demos/Meetup Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linus Torvalds           24259\n",
       "David S. Miller           9563\n",
       "Mark Brown                6917\n",
       "Takashi Iwai              6293\n",
       "Al Viro                   6064\n",
       "H Hartley Sweeten         5942\n",
       "Ingo Molnar               5462\n",
       "Mauro Carvalho Chehab     5384\n",
       "Arnd Bergmann             5305\n",
       "Greg Kroah-Hartman        4687\n",
       "Name: author, dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../../../linux/git_demo_timestamp.csv\", sep=\";\")\n",
    "df.author.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fef79256a0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MNv+w7gwU88bBmDdzie4gRKHEQoZ6xmW/CsBStH3Sd8yZYtk6wgK/nnOriD7XB4Db4XR/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\nxhhjjDGBZYWMMcYYYwLr/wccpqHLIZ6t3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fef78fd320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df.author.value_counts().head(10).plot(kind='pie', figsize=(8,8), label=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  723214\n",
       "unique                 691746\n",
       "top       2017-11-01 03:56:19\n",
       "freq                      137\n",
       "first     2005-04-16 15:20:36\n",
       "last      2017-12-31 16:52:15\n",
       "Name: timestamp_local, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timestamp_local'] = pd.to_datetime(df['timestamp_local'])\n",
    "df.timestamp_local.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fef7a510b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iFEZ88J6kHuBF289Kmga8ncYF4+3ACmB9eX2gdNkOfErSx4DX0LjwvMv2aUkny8XsncCt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\ngWZGro0UIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fef79ac128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.timestamp_local.dt.year.value_counts(sort=False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fef6ea6080>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fef9d49978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.timestamp_local.dt.hour.value_counts(sort=False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alternative: Using original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>unix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sun Dec 31 14:47:43 2017 -0800</td>\n",
       "      <td>1514760463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sun Dec 31 13:13:56 2017 -0800</td>\n",
       "      <td>1514754836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sun Dec 31 13:03:05 2017 -0800</td>\n",
       "      <td>1514754185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sun Dec 31 12:30:34 2017 -0800</td>\n",
       "      <td>1514752234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sun Dec 31 12:29:02 2017 -0800</td>\n",
       "      <td>1514752142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             time        unix\n",
       "0  Sun Dec 31 14:47:43 2017 -0800  1514760463\n",
       "1  Sun Dec 31 13:13:56 2017 -0800  1514754836\n",
       "2  Sun Dec 31 13:03:05 2017 -0800  1514754185\n",
       "3  Sun Dec 31 12:30:34 2017 -0800  1514752234\n",
       "4  Sun Dec 31 12:29:02 2017 -0800  1514752142"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\n",
    "    r\"C:/Temp/git_time_unix.csv\",\n",
    "    sep=\"\\t\",\n",
    "    encoding='latin-1',\n",
    "    names = ['time', 'unix']\n",
    ")\n",
    "#df['unix_time'] = pd.to_datetime(df['unix'], unit=\"s\")\n",
    "#df['read_time'] = pd.to_datetime(df['time'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'pandas.api' from 'C:\\\\dev\\\\apps\\\\Anaconda3\\\\lib\\\\site-packages\\\\pandas\\\\api\\\\__init__.py'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>unix</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sun Dec 31 14:47:43 2017 -0800</td>\n",
       "      <td>1514760463</td>\n",
       "      <td>2017-12-31 14:47:43</td>\n",
       "      <td>-0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sun Dec 31 13:13:56 2017 -0800</td>\n",
       "      <td>1514754836</td>\n",
       "      <td>2017-12-31 13:13:56</td>\n",
       "      <td>-0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sun Dec 31 13:03:05 2017 -0800</td>\n",
       "      <td>1514754185</td>\n",
       "      <td>2017-12-31 13:03:05</td>\n",
       "      <td>-0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sun Dec 31 12:30:34 2017 -0800</td>\n",
       "      <td>1514752234</td>\n",
       "      <td>2017-12-31 12:30:34</td>\n",
       "      <td>-0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sun Dec 31 12:29:02 2017 -0800</td>\n",
       "      <td>1514752142</td>\n",
       "      <td>2017-12-31 12:29:02</td>\n",
       "      <td>-0800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             time        unix           timestamp timezone\n",
       "0  Sun Dec 31 14:47:43 2017 -0800  1514760463 2017-12-31 14:47:43    -0800\n",
       "1  Sun Dec 31 13:13:56 2017 -0800  1514754836 2017-12-31 13:13:56    -0800\n",
       "2  Sun Dec 31 13:03:05 2017 -0800  1514754185 2017-12-31 13:03:05    -0800\n",
       "3  Sun Dec 31 12:30:34 2017 -0800  1514752234 2017-12-31 12:30:34    -0800\n",
       "4  Sun Dec 31 12:29:02 2017 -0800  1514752142 2017-12-31 12:29:02    -0800"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['timestamp', 'timezone']] = df.time.str.rsplit(\" \", n=1, expand=True)\n",
    "df['timestamp'] = pd.to_datetime(df.timestamp)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": 33,
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    {
     "data": {
      "text/plain": [
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       "2013-09-19 12:59:00     22\n",
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       "                      ... \n",
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       "Name: 0, Length: 692468, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.to_datetime(df.timeinfo.str.split(\" \", expand=True)[0], unit=\"s\")\n",
    "t.value_counts()"
   ]
  }
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