\n",
        "    return int(P[N])\n",
        "taglio_polinomial(int(input()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGwRh-BHT7Ey",
        "outputId": "edb62cc8-cc6c-42d6-8166-bcb6e02ba6eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "$T(N)=C_1 +\\sum_{i=1}^{N}\\sum_{j=1}^{i}C_2$\n",
        "\n",
        "$T(N)=O(1)+\\sum_{i=1}^{N}\\sum_{j=1}^{i}O(1)$\n",
        "\n",
        "$T(N)=O(1)+\\sum_{i=1}^{N}(i-0+1)O(1)$\n",
        "\n",
        "$T(N)=O(1)+\\sum_{i=1}^{N}(i+1)O(1)$\n",
        "\n",
        "$T(N)=O(1)+\\sum_{i=1}^{N}(i+1)O(1)$\n",
        "\n",
        "Since $1 \\leq i \\leq N$ we can consider $i+1=O(N)$ then:\n",
        "\n",
        "$T(N)=O(1)+\\sum_{i=1}^{N}O(N)O(1)$\n",
        "\n",
        "$=O(1)+\\sum_{i=1}^{N}O(N)$\n",
        "\n",
        "$T(N)= O(1)+ (N-1+1)O(N)$\n",
        "\n",
        "$T(N)= O(1)+ O(N)O(N)= O(N^2)$"
      ],
      "metadata": {
        "id": "yzin4pkFcVqh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Evaluate the running time of your algorithm for the sizes (Ns) in the range of [2, 25], plot the results, and interpret it (if you have implemented two algorithms, please include both in the plot and compare them)."
      ],
      "metadata": {
        "id": "CVrVzNgoTOUn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "start1 = time.time()\n",
        "for N in range(2,26):\n",
        "    tagliaN(N)\n",
        "end1 = time.time()\n",
        "delta_time1= end1 - start1\n",
        "print(f\"Execution time of the recursive function tagliaN() is: {round(delta_time1,5)*1000} milliseconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVYZzsaNUyNJ",
        "outputId": "622c79f6-1556-4a61-9df8-b36d75977788"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Execution time of the recursive function tagliaN() is: 90.47 milliseconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start2 = time.time()\n",
        "for N in range(2,26):\n",
        "    taglio_polinomial(N)\n",
        "end2 = time.time()\n",
        "delta_time2= end2 - start2\n",
        "print(f\"Execution time of the polinomial function taglio_polinomial() is: {round(delta_time2,5)*1000} milliseconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bx3wPUpqU-fs",
        "outputId": "a5b42166-3244-4cd8-bfe3-401174f93d7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Execution time of the polinomial function taglio_polinomial() is: 4.67 milliseconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "r = range(2, 26)\n",
        "start_rec = time.time()\n",
        "dt_rec = []\n",
        "for N in r: \n",
        "    tagliaN(N)\n",
        "    dt_rec.append(time.time()-start_rec)\n",
        "plt.plot(r, dt_rec, color=\"blue\")\n",
        "\n",
        "start_pol = time.time()\n",
        "dt_pol = []\n",
        "for N in r: \n",
        "    taglio_polinomial(N)\n",
        "    dt_pol.append(time.time()-start_pol)\n",
        "plt.plot(r, dt_pol, color=\"orange\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "JiuSGa0pVNtj",
        "outputId": "01c3e9e9-4406-4e81-9e78-c8de83985e5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f69c1401490>]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###(Optional, mini bonus) Is the algorithm of question 3 optimal? If you belive it, can you prove it? If not, can you find a faster algorithm? In case you found a faster algorithm implement it and explain your algorithm in detail."
      ],
      "metadata": {
        "id": "m6Ceo_ufTSJU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We implemented two different version of this algorithm:\n",
        "\n",
        "the idea of this algorithm is based on a lateral observation. Mainly the polinomyal version of the algorithm build a vector where for each index $i$ we have the solution for a ribbon with length $i$.\n",
        "So I decided to focus on studying that sequence of numbers. I decomposed each element of the sequence in a product of prime numbers, and I observed that every member is a product of $2^m$ and $3^n$ where $m \\in \\left \\{ 0,1,2 \\right\\}$ and $n$ is integer positive number and increase every 3 number.\n",
        "\n",
        "Therefore in the first version i build the same vector than the polynomial algorithm but in a easier way.\n",
        "After that I observed that I only need the last position of the vector and I write the exponents $m$, $n$ in a different way: $m$ is the remainder of the division of the index (the lenght of the ribbon) by three, and $n$ is the whole part of the same division."
      ],
      "metadata": {
        "id": "WMRRwT65XQwQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cut(N):\n",
        "    C=np.zeros(N)\n",
        "    C[0]=1\n",
        "    C[1]=2\n",
        "    f=0\n",
        "    g=2\n",
        "    h=1\n",
        "    for i in range(1,N-1):\n",
        "        \n",
        "        if i%3 == 0:\n",
        "            C[i+1]=3**(g)\n",
        "            g+=1\n",
        "        elif (i-1)%3 == 0:\n",
        "            C[i+1]=4*(3**(f))\n",
        "            f+=1\n",
        "        elif (i-2)%3 == 0:\n",
        "            C[i+1]=2*(3**(h))\n",
        "            h+=1\n",
        "            \n",
        "    return C[N-2]"
      ],
      "metadata": {
        "id": "WpVmvvVlV63a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_cut = time.time()\n",
        "dt_cut = []\n",
        "for N in r: \n",
        "    cut(N)\n",
        "    dt_cut.append(time.time()-start_cut)\n",
        "plt.plot(r, dt_cut, color=\"orange\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "h4TbY07bWS4r",
        "outputId": "255b878c-9e49-48d2-ae7e-d51cc28f7caf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f69ae536790>]"
            ]
          },
          "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_cut = time.time()\n",
        "for N in range(2,26):\n",
        "    cut(N)\n",
        "end_cut = time.time()\n",
        "delta_time_cut= end_cut - start_cut\n",
        "print(f\"Execution time of the polinomial function taglio_polinom() is: {round(delta_time2,5)*1000} milliseconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2cczxBTWVPX",
        "outputId": "e1cf521a-81fa-4855-9f13-1fd88e5eb07e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Execution time of the polinomial function taglio_polinom() is: 4.67 milliseconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cut_better(N):\n",
        "    if N <= 1:\n",
        "        return 0\n",
        "    elif N == 2:\n",
        "        return 1\n",
        "    elif N == 3:\n",
        "        return 2\n",
        "    else:\n",
        "        if N%3 == 0:\n",
        "            M=3**(N//3)\n",
        "        \n",
        "        elif (N-1)%3 == 0:\n",
        "            M=4*(3**(N//3 -1))\n",
        "        \n",
        "        elif (N-2)%3 == 0:\n",
        "            M=2*(3**(N//3))\n",
        "            \n",
        "    return M\n",
        "\n",
        "cut_better(int(input()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6HDMQh7jWI-6",
        "outputId": "7d044714-25d6-4699-a7f1-bb74fe6ab323"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "25\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8748"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_cutB = time.time()\n",
        "dt_cutB = []\n",
        "for N in r: \n",
        "    cut_better(N)\n",
        "    dt_cutB.append(time.time()-start_cutB)\n",
        "plt.plot(r, dt_cutB, color=\"purple\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "DmMMH8UHWlTj",
        "outputId": "8d3d064c-4036-4049-f185-0537d597962f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6a0f054ed0>]"
            ]
          },
          "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start4 = time.time()\n",
        "for N in range(2,26):\n",
        "    cut_better(N)\n",
        "end4 = time.time()\n",
        "delta_time4= end4 - start4\n",
        "print(f\"Execution time of the function cut_better() is: {round(delta_time4,5)*1000} milliseconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "My5dzAN9WsIY",
        "outputId": "6a7b3010-8ba8-48db-e5ab-f3bf2af2dfc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Execution time of the function cut_better() is: 0.12000000000000001 milliseconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GIyMz2fVcDbN"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "VEdOLrYW44YQ",
        "g3hXGkDi-STT",
        "drmW5loHHfKQ",
        "sf66iM8W7XUS",
        "ub42grWEfCgk",
        "nO637-xkNigo",
        "UMEF8Hf3PuK5"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}