show_statistics_on_comment_time.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from bidi.algorithm import get_display\n",
    "import re\n",
    "import multiprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_type = 'post'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "field_name = 'message'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "threads_df = pd.read_csv(f'{file_type}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = threads_df[threads_df['date'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[~df['date'].str.contains('post')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>thread_id</th>\n",
       "      <th>post_id</th>\n",
       "      <th>user_name</th>\n",
       "      <th>date</th>\n",
       "      <th>message</th>\n",
       "      <th>cite1</th>\n",
       "      <th>cite2</th>\n",
       "      <th>cite3</th>\n",
       "      <th>cite4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, thread_id, post_id, user_name, date, message, cite1, cite2, cite3, cite4]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['date'].str.contains('post')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "threads_df = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "threads_df['timestamp'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = threads_df.drop_duplicates(subset=['thread_id','post_id','user_name','message','timestamp'], keep=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/ipykernel_launcher.py:1: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/ipykernel_launcher.py:2: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/ipykernel_launcher.py:3: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/ipykernel_launcher.py:4: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/anaconda/envs/azureml_py36/lib/python3.6/site-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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/anaconda/envs/azureml_py36/lib/python3.6/site-packages/ipykernel_launcher.py:6: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "dataset['year'] = dataset.timestamp.dt.year\n",
    "dataset['month'] = dataset.timestamp.dt.month\n",
    "dataset['day'] = dataset.timestamp.dt.day\n",
    "dataset['hour'] = dataset.timestamp.dt.hour\n",
    "dataset['day_of_week_name'] = dataset.timestamp.dt.weekday_name\n",
    "dataset['quarter'] = dataset.timestamp.dt.quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019    187255\n",
       "2020    114001\n",
       "2018      3592\n",
       "2017       354\n",
       "2016        70\n",
       "2013         5\n",
       "2015         3\n",
       "2011         1\n",
       "2010         1\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    80468\n",
       "1    76825\n",
       "4    75944\n",
       "2    72045\n",
       "Name: quarter, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['quarter'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55f5aee208>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['hour'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55f5c3b128>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['month'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2010-09-08 22:01:00')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['timestamp'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}