Swipes Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvSZZ6IDUksX"
      },
      "source": [
        "#Swipes data cleaning"
      ]
    },
    {
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      "execution_count": 5,
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      "source": [
        "import pandas as pd\n",
        "# url2=\"https://raw.githubusercontent.com/kushalpoddar/idyll-dummy-dataset/main/swipes.csv\"\n",
        "# swipes=pd.read_csv(url2,low_memory=False)\n",
        "swipes=pd.read_csv('./swipes.csv',low_memory=False)   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4vZZx0ArD3T"
      },
      "source": [
        "Devising a scoring function. Profile score is a user’s profile score\n",
        "having parameters like verified, if 100% completion, number of users\n",
        "liked him/her, number of matches received, paid subscription or a free\n",
        "profile, verified user or not, number of likes done vs received, dislike\n",
        "by likes ratio performed and received,"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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        "outputId": "abc56251-6b19-4072-af94-70d68c55a83a"
      },
      "outputs": [
        {
          "name": "stdout",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 66622 entries, 0 to 66621\n",
            "Data columns (total 14 columns):\n",
            " #   Column                 Non-Null Count  Dtype \n",
            "---  ------                 --------------  ----- \n",
            " 0   id                     66622 non-null  int64 \n",
            " 1   first_like_unlike_at   66622 non-null  object\n",
            " 2   first_msg              169 non-null    object\n",
            " 3   first_type             66622 non-null  object\n",
            " 4   is_unmatch             66622 non-null  bool  \n",
            " 5   like_count             66622 non-null  int64 \n",
            " 6   p1                     66622 non-null  int64 \n",
            " 7   p1_extend_at           102 non-null    object\n",
            " 8   p2                     66622 non-null  int64 \n",
            " 9   p2_extend_at           151 non-null    object\n",
            " 10  second_like_unlike_at  4730 non-null   object\n",
            " 11  second_msg             26 non-null     object\n",
            " 12  second_type            4730 non-null   object\n",
            " 13  unmatch_on             147 non-null    object\n",
            "dtypes: bool(1), int64(4), object(9)\n",
            "memory usage: 6.7+ MB\n"
          ]
        }
      ],
      "source": [
        "swipes.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 66622 entries, 0 to 66621\n",
            "Data columns (total 9 columns):\n",
            " #   Column                 Non-Null Count  Dtype \n",
            "---  ------                 --------------  ----- \n",
            " 0   first_like_unlike_at   66622 non-null  object\n",
            " 1   first_type             66622 non-null  object\n",
            " 2   is_unmatch             66622 non-null  bool  \n",
            " 3   like_count             66622 non-null  int64 \n",
            " 4   p1                     66622 non-null  int64 \n",
            " 5   p2                     66622 non-null  int64 \n",
            " 6   second_like_unlike_at  4730 non-null   object\n",
            " 7   second_type            4730 non-null   object\n",
            " 8   unmatch_on             147 non-null    object\n",
            "dtypes: bool(1), int64(3), object(5)\n",
            "memory usage: 4.1+ MB\n"
          ]
        }
      ],
      "source": [
        "cols=['first_like_unlike_at','first_type','is_unmatch','like_count','p1','p2','second_like_unlike_at','second_type','unmatch_on']\n",
        "swipes=swipes[cols]\n",
        "swipes.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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              "66475"
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          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "swipes['unmatch_on'].isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
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        {
          "data": {
            "text/plain": [
              "first_like_unlike_at         0\n",
              "first_type                   0\n",
              "is_unmatch                   0\n",
              "like_count                   0\n",
              "p1                           0\n",
              "p2                           0\n",
              "second_like_unlike_at    61892\n",
              "second_type              61892\n",
              "unmatch_on               66475\n",
              "dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
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        }
      ],
      "source": [
        "swipes.isna().sum()"
      ]
    },
    {
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      "execution_count": 10,
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              "11      663\n",
              "1238    602\n",
              "2       351\n",
              "1098    318\n",
              "831     276\n",
              "       ... \n",
              "112       2\n",
              "1192      2\n",
              "1142      1\n",
              "1188      1\n",
              "643       1\n",
              "Name: p1, Length: 1055, dtype: int64"
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          },
          "execution_count": 10,
          "metadata": {},
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      ],
      "source": [
        "swipes.p1.value_counts()"
      ]
    },
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              "170     358\n",
              "160     350\n",
              "224     348\n",
              "450     347\n",
              "404     347\n",
              "       ... \n",
              "243       1\n",
              "28        1\n",
              "27        1\n",
              "942       1\n",
              "1402      1\n",
              "Name: p2, Length: 1042, dtype: int64"
            ]
          },
          "execution_count": 11,
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      ],
      "source": [
        "swipes.p2.value_counts()"
      ]
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      "source": [
        "swipes.p1.isna().sum()"
      ]
    },
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>first_like_unlike_at</th>\n",
              "      <th>first_type</th>\n",
              "      <th>is_unmatch</th>\n",
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              "      <th>66540</th>\n",
              "      <td>2023-03-03T17:58:43.895Z</td>\n",
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              "      <td>1</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>2023-03-03T17:59:01.925Z</td>\n",
              "      <td>LIKE</td>\n",
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            ],
            "text/plain": [
              "           first_like_unlike_at first_type  is_unmatch  like_count    p1  \\\n",
              "66540  2023-03-03T17:58:43.895Z       LIKE       False           1  1404   \n",
              "66541  2023-03-03T17:58:48.321Z    DISLIKE       False           1  1404   \n",
              "66542  2023-03-03T17:58:53.994Z       LIKE       False           1  1404   \n",
              "66543  2023-03-03T17:58:57.018Z       LIKE       False           1  1404   \n",
              "66544  2023-03-03T17:59:01.925Z       LIKE       False           1  1404   \n",
              "\n",
              "         p2 second_like_unlike_at second_type unmatch_on  \n",
              "66540  1064                   NaN         NaN        NaN  \n",
              "66541  1098                   NaN         NaN        NaN  \n",
              "66542   940                   NaN         NaN        NaN  \n",
              "66543  1223                   NaN         NaN        NaN  \n",
              "66544  1178                   NaN         NaN        NaN  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
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      ],
      "source": [
        "swipes = swipes.sort_values(by=['p1','first_like_unlike_at'], ascending=[True,True])\n",
        "swipes.tail()"
      ]
    },
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      "cell_type": "code",
      "execution_count": 14,
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              "11      663\n",
              "1238    602\n",
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              "       ... \n",
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        "swipes['p1'].value_counts()\n"
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              "       ... \n",
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              "1060      1\n",
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              "True       147\n",
              "Name: is_unmatch, dtype: int64"
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              "      <td>2023-02-18T17:36:26.651Z</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "      <td>1252</td>\n",
              "      <td>7</td>\n",
              "      <td>2023-02-21T07:31:15.678Z</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>2023-02-24T07:40:33.784Z</td>\n",
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              "      <td>2023-02-20T03:54:31.612Z</td>\n",
              "      <td>LIKE</td>\n",
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              "      <td>2</td>\n",
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              "      <td>7</td>\n",
              "      <td>2023-02-21T07:29:20.974Z</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>2023-02-23T17:06:34.654Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54698</th>\n",
              "      <td>2023-02-22T20:41:23.734Z</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "      <td>1306</td>\n",
              "      <td>1238</td>\n",
              "      <td>2023-02-24T11:41:18.643Z</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>2023-03-02T07:41:35.831Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54700</th>\n",
              "      <td>2023-02-22T20:41:35.524Z</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "      <td>1306</td>\n",
              "      <td>1227</td>\n",
              "      <td>2023-02-26T08:09:45.056Z</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>2023-03-02T07:41:11.864Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58849</th>\n",
              "      <td>2023-02-25T08:20:53.243Z</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "      <td>1322</td>\n",
              "      <td>1337</td>\n",
              "      <td>2023-02-25T08:24:01.112Z</td>\n",
              "      <td>DISLIKE</td>\n",
              "      <td>2023-02-25T09:03:35.883Z</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>147 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           first_like_unlike_at first_type  is_unmatch  like_count    p1  \\\n",
              "212    2023-02-08T05:13:13.430Z       LIKE        True           2     0   \n",
              "9002   2023-02-09T13:26:31.608Z       LIKE        True           2     2   \n",
              "30545  2023-02-14T19:54:16.975Z       LIKE        True           2     2   \n",
              "30554  2023-02-14T19:54:36.951Z    DISLIKE        True           2     2   \n",
              "11489  2023-02-10T06:29:50.741Z       LIKE        True           2     3   \n",
              "...                         ...        ...         ...         ...   ...   \n",
              "45540  2023-02-18T17:36:26.651Z       LIKE        True           2  1252   \n",
              "48233  2023-02-20T03:54:31.612Z       LIKE        True           2  1270   \n",
              "54698  2023-02-22T20:41:23.734Z       LIKE        True           2  1306   \n",
              "54700  2023-02-22T20:41:35.524Z       LIKE        True           2  1306   \n",
              "58849  2023-02-25T08:20:53.243Z       LIKE        True           2  1322   \n",
              "\n",
              "         p2     second_like_unlike_at second_type                unmatch_on  \n",
              "212      35  2023-02-09T06:39:18.142Z        LIKE  2023-02-11T11:31:48.390Z  \n",
              "9002    468  2023-02-10T02:21:28.070Z        LIKE  2023-02-13T19:27:37.333Z  \n",
              "30545  1029  2023-02-15T00:36:53.312Z        LIKE  2023-02-16T17:45:39.455Z  \n",
              "30554   982  2023-02-24T15:50:59.927Z     DISLIKE  2023-02-24T15:51:06.011Z  \n",
              "11489   259  2023-02-10T08:52:55.902Z        LIKE  2023-02-10T09:29:00.331Z  \n",
              "...     ...                       ...         ...                       ...  \n",
              "45540     7  2023-02-21T07:31:15.678Z        LIKE  2023-02-24T07:40:33.784Z  \n",
              "48233     7  2023-02-21T07:29:20.974Z        LIKE  2023-02-23T17:06:34.654Z  \n",
              "54698  1238  2023-02-24T11:41:18.643Z        LIKE  2023-03-02T07:41:35.831Z  \n",
              "54700  1227  2023-02-26T08:09:45.056Z        LIKE  2023-03-02T07:41:11.864Z  \n",
              "58849  1337  2023-02-25T08:24:01.112Z     DISLIKE  2023-02-25T09:03:35.883Z  \n",
              "\n",
              "[147 rows x 9 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "swipes[swipes['is_unmatch']==1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGlD-6GCfjKH",
        "outputId": "52b72973-1b6a-41a6-c8f2-8036e9aab3b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 66622 entries, 212 to 66544\n",
            "Data columns (total 9 columns):\n",
            " #   Column                 Non-Null Count  Dtype              \n",
            "---  ------                 --------------  -----              \n",
            " 0   first_like_unlike_at   66622 non-null  datetime64[ns, UTC]\n",
            " 1   first_type             66622 non-null  object             \n",
            " 2   is_unmatch             66622 non-null  bool               \n",
            " 3   like_count             66622 non-null  int64              \n",
            " 4   p1                     66622 non-null  int64              \n",
            " 5   p2                     66622 non-null  int64              \n",
            " 6   second_like_unlike_at  4730 non-null   datetime64[ns, UTC]\n",
            " 7   second_type            4730 non-null   object             \n",
            " 8   unmatch_on             147 non-null    datetime64[ns, UTC]\n",
            "dtypes: bool(1), datetime64[ns, UTC](3), int64(3), object(2)\n",
            "memory usage: 4.6+ MB\n"
          ]
        }
      ],
      "source": [
        "swipes['unmatch_on'] = pd.to_datetime(swipes['unmatch_on'])\n",
        "swipes['first_like_unlike_at'] = pd.to_datetime(swipes['first_like_unlike_at'])\n",
        "swipes['second_like_unlike_at'] = pd.to_datetime(swipes['second_like_unlike_at'])\n",
        "swipes.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3XfCkBhhim_"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "G6hXQ_XZhiOY",
        "outputId": "d0042979-f3f4-421f-b46b-d0bee4f90510"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>first_like_unlike_at</th>\n",
              "      <th>first_type</th>\n",
              "      <th>is_unmatch</th>\n",
              "      <th>like_count</th>\n",
              "      <th>p1</th>\n",
              "      <th>p2</th>\n",
              "      <th>second_like_unlike_at</th>\n",
              "      <th>second_type</th>\n",
              "      <th>unmatch_on</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>2023-02-08 05:13:13.430000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>2023-02-09 06:39:18.142000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>2023-02-11 11:31:48.390000+00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>2023-02-08 05:13:24.752000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>2023-02-08 05:13:33.234000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>2023-02-09 06:40:14.134000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>2023-02-08 05:13:41.509000+00:00</td>\n",
              "      <td>DISLIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>2023-02-08 05:13:46.215000+00:00</td>\n",
              "      <td>DISLIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                first_like_unlike_at first_type  is_unmatch  like_count  p1  \\\n",
              "212 2023-02-08 05:13:13.430000+00:00       LIKE        True           2   0   \n",
              "213 2023-02-08 05:13:24.752000+00:00       LIKE       False           1   0   \n",
              "214 2023-02-08 05:13:33.234000+00:00       LIKE       False           2   0   \n",
              "215 2023-02-08 05:13:41.509000+00:00    DISLIKE       False           1   0   \n",
              "216 2023-02-08 05:13:46.215000+00:00    DISLIKE       False           1   0   \n",
              "\n",
              "     p2            second_like_unlike_at second_type  \\\n",
              "212  35 2023-02-09 06:39:18.142000+00:00        LIKE   \n",
              "213  36                              NaT         NaN   \n",
              "214  37 2023-02-09 06:40:14.134000+00:00        LIKE   \n",
              "215  38                              NaT         NaN   \n",
              "216  39                              NaT         NaN   \n",
              "\n",
              "                          unmatch_on  \n",
              "212 2023-02-11 11:31:48.390000+00:00  \n",
              "213                              NaT  \n",
              "214                              NaT  \n",
              "215                              NaT  \n",
              "216                              NaT  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "swipes.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmqCxmFLXZap"
      },
      "source": [
        "swipes.csv - Dataset of ~60k swipes by profiles.\n",
        "\n",
        "id - Primary ID of the swipe or match\n",
        "\n",
        "p1 - Primary ID of the first person performing the swipe. If A swipes B then p1 is A\n",
        "\n",
        "p2 - Primary ID of the person who was swiped by p1. If A swipes B then p2 is B\n",
        "\n",
        "first_type - LIKE or DISLIKE performed by p1\n",
        "\n",
        "is_unmatch - If the match was unmatched by any user.\n",
        "\n",
        "unmatch_on - Timestamp of unmatch\n",
        "\n",
        "like_count - 1 if single swipe is done. 2 if both have swiped each other\n",
        "\n",
        "second_type - LIKE or DISLIKE performed by p2 (Only present if like_count is 2) and 2nd user performed the action.\n",
        "\n",
        "p1_extend_at and p2_extend_at are irrelevant."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "vf8iSpbbHRpL",
        "outputId": "74b11c59-39b6-4dc8-e3ae-5e8b25d4b429"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>first_like_unlike_at</th>\n",
              "      <th>first_type</th>\n",
              "      <th>is_unmatch</th>\n",
              "      <th>like_count</th>\n",
              "      <th>p1</th>\n",
              "      <th>p2</th>\n",
              "      <th>second_like_unlike_at</th>\n",
              "      <th>second_type</th>\n",
              "      <th>unmatch_on</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7376</th>\n",
              "      <td>2023-02-09 07:24:46.746000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>385</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7377</th>\n",
              "      <td>2023-02-09 07:24:51.520000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7379</th>\n",
              "      <td>2023-02-09 07:24:54.276000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>77</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7384</th>\n",
              "      <td>2023-02-09 07:25:08.552000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7385</th>\n",
              "      <td>2023-02-09 07:25:10.423000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>321</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64510</th>\n",
              "      <td>2023-03-01 12:13:03.699000+00:00</td>\n",
              "      <td>DISLIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1337</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64511</th>\n",
              "      <td>2023-03-01 12:13:06.092000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>523</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64512</th>\n",
              "      <td>2023-03-01 12:13:08.077000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>924</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64513</th>\n",
              "      <td>2023-03-01 12:13:14.760000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1290</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64514</th>\n",
              "      <td>2023-03-01 12:13:16.830000+00:00</td>\n",
              "      <td>LIKE</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1079</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>69 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  first_like_unlike_at first_type  is_unmatch  like_count  p1  \\\n",
              "7376  2023-02-09 07:24:46.746000+00:00       LIKE       False           1   1   \n",
              "7377  2023-02-09 07:24:51.520000+00:00       LIKE       False           1   1   \n",
              "7379  2023-02-09 07:24:54.276000+00:00       LIKE       False           1   1   \n",
              "7384  2023-02-09 07:25:08.552000+00:00       LIKE       False           1   1   \n",
              "7385  2023-02-09 07:25:10.423000+00:00       LIKE       False           1   1   \n",
              "...                                ...        ...         ...         ...  ..   \n",
              "64510 2023-03-01 12:13:03.699000+00:00    DISLIKE       False           1   1   \n",
              "64511 2023-03-01 12:13:06.092000+00:00       LIKE       False           1   1   \n",
              "64512 2023-03-01 12:13:08.077000+00:00       LIKE       False           1   1   \n",
              "64513 2023-03-01 12:13:14.760000+00:00       LIKE       False           1   1   \n",
              "64514 2023-03-01 12:13:16.830000+00:00       LIKE       False           1   1   \n",
              "\n",
              "         p2 second_like_unlike_at second_type unmatch_on  \n",
              "7376    385                   NaT         NaN        NaT  \n",
              "7377    160                   NaT         NaN        NaT  \n",
              "7379     77                   NaT         NaN        NaT  \n",
              "7384     73                   NaT         NaN        NaT  \n",
              "7385    321                   NaT         NaN        NaT  \n",
              "...     ...                   ...         ...        ...  \n",
              "64510  1337                   NaT         NaN        NaT  \n",
              "64511   523                   NaT         NaN        NaT  \n",
              "64512   924                   NaT         NaN        NaT  \n",
              "64513  1290                   NaT         NaN        NaT  \n",
              "64514  1079                   NaT         NaN        NaT  \n",
              "\n",
              "[69 rows x 9 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "swipes[swipes['p1']==1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "Rdo2wC4C3cf4"
      },
      "outputs": [],
      "source": [
        "profiles1=[]\n",
        "for i in range(1405):\n",
        "  temp=swipes[swipes['p1']==i]\n",
        "  # temp2={i:temp}\n",
        "  profiles1.append(temp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wXbDLaPHf4_",
        "outputId": "1e72c4be-8eb7-4401-e7eb-868832503946"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                  first_like_unlike_at first_type  is_unmatch  like_count  p1  \\\n",
            "212   2023-02-08 05:13:13.430000+00:00       LIKE        True           2   0   \n",
            "213   2023-02-08 05:13:24.752000+00:00       LIKE       False           1   0   \n",
            "214   2023-02-08 05:13:33.234000+00:00       LIKE       False           2   0   \n",
            "215   2023-02-08 05:13:41.509000+00:00    DISLIKE       False           1   0   \n",
            "216   2023-02-08 05:13:46.215000+00:00    DISLIKE       False           1   0   \n",
            "...                                ...        ...         ...         ...  ..   \n",
            "62883 2023-02-27 20:40:44.524000+00:00       LIKE       False           1   0   \n",
            "62884 2023-02-27 20:40:48.367000+00:00       LIKE       False           1   0   \n",
            "62885 2023-02-27 20:40:55.653000+00:00    DISLIKE       False           1   0   \n",
            "63877 2023-02-28 19:51:48.800000+00:00    DISLIKE       False           1   0   \n",
            "63878 2023-02-28 19:52:01.085000+00:00    DISLIKE       False           1   0   \n",
            "\n",
            "         p2            second_like_unlike_at second_type  \\\n",
            "212      35 2023-02-09 06:39:18.142000+00:00        LIKE   \n",
            "213      36                              NaT         NaN   \n",
            "214      37 2023-02-09 06:40:14.134000+00:00        LIKE   \n",
            "215      38                              NaT         NaN   \n",
            "216      39                              NaT         NaN   \n",
            "...     ...                              ...         ...   \n",
            "62883  1337                              NaT         NaN   \n",
            "62884  1357                              NaT         NaN   \n",
            "62885  1358                              NaT         NaN   \n",
            "63877  1375                              NaT         NaN   \n",
            "63878  1371                              NaT         NaN   \n",
            "\n",
            "                            unmatch_on  \n",
            "212   2023-02-11 11:31:48.390000+00:00  \n",
            "213                                NaT  \n",
            "214                                NaT  \n",
            "215                                NaT  \n",
            "216                                NaT  \n",
            "...                                ...  \n",
            "62883                              NaT  \n",
            "62884                              NaT  \n",
            "62885                              NaT  \n",
            "63877                              NaT  \n",
            "63878                              NaT  \n",
            "\n",
            "[230 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "print(profiles1[])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                first_like_unlike_at first_type  is_unmatch  like_count   p1  \\\n",
            "0   2023-02-07 06:12:59.013000+00:00    DISLIKE       False           2    2   \n",
            "3   2023-02-07 07:10:01.566000+00:00    DISLIKE       False           1    4   \n",
            "11  2023-02-07 09:11:01.334000+00:00       LIKE       False           2   11   \n",
            "32  2023-02-07 10:36:50.184000+00:00    DISLIKE       False           1   16   \n",
            "41  2023-02-07 12:50:15.249000+00:00    DISLIKE       False           1   21   \n",
            "62  2023-02-07 13:08:25.798000+00:00       LIKE       False           2   22   \n",
            "67  2023-02-07 13:28:14.048000+00:00       LIKE       False           2   23   \n",
            "102 2023-02-07 16:26:24.052000+00:00    DISLIKE       False           2   30   \n",
            "423 2023-02-08 14:20:58.551000+00:00       LIKE       False           2  110   \n",
            "444 2023-02-08 14:22:16.393000+00:00    DISLIKE       False           1  112   \n",
            "449 2023-02-08 14:22:55.049000+00:00    DISLIKE       False           1  113   \n",
            "\n",
            "     p2            second_like_unlike_at second_type unmatch_on  \n",
            "0     0 2023-02-07 11:15:48.194000+00:00     DISLIKE        NaT  \n",
            "3     0                              NaT         NaN        NaT  \n",
            "11    0 2023-02-07 11:16:27.277000+00:00     DISLIKE        NaT  \n",
            "32    0                              NaT         NaN        NaT  \n",
            "41    0                              NaT         NaN        NaT  \n",
            "62    0 2023-02-07 13:35:50.442000+00:00        LIKE        NaT  \n",
            "67    0 2023-02-07 13:36:00.090000+00:00     DISLIKE        NaT  \n",
            "102   0 2023-02-08 05:13:03.752000+00:00        LIKE        NaT  \n",
            "423   0 2023-02-08 15:13:27.349000+00:00        LIKE        NaT  \n",
            "444   0                              NaT         NaN        NaT  \n",
            "449   0                              NaT         NaN        NaT  \n",
            "1405\n"
          ]
        }
      ],
      "source": [
        "profiles2=[]\n",
        "for i in range(1405):\n",
        "  temp=swipes[swipes['p2']==i]\n",
        "  # temp2={i:temp}\n",
        "  profiles2.append(temp)\n",
        "print(profiles2[0])\n",
        "print(len(profiles2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[\"Jawaharlal Nehru University\"]                    491\n",
              "[]                                                 226\n",
              "[\"JNU\"]                                            118\n",
              "[\"Jnu\"]                                             76\n",
              "[\"Miranda House\"]                                   48\n",
              "                                                  ... \n",
              "[\"IIT bhu\"]                                          1\n",
              "[\"Indian Institute of Technology BHU Varanasi\"]      1\n",
              "[\"Jawarlal Nehru University\"]                        1\n",
              "[\"Iit delhi\"]                                        1\n",
              "[\"Dps\"]                                              1\n",
              "Name: college, Length: 245, dtype: int64"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "users=pd.read_csv('./users.csv')\n",
        "users['college'].value_counts()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.2"
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  "nbformat_minor": 0
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