notebooks/PandasKickoff.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring data using Pandas\n",
    "\n",
    "![Pandas](pandas.jpg)\n",
    "\n",
    "So far we explored Python and a few native libraries. Now we will play a little to simplify our life with tools to conduct some **data analysis**.\n",
    "\n",
    "**Pandas** is the most popular library (so far) to import and handle data in Python.\n",
    "\n",
    "### Let's import some data from a CSV file\n",
    "\n",
    "**When downloading my ipynb, remember to also get the `commits_pr.csv` file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "cpr = pandas.read_csv(\"commits_pr.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It became this easy to read a CSV file!!!\n",
    "And more... Look at what my `cpr` is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes! A DataFrame. And it reads really nice, look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "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>user</th>\n",
       "      <th>project_name</th>\n",
       "      <th>prog_lang</th>\n",
       "      <th>pull_req_number</th>\n",
       "      <th>num_commits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>user1</td>\n",
       "      <td>php-src</td>\n",
       "      <td>C</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user2</td>\n",
       "      <td>activeadmin</td>\n",
       "      <td>ruby</td>\n",
       "      <td>3325</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>user3</td>\n",
       "      <td>YouCompleteMe</td>\n",
       "      <td>python</td>\n",
       "      <td>2128</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>user4</td>\n",
       "      <td>requests</td>\n",
       "      <td>python</td>\n",
       "      <td>2663</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>user5</td>\n",
       "      <td>ipython</td>\n",
       "      <td>python</td>\n",
       "      <td>7901</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>user6</td>\n",
       "      <td>haste-compiler</td>\n",
       "      <td>haskell</td>\n",
       "      <td>407</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>user7</td>\n",
       "      <td>select2</td>\n",
       "      <td>javascript</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>user8</td>\n",
       "      <td>django</td>\n",
       "      <td>python</td>\n",
       "      <td>8608</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>user9</td>\n",
       "      <td>folly</td>\n",
       "      <td>C++</td>\n",
       "      <td>206</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>user10</td>\n",
       "      <td>django</td>\n",
       "      <td>python</td>\n",
       "      <td>4745</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>user11</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>5561</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>user12</td>\n",
       "      <td>Chart.js</td>\n",
       "      <td>javascript</td>\n",
       "      <td>2116</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>user13</td>\n",
       "      <td>react</td>\n",
       "      <td>javascript</td>\n",
       "      <td>3753</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>user14</td>\n",
       "      <td>codecombat</td>\n",
       "      <td>coffeescript</td>\n",
       "      <td>348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>user14</td>\n",
       "      <td>duckduckgo</td>\n",
       "      <td>perl</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user    project_name     prog_lang  pull_req_number  num_commits\n",
       "0    user1         php-src             C              122            1\n",
       "1    user2     activeadmin          ruby             3325            1\n",
       "2    user3   YouCompleteMe        python             2128            2\n",
       "3    user4        requests        python             2663            1\n",
       "4    user5         ipython        python             7901            1\n",
       "5    user6  haste-compiler       haskell              407            1\n",
       "6    user7         select2    javascript             1987            1\n",
       "7    user8          django        python             8608            3\n",
       "8    user9           folly           C++              206            1\n",
       "9   user10          django        python             4745            2\n",
       "10  user11   homebrew-cask          ruby             5561            1\n",
       "11  user12        Chart.js    javascript             2116            1\n",
       "12  user13           react    javascript             3753          115\n",
       "13  user14      codecombat  coffeescript              348            1\n",
       "14  user14      duckduckgo          perl               30            1"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.head(15)\n",
    "### We can use head() and tail() functions to see a bit less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before moving forward... Explaining a little about this dataset.\n",
    "\n",
    "This dataset represents a series of Pull Requests made to a subset of projects hosted by GitHub. We worked on this data to capture a specific type of contributor, which we called *casual contributor*. These contributors are known by having a single pull request accepted in a project and not coming back (i.e., they have no long-term commitment to the project).\n",
    "\n",
    "In this specific dataset, you will find the following columns:\n",
    "\n",
    "* `user`: represent a user on GitHub (anonymized here)\n",
    "* `project_name`: the name of GitHub project in which the pull request was accepted\n",
    "* `prog_lang`: programming language of the project\n",
    "* `pull_req_num`: unique identifier of the pull request\n",
    "* `num_commits`: number of commits sent within that specific pull request\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some information about the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimensions/shape of the dataset (lines vs. columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42092, 5)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the column names?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user', 'project_name', 'prog_lang', 'pull_req_number', 'num_commits'], dtype='object')"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the datatype per column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user               object\n",
       "project_name       object\n",
       "prog_lang          object\n",
       "pull_req_number     int64\n",
       "num_commits         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some more information: `info()` method prints information including the index dtype and column dtypes, non-null values and memory usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42092 entries, 0 to 42091\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   user             42092 non-null  object\n",
      " 1   project_name     42092 non-null  object\n",
      " 2   prog_lang        42092 non-null  object\n",
      " 3   pull_req_number  42092 non-null  int64 \n",
      " 4   num_commits      42092 non-null  int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "cpr.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the type of a specific column???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cpr[\"num_commits\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A *serie* is a list, with one dimension, indexed. Each column of a dataframe is a series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before moving ahead, we can use the types to filter some columns. \n",
    "\n",
    "Let's say we want only the columns that store `int`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pull_req_number', 'num_commits'], dtype='object')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_columns = cpr.dtypes[cpr.dtypes == \"int64\"].index\n",
    "int_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now... I just want to see these columns... **BOOM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "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>pull_req_number</th>\n",
       "      <th>num_commits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3325</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2128</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2663</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7901</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pull_req_number  num_commits\n",
       "0              122            1\n",
       "1             3325            1\n",
       "2             2128            2\n",
       "3             2663            1\n",
       "4             7901            1"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr[int_columns].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What about statistical information about my DataFrame?\n",
    "\n",
    "`describe()` method provides a summary of numeric values in your dataset: mean, standard deviation, minimum, maximum, 1st quartile, 2nd quartile (median), 3rd quartile of the columns with numeric values. It also counts the number of variables in the dataset (are there missing variables?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>pull_req_number</th>\n",
       "      <th>num_commits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42092.000000</td>\n",
       "      <td>42092.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4452.145681</td>\n",
       "      <td>3.824242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6152.304478</td>\n",
       "      <td>20.760123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>628.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5534.250000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38174.000000</td>\n",
       "      <td>385.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pull_req_number   num_commits\n",
       "count     42092.000000  42092.000000\n",
       "mean       4452.145681      3.824242\n",
       "std        6152.304478     20.760123\n",
       "min           1.000000      1.000000\n",
       "25%         628.000000      1.000000\n",
       "50%        2007.000000      1.000000\n",
       "75%        5534.250000      2.000000\n",
       "max       38174.000000    385.000000"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do it for a Series..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    42092.000000\n",
       "mean         3.824242\n",
       "std         20.760123\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          2.000000\n",
       "max        385.000000\n",
       "Name: num_commits, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cpr[\"num_commits\"].describe()\n",
    "cpr.num_commits.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     42092\n",
       "unique       17\n",
       "top        ruby\n",
       "freq       8147\n",
       "Name: prog_lang, dtype: object"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LOOK at this with a non-numeric column\n",
    "cpr.prog_lang.describe() #either way work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can get specific information per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.num_commits.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8242421362729258"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.num_commits.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.76012335707578"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.num_commits.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------####\n",
    "### Playing with the data: sorting\n",
    "\n",
    "We can sort our data easily using pandas.\n",
    "\n",
    "In this example, sorting by Programming Language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpr.sort_values(\"pull_req_number\", ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sort using *many columns*, by using a list (sort will happen from the first item to the last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>user</th>\n",
       "      <th>project_name</th>\n",
       "      <th>prog_lang</th>\n",
       "      <th>pull_req_number</th>\n",
       "      <th>num_commits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29147</th>\n",
       "      <td>user25542</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38174</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40843</th>\n",
       "      <td>user35812</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38168</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21764</th>\n",
       "      <td>user19011</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38167</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30069</th>\n",
       "      <td>user26309</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38144</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37715</th>\n",
       "      <td>user33014</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35947</th>\n",
       "      <td>user31454</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9231</th>\n",
       "      <td>user8106</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38128</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34552</th>\n",
       "      <td>user30231</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38121</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12884</th>\n",
       "      <td>user11308</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5483</th>\n",
       "      <td>user4826</td>\n",
       "      <td>homebrew-cask</td>\n",
       "      <td>ruby</td>\n",
       "      <td>38073</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user   project_name prog_lang  pull_req_number  num_commits\n",
       "29147  user25542  homebrew-cask      ruby            38174            1\n",
       "40843  user35812  homebrew-cask      ruby            38168            1\n",
       "21764  user19011  homebrew-cask      ruby            38167            2\n",
       "30069  user26309  homebrew-cask      ruby            38144            1\n",
       "37715  user33014  homebrew-cask      ruby            38136            1\n",
       "35947  user31454  homebrew-cask      ruby            38135            1\n",
       "9231    user8106  homebrew-cask      ruby            38128            1\n",
       "34552  user30231  homebrew-cask      ruby            38121            4\n",
       "12884  user11308  homebrew-cask      ruby            38086            1\n",
       "5483    user4826  homebrew-cask      ruby            38073            1"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to keep the sorted version, you can use the parameter `inplace`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpr.sort_values([\"num_commits\"], ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\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>user</th>\n",
       "      <th>project_name</th>\n",
       "      <th>prog_lang</th>\n",
       "      <th>pull_req_number</th>\n",
       "      <th>num_commits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>user34165</td>\n",
       "      <td>three.js</td>\n",
       "      <td>javascript</td>\n",
       "      <td>7832</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>user640</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>6866</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7335</th>\n",
       "      <td>user6426</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3506</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19587</th>\n",
       "      <td>user17126</td>\n",
       "      <td>jenkins</td>\n",
       "      <td>java</td>\n",
       "      <td>2718</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35826</th>\n",
       "      <td>user31347</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3230</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13300</th>\n",
       "      <td>user11672</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>16576</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3601</th>\n",
       "      <td>user3214</td>\n",
       "      <td>three.js</td>\n",
       "      <td>javascript</td>\n",
       "      <td>7808</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13873</th>\n",
       "      <td>user12167</td>\n",
       "      <td>spring-framework</td>\n",
       "      <td>java</td>\n",
       "      <td>642</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26360</th>\n",
       "      <td>user23077</td>\n",
       "      <td>Faker</td>\n",
       "      <td>php</td>\n",
       "      <td>660</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>user16293</td>\n",
       "      <td>libgdx</td>\n",
       "      <td>java</td>\n",
       "      <td>814</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user      project_name   prog_lang  pull_req_number  num_commits\n",
       "38987  user34165          three.js  javascript             7832          385\n",
       "705      user640         cocos2d-x         C++             6866          364\n",
       "7335    user6426             redis           C             3506          315\n",
       "19587  user17126           jenkins        java             2718          307\n",
       "35826  user31347             redis           C             3230          290\n",
       "13300  user11672         cocos2d-x         C++            16576          281\n",
       "3601    user3214          three.js  javascript             7808          277\n",
       "13873  user12167  spring-framework        java              642          273\n",
       "26360  user23077             Faker         php              660          259\n",
       "18632  user16293            libgdx        java              814          258"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.head(10)\n",
    "#cpr = pandas.read_csv(\"commits_pr.csv\") #--> to return to the original order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting the occurences of variables\n",
    "\n",
    "So, to count the occurrences in a column we have to select the column first, and use the method `value_counts()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ruby            8147\n",
       "javascript      7052\n",
       "python          4092\n",
       "php             4069\n",
       "C++             2785\n",
       "java            2596\n",
       "C               2196\n",
       "go              2103\n",
       "coffeescript    2066\n",
       "scala           1823\n",
       "objective-c     1801\n",
       "haskell          950\n",
       "clojure          882\n",
       "perl             663\n",
       "erlang           500\n",
       "typescript       343\n",
       "Perl              24\n",
       "Name: prog_lang, dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.prog_lang.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But... I just want to know what are the languages out there. Is there a way?\n",
    "\n",
    "*Always*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['javascript', 'C++', 'C', 'java', 'php', 'objective-c', 'python',\n",
       "       'ruby', 'typescript', 'go', 'coffeescript', 'scala', 'erlang',\n",
       "       'perl', 'clojure', 'haskell', 'Perl'], dtype=object)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr[\"prog_lang\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OK! Let's do something else... Like, selecting columns and filtering data\n",
    "\n",
    "Let's say that I just want to look at the columns programming language, project name and number of commits. \n",
    "\n",
    "I can select them and create a new DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = [\"prog_lang\", \"project_name\", \"num_commits\"]\n",
    "type(selected_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>prog_lang</th>\n",
       "      <th>project_name</th>\n",
       "      <th>num_commits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>javascript</td>\n",
       "      <td>three.js</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>C++</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7335</th>\n",
       "      <td>C</td>\n",
       "      <td>redis</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19587</th>\n",
       "      <td>java</td>\n",
       "      <td>jenkins</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35826</th>\n",
       "      <td>C</td>\n",
       "      <td>redis</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13300</th>\n",
       "      <td>C++</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3601</th>\n",
       "      <td>javascript</td>\n",
       "      <td>three.js</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13873</th>\n",
       "      <td>java</td>\n",
       "      <td>spring-framework</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26360</th>\n",
       "      <td>php</td>\n",
       "      <td>Faker</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>java</td>\n",
       "      <td>libgdx</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prog_lang      project_name  num_commits\n",
       "38987  javascript          three.js          385\n",
       "705           C++         cocos2d-x          364\n",
       "7335            C             redis          315\n",
       "19587        java           jenkins          307\n",
       "35826           C             redis          290\n",
       "13300         C++         cocos2d-x          281\n",
       "3601   javascript          three.js          277\n",
       "13873        java  spring-framework          273\n",
       "26360         php             Faker          259\n",
       "18632        java            libgdx          258"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = [\"prog_lang\", \"project_name\", \"num_commits\"]\n",
    "my_subset = cpr[selected_columns]\n",
    "my_subset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if now I want to filter those projects written in `C` language?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>pull_req_number</th>\n",
       "      <th>num_commits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1305.000000</td>\n",
       "      <td>1305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2852.606897</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2889.262660</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>544.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4205.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12987.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pull_req_number  num_commits\n",
       "count      1305.000000       1305.0\n",
       "mean       2852.606897          1.0\n",
       "std        2889.262660          0.0\n",
       "min           1.000000          1.0\n",
       "25%         544.000000          1.0\n",
       "50%        1872.000000          1.0\n",
       "75%        4205.000000          1.0\n",
       "max       12987.000000          1.0"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_C = cpr[(cpr[\"prog_lang\"]=='C') & (cpr[\"num_commits\"]==1)]\n",
    "only_C.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can filter whatever we want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>pull_req_number</th>\n",
       "      <th>num_commits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29655.000000</td>\n",
       "      <td>29655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4183.982769</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6036.266784</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>566.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1768.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4969.500000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38174.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pull_req_number  num_commits\n",
       "count     29655.000000      29655.0\n",
       "mean       4183.982769          1.0\n",
       "std        6036.266784          0.0\n",
       "min           1.000000          1.0\n",
       "25%         566.000000          1.0\n",
       "50%        1768.000000          1.0\n",
       "75%        4969.500000          1.0\n",
       "max       38174.000000          1.0"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_commit = cpr[cpr[\"num_commits\"] == 1]\n",
    "single_commit.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create filters in variables, and use whenever we want, as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_commit = cpr[\"num_commits\"]==1\n",
    "language_C = cpr[\"prog_lang\"]==\"C\"\n",
    "multi_commit = cpr[\"num_commits\"]>1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>project_name</th>\n",
       "      <th>prog_lang</th>\n",
       "      <th>pull_req_number</th>\n",
       "      <th>num_commits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4141</th>\n",
       "      <td>user3682</td>\n",
       "      <td>the_silver_searcher</td>\n",
       "      <td>C</td>\n",
       "      <td>948</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36595</th>\n",
       "      <td>user32015</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>941</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41821</th>\n",
       "      <td>user36684</td>\n",
       "      <td>memcached</td>\n",
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       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18610</th>\n",
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       "      <td>jq</td>\n",
       "      <td>C</td>\n",
       "      <td>944</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <td>user2381</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34724</th>\n",
       "      <td>user30381</td>\n",
       "      <td>gumbo-parser</td>\n",
       "      <td>C</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7614</th>\n",
       "      <td>user6673</td>\n",
       "      <td>scikit-learn</td>\n",
       "      <td>C</td>\n",
       "      <td>945</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14865</th>\n",
       "      <td>user13010</td>\n",
       "      <td>the_silver_searcher</td>\n",
       "      <td>C</td>\n",
       "      <td>957</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5910</th>\n",
       "      <td>user5193</td>\n",
       "      <td>scikit-learn</td>\n",
       "      <td>C</td>\n",
       "      <td>956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26546</th>\n",
       "      <td>user23246</td>\n",
       "      <td>masscan</td>\n",
       "      <td>C</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user         project_name prog_lang  pull_req_number  num_commits\n",
       "4141    user3682  the_silver_searcher         C              948            1\n",
       "36595  user32015                redis         C              941            1\n",
       "41821  user36684            memcached         C               69            1\n",
       "18610  user16272                   jq         C              944            1\n",
       "2652    user2381                redis         C               69            1\n",
       "34724  user30381         gumbo-parser         C               69            1\n",
       "7614    user6673         scikit-learn         C              945            1\n",
       "14865  user13010  the_silver_searcher         C              957            1\n",
       "5910    user5193         scikit-learn         C              956            1\n",
       "26546  user23246              masscan         C               66            1"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr[one_commit & language_C].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And... we can use OR (|) and AND(&) to play!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>36595</th>\n",
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       "      <td>redis</td>\n",
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       "      <th>7614</th>\n",
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       "      <td>1</td>\n",
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       "      <th>14865</th>\n",
       "      <td>user13010</td>\n",
       "      <td>the_silver_searcher</td>\n",
       "      <td>C</td>\n",
       "      <td>957</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5910</th>\n",
       "      <td>user5193</td>\n",
       "      <td>scikit-learn</td>\n",
       "      <td>C</td>\n",
       "      <td>956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26546</th>\n",
       "      <td>user23246</td>\n",
       "      <td>masscan</td>\n",
       "      <td>C</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user         project_name prog_lang  pull_req_number  num_commits\n",
       "4141    user3682  the_silver_searcher         C              948            1\n",
       "36595  user32015                redis         C              941            1\n",
       "41821  user36684            memcached         C               69            1\n",
       "18610  user16272                   jq         C              944            1\n",
       "2652    user2381                redis         C               69            1\n",
       "34724  user30381         gumbo-parser         C               69            1\n",
       "7614    user6673         scikit-learn         C              945            1\n",
       "14865  user13010  the_silver_searcher         C              957            1\n",
       "5910    user5193         scikit-learn         C              956            1\n",
       "26546  user23246              masscan         C               66            1"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr[one_commit & language_C].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What if we want the pull requests with more than one commit for the projects written in \"C\" + those with 2 commits for the projects written in \"typescript\"???\n",
    "\n",
    "Let's do it!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7335</th>\n",
       "      <td>user6426</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3506</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35826</th>\n",
       "      <td>user31347</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3230</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30507</th>\n",
       "      <td>user26699</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3759</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>user271</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3633</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22522</th>\n",
       "      <td>user19679</td>\n",
       "      <td>php-src</td>\n",
       "      <td>C</td>\n",
       "      <td>2571</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2396</th>\n",
       "      <td>user2153</td>\n",
       "      <td>godot</td>\n",
       "      <td>C</td>\n",
       "      <td>6557</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36710</th>\n",
       "      <td>user32118</td>\n",
       "      <td>php-src</td>\n",
       "      <td>C</td>\n",
       "      <td>328</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20127</th>\n",
       "      <td>user17580</td>\n",
       "      <td>scikit-learn</td>\n",
       "      <td>C</td>\n",
       "      <td>6200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26014</th>\n",
       "      <td>user22777</td>\n",
       "      <td>scikit-learn</td>\n",
       "      <td>C</td>\n",
       "      <td>7111</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28583</th>\n",
       "      <td>user25031</td>\n",
       "      <td>scikit-learn</td>\n",
       "      <td>C</td>\n",
       "      <td>6758</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>953 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            user  project_name prog_lang  pull_req_number  num_commits\n",
       "7335    user6426         redis         C             3506          315\n",
       "35826  user31347         redis         C             3230          290\n",
       "30507  user26699         redis         C             3759          253\n",
       "297      user271         redis         C             3633          252\n",
       "22522  user19679       php-src         C             2571          251\n",
       "...          ...           ...       ...              ...          ...\n",
       "2396    user2153         godot         C             6557            2\n",
       "36710  user32118       php-src         C              328            2\n",
       "20127  user17580  scikit-learn         C             6200            2\n",
       "26014  user22777  scikit-learn         C             7111            2\n",
       "28583  user25031  scikit-learn         C             6758            2\n",
       "\n",
       "[953 rows x 5 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_commits = cpr[\"num_commits\"]==2\n",
    "typescript = cpr[\"prog_lang\"]==\"typescript\"\n",
    "\n",
    "cpr[(multi_commit & language_C) | (two_commits & typescript)]\n",
    "\n",
    "cpr[((cpr[\"num_commits\"]>1) & (cpr[\"prog_lang\"]==\"C\")) | ((cpr[\"num_commits\"]==2) & (cpr[\"prog_lang\"]==\"typescript\"))]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if I wanted to convert number of commits into a feature by creating bands of values that we define:\n",
    "* 1 commit = group 1\n",
    "* 2 - 5 commits = group 2\n",
    "* 6 - 20 commits = group 3\n",
    "*  more than 20 = group 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "commits_from_2_to_5 = (cpr[\"num_commits\"]<=5) & (cpr[\"num_commits\"]>=2)\n",
    "\n",
    "cpr.loc[one_commit, \"group_commit\"]=1\n",
    "cpr.loc[commits_from_2_to_5, \"group_commit\"]=2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>38987</th>\n",
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       "      <td>user640</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>6866</td>\n",
       "      <td>364</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7335</th>\n",
       "      <td>user6426</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3506</td>\n",
       "      <td>315</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19587</th>\n",
       "      <td>user17126</td>\n",
       "      <td>jenkins</td>\n",
       "      <td>java</td>\n",
       "      <td>2718</td>\n",
       "      <td>307</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35826</th>\n",
       "      <td>user31347</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3230</td>\n",
       "      <td>290</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user project_name   prog_lang  pull_req_number  num_commits  \\\n",
       "38987  user34165     three.js  javascript             7832          385   \n",
       "705      user640    cocos2d-x         C++             6866          364   \n",
       "7335    user6426        redis           C             3506          315   \n",
       "19587  user17126      jenkins        java             2718          307   \n",
       "35826  user31347        redis           C             3230          290   \n",
       "\n",
       "       group_commit  \n",
       "38987           NaN  \n",
       "705             NaN  \n",
       "7335            NaN  \n",
       "19587           NaN  \n",
       "35826           NaN  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I challenge you:\n",
    "\n",
    "What if: I wanted to know what is the average of num_commits for those pull requests in group_commit 2???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.62746814512207"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_two = cpr[\"group_commit\"]==2\n",
    "group_two_dataframe = cpr[group_two]\n",
    "group_two_dataframe.num_commits.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I challenge you (2):\n",
    "\n",
    "Can you do that average per language?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "javascript: \t4.074305161656268\n",
      "C++: \t8.20754039497307\n",
      "C: \t6.885245901639344\n",
      "java: \t5.972265023112481\n",
      "php: \t5.163430818382895\n",
      "objective-c: \t3.425319267073848\n",
      "python: \t2.5386119257087\n",
      "ruby: \t2.2452436479685773\n",
      "typescript: \t4.766763848396502\n",
      "go: \t2.2030432715168806\n",
      "coffeescript: \t2.304937076476283\n",
      "scala: \t2.571036752605595\n",
      "erlang: \t2.708\n",
      "perl: \t2.6561085972850678\n",
      "clojure: \t1.9682539682539681\n",
      "haskell: \t2.4063157894736844\n",
      "Perl: \t1.7916666666666667\n"
     ]
    }
   ],
   "source": [
    "for lang in cpr[\"prog_lang\"].unique():\n",
    "    data = cpr[cpr[\"prog_lang\"] == lang]\n",
    "    print(lang + \": \\t\" + str(data.num_commits.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">pull_req_number</th>\n",
       "      <th colspan=\"8\" halign=\"left\">num_commits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prog_lang</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2196.0</td>\n",
       "      <td>3340.718124</td>\n",
       "      <td>3113.785559</td>\n",
       "      <td>1.0</td>\n",
       "      <td>704.75</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>5143.50</td>\n",
       "      <td>13040.0</td>\n",
       "      <td>2196.0</td>\n",
       "      <td>6.885246</td>\n",
       "      <td>31.142637</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C++</th>\n",
       "      <td>2785.0</td>\n",
       "      <td>5393.853142</td>\n",
       "      <td>5216.342089</td>\n",
       "      <td>1.0</td>\n",
       "      <td>954.00</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>9090.00</td>\n",
       "      <td>20150.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>8.207540</td>\n",
       "      <td>36.843965</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perl</th>\n",
       "      <td>24.0</td>\n",
       "      <td>66.625000</td>\n",
       "      <td>56.155694</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.75</td>\n",
       "      <td>62.5</td>\n",
       "      <td>118.75</td>\n",
       "      <td>163.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.791667</td>\n",
       "      <td>2.358687</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clojure</th>\n",
       "      <td>882.0</td>\n",
       "      <td>601.233560</td>\n",
       "      <td>659.704443</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.50</td>\n",
       "      <td>297.0</td>\n",
       "      <td>941.75</td>\n",
       "      <td>2324.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1.968254</td>\n",
       "      <td>8.607807</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coffeescript</th>\n",
       "      <td>2066.0</td>\n",
       "      <td>1155.201355</td>\n",
       "      <td>1092.298069</td>\n",
       "      <td>2.0</td>\n",
       "      <td>341.00</td>\n",
       "      <td>808.0</td>\n",
       "      <td>1580.75</td>\n",
       "      <td>4625.0</td>\n",
       "      <td>2066.0</td>\n",
       "      <td>2.304937</td>\n",
       "      <td>10.824595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>erlang</th>\n",
       "      <td>500.0</td>\n",
       "      <td>369.026000</td>\n",
       "      <td>375.595054</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.50</td>\n",
       "      <td>248.0</td>\n",
       "      <td>540.50</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>2.708000</td>\n",
       "      <td>13.874427</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go</th>\n",
       "      <td>2103.0</td>\n",
       "      <td>2189.967665</td>\n",
       "      <td>1994.589005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>566.50</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>3448.00</td>\n",
       "      <td>8674.0</td>\n",
       "      <td>2103.0</td>\n",
       "      <td>2.203043</td>\n",
       "      <td>8.442572</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>haskell</th>\n",
       "      <td>950.0</td>\n",
       "      <td>1027.640000</td>\n",
       "      <td>1073.496504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>284.00</td>\n",
       "      <td>553.5</td>\n",
       "      <td>1410.50</td>\n",
       "      <td>4009.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2.406316</td>\n",
       "      <td>10.041597</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>java</th>\n",
       "      <td>2596.0</td>\n",
       "      <td>4886.220724</td>\n",
       "      <td>6770.891969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>563.50</td>\n",
       "      <td>1603.0</td>\n",
       "      <td>5576.75</td>\n",
       "      <td>26501.0</td>\n",
       "      <td>2596.0</td>\n",
       "      <td>5.972265</td>\n",
       "      <td>31.208985</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>javascript</th>\n",
       "      <td>7052.0</td>\n",
       "      <td>4635.527935</td>\n",
       "      <td>3896.028860</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1500.75</td>\n",
       "      <td>3315.0</td>\n",
       "      <td>7329.25</td>\n",
       "      <td>16129.0</td>\n",
       "      <td>7052.0</td>\n",
       "      <td>4.074305</td>\n",
       "      <td>21.794352</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective-c</th>\n",
       "      <td>1801.0</td>\n",
       "      <td>958.897835</td>\n",
       "      <td>946.184484</td>\n",
       "      <td>1.0</td>\n",
       "      <td>241.00</td>\n",
       "      <td>571.0</td>\n",
       "      <td>1502.00</td>\n",
       "      <td>3975.0</td>\n",
       "      <td>1801.0</td>\n",
       "      <td>3.425319</td>\n",
       "      <td>19.272465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perl</th>\n",
       "      <td>663.0</td>\n",
       "      <td>406.612368</td>\n",
       "      <td>313.133880</td>\n",
       "      <td>2.0</td>\n",
       "      <td>134.50</td>\n",
       "      <td>334.0</td>\n",
       "      <td>647.00</td>\n",
       "      <td>1166.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>2.656109</td>\n",
       "      <td>13.405572</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>php</th>\n",
       "      <td>4069.0</td>\n",
       "      <td>3331.454411</td>\n",
       "      <td>3263.961858</td>\n",
       "      <td>1.0</td>\n",
       "      <td>680.00</td>\n",
       "      <td>2377.0</td>\n",
       "      <td>4930.00</td>\n",
       "      <td>14762.0</td>\n",
       "      <td>4069.0</td>\n",
       "      <td>5.163431</td>\n",
       "      <td>24.771018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>4092.0</td>\n",
       "      <td>3026.508309</td>\n",
       "      <td>3073.273573</td>\n",
       "      <td>1.0</td>\n",
       "      <td>719.75</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>4601.00</td>\n",
       "      <td>13940.0</td>\n",
       "      <td>4092.0</td>\n",
       "      <td>2.538612</td>\n",
       "      <td>12.839750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ruby</th>\n",
       "      <td>8147.0</td>\n",
       "      <td>9085.637658</td>\n",
       "      <td>9873.403196</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1390.00</td>\n",
       "      <td>4363.0</td>\n",
       "      <td>15018.50</td>\n",
       "      <td>38174.0</td>\n",
       "      <td>8147.0</td>\n",
       "      <td>2.245244</td>\n",
       "      <td>12.142810</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scala</th>\n",
       "      <td>1823.0</td>\n",
       "      <td>4129.870543</td>\n",
       "      <td>6471.173247</td>\n",
       "      <td>1.0</td>\n",
       "      <td>457.50</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>3692.50</td>\n",
       "      <td>23589.0</td>\n",
       "      <td>1823.0</td>\n",
       "      <td>2.571037</td>\n",
       "      <td>10.647855</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>typescript</th>\n",
       "      <td>343.0</td>\n",
       "      <td>4362.323615</td>\n",
       "      <td>5465.178557</td>\n",
       "      <td>1.0</td>\n",
       "      <td>135.50</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>8213.50</td>\n",
       "      <td>18077.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>4.766764</td>\n",
       "      <td>24.168835</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pull_req_number                                                  \\\n",
       "                       count         mean          std  min      25%     50%   \n",
       "prog_lang                                                                      \n",
       "C                     2196.0  3340.718124  3113.785559  1.0   704.75  2427.0   \n",
       "C++                   2785.0  5393.853142  5216.342089  1.0   954.00  3439.0   \n",
       "Perl                    24.0    66.625000    56.155694  1.0    10.75    62.5   \n",
       "clojure                882.0   601.233560   659.704443  1.0   110.50   297.0   \n",
       "coffeescript          2066.0  1155.201355  1092.298069  2.0   341.00   808.0   \n",
       "erlang                 500.0   369.026000   375.595054  1.0    92.50   248.0   \n",
       "go                    2103.0  2189.967665  1994.589005  1.0   566.50  1530.0   \n",
       "haskell                950.0  1027.640000  1073.496504  1.0   284.00   553.5   \n",
       "java                  2596.0  4886.220724  6770.891969  1.0   563.50  1603.0   \n",
       "javascript            7052.0  4635.527935  3896.028860  1.0  1500.75  3315.0   \n",
       "objective-c           1801.0   958.897835   946.184484  1.0   241.00   571.0   \n",
       "perl                   663.0   406.612368   313.133880  2.0   134.50   334.0   \n",
       "php                   4069.0  3331.454411  3263.961858  1.0   680.00  2377.0   \n",
       "python                4092.0  3026.508309  3073.273573  1.0   719.75  1745.0   \n",
       "ruby                  8147.0  9085.637658  9873.403196  1.0  1390.00  4363.0   \n",
       "scala                 1823.0  4129.870543  6471.173247  1.0   457.50  1367.0   \n",
       "typescript             343.0  4362.323615  5465.178557  1.0   135.50  1257.0   \n",
       "\n",
       "                                num_commits                                 \\\n",
       "                   75%      max       count      mean        std  min  25%   \n",
       "prog_lang                                                                    \n",
       "C              5143.50  13040.0      2196.0  6.885246  31.142637  1.0  1.0   \n",
       "C++            9090.00  20150.0      2785.0  8.207540  36.843965  1.0  1.0   \n",
       "Perl            118.75    163.0        24.0  1.791667   2.358687  1.0  1.0   \n",
       "clojure         941.75   2324.0       882.0  1.968254   8.607807  1.0  1.0   \n",
       "coffeescript   1580.75   4625.0      2066.0  2.304937  10.824595  1.0  1.0   \n",
       "erlang          540.50   1959.0       500.0  2.708000  13.874427  1.0  1.0   \n",
       "go             3448.00   8674.0      2103.0  2.203043   8.442572  1.0  1.0   \n",
       "haskell        1410.50   4009.0       950.0  2.406316  10.041597  1.0  1.0   \n",
       "java           5576.75  26501.0      2596.0  5.972265  31.208985  1.0  1.0   \n",
       "javascript     7329.25  16129.0      7052.0  4.074305  21.794352  1.0  1.0   \n",
       "objective-c    1502.00   3975.0      1801.0  3.425319  19.272465  1.0  1.0   \n",
       "perl            647.00   1166.0       663.0  2.656109  13.405572  1.0  1.0   \n",
       "php            4930.00  14762.0      4069.0  5.163431  24.771018  1.0  1.0   \n",
       "python         4601.00  13940.0      4092.0  2.538612  12.839750  1.0  1.0   \n",
       "ruby          15018.50  38174.0      8147.0  2.245244  12.142810  1.0  1.0   \n",
       "scala          3692.50  23589.0      1823.0  2.571037  10.647855  1.0  1.0   \n",
       "typescript     8213.50  18077.0       343.0  4.766764  24.168835  1.0  1.0   \n",
       "\n",
       "                               \n",
       "              50%  75%    max  \n",
       "prog_lang                      \n",
       "C             1.0  2.0  315.0  \n",
       "C++           1.0  2.0  364.0  \n",
       "Perl          1.0  1.0   12.0  \n",
       "clojure       1.0  1.0  178.0  \n",
       "coffeescript  1.0  2.0  250.0  \n",
       "erlang        1.0  2.0  250.0  \n",
       "go            1.0  2.0  250.0  \n",
       "haskell       1.0  2.0  172.0  \n",
       "java          1.0  2.0  307.0  \n",
       "javascript    1.0  2.0  385.0  \n",
       "objective-c   1.0  2.0  256.0  \n",
       "perl          1.0  2.0  250.0  \n",
       "php           1.0  2.0  259.0  \n",
       "python        1.0  2.0  251.0  \n",
       "ruby          1.0  2.0  250.0  \n",
       "scala         1.0  2.0  250.0  \n",
       "typescript    1.0  2.0  250.0  "
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr.groupby([\"prog_lang\"]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-258-eb310c3d47d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"user\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mdescribe\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   1450\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1451\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_group_selection_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1452\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1453\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1454\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    733\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0moption_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mode.chained_assignment\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    734\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 735\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_python_apply_general\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    736\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    737\u001b[0m                 \u001b[0;31m# gh-20949\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_python_apply_general\u001b[0;34m(self, f)\u001b[0m\n\u001b[1;32m    749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_python_apply_general\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 751\u001b[0;31m         \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmutated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selected_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    752\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m         return self._wrap_applied_output(\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, data, axis)\u001b[0m\n\u001b[1;32m    170\u001b[0m         ):\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m                 \u001b[0mresult_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmutated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msplitter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfast_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mlibreduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidApply\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mfast_apply\u001b[0;34m(self, f, names)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m         \u001b[0msdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_sorted_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlibreduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_frame_axis0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstarts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mends\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_chop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msdata\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/reduction.pyx\u001b[0m in \u001b[0;36mpandas._libs.reduction.apply_frame_axis0\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1450\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1451\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_group_selection_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1452\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1453\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1454\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdescribe\u001b[0;34m(self, percentiles, include, exclude)\u001b[0m\n\u001b[1;32m   9938\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9940\u001b[0;31m         \u001b[0mldesc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdescribe_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9941\u001b[0m         \u001b[0;31m# set a convenient order for rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9942\u001b[0m         \u001b[0mnames\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   9938\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9940\u001b[0;31m         \u001b[0mldesc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdescribe_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9941\u001b[0m         \u001b[0;31m# set a convenient order for rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9942\u001b[0m         \u001b[0mnames\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdescribe_1d\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m   9917\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mdescribe_categorical_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9918\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mis_numeric_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9919\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mdescribe_numeric_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9920\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mis_timedelta64_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9921\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mdescribe_numeric_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdescribe_numeric_1d\u001b[0;34m(series)\u001b[0m\n\u001b[1;32m   9871\u001b[0m                 \u001b[0;34m[\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9872\u001b[0m                 \u001b[0;34m+\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpercentiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9873\u001b[0;31m                 \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9874\u001b[0m             )\n\u001b[1;32m   9875\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstat_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, q, interpolation)\u001b[0m\n\u001b[1;32m   2203\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFloat64Index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m             \u001b[0;31m# scalar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    251\u001b[0m                     \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mis_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, index, **kwargs)\u001b[0m\n\u001b[1;32m   4028\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4029\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4030\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4031\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4032\u001b[0m     def drop(\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   4529\u001b[0m         if all(\n\u001b[1;32m   4530\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4531\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4532\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4533\u001b[0m         ):\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   4530\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4531\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4532\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4533\u001b[0m         ):\n\u001b[1;32m   4534\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36midentical\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   4087\u001b[0m         \"\"\"\n\u001b[1;32m   4088\u001b[0m         return (\n\u001b[0;32m-> 4089\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4090\u001b[0m             and all(\n\u001b[1;32m   4091\u001b[0m                 (\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/numeric.py\u001b[0m in \u001b[0;36mequals\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    470\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    471\u001b[0m             \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ndarray_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ndarray_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 472\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_isnan\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_isnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    473\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    474\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_isnan\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1759\u001b[0m         \"\"\"\n\u001b[1;32m   1760\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_na\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1761\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1762\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1763\u001b[0m             \u001b[0;31m# shouldn't reach to this condition by checking hasnans beforehand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/missing.py\u001b[0m in \u001b[0;36misna\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0mName\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m     \"\"\"\n\u001b[0;32m--> 126\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_isna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/missing.py\u001b[0m in \u001b[0;36m_isna_new\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m    150\u001b[0m         ),\n\u001b[1;32m    151\u001b[0m     ):\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_isna_ndarraylike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCGeneric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cpr.groupby([\"user\"]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Some more... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's work with a new dataset... \n",
    "\n",
    "This is not only related to casual contributors, but all contributors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th>num_comments</th>\n",
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       "      <th>52041</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1320472.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5865.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>52036</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
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       "      <td>10000.0</td>\n",
       "      <td>1321513.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5905.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>54883</th>\n",
       "      <td>user_13227</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1549480.0</td>\n",
       "      <td>843841.0</td>\n",
       "      <td>9726.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>52033</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1320976.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5892.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>61760</th>\n",
       "      <td>user_13751</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1163795.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5241.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>52034</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1321296.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5905.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>61813</th>\n",
       "      <td>user_13757</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1324952.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5903.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>52035</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1321419.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5890.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>61841</th>\n",
       "      <td>user_13764</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1539898.0</td>\n",
       "      <td>21568.0</td>\n",
       "      <td>6574.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>54495</th>\n",
       "      <td>user_13188</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1249461.0</td>\n",
       "      <td>1129333.0</td>\n",
       "      <td>8848.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>52031</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1320618.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5887.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>52092</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1069118.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5218.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>52091</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1058251.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5217.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>52090</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1058160.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5217.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>52089</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1226271.0</td>\n",
       "      <td>21708.0</td>\n",
       "      <td>5737.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>52088</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1226229.0</td>\n",
       "      <td>21708.0</td>\n",
       "      <td>5737.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>52087</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1228511.0</td>\n",
       "      <td>21701.0</td>\n",
       "      <td>6558.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>52086</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1058254.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5217.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>52032</th>\n",
       "      <td>user_13168</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1320671.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5887.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55017</th>\n",
       "      <td>user_13245</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1168604.0</td>\n",
       "      <td>24870.0</td>\n",
       "      <td>5248.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             user project_name prog_lang  num_commits  additions  deletions  \\\n",
       "52041  user_13168    cocos2d-x       C++      10000.0  1320472.0    24870.0   \n",
       "52036  user_13168    cocos2d-x       C++      10000.0  1321513.0    24870.0   \n",
       "54883  user_13227    cocos2d-x       C++      10000.0  1549480.0   843841.0   \n",
       "52033  user_13168    cocos2d-x       C++      10000.0  1320976.0    24870.0   \n",
       "61760  user_13751    cocos2d-x       C++      10000.0  1163795.0    24870.0   \n",
       "52034  user_13168    cocos2d-x       C++      10000.0  1321296.0    24870.0   \n",
       "61813  user_13757    cocos2d-x       C++      10000.0  1324952.0    24870.0   \n",
       "52035  user_13168    cocos2d-x       C++      10000.0  1321419.0    24870.0   \n",
       "61841  user_13764    cocos2d-x       C++      10000.0  1539898.0    21568.0   \n",
       "54495  user_13188    cocos2d-x       C++      10000.0  1249461.0  1129333.0   \n",
       "52031  user_13168    cocos2d-x       C++      10000.0  1320618.0    24870.0   \n",
       "52092  user_13168    cocos2d-x       C++      10000.0  1069118.0    24870.0   \n",
       "52091  user_13168    cocos2d-x       C++      10000.0  1058251.0    24870.0   \n",
       "52090  user_13168    cocos2d-x       C++      10000.0  1058160.0    24870.0   \n",
       "52089  user_13168    cocos2d-x       C++      10000.0  1226271.0    21708.0   \n",
       "52088  user_13168    cocos2d-x       C++      10000.0  1226229.0    21708.0   \n",
       "52087  user_13168    cocos2d-x       C++      10000.0  1228511.0    21701.0   \n",
       "52086  user_13168    cocos2d-x       C++      10000.0  1058254.0    24870.0   \n",
       "52032  user_13168    cocos2d-x       C++      10000.0  1320671.0    24870.0   \n",
       "55017  user_13245    cocos2d-x       C++      10000.0  1168604.0    24870.0   \n",
       "\n",
       "       files_changed  num_comments  \n",
       "52041         5865.0           0.0  \n",
       "52036         5905.0           0.0  \n",
       "54883         9726.0           0.0  \n",
       "52033         5892.0           0.0  \n",
       "61760         5241.0           0.0  \n",
       "52034         5905.0           0.0  \n",
       "61813         5903.0           0.0  \n",
       "52035         5890.0           0.0  \n",
       "61841         6574.0           0.0  \n",
       "54495         8848.0           0.0  \n",
       "52031         5887.0           0.0  \n",
       "52092         5218.0           0.0  \n",
       "52091         5217.0           2.0  \n",
       "52090         5217.0           0.0  \n",
       "52089         5737.0           0.0  \n",
       "52088         5737.0           0.0  \n",
       "52087         6558.0           0.0  \n",
       "52086         5217.0           0.0  \n",
       "52032         5887.0           0.0  \n",
       "55017         5248.0           0.0  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commits_complete = pandas.read_csv('commit_complete.csv')\n",
    "commits_complete.sort_values('num_commits', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2448588093221469"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commits_complete['deletions'].corr(commits_complete['additions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "               num_commits  additions  deletions  files_changed  num_comments\n",
       "num_commits       1.000000   0.657321   0.151074       0.605152     -0.007297\n",
       "additions         0.657321   1.000000   0.244859       0.749543     -0.002682\n",
       "deletions         0.151074   0.244859   1.000000       0.566905      0.011876\n",
       "files_changed     0.605152   0.749543   0.566905       1.000000      0.003657\n",
       "num_comments     -0.007297  -0.002682   0.011876       0.003657      1.000000"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commits_complete.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
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       "        }    #T_b4ec94e8_14ad_11eb_930b_acde48001122row4_col1 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_b4ec94e8_14ad_11eb_930b_acde48001122row4_col2 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_b4ec94e8_14ad_11eb_930b_acde48001122row4_col3 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_b4ec94e8_14ad_11eb_930b_acde48001122row4_col4 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >num_commits</th>        <th class=\"col_heading level0 col1\" >additions</th>        <th class=\"col_heading level0 col2\" >deletions</th>        <th class=\"col_heading level0 col3\" >files_changed</th>        <th class=\"col_heading level0 col4\" >num_comments</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122level0_row0\" class=\"row_heading level0 row0\" >num_commits</th>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row0_col1\" class=\"data row0 col1\" >0.657321</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row0_col2\" class=\"data row0 col2\" >0.151074</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row0_col3\" class=\"data row0 col3\" >0.605152</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row0_col4\" class=\"data row0 col4\" >-0.007297</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122level0_row1\" class=\"row_heading level0 row1\" >additions</th>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row1_col0\" class=\"data row1 col0\" >0.657321</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row1_col2\" class=\"data row1 col2\" >0.244859</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row1_col3\" class=\"data row1 col3\" >0.749543</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row1_col4\" class=\"data row1 col4\" >-0.002682</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122level0_row2\" class=\"row_heading level0 row2\" >deletions</th>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row2_col0\" class=\"data row2 col0\" >0.151074</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row2_col1\" class=\"data row2 col1\" >0.244859</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row2_col3\" class=\"data row2 col3\" >0.566905</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row2_col4\" class=\"data row2 col4\" >0.011876</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122level0_row3\" class=\"row_heading level0 row3\" >files_changed</th>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row3_col0\" class=\"data row3 col0\" >0.605152</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row3_col1\" class=\"data row3 col1\" >0.749543</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row3_col2\" class=\"data row3 col2\" >0.566905</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row3_col4\" class=\"data row3 col4\" >0.003657</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122level0_row4\" class=\"row_heading level0 row4\" >num_comments</th>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row4_col0\" class=\"data row4 col0\" >-0.007297</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row4_col1\" class=\"data row4 col1\" >-0.002682</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row4_col2\" class=\"data row4 col2\" >0.011876</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row4_col3\" class=\"data row4 col3\" >0.003657</td>\n",
       "                        <td id=\"T_b4ec94e8_14ad_11eb_930b_acde48001122row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc1f8e6dc50>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commits_complete.corr(method='pearson').style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can we play with graphics?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot types:**\n",
    "- 'line' : line plot (default)\n",
    "- 'bar' : vertical bar plot\n",
    "- 'barh' : horizontal bar plot\n",
    "- 'hist' : histogram\n",
    "- 'box' : boxplot\n",
    "- 'kde' : Kernel Density Estimation plot\n",
    "- 'density' : same as 'kde'\n",
    "- 'area' : area plot\n",
    "- 'pie' : pie plot\n",
    "- 'scatter' : scatter plot\n",
    "- 'hexbin' : hexbin plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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>user</th>\n",
       "      <th>project_name</th>\n",
       "      <th>prog_lang</th>\n",
       "      <th>pull_req_number</th>\n",
       "      <th>num_commits</th>\n",
       "      <th>group_commit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>user34165</td>\n",
       "      <td>three.js</td>\n",
       "      <td>javascript</td>\n",
       "      <td>7832</td>\n",
       "      <td>385</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>user640</td>\n",
       "      <td>cocos2d-x</td>\n",
       "      <td>C++</td>\n",
       "      <td>6866</td>\n",
       "      <td>364</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7335</th>\n",
       "      <td>user6426</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3506</td>\n",
       "      <td>315</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19587</th>\n",
       "      <td>user17126</td>\n",
       "      <td>jenkins</td>\n",
       "      <td>java</td>\n",
       "      <td>2718</td>\n",
       "      <td>307</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35826</th>\n",
       "      <td>user31347</td>\n",
       "      <td>redis</td>\n",
       "      <td>C</td>\n",
       "      <td>3230</td>\n",
       "      <td>290</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4243</th>\n",
       "      <td>user3774</td>\n",
       "      <td>django</td>\n",
       "      <td>python</td>\n",
       "      <td>6768</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37295</th>\n",
       "      <td>user32646</td>\n",
       "      <td>scalatra</td>\n",
       "      <td>scala</td>\n",
       "      <td>329</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40657</th>\n",
       "      <td>user35646</td>\n",
       "      <td>react</td>\n",
       "      <td>javascript</td>\n",
       "      <td>6746</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28583</th>\n",
       "      <td>user25031</td>\n",
       "      <td>scikit-learn</td>\n",
       "      <td>C</td>\n",
       "      <td>6758</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27959</th>\n",
       "      <td>user24491</td>\n",
       "      <td>angular.js</td>\n",
       "      <td>javascript</td>\n",
       "      <td>7086</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12437 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            user  project_name   prog_lang  pull_req_number  num_commits  \\\n",
       "38987  user34165      three.js  javascript             7832          385   \n",
       "705      user640     cocos2d-x         C++             6866          364   \n",
       "7335    user6426         redis           C             3506          315   \n",
       "19587  user17126       jenkins        java             2718          307   \n",
       "35826  user31347         redis           C             3230          290   \n",
       "...          ...           ...         ...              ...          ...   \n",
       "4243    user3774        django      python             6768            2   \n",
       "37295  user32646      scalatra       scala              329            2   \n",
       "40657  user35646         react  javascript             6746            2   \n",
       "28583  user25031  scikit-learn           C             6758            2   \n",
       "27959  user24491    angular.js  javascript             7086            2   \n",
       "\n",
       "       group_commit  \n",
       "38987           NaN  \n",
       "705             NaN  \n",
       "7335            NaN  \n",
       "19587           NaN  \n",
       "35826           NaN  \n",
       "...             ...  \n",
       "4243            2.0  \n",
       "37295           2.0  \n",
       "40657           2.0  \n",
       "28583           2.0  \n",
       "27959           2.0  \n",
       "\n",
       "[12437 rows x 6 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpr[multi_commit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc21b54ee90>"
      ]
     },
     "execution_count": 218,
     "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": [
    "cpr[cpr[\"num_commits\"]>7].num_commits.plot.hist(bins=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc2018f3a50>"
      ]
     },
     "execution_count": 221,
     "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": [
    "cpr[cpr[\"prog_lang\"]==\"C\"].num_commits.plot.hist(bins=20, color=\"red\", alpha=0.5)\n",
    "cpr[cpr[\"prog_lang\"]==\"java\"].num_commits.plot.hist(bins=20, alpha=0.5).legend([\"C\", \"Java\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc21b7a8410>"
      ]
     },
     "execution_count": 231,
     "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": [
    "cpr.prog_lang.str.lower().value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc201b7cc90>"
      ]
     },
     "execution_count": 234,
     "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": [
    "cpr[cpr[\"prog_lang\"]== \"C\"].project_name.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc22184d510>"
      ]
     },
     "execution_count": 237,
     "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": [
    "commits_complete.plot.scatter(x = \"additions\", y = \"num_commits\", color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2218107d0>"
      ]
     },
     "execution_count": 238,
     "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": [
    "lang_c = cpr.prog_lang==\"C\"\n",
    "lang_java = cpr.prog_lang==\"java\"\n",
    "lang_php = cpr.prog_lang==\"php\"\n",
    "\n",
    "\n",
    "cpr[(lang_c) | (lang_java) | (lang_php)].boxplot(by='prog_lang', column=['num_commits'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '')"
      ]
     },
     "execution_count": 240,
     "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": [
    "\n",
    "plot = cpr[(lang_c) | (lang_java) | (lang_php)].boxplot(by='prog_lang', column=['num_commits'], showfliers=False, grid=True)\n",
    "\n",
    "plot.set_xlabel(\"Language\")\n",
    "plot.set_ylabel(\"# of commits\")\n",
    "plot.set_title(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Just to show...**\n",
    "\n",
    "that it is possible to do statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=2481768.0, pvalue=2.1763470665307134e-20)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "stats.mannwhitneyu(cpr[(lang_c)].num_commits, cpr[(lang_java)].num_commits)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prog_lang': {38987: 'javascript',\n",
       "  705: 'C++',\n",
       "  7335: 'C',\n",
       "  19587: 'java',\n",
       "  35826: 'C',\n",
       "  13300: 'C++',\n",
       "  3601: 'javascript',\n",
       "  13873: 'java',\n",
       "  26360: 'php',\n",
       "  18632: 'java',\n",
       "  11805: 'objective-c',\n",
       "  30507: 'C',\n",
       "  29618: 'php',\n",
       "  31455: 'java',\n",
       "  297: 'C',\n",
       "  34902: 'python',\n",
       "  4035: 'javascript',\n",
       "  22522: 'C',\n",
       "  17330: 'javascript',\n",
       "  15688: 'java',\n",
       "  4876: 'C',\n",
       "  39577: 'php',\n",
       "  11332: 'python',\n",
       "  40015: 'java',\n",
       "  4059: 'php',\n",
       "  31301: 'C++',\n",
       "  37120: 'ruby',\n",
       "  41066: 'objective-c',\n",
       "  14922: 'javascript',\n",
       "  27210: 'C++',\n",
       "  5053: 'javascript',\n",
       "  12142: 'typescript',\n",
       "  12477: 'ruby',\n",
       "  32510: 'javascript',\n",
       "  1142: 'python',\n",
       "  6891: 'objective-c',\n",
       "  2762: 'C++',\n",
       "  5620: 'javascript',\n",
       "  14943: 'java',\n",
       "  40423: 'javascript',\n",
       "  9063: 'C++',\n",
       "  20683: 'javascript',\n",
       "  12604: 'java',\n",
       "  2405: 'C',\n",
       "  28796: 'objective-c',\n",
       "  15686: 'javascript',\n",
       "  22431: 'C',\n",
       "  14461: 'python',\n",
       "  14910: 'javascript',\n",
       "  41780: 'javascript',\n",
       "  16113: 'C++',\n",
       "  10328: 'php',\n",
       "  30588: 'C',\n",
       "  9786: 'C++',\n",
       "  4649: 'C++',\n",
       "  22163: 'java',\n",
       "  29884: 'java',\n",
       "  13830: 'C++',\n",
       "  40839: 'objective-c',\n",
       "  35238: 'ruby',\n",
       "  40409: 'C',\n",
       "  29672: 'java',\n",
       "  1176: 'go',\n",
       "  10383: 'javascript',\n",
       "  34075: 'javascript',\n",
       "  20235: 'java',\n",
       "  40777: 'C++',\n",
       "  7480: 'javascript',\n",
       "  19363: 'C++',\n",
       "  36461: 'C++',\n",
       "  28: 'C++',\n",
       "  4428: 'php',\n",
       "  19633: 'javascript',\n",
       "  34970: 'javascript',\n",
       "  10806: 'C++',\n",
       "  29217: 'javascript',\n",
       "  19506: 'C',\n",
       "  4831: 'C++',\n",
       "  2867: 'php',\n",
       "  26824: 'java',\n",
       "  15548: 'javascript',\n",
       "  28758: 'C++',\n",
       "  8165: 'C',\n",
       "  33367: 'C++',\n",
       "  5971: 'C++',\n",
       "  16470: 'C++',\n",
       "  25816: 'C++',\n",
       "  12927: 'ruby',\n",
       "  31286: 'C++',\n",
       "  33317: 'C++',\n",
       "  1566: 'C',\n",
       "  22432: 'C++',\n",
       "  23703: 'ruby',\n",
       "  18208: 'java',\n",
       "  32302: 'php',\n",
       "  36643: 'coffeescript',\n",
       "  3193: 'C',\n",
       "  22876: 'javascript',\n",
       "  16: 'javascript',\n",
       "  664: 'php',\n",
       "  20426: 'C',\n",
       "  6989: 'C++',\n",
       "  35554: 'python',\n",
       "  35576: 'C++',\n",
       "  12693: 'C',\n",
       "  28248: 'C++',\n",
       "  15956: 'C',\n",
       "  8604: 'javascript',\n",
       "  6310: 'scala',\n",
       "  26352: 'php',\n",
       "  20363: 'php',\n",
       "  37354: 'C++',\n",
       "  9200: 'C++',\n",
       "  40001: 'C',\n",
       "  31004: 'ruby',\n",
       "  31474: 'java',\n",
       "  15217: 'java',\n",
       "  41782: 'java',\n",
       "  20576: 'ruby',\n",
       "  25058: 'C',\n",
       "  39329: 'java',\n",
       "  17138: 'C',\n",
       "  17616: 'ruby',\n",
       "  28181: 'C++',\n",
       "  38557: 'C++',\n",
       "  10686: 'php',\n",
       "  31570: 'php',\n",
       "  21048: 'javascript',\n",
       "  15664: 'java',\n",
       "  7429: 'java',\n",
       "  37298: 'java',\n",
       "  41238: 'java',\n",
       "  20855: 'javascript',\n",
       "  31328: 'C',\n",
       "  20729: 'php',\n",
       "  14378: 'java',\n",
       "  21663: 'javascript',\n",
       "  37144: 'javascript',\n",
       "  30449: 'erlang',\n",
       "  15732: 'C++',\n",
       "  17139: 'ruby',\n",
       "  35318: 'C++',\n",
       "  6051: 'C++',\n",
       "  40946: 'javascript',\n",
       "  41116: 'php',\n",
       "  23724: 'ruby',\n",
       "  6957: 'ruby',\n",
       "  30467: 'C',\n",
       "  32416: 'perl',\n",
       "  14485: 'java',\n",
       "  37005: 'C++',\n",
       "  27076: 'php',\n",
       "  17392: 'javascript',\n",
       "  23564: 'ruby',\n",
       "  16291: 'java',\n",
       "  8253: 'javascript',\n",
       "  25897: 'php',\n",
       "  2771: 'php',\n",
       "  33262: 'C++',\n",
       "  16321: 'objective-c',\n",
       "  41939: 'php',\n",
       "  9807: 'C++',\n",
       "  38261: 'objective-c',\n",
       "  24963: 'java',\n",
       "  22373: 'ruby',\n",
       "  40570: 'python',\n",
       "  18890: 'objective-c',\n",
       "  20078: 'java',\n",
       "  32240: 'javascript',\n",
       "  7970: 'C',\n",
       "  31410: 'php',\n",
       "  39576: 'C++',\n",
       "  22106: 'php',\n",
       "  20860: 'java',\n",
       "  40186: 'ruby',\n",
       "  40070: 'java',\n",
       "  9436: 'C++',\n",
       "  8538: 'C++',\n",
       "  39993: 'typescript',\n",
       "  5306: 'C++',\n",
       "  27999: 'javascript',\n",
       "  27330: 'java',\n",
       "  17793: 'C',\n",
       "  5164: 'javascript',\n",
       "  34985: 'java',\n",
       "  22610: 'java',\n",
       "  12618: 'java',\n",
       "  4954: 'ruby',\n",
       "  3227: 'typescript',\n",
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       "  8941: 'java',\n",
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       "  25654: 'php',\n",
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       "  37817: 'javascript',\n",
       "  9082: 'php',\n",
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       "  14790: 'php',\n",
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       "  3924: 'ruby',\n",
       "  40938: 'Perl',\n",
       "  9555: 'C++',\n",
       "  5817: 'clojure',\n",
       "  21372: 'python',\n",
       "  30578: 'javascript',\n",
       "  31624: 'perl',\n",
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       "  32141: 'python',\n",
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       "  27924: 'python',\n",
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       "  21505: 'php',\n",
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       "  23356: 'ruby',\n",
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       "  6459: 'php',\n",
       "  1050: 'php',\n",
       "  35480: 'php',\n",
       "  21380: 'ruby',\n",
       "  1826: 'objective-c',\n",
       "  ...},\n",
       " 'project_name': {38987: 'three.js',\n",
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       "  7335: 'redis',\n",
       "  19587: 'jenkins',\n",
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       "  26360: 'Faker',\n",
       "  18632: 'libgdx',\n",
       "  11805: 'AFNetworking',\n",
       "  30507: 'redis',\n",
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       "  31455: 'spring-framework',\n",
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       "  14922: 'react',\n",
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       "  12477: 'resque',\n",
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       "  14943: 'retrofit',\n",
       "  40423: 'angular.js',\n",
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       "  41780: 'meteor',\n",
       "  16113: 'TrinityCore',\n",
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       "  30588: 'ccv',\n",
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       "  4649: 'TrinityCore',\n",
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       "  20235: 'netty',\n",
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       "  19363: 'bitcoin',\n",
       "  36461: 'TrinityCore',\n",
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       "  10806: 'bitcoin',\n",
       "  29217: 'jquery',\n",
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       "  2867: 'yii2',\n",
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       "  5971: 'TrinityCore',\n",
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       "  16: 'angular.js',\n",
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       "  20426: 'godot',\n",
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       "  35554: 'flask',\n",
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       "  28248: 'xbmc',\n",
       "  15956: 'php-src',\n",
       "  8604: 'three.js',\n",
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       "  26352: 'CodeIgniter',\n",
       "  20363: 'cakephp',\n",
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       "  40001: 'scikit-learn',\n",
       "  31004: 'discourse',\n",
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       "  41782: 'RxJava',\n",
       "  20576: 'devise',\n",
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       "  39329: 'netty',\n",
       "  17138: 'php-src',\n",
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       "  37298: 'elasticsearch',\n",
       "  41238: 'elasticsearch',\n",
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       "  21663: 'Semantic-UI',\n",
       "  37144: 'jquery',\n",
       "  30449: 'ejabberd',\n",
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       "  17139: 'jekyll',\n",
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       "  6957: 'rails',\n",
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       "  16321: 'OpenEmu',\n",
       "  41939: 'laravel',\n",
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       "  24963: 'netty',\n",
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       "  22106: 'cakephp',\n",
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       "  39993: 'primecoin',\n",
       "  5306: 'xbmc',\n",
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       "  27330: 'ActionBarSherlock',\n",
       "  17793: 'emscripten',\n",
       "  5164: 'jquery',\n",
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       "  12618: 'spring-framework',\n",
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       "  13433: 'laravel',\n",
       "  20938: 'TrinityCore',\n",
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       "  9737: 'jquery',\n",
       "  17955: 'elasticsearch',\n",
       "  34989: 'laravel',\n",
       "  9776: 'bitcoin',\n",
       "  23459: 'SlidingMenu',\n",
       "  12367: 'cakephp',\n",
       "  75: 'redis',\n",
       "  199: 'reddit',\n",
       "  21848: 'jquery',\n",
       "  34425: 'TrinityCore',\n",
       "  1163: 'TrinityCore',\n",
       "  12615: 'react',\n",
       "  36927: 'rails',\n",
       "  15: 'ReactiveCocoa',\n",
       "  35098: 'react',\n",
       "  22347: 'jquery',\n",
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       "  5169: 'cakephp',\n",
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       "  13833: 'bitcoin',\n",
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       "  23718: 'laravel',\n",
       "  34302: 'bitcoin',\n",
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       "  20965: 'bitcoin',\n",
       "  29414: 'xbmc',\n",
       "  10536: 'libgdx',\n",
       "  31945: 'three.js',\n",
       "  28381: 'scikit-learn',\n",
       "  38526: 'Mantle',\n",
       "  41434: 'jquery',\n",
       "  24418: 'brackets',\n",
       "  1103: 'coffeescript',\n",
       "  10947: 'laravel',\n",
       "  26761: 'laravel',\n",
       "  26156: 'php-src',\n",
       "  8058: 'youtube-dl',\n",
       "  12057: 'sbt',\n",
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       "  34499: 'duckduckgo',\n",
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       "  24205: 'laravel',\n",
       "  9587: 'brackets',\n",
       "  34151: 'bitcoin',\n",
       "  22925: 'cphalcon',\n",
       "  39256: 'laravel',\n",
       "  8887: 'moment',\n",
       "  22113: 'laravel',\n",
       "  32945: 'laravel',\n",
       "  21407: 'laravel',\n",
       "  25632: 'laravel',\n",
       "  34719: 'laravel',\n",
       "  11227: 'laravel',\n",
       "  32175: 'laravel',\n",
       "  22348: 'jenkins',\n",
       "  22028: 'instaparse',\n",
       "  39583: 'resque',\n",
       "  18764: 'xbmc',\n",
       "  19515: 'CodeIgniter',\n",
       "  19195: 'yesod',\n",
       "  5612: 'xbmc',\n",
       "  8518: 'laravel',\n",
       "  21091: 'yesod',\n",
       "  14226: 'Semantic-UI',\n",
       "  29084: 'yesod',\n",
       "  30634: 'bootstrap-tour',\n",
       "  23817: 'ActionBarSherlock',\n",
       "  13517: 'react',\n",
       "  9159: 'bootstrap-tour',\n",
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       "  16745: 'modern-cljs',\n",
       "  37485: 'octopress',\n",
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       "  14217: 'bootbox',\n",
       "  28285: 'finagle',\n",
       "  6366: 'three.js',\n",
       "  38819: 'CodeIgniter',\n",
       "  9060: 'laravel',\n",
       "  36471: 'CodeIgniter',\n",
       "  6876: 'cocos2d-x',\n",
       "  36506: 'cakephp',\n",
       "  41486: 'playframework',\n",
       "  1354: 'CodeIgniter',\n",
       "  990: 'redis',\n",
       "  16773: 'cphalcon',\n",
       "  39505: 'jquery',\n",
       "  29043: 'angular.js',\n",
       "  20225: 'Slim',\n",
       "  33061: 'diaspora',\n",
       "  18144: 'spring-framework',\n",
       "  21294: 'folly',\n",
       "  7045: 'three.js',\n",
       "  33698: 'scikit-learn',\n",
       "  32606: 'php-src',\n",
       "  25834: 'ipython',\n",
       "  16425: 'elasticsearch',\n",
       "  17805: 'MagicalRecord',\n",
       "  3542: 'cakephp',\n",
       "  27586: 'appjs',\n",
       "  12707: 'laravel',\n",
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       "  34230: 'TrinityCore',\n",
       "  9833: 'three.js',\n",
       "  18237: 'TypeScript',\n",
       "  35355: 'syncthing',\n",
       "  10043: 'chosen',\n",
       "  19767: 'laravel',\n",
       "  33350: 'socket.io',\n",
       "  11736: 'scikit-learn',\n",
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       "  12: 'react',\n",
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       "  10103: 'scikit-learn',\n",
       "  22987: 'influxdb',\n",
       "  28149: 'three.js',\n",
       "  10711: 'three.js',\n",
       "  20281: 'html5-boilerplate',\n",
       "  35007: 'three.js',\n",
       "  314: 'Semantic-UI',\n",
       "  30653: 'angular.js',\n",
       "  706: 'RestKit',\n",
       "  23033: 'MagicalRecord',\n",
       "  16323: 'hhvm',\n",
       "  36002: 'CodeIgniter',\n",
       "  19144: 'Framer',\n",
       "  25381: 'jQuery-File-Upload',\n",
       "  2386: 'node',\n",
       "  31218: 'laravel',\n",
       "  18730: 'moment',\n",
       "  20221: 'laravel',\n",
       "  5366: 'laravel',\n",
       "  23610: 'playframework',\n",
       "  28522: 'hub',\n",
       "  10940: 'chosen',\n",
       "  6509: 'rails',\n",
       "  40692: 'bitcoin',\n",
       "  40024: 'angular.js',\n",
       "  31216: 'emscripten',\n",
       "  41406: 'laravel',\n",
       "  33595: 'mongo',\n",
       "  41214: 'laravel',\n",
       "  25246: 'bitcoin',\n",
       "  35897: 'contrib',\n",
       "  27026: 'redis',\n",
       "  32130: 'CodeIgniter',\n",
       "  21078: 'brackets',\n",
       "  22780: 'jquery',\n",
       "  29243: 'three.js',\n",
       "  36431: 'gogs',\n",
       "  28170: 'laravel',\n",
       "  7943: 'mongo',\n",
       "  13124: 'laravel',\n",
       "  24620: 'jquery',\n",
       "  8038: 'netty',\n",
       "  30184: 'laravel',\n",
       "  11751: 'laravel',\n",
       "  30949: 'etcd',\n",
       "  12202: 'scikit-learn',\n",
       "  24546: 'discourse',\n",
       "  31258: 'three.js',\n",
       "  5558: 'emscripten',\n",
       "  3540: 'phantomjs',\n",
       "  2053: 'Chart.js',\n",
       "  25541: 'laravel',\n",
       "  9842: 'cphalcon',\n",
       "  9104: 'laravel',\n",
       "  9960: 'mongo',\n",
       "  35965: 'retrofit',\n",
       "  20019: 'akka',\n",
       "  26577: 'beego',\n",
       "  27939: 'RxJava',\n",
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       "  304: 'cocos2d-x',\n",
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       "  14790: 'CodeIgniter',\n",
       "  9397: 'CodeIgniter',\n",
       "  27750: 'cakephp',\n",
       "  11597: 'yii2',\n",
       "  26420: 'meteor',\n",
       "  41385: 'devise',\n",
       "  16776: 'ipython',\n",
       "  41968: 'three.js',\n",
       "  21939: 'scalding',\n",
       "  3924: 'rails',\n",
       "  40938: 'sshuttle',\n",
       "  9555: 'xbmc',\n",
       "  5817: 'Korma',\n",
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       "  30578: 'select2',\n",
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       "  32983: 'bitcoin',\n",
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       "  5988: 'discourse',\n",
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       "  2816: 'rocksdb',\n",
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       "  13398: 'laravel',\n",
       "  27924: 'django',\n",
       "  22388: 'RestKit',\n",
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       "  1050: 'cakephp',\n",
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       "  1826: 'GPUImage',\n",
       "  ...},\n",
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       "  10294: 24,\n",
       "  12307: 24,\n",
       "  30912: 24,\n",
       "  4540: 24,\n",
       "  20014: 24,\n",
       "  18222: 24,\n",
       "  37711: 24,\n",
       "  31045: 24,\n",
       "  7205: 24,\n",
       "  21846: 24,\n",
       "  41672: 23,\n",
       "  24092: 23,\n",
       "  28648: 23,\n",
       "  15035: 23,\n",
       "  5491: 23,\n",
       "  8263: 23,\n",
       "  24422: 23,\n",
       "  9046: 23,\n",
       "  6695: 23,\n",
       "  1644: 23,\n",
       "  41806: 23,\n",
       "  33906: 23,\n",
       "  12308: 23,\n",
       "  36193: 22,\n",
       "  16260: 22,\n",
       "  28325: 22,\n",
       "  33586: 22,\n",
       "  13981: 22,\n",
       "  21664: 22,\n",
       "  32839: 22,\n",
       "  18426: 22,\n",
       "  5621: 22,\n",
       "  16279: 22,\n",
       "  22922: 22,\n",
       "  4401: 22,\n",
       "  14967: 22,\n",
       "  17113: 22,\n",
       "  16232: 22,\n",
       "  9478: 22,\n",
       "  13480: 21,\n",
       "  6981: 21,\n",
       "  30011: 21,\n",
       "  35630: 21,\n",
       "  17515: 21,\n",
       "  15373: 21,\n",
       "  9698: 21,\n",
       "  10687: 21,\n",
       "  35029: 21,\n",
       "  19680: 21,\n",
       "  9154: 21,\n",
       "  35921: 21,\n",
       "  5343: 21,\n",
       "  17683: 21,\n",
       "  1692: 21,\n",
       "  4817: 21,\n",
       "  7860: 20,\n",
       "  29607: 20,\n",
       "  9053: 20,\n",
       "  10490: 20,\n",
       "  2930: 20,\n",
       "  12886: 20,\n",
       "  20546: 20,\n",
       "  34504: 20,\n",
       "  41030: 20,\n",
       "  33954: 20,\n",
       "  27800: 20,\n",
       "  10973: 20,\n",
       "  4898: 20,\n",
       "  27724: 20,\n",
       "  4501: 20,\n",
       "  36381: 20,\n",
       "  24338: 20,\n",
       "  24630: 20,\n",
       "  682: 20,\n",
       "  39285: 20,\n",
       "  37830: 20,\n",
       "  38092: 20,\n",
       "  41983: 20,\n",
       "  7348: 20,\n",
       "  32560: 20,\n",
       "  37293: 19,\n",
       "  30323: 19,\n",
       "  19589: 19,\n",
       "  22030: 19,\n",
       "  37930: 19,\n",
       "  13690: 19,\n",
       "  6851: 19,\n",
       "  32522: 19,\n",
       "  16222: 19,\n",
       "  25815: 19,\n",
       "  12818: 19,\n",
       "  13964: 19,\n",
       "  32133: 19,\n",
       "  37327: 19,\n",
       "  9589: 19,\n",
       "  2901: 19,\n",
       "  22176: 19,\n",
       "  28392: 19,\n",
       "  7916: 19,\n",
       "  24580: 19,\n",
       "  41277: 19,\n",
       "  8850: 19,\n",
       "  39237: 19,\n",
       "  929: 19,\n",
       "  255: 19,\n",
       "  31241: 19,\n",
       "  7510: 19,\n",
       "  28671: 19,\n",
       "  36133: 18,\n",
       "  4942: 18,\n",
       "  15677: 18,\n",
       "  24880: 18,\n",
       "  8645: 18,\n",
       "  30665: 18,\n",
       "  6992: 18,\n",
       "  1353: 18,\n",
       "  24990: 18,\n",
       "  28310: 18,\n",
       "  35785: 18,\n",
       "  21101: 18,\n",
       "  20675: 18,\n",
       "  35384: 18,\n",
       "  11357: 18,\n",
       "  26407: 17,\n",
       "  17442: 17,\n",
       "  16599: 17,\n",
       "  28544: 17,\n",
       "  29478: 17,\n",
       "  9352: 17,\n",
       "  24032: 17,\n",
       "  354: 17,\n",
       "  10219: 17,\n",
       "  32733: 17,\n",
       "  40806: 17,\n",
       "  475: 17,\n",
       "  18015: 17,\n",
       "  12377: 17,\n",
       "  31209: 17,\n",
       "  20104: 17,\n",
       "  16294: 17,\n",
       "  2306: 17,\n",
       "  19775: 17,\n",
       "  3677: 17,\n",
       "  8694: 17,\n",
       "  18599: 17,\n",
       "  21454: 17,\n",
       "  1015: 17,\n",
       "  19997: 17,\n",
       "  34389: 16,\n",
       "  14592: 16,\n",
       "  28953: 16,\n",
       "  20129: 16,\n",
       "  28322: 16,\n",
       "  9769: 16,\n",
       "  18432: 16,\n",
       "  10831: 16,\n",
       "  4861: 16,\n",
       "  22381: 16,\n",
       "  27145: 16,\n",
       "  33249: 16,\n",
       "  7522: 16,\n",
       "  1051: 16,\n",
       "  35372: 16,\n",
       "  30600: 16,\n",
       "  26001: 16,\n",
       "  30710: 16,\n",
       "  25316: 16,\n",
       "  6510: 16,\n",
       "  9739: 16,\n",
       "  26091: 16,\n",
       "  41005: 16,\n",
       "  12085: 16,\n",
       "  38375: 16,\n",
       "  31023: 16,\n",
       "  36303: 16,\n",
       "  2071: 16,\n",
       "  2957: 16,\n",
       "  36095: 16,\n",
       "  12221: 16,\n",
       "  10705: 16,\n",
       "  12696: 16,\n",
       "  6267: 16,\n",
       "  14775: 16,\n",
       "  24631: 16,\n",
       "  24420: 15,\n",
       "  23005: 15,\n",
       "  38998: 15,\n",
       "  12803: 15,\n",
       "  12492: 15,\n",
       "  38494: 15,\n",
       "  4994: 15,\n",
       "  18831: 15,\n",
       "  18214: 15,\n",
       "  14075: 15,\n",
       "  41046: 15,\n",
       "  32417: 15,\n",
       "  18763: 15,\n",
       "  29176: 15,\n",
       "  21832: 15,\n",
       "  1693: 15,\n",
       "  23771: 15,\n",
       "  30094: 15,\n",
       "  3004: 15,\n",
       "  2116: 15,\n",
       "  4599: 15,\n",
       "  15067: 15,\n",
       "  29239: 15,\n",
       "  20976: 15,\n",
       "  14058: 15,\n",
       "  32991: 15,\n",
       "  35729: 15,\n",
       "  2099: 14,\n",
       "  31508: 14,\n",
       "  12612: 14,\n",
       "  32455: 14,\n",
       "  17161: 14,\n",
       "  15643: 14,\n",
       "  20120: 14,\n",
       "  124: 14,\n",
       "  41957: 14,\n",
       "  24207: 14,\n",
       "  17281: 14,\n",
       "  13401: 14,\n",
       "  11038: 14,\n",
       "  38814: 14,\n",
       "  37998: 14,\n",
       "  40454: 14,\n",
       "  39363: 14,\n",
       "  4740: 14,\n",
       "  6676: 14,\n",
       "  32216: 14,\n",
       "  15702: 14,\n",
       "  25025: 14,\n",
       "  39676: 14,\n",
       "  36466: 14,\n",
       "  36324: 14,\n",
       "  19356: 14,\n",
       "  8801: 14,\n",
       "  3405: 14,\n",
       "  4647: 14,\n",
       "  21693: 14,\n",
       "  5760: 14,\n",
       "  37858: 14,\n",
       "  36862: 14,\n",
       "  38498: 14,\n",
       "  29202: 14,\n",
       "  2893: 14,\n",
       "  39794: 14,\n",
       "  341: 14,\n",
       "  30824: 14,\n",
       "  23602: 14,\n",
       "  22248: 14,\n",
       "  10032: 14,\n",
       "  8063: 14,\n",
       "  477: 14,\n",
       "  39557: 14,\n",
       "  15789: 14,\n",
       "  3436: 14,\n",
       "  15943: 14,\n",
       "  13483: 13,\n",
       "  13438: 13,\n",
       "  10410: 13,\n",
       "  4214: 13,\n",
       "  31050: 13,\n",
       "  24016: 13,\n",
       "  21195: 13,\n",
       "  5228: 13,\n",
       "  24922: 13,\n",
       "  32906: 13,\n",
       "  14161: 13,\n",
       "  30666: 13,\n",
       "  2628: 13,\n",
       "  29720: 13,\n",
       "  19764: 13,\n",
       "  36136: 13,\n",
       "  38128: 13,\n",
       "  14025: 13,\n",
       "  39370: 13,\n",
       "  20434: 13,\n",
       "  19831: 13,\n",
       "  17505: 13,\n",
       "  3456: 13,\n",
       "  31704: 13,\n",
       "  21381: 13,\n",
       "  35789: 13,\n",
       "  2102: 13,\n",
       "  2269: 13,\n",
       "  33046: 13,\n",
       "  37900: 13,\n",
       "  19975: 13,\n",
       "  6544: 13,\n",
       "  17526: 13,\n",
       "  8941: 13,\n",
       "  3796: 13,\n",
       "  2366: 13,\n",
       "  30134: 13,\n",
       "  11700: 13,\n",
       "  38226: 13,\n",
       "  9096: 13,\n",
       "  30236: 13,\n",
       "  39171: 13,\n",
       "  16801: 13,\n",
       "  27019: 13,\n",
       "  18029: 13,\n",
       "  26397: 13,\n",
       "  18374: 13,\n",
       "  35006: 13,\n",
       "  14921: 13,\n",
       "  18528: 13,\n",
       "  34878: 13,\n",
       "  11476: 13,\n",
       "  10024: 13,\n",
       "  13699: 13,\n",
       "  20569: 13,\n",
       "  11868: 13,\n",
       "  16557: 13,\n",
       "  33812: 13,\n",
       "  27917: 13,\n",
       "  13546: 13,\n",
       "  4754: 13,\n",
       "  13101: 13,\n",
       "  21824: 13,\n",
       "  40187: 12,\n",
       "  37538: 12,\n",
       "  15272: 12,\n",
       "  18126: 12,\n",
       "  11944: 12,\n",
       "  24999: 12,\n",
       "  20059: 12,\n",
       "  32080: 12,\n",
       "  33690: 12,\n",
       "  15210: 12,\n",
       "  39098: 12,\n",
       "  25654: 12,\n",
       "  9223: 12,\n",
       "  304: 12,\n",
       "  37817: 12,\n",
       "  9082: 12,\n",
       "  22423: 12,\n",
       "  29713: 12,\n",
       "  14790: 12,\n",
       "  9397: 12,\n",
       "  27750: 12,\n",
       "  11597: 12,\n",
       "  26420: 12,\n",
       "  41385: 12,\n",
       "  16776: 12,\n",
       "  41968: 12,\n",
       "  21939: 12,\n",
       "  3924: 12,\n",
       "  40938: 12,\n",
       "  9555: 12,\n",
       "  5817: 12,\n",
       "  21372: 12,\n",
       "  30578: 12,\n",
       "  31624: 12,\n",
       "  3187: 12,\n",
       "  35281: 12,\n",
       "  32983: 12,\n",
       "  32141: 12,\n",
       "  28400: 12,\n",
       "  5988: 12,\n",
       "  303: 12,\n",
       "  24500: 12,\n",
       "  41756: 12,\n",
       "  9883: 12,\n",
       "  17604: 12,\n",
       "  28415: 12,\n",
       "  2816: 12,\n",
       "  38636: 12,\n",
       "  15780: 12,\n",
       "  6570: 12,\n",
       "  19318: 12,\n",
       "  2480: 12,\n",
       "  15248: 12,\n",
       "  18188: 12,\n",
       "  32112: 12,\n",
       "  13398: 12,\n",
       "  27924: 12,\n",
       "  22388: 12,\n",
       "  5986: 12,\n",
       "  4934: 12,\n",
       "  21505: 11,\n",
       "  23873: 11,\n",
       "  12574: 11,\n",
       "  23356: 11,\n",
       "  18324: 11,\n",
       "  6459: 11,\n",
       "  1050: 11,\n",
       "  35480: 11,\n",
       "  21380: 11,\n",
       "  1826: 11,\n",
       "  ...}}"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_subset.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpr.to_csv('test.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Go for the HW"
   ]
  }
 ],
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