TwitterPartial_numeric.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TwitterParital Data Leak"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Written by: Thai Nguyen\n",
    "\n",
    "LinkedIn/Github: @thai22011\n",
    "\n",
    "Date: Dec 26, 2022"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment\n",
    "https://towardsdatascience.com/4-libraries-that-can-perform-eda-in-one-line-of-python-code-b13938a06ae\n",
    "\n",
    "https://conda-forge.org/docs/user/introduction.html#\n",
    "\n",
    "`ujson` helps load the json faster\n",
    "\n",
    "```\n",
    "conda create -n twitter_env python=3.8 numpy pandas matplotlib seaborn statsmodels scikit-learn jupyter jupyterlab plotly\n",
    "conda activate twitter_env\n",
    "conda install -c conda-forge fbprophet\n",
    "conda install -c conda-forge pandas-profiling\n",
    "conda install -c conda-forge sweetviz\n",
    "conda install -c conda-forge autoviz\n",
    "conda install -c conda-forge dtale\n",
    "conda install ujson\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import ujson"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the numeric dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data\n",
    "twitter_df2_numeric = pd.read_pickle('data/twitter_df2_numeric.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save as csv\n",
    "twitter_df2_numeric.to_csv('data/twitter_df2_numeric.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "         protected  followers_count  friends_count  listed_count  \\\n",
       "0                0             1217            580            27   \n",
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       "5485634          0                1              4             0   \n",
       "\n",
       "                        created_at  favourites_count  verified  statuses_count  \n",
       "0        2006-05-01 05:51:29+00:00                64         0             163  \n",
       "1        2006-05-23 23:55:27+00:00             13606         0           25942  \n",
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       "5485634  2022-01-08 01:15:40+00:00                 1         0               0  \n",
       "\n",
       "[5485635 rows x 8 columns]"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "twitter_df2_numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    99.916892\n",
       "True      0.083108\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicated rows\n",
    "twitter_df2_numeric.duplicated().value_counts()/twitter_df2_numeric.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "protected           0\n",
       "followers_count     0\n",
       "friends_count       0\n",
       "listed_count        0\n",
       "created_at          0\n",
       "favourites_count    0\n",
       "verified            0\n",
       "statuses_count      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "twitter_df2_numeric.isna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I plan to locate the location of each user by `location` or `phone` with area codes locating. However, significant amounts of phone are missing and locations are not available as well. Unless Twitter collects IP addresses, there is difficult to locate the user location.\n",
    "\n",
    "In addition, when `location` has no null value, it is a wrong because `location` had many blank rows by observation, which meant space isn't null this the dataframe. Let's dig a bit deeper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "         protected  followers_count  friends_count  listed_count  \\\n",
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       "\n",
       "                        created_at  favourites_count  verified  statuses_count  \n",
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       "1418     2007-03-29 20:32:44+00:00                 0         0               0  \n",
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       "5485622  2022-01-07 19:27:03+00:00                 0         0               0  \n",
       "\n",
       "[418090 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_df2_numeric[(twitter_df2_numeric['favourites_count'] == 0) & (twitter_df2_numeric['followers_count'] == 0) & (twitter_df2_numeric['statuses_count'] == 0) & (twitter_df2_numeric['friends_count'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
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       "      <td>2006-11-26 23:09:25+00:00</td>\n",
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       "      <td>0</td>\n",
       "      <td>74</td>\n",
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       "      <td>2006-12-07 23:26:52+00:00</td>\n",
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       "      <td>2006-12-12 17:32:09+00:00</td>\n",
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       "         protected  followers_count  friends_count  listed_count  \\\n",
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       "\n",
       "                        created_at  favourites_count  verified  statuses_count  \n",
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       "...                            ...               ...       ...             ...  \n",
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       "5485628  2022-01-07 21:40:49+00:00                 0         0               1  \n",
       "\n",
       "[585511 rows x 8 columns]"
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_df2_numeric[(twitter_df2_numeric['favourites_count'] == 0) & (twitter_df2_numeric['followers_count'] == 0) & (twitter_df2_numeric['friends_count'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "         protected  followers_count  friends_count  listed_count  \\\n",
       "46               1                0              0             1   \n",
       "60               1                0              0             0   \n",
       "120              1                0              0             0   \n",
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       "                        created_at  favourites_count  verified  statuses_count  \n",
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       "5485628  2022-01-07 21:40:49+00:00                 0         0               1  \n",
       "\n",
       "[636231 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_df2_numeric[(twitter_df2_numeric['followers_count'] == 0) & (twitter_df2_numeric['friends_count'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>1301033 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "         protected  followers_count  friends_count  listed_count  \\\n",
       "30               1              203              0             1   \n",
       "41               0               44              0             3   \n",
       "43               0              763              0            49   \n",
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       "5485622          0                0              0             0   \n",
       "5485628          0                0              0             0   \n",
       "\n",
       "                        created_at  favourites_count  verified  statuses_count  \n",
       "30       2006-08-28 20:22:10+00:00                14         0               7  \n",
       "41       2006-09-20 08:36:58+00:00                 0         0               0  \n",
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       "46       2006-10-01 05:00:10+00:00                 0         0               2  \n",
       "60       2006-10-23 18:28:04+00:00                 0         0               3  \n",
       "...                            ...               ...       ...             ...  \n",
       "5485610  2022-01-07 17:01:10+00:00                 0         0               0  \n",
       "5485618  2022-01-07 18:08:54+00:00                 0         0               0  \n",
       "5485620  2022-01-07 18:59:50+00:00                 0         0               0  \n",
       "5485622  2022-01-07 19:27:03+00:00                 0         0               0  \n",
       "5485628  2022-01-07 21:40:49+00:00                 0         0               1  \n",
       "\n",
       "[1301033 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_df2_numeric[(twitter_df2_numeric['friends_count'] == 0)]"
   ]
  },
  {
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   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protected</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>created_at</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>verified</th>\n",
       "      <th>statuses_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2006-09-29 10:23:24+00:00</td>\n",
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       "      <td>2006-10-01 05:00:10+00:00</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2006-10-23 18:28:04+00:00</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2006-10-24 13:34:02+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>2022-01-07 21:22:56+00:00</td>\n",
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       "      <td>2022-01-07 21:29:19+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5485628</th>\n",
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       "      <td>2022-01-07 21:40:49+00:00</td>\n",
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       "<p>2656856 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         protected  followers_count  friends_count  listed_count  \\\n",
       "41               0               44              0             3   \n",
       "45               0               37             11             1   \n",
       "46               1                0              0             1   \n",
       "60               1                0              0             0   \n",
       "65               1                3              0             0   \n",
       "...            ...              ...            ...           ...   \n",
       "5485626          0                0              3             0   \n",
       "5485627          0                0              1             0   \n",
       "5485628          0                0              0             0   \n",
       "5485630          0                0              1             0   \n",
       "5485631          0                0              2             0   \n",
       "\n",
       "                        created_at  favourites_count  verified  statuses_count  \n",
       "41       2006-09-20 08:36:58+00:00                 0         0               0  \n",
       "45       2006-09-29 10:23:24+00:00                 0         0               0  \n",
       "46       2006-10-01 05:00:10+00:00                 0         0               2  \n",
       "60       2006-10-23 18:28:04+00:00                 0         0               3  \n",
       "65       2006-10-24 13:34:02+00:00                 0         0              21  \n",
       "...                            ...               ...       ...             ...  \n",
       "5485626  2022-01-07 21:22:56+00:00                 0         0               0  \n",
       "5485627  2022-01-07 21:29:19+00:00                 0         0               1  \n",
       "5485628  2022-01-07 21:40:49+00:00                 0         0               1  \n",
       "5485630  2022-01-07 22:07:56+00:00                 0         0               0  \n",
       "5485631  2022-01-08 00:09:17+00:00                 0         0               0  \n",
       "\n",
       "[2656856 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_df2_numeric[(twitter_df2_numeric['favourites_count'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5482283\n",
       "1       3352\n",
       "Name: verified, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for Percentage of `verified` and `protected` accounts\n",
    "twitter_df2_numeric['verified'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    91.182201\n",
       "1     8.817799\n",
       "Name: protected, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for Percentage of `verified` and `protected` accounts\n",
    "twitter_df2_sample['protected'].value_counts()/twitter_df2_sample.shape[0]*100"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This made sense why Elon Musk want to roll out the `verified` feature as a paid subsription with extra benefits as 99.9% of all Twitter users are non-verified. \n",
    "\n",
    "Regarding the protected accounts, which means accounts are privated and unseen unless being followed"
   ]
  }
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