notebooks/Developers' Habits (IntelliJ Edition).ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\" style=\"margin-top: 1em;\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Gaining-the-data\" data-toc-modified-id=\"Gaining-the-data-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Gaining the data</a></span></li><li><span><a href=\"#Wrangling-the-raw-data\" data-toc-modified-id=\"Wrangling-the-raw-data-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Wrangling the raw data</a></span></li><li><span><a href=\"#Refining-the-dataset\" data-toc-modified-id=\"Refining-the-dataset-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Refining the dataset</a></span><ul class=\"toc-item\"><li><span><a href=\"#Adding-weekdays\" data-toc-modified-id=\"Adding-weekdays-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Adding weekdays</a></span></li><li><span><a href=\"#Adding-working-hours\" data-toc-modified-id=\"Adding-working-hours-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Adding working hours</a></span></li></ul></li><li><span><a href=\"#Analyzing-the-data\" data-toc-modified-id=\"Analyzing-the-data-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Analyzing the data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Developers'-timezones\" data-toc-modified-id=\"Developers'-timezones-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Developers' timezones</a></span></li><li><span><a href=\"#Weekdays-with-the-most-commits\" data-toc-modified-id=\"Weekdays-with-the-most-commits-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Weekdays with the most commits</a></span></li><li><span><a href=\"#Working-behavior-on-weekends\" data-toc-modified-id=\"Working-behavior-on-weekends-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Working behavior on weekends</a></span></li><li><span><a href=\"#Usual-working-hours\" data-toc-modified-id=\"Usual-working-hours-5.4\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>Usual working hours</a></span></li><li><span><a href=\"#Signs-of-overtime\" data-toc-modified-id=\"Signs-of-overtime-5.5\"><span class=\"toc-item-num\">5.5&nbsp;&nbsp;</span>Signs of overtime</a></span></li></ul></li><li><span><a href=\"#Closing-remarks\" data-toc-modified-id=\"Closing-remarks-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Closing remarks</a></span></li></ul></div>"
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    "# Introduction\n",
    "In this blog post / notebook, we want to take a look at how much information you can extract from a simple [Git log](https://git-scm.com/docs/git-log) output. We want to know\n",
    "\n",
    "* where the developers come from\n",
    "* on which weekdays the developers don't work\n",
    "* which developers are working on weekends\n",
    "* what the normal working hours are and\n",
    "* if the is any sight of overtime periods.\n",
    "\n",
    "We use [Pandas](https://pandas.pydata.org/) as data analysis toolkit to accomplish these tasks for the big open source project [IntelliJ](https://github.com/JetBrains/intellij-community)."
   ]
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    "# Gaining the data\n",
    "\n",
    "To get the necessary data, we clone the Git repository with\n",
    "\n",
    "```\n",
    "git clone https://github.com/JetBrains/intellij-community.git\n",
    "```\n",
    "\n",
    "and create a latin-1 encoded log file of the complete commit history with\n",
    "\n",
    "```\n",
    "git log --date=raw --encoding=LATIN-1 --pretty=\"%ad%x09%aN%x09%ae\" > git_timestamp_author_email.log\n",
    "```\n",
    "\n",
    "on the command line.\n",
    "\n",
    "\n",
    "This gives us a nice file with the following content:\n",
    "\n",
    "```\n",
    "1514469569 +0300\tKirill Kirichenko\tkirill.kirichenko@jetbrains.com\n",
    "1514469402 +0100\tAnna Kozlova\tanna.kozlova@jetbrains.com\n",
    "1514469119 +0100\tVladimir Krivosheev\tvladimir.krivosheev@jetbrains.com\n",
    "1514468066 +0100\tVladimir Krivosheev\tvladimir.krivosheev@jetbrains.com\n",
    "1514462548 +0100\tVladimir Krivosheev\tvladimir.krivosheev@jetbrains.com\n",
    "...\n",
    "```\n",
    "\n",
    "It includes the UNIX timestamp (in seconds since epoch), a whitespace, the time zone (where the authors live in), a tab separator, the name of the author, a tab and the email address of the author.\n",
    "\n",
    "Note:\n",
    "* We use the `--date=raw` option that returns the UNIX timestamp in the UTC time zone and the author's time zone. We use the UNIX timestamp because Pandas can parse that data format very efficient. We need also the time zone because we want to know when each committer works at their local time.\n",
    "* We've created and implicitly used a `.mailmap` file for this task to map multiple author names and email addresses to the same person. You can find this file in this [GitHub Gist](https://gist.github.com/feststelltaste/c341ca49c37e143b22bb9adeb84bf89e). To use it, the file has to be put into the root of the Git repository before the execution of the `git log` command."
   ]
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   "source": [
    "# Wrangling the raw data"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "We import the data by using Pandas' `read_csv` function and the appropriate parameters."
   ]
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       "      <th>unix_timestamp</th>\n",
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       "      <td>Kirill Kirichenko</td>\n",
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       "      <td>Anna Kozlova</td>\n",
       "      <td>anna.kozlova@jetbrains.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1514469119 +0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     unix_timestamp               author                              email\n",
       "0  1514469569 +0300    Kirill Kirichenko    kirill.kirichenko@jetbrains.com\n",
       "1  1514469402 +0100         Anna Kozlova         anna.kozlova@jetbrains.com\n",
       "2  1514469119 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com\n",
       "3  1514468066 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com\n",
       "4  1514462548 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "raw = pd.read_csv(\n",
    "    r'../../intellij-community/git_timestamp_author_email.log',\n",
    "    sep=\"\\t\",\n",
    "    encoding=\"latin-1\",\n",
    "    header=None,\n",
    "    names=['unix_timestamp', 'author', 'email'])\n",
    "\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split the information in `unix_timestamp` into the separate columns `timestamp` and `timezone`."
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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       "     unix_timestamp               author                              email  \\\n",
       "0  1514469569 +0300    Kirill Kirichenko    kirill.kirichenko@jetbrains.com   \n",
       "1  1514469402 +0100         Anna Kozlova         anna.kozlova@jetbrains.com   \n",
       "2  1514469119 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "3  1514468066 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "4  1514462548 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "\n",
       "    timestamp timezone  \n",
       "0  1514469569    +0300  \n",
       "1  1514469402    +0100  \n",
       "2  1514469119    +0100  \n",
       "3  1514468066    +0100  \n",
       "4  1514462548    +0100  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw[['timestamp', 'timezone']] = raw['unix_timestamp'].str.split(\" \", expand=True)\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also create a new numeric column for the timezone offset for later calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     unix_timestamp               author                              email  \\\n",
       "0  1514469569 +0300    Kirill Kirichenko    kirill.kirichenko@jetbrains.com   \n",
       "1  1514469402 +0100         Anna Kozlova         anna.kozlova@jetbrains.com   \n",
       "2  1514469119 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "3  1514468066 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "4  1514462548 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "\n",
       "    timestamp timezone  timezone_offset  \n",
       "0  1514469569    +0300              3.0  \n",
       "1  1514469402    +0100              1.0  \n",
       "2  1514469119    +0100              1.0  \n",
       "3  1514468066    +0100              1.0  \n",
       "4  1514462548    +0100              1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw['timezone_offset'] = pd.to_numeric(raw['timezone']) / 100.0\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a real time column, we convert the information in `timestamp` accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Anna Kozlova</td>\n",
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       "      <td>1514469119 +0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>2017-12-28 13:51:59</td>\n",
       "      <td>+0100</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Vladimir Krivosheev</td>\n",
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       "      <td>1514462548 +0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>2017-12-28 12:02:28</td>\n",
       "      <td>+0100</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "     unix_timestamp               author                              email  \\\n",
       "0  1514469569 +0300    Kirill Kirichenko    kirill.kirichenko@jetbrains.com   \n",
       "1  1514469402 +0100         Anna Kozlova         anna.kozlova@jetbrains.com   \n",
       "2  1514469119 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "3  1514468066 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "4  1514462548 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "\n",
       "            timestamp timezone  timezone_offset  \n",
       "0 2017-12-28 13:59:29    +0300              3.0  \n",
       "1 2017-12-28 13:56:42    +0100              1.0  \n",
       "2 2017-12-28 13:51:59    +0100              1.0  \n",
       "3 2017-12-28 13:34:26    +0100              1.0  \n",
       "4 2017-12-28 12:02:28    +0100              1.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw['timestamp'] = pd.to_datetime(raw['timestamp'], unit=\"s\")\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also calculate the local time of each commit by adding the `timezone_offset` data to the `timestamp` entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>unix_timestamp</th>\n",
       "      <th>author</th>\n",
       "      <th>email</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>timezone</th>\n",
       "      <th>timezone_offset</th>\n",
       "      <th>timestamp_local</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1514469569 +0300</td>\n",
       "      <td>Kirill Kirichenko</td>\n",
       "      <td>kirill.kirichenko@jetbrains.com</td>\n",
       "      <td>2017-12-28 13:59:29</td>\n",
       "      <td>+0300</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2017-12-28 16:59:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1514469402 +0100</td>\n",
       "      <td>Anna Kozlova</td>\n",
       "      <td>anna.kozlova@jetbrains.com</td>\n",
       "      <td>2017-12-28 13:56:42</td>\n",
       "      <td>+0100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-12-28 14:56:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1514469119 +0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>2017-12-28 13:51:59</td>\n",
       "      <td>+0100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-12-28 14:51:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1514468066 +0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>2017-12-28 13:34:26</td>\n",
       "      <td>+0100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-12-28 14:34:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1514462548 +0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>2017-12-28 12:02:28</td>\n",
       "      <td>+0100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-12-28 13:02:28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     unix_timestamp               author                              email  \\\n",
       "0  1514469569 +0300    Kirill Kirichenko    kirill.kirichenko@jetbrains.com   \n",
       "1  1514469402 +0100         Anna Kozlova         anna.kozlova@jetbrains.com   \n",
       "2  1514469119 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "3  1514468066 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "4  1514462548 +0100  Vladimir Krivosheev  vladimir.krivosheev@jetbrains.com   \n",
       "\n",
       "            timestamp timezone  timezone_offset     timestamp_local  \n",
       "0 2017-12-28 13:59:29    +0300              3.0 2017-12-28 16:59:29  \n",
       "1 2017-12-28 13:56:42    +0100              1.0 2017-12-28 14:56:42  \n",
       "2 2017-12-28 13:51:59    +0100              1.0 2017-12-28 14:51:59  \n",
       "3 2017-12-28 13:34:26    +0100              1.0 2017-12-28 14:34:26  \n",
       "4 2017-12-28 12:02:28    +0100              1.0 2017-12-28 13:02:28  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw[\"timestamp_local\"] = raw['timestamp'] + pd.to_timedelta(raw['timezone_offset'], unit='h')\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At last, we copy only the needed data from the `raw` dataset into the new DataFrame `git_authors`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>timestamp_local</th>\n",
       "      <th>timezone</th>\n",
       "      <th>author</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-28 16:59:29</td>\n",
       "      <td>+0300</td>\n",
       "      <td>Kirill Kirichenko</td>\n",
       "      <td>kirill.kirichenko@jetbrains.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-28 14:56:42</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Anna Kozlova</td>\n",
       "      <td>anna.kozlova@jetbrains.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-28 14:51:59</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-28 14:34:26</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-28 13:02:28</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      timestamp_local timezone               author  \\\n",
       "0 2017-12-28 16:59:29    +0300    Kirill Kirichenko   \n",
       "1 2017-12-28 14:56:42    +0100         Anna Kozlova   \n",
       "2 2017-12-28 14:51:59    +0100  Vladimir Krivosheev   \n",
       "3 2017-12-28 14:34:26    +0100  Vladimir Krivosheev   \n",
       "4 2017-12-28 13:02:28    +0100  Vladimir Krivosheev   \n",
       "\n",
       "                               email  \n",
       "0    kirill.kirichenko@jetbrains.com  \n",
       "1         anna.kozlova@jetbrains.com  \n",
       "2  vladimir.krivosheev@jetbrains.com  \n",
       "3  vladimir.krivosheev@jetbrains.com  \n",
       "4  vladimir.krivosheev@jetbrains.com  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "git_authors = raw[['timestamp_local', 'timezone', 'author', 'email']].copy()\n",
    "git_authors.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Refining the dataset\n",
    "In this section, we add some additional information to the `DataFrame` to accomplish our tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding weekdays\n",
    "First, we add the information about the weekdays based on the `dayofweek` information of the `timestamp_local` column. We round down possible non-integer values and extract the weekday name from the `calender.day_name` list. Because we want to preserve the order of the weekdays, we convert the `day` entries to a `Categorial` data type, too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>timestamp_local</th>\n",
       "      <th>timezone</th>\n",
       "      <th>author</th>\n",
       "      <th>email</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-28 16:59:29</td>\n",
       "      <td>+0300</td>\n",
       "      <td>Kirill Kirichenko</td>\n",
       "      <td>kirill.kirichenko@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-28 14:56:42</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Anna Kozlova</td>\n",
       "      <td>anna.kozlova@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-28 14:51:59</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-28 14:34:26</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-28 13:02:28</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      timestamp_local timezone               author  \\\n",
       "0 2017-12-28 16:59:29    +0300    Kirill Kirichenko   \n",
       "1 2017-12-28 14:56:42    +0100         Anna Kozlova   \n",
       "2 2017-12-28 14:51:59    +0100  Vladimir Krivosheev   \n",
       "3 2017-12-28 14:34:26    +0100  Vladimir Krivosheev   \n",
       "4 2017-12-28 13:02:28    +0100  Vladimir Krivosheev   \n",
       "\n",
       "                               email       day  \n",
       "0    kirill.kirichenko@jetbrains.com  Thursday  \n",
       "1         anna.kozlova@jetbrains.com  Thursday  \n",
       "2  vladimir.krivosheev@jetbrains.com  Thursday  \n",
       "3  vladimir.krivosheev@jetbrains.com  Thursday  \n",
       "4  vladimir.krivosheev@jetbrains.com  Thursday  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import calendar\n",
    "\n",
    "git_authors['day'] = git_authors[\"timestamp_local\"].apply(lambda x : calendar.day_name[int(x.dayofweek)])\n",
    "git_authors['day'] = pd.Categorical(git_authors['day'], categories=calendar.day_name, ordered=True)\n",
    "git_authors.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding working hours\n",
    "For the working hour analysis, we extract the hour information from the `timestamp_local` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>timestamp_local</th>\n",
       "      <th>timezone</th>\n",
       "      <th>author</th>\n",
       "      <th>email</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-28 16:59:29</td>\n",
       "      <td>+0300</td>\n",
       "      <td>Kirill Kirichenko</td>\n",
       "      <td>kirill.kirichenko@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-28 14:56:42</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Anna Kozlova</td>\n",
       "      <td>anna.kozlova@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-28 14:51:59</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-28 14:34:26</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-28 13:02:28</td>\n",
       "      <td>+0100</td>\n",
       "      <td>Vladimir Krivosheev</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      timestamp_local timezone               author  \\\n",
       "0 2017-12-28 16:59:29    +0300    Kirill Kirichenko   \n",
       "1 2017-12-28 14:56:42    +0100         Anna Kozlova   \n",
       "2 2017-12-28 14:51:59    +0100  Vladimir Krivosheev   \n",
       "3 2017-12-28 14:34:26    +0100  Vladimir Krivosheev   \n",
       "4 2017-12-28 13:02:28    +0100  Vladimir Krivosheev   \n",
       "\n",
       "                               email       day  hour  \n",
       "0    kirill.kirichenko@jetbrains.com  Thursday    16  \n",
       "1         anna.kozlova@jetbrains.com  Thursday    14  \n",
       "2  vladimir.krivosheev@jetbrains.com  Thursday    14  \n",
       "3  vladimir.krivosheev@jetbrains.com  Thursday    14  \n",
       "4  vladimir.krivosheev@jetbrains.com  Thursday    13  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "git_authors['hour'] = git_authors['timestamp_local'].dt.hour\n",
    "git_authors.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the data\n",
    "With the prepared `git_authors` DataFrame, we are now able to deliver insights into the past years of development."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Developers' timezones\n",
    "First, we want to know where the developers roughly live. For this, we plot the values of the `timezone` columns as a pie chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1243beb3ef0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ltSvwOefc3s65fZxzZxC2Z1yptKRsBse5rkydo3Eu8aGkuwcjfdHHpdHHq4a8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\nREQkNlRaIiISGyotERGJDZWWiIjExv8HEmeAyi2No5oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1243ad9dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "git_authors['timezone'].value_counts().plot(kind='pie', figsize=(7,7), title=\"Developer's timezones\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Result**\n",
    "\n",
    "The majority of the developers' lives in the time zones +0300 and +0400. Looking at a time zone map ([source](https://commons.wikimedia.org/wiki/File:Standard_World_Time_Zones.png)) and on the [website](https://www.jetbrains.com/company/contacts/) of the company that mainly maintains the project, we can defer that most of the developers are probably working from Russia.\n",
    "\n",
    "![](resources/developers_habits_timezone.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weekdays with the most commits\n",
    "\n",
    "Next, we want to know on which days the developers are working during the week. We count by the weekdays but avoid sorting the results to keep the order along our categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Monday       38605\n",
       "Tuesday      40611\n",
       "Wednesday    40988\n",
       "Thursday     40487\n",
       "Friday       37459\n",
       "Saturday      6896\n",
       "Sunday        5355\n",
       "Name: day, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commits_per_weekday = git_authors['day'].value_counts(sort=False)\n",
    "commits_per_weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the result as a standard bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1243c152438>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gzH9U0vmSdul/aBER0Ta91FT+0fbDkl4CvIrqdfMn9zesiIhoo16SSmdMk/2BU2x/B1i7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\nUZsklYiIqE2SSkRE1Ob/A3NWpPd4sOKyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1243c0eed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = commits_per_weekday.plot(kind='bar', title=\"Commits per weekday\")\n",
    "ax.set_xlabel('weekday')\n",
    "ax.set_ylabel('# commits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Result:** \n",
    "\n",
    "Most of the commits occur during normal working days. There are just a few commits on weekends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working behavior on weekends\n",
    "We take a look at the type of weekend workers. We want to see, if the commits come from the main developing company JetBrains or from other voluntary contributors that work on the open source project on weekends.\n",
    "\n",
    "As an approximation, we use the domain name that is included in the author's email addresses to decide if an author is an employee of JetBrains or not. We use a separate DataFrame `weekend_workers` for this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>day</th>\n",
       "      <th>email</th>\n",
       "      <th>employee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>kirill.kirichenko@jetbrains.com</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>anna.kozlova@jetbrains.com</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>vladimir.krivosheev@jetbrains.com</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day                              email  employee\n",
       "0  Thursday    kirill.kirichenko@jetbrains.com      True\n",
       "1  Thursday         anna.kozlova@jetbrains.com      True\n",
       "2  Thursday  vladimir.krivosheev@jetbrains.com      True\n",
       "3  Thursday  vladimir.krivosheev@jetbrains.com      True\n",
       "4  Thursday  vladimir.krivosheev@jetbrains.com      True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekend_workers = git_authors[['day', 'email']].copy()\n",
    "weekend_workers['employee'] = weekend_workers['email'].str.lower().str.endswith(\"@jetbrains.com\")\n",
    "weekend_workers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We group and count the weekdays and employee information and store the result in the new DataFrame `commits_per_weekday_employee`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">email</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>6428</td>\n",
       "      <td>32177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>6411</td>\n",
       "      <td>34200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>6515</td>\n",
       "      <td>34473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>6658</td>\n",
       "      <td>33829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>5720</td>\n",
       "      <td>31739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>1353</td>\n",
       "      <td>5543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>933</td>\n",
       "      <td>4422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          email       \n",
       "employee  False  True \n",
       "day                   \n",
       "Monday     6428  32177\n",
       "Tuesday    6411  34200\n",
       "Wednesday  6515  34473\n",
       "Thursday   6658  33829\n",
       "Friday     5720  31739\n",
       "Saturday   1353   5543\n",
       "Sunday      933   4422"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commits_per_weekday_employee = weekend_workers.groupby(['day', 'employee']).count().unstack()\n",
    "commits_per_weekday_employee"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to spot differences more easily, we calculate the ratio between the employed developers and all developers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">email</th>\n",
       "      <th>employed_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>6428</td>\n",
       "      <td>32177</td>\n",
       "      <td>0.833493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>6411</td>\n",
       "      <td>34200</td>\n",
       "      <td>0.842136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>6515</td>\n",
       "      <td>34473</td>\n",
       "      <td>0.841051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>6658</td>\n",
       "      <td>33829</td>\n",
       "      <td>0.835552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>5720</td>\n",
       "      <td>31739</td>\n",
       "      <td>0.847300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>1353</td>\n",
       "      <td>5543</td>\n",
       "      <td>0.803799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>933</td>\n",
       "      <td>4422</td>\n",
       "      <td>0.825770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          email        employed_ratio\n",
       "employee  False   True               \n",
       "day                                  \n",
       "Monday     6428  32177       0.833493\n",
       "Tuesday    6411  34200       0.842136\n",
       "Wednesday  6515  34473       0.841051\n",
       "Thursday   6658  33829       0.835552\n",
       "Friday     5720  31739       0.847300\n",
       "Saturday   1353   5543       0.803799\n",
       "Sunday      933   4422       0.825770"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commits_per_weekday_employee['employed_ratio'] = \\\n",
    "  commits_per_weekday_employee['email'][True] / \\\n",
    "    commits_per_weekday_employee['email'].sum(axis=1)\n",
    "commits_per_weekday_employee"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot this new information in a second bar chart to see possible differences in the committing behavior between IntelliJ employees and other contributors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1243c18a710>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZHREhqRWOZ0l6b9VFdeBRSX9POlnE65XO5fnCimsq40P571srrWL0lgI/kzQP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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1243c224908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = commits_per_weekday_employee['employed_ratio'].plot(\n",
    "    kind='bar', color='g', title=\"Ratio of commits from employed authors\")\n",
    "ax.set_xlabel(\"weekdays\")\n",
    "ax.set_xlabel(\"ratio\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Result**\n",
    "\n",
    "There is only a slight, non-significant difference between the ratio of employed and non-employed contributors on weekends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usual working hours\n",
    "To find out about the working habits of the contributors, we group the commits by `hour` and count the entries (in this case we choose `author`) to see if there are any irregularities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour\n",
       "0    2083\n",
       "1    1397\n",
       "2     825\n",
       "3     485\n",
       "4     311\n",
       "Name: author, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_hours = git_authors.groupby(['hour'])['author'].count()\n",
    "working_hours.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we plot the results with a standard bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1243e072da0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EfKUdiZiZWedr5or6ccCXgB1Y/4p6X7tiZmbraWb460cU/z7YV9SbmVldzRSVVRExu+WZ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1243e085c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = working_hours.plot(kind='bar')\n",
    "ax.set_title(\"Distribution of working hours\")\n",
    "ax.yaxis.set_label_text(\"# commits\")\n",
    "ax.xaxis.set_label_text(\"hour\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Result**\n",
    "\n",
    "The distribution of the working hours is a nice Gaussian distribution. It seems that this open source project is mainly developed by developers in their full-time jobs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Signs of overtime \n",
    "\n",
    "At last, we have a look at possible overtime periods. For this, we first group all commits on a weekly basis per authors. As grouping function, we choose `max()` to get the hour where each author committed at latest per week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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></th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp_local</th>\n",
       "      <th>author</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-11-07</th>\n",
       "      <th>Olesya Smirnova</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2004-11-14</th>\n",
       "      <th>Alexey Kudravtsev</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olesya Smirnova</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2004-11-21</th>\n",
       "      <th>Anna Kozlova</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olesya Smirnova</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   hour\n",
       "timestamp_local author                 \n",
       "2004-11-07      Olesya Smirnova      20\n",
       "2004-11-14      Alexey Kudravtsev    16\n",
       "                Olesya Smirnova      22\n",
       "2004-11-21      Anna Kozlova         21\n",
       "                Olesya Smirnova      19"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_hour_per_week  = git_authors.groupby(\n",
    "    [\n",
    "        pd.Grouper(key='timestamp_local', freq='1w'), \n",
    "        'author']\n",
    ")[['hour']].max()\n",
    "\n",
    "latest_hour_per_week.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to know if there were any stressful time periods that forced the developers to work overtime over a longer period of time. We calculate the mean of all late stays of all authors for each week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp_local</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-11-07</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-11-14</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-11-21</th>\n",
       "      <td>13.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-05</th>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-12</th>\n",
       "      <td>15.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      hour\n",
       "timestamp_local           \n",
       "2004-11-07       20.000000\n",
       "2004-11-14       19.000000\n",
       "2004-11-21       13.666667\n",
       "2004-12-05       16.666667\n",
       "2004-12-12       15.333333"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_latest_hours_per_week = latest_hour_per_week.reset_index().groupby('timestamp_local').mean()\n",
    "mean_latest_hours_per_week.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also create a trend line that shows how the contributors are working over the span of the past years. We use the `polyfit` function from `numpy` for this which needs a numeric index to calculate the polynomial coefficients later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 684)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_index = range(0, len(mean_latest_hours_per_week.index.week))\n",
    "numeric_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then calculate the coeffiecients with a three-dimensional polynomial based on the hours of the `mean_latest_hours_per_week` DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.35626413e-08,   2.54703899e-05,  -9.85330587e-03,\n",
       "         2.02637516e+01])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "coefficients = np.polyfit(numeric_index, mean_latest_hours_per_week.hour, 3)\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For visualization, we decrease the number of degrees and calculate the y-coordinates for all weeks that are encoded in `numeric_index`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20.2637516 ,  20.25392374,  20.24414669,  20.23442028,  20.2247444 ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polynomial = np.poly1d(coefficients)\n",
    "ys = polynomial(numeric_index)\n",
    "ys[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At last, we plot the results of the `mean_latest_hours_per_week` DataFrame as well as the trend line in one line plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1243ddaae80>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u7tjUa/UiBU5QPqWOdkRYN4XGO+XOnTuIxWK4fPnyQVeFOMJ0tQjThSOPHz/u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\nHzPbqZbI+FwEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1243df05a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = mean_latest_hours_per_week['hour'].plot(\n",
    "    figsize=(10, 6), color='k', alpha=0.2, title=\"Late hours per weeks\")\n",
    "ax.set_xlabel(\"time\")\n",
    "ax.set_ylabel(\"hour\")\n",
    "ax.plot(mean_latest_hours_per_week.index, ys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Result**\n",
    "\n",
    "We can see that the late hours are decreasing over time (almost two hours during the last 12 years). There is no sign of regular death marches. In general, this could be a good sign, leading to a work-life balance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Closing remarks\n",
    "\n",
    "We've seen that various metrics and results can be easily created from a simple Git log output file. With Pandas, it's possible to get to know the habits of the developers of software projects."
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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