04 Capstone 1 FCC Submissions Frequency.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This Notebook will <li> import the main cleaned dataset <li> convert and summarize the submission time by second and by minute <li> plot and annotate the frequency of submissions by second and by minute for a selected 24 period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages and set to display charts in document\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the main dataset, 3 million records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2698: DtypeWarning: Columns (10,13,14,15,16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#Read in Dataset\n",
    "df = pd.read_csv('df_5.csv',encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Slice the dataset for just the dates\n",
    "df = df[['id','date_submission']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date_submission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59f8b2bb50b733300f069dc9</td>\n",
       "      <td>2017-07-11T15:53:55.361Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59f8b2bb50b733300f069dca</td>\n",
       "      <td>2017-07-11T15:53:55.390Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59f8b2bb50b733300f069dcb</td>\n",
       "      <td>2017-07-11T15:53:55.449Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59f8b2bb50b733300f069dcc</td>\n",
       "      <td>2017-07-11T15:53:55.473Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59f8b2bb50b733300f069dcd</td>\n",
       "      <td>2017-07-11T15:53:55.512Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id           date_submission\n",
       "0  59f8b2bb50b733300f069dc9  2017-07-11T15:53:55.361Z\n",
       "1  59f8b2bb50b733300f069dca  2017-07-11T15:53:55.390Z\n",
       "2  59f8b2bb50b733300f069dcb  2017-07-11T15:53:55.449Z\n",
       "3  59f8b2bb50b733300f069dcc  2017-07-11T15:53:55.473Z\n",
       "4  59f8b2bb50b733300f069dcd  2017-07-11T15:53:55.512Z"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Explore the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the submission date column to datetime and convert to seconds and to minutes. Group by count to get to create a column of the total number of submissions in that second "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert time text time to seconds segments\n",
    "df['date_sec']= pd.to_datetime(df['date_submission'])\n",
    "df['date_sec']= df['date_sec'].values.astype('datetime64[s]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert time text time to minute segments\n",
    "df['date_min']= pd.to_datetime(df['date_submission'])\n",
    "df['date_min']= df['date_min'].values.astype('datetime64[m]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#group by and count the submissions in that second\n",
    "df['freq_time'] = df.groupby('date_sec')['date_sec'].transform('count')\n",
    "df['freq_time'] = pd.to_numeric(df['freq_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_seconds = df['freq_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12\n",
       "1    12\n",
       "2    12\n",
       "3    12\n",
       "4    12\n",
       "Name: freq_time, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore the seconds series\n",
    "df_seconds.head()\n",
    "#129,286(!) records submitted on July 17, 2017 at 8:53pm 8seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select and graph +/- 24 hrs from 2017-07-17 20:53:08\n",
    "df['date_sec'] = pd.to_datetime(df['date_sec'])  \n",
    "mask = (df['date_sec'] >= '2017-07-16 20:53:08') & (df['date_sec'] <= '2017-07-18 20:53:08')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date_submission</th>\n",
       "      <th>date_sec</th>\n",
       "      <th>date_min</th>\n",
       "      <th>freq_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2230012</th>\n",
       "      <td>59f8ded450b733300f28b67f</td>\n",
       "      <td>2017-07-17T00:00:00.000Z</td>\n",
       "      <td>2017-07-17 00:00:00</td>\n",
       "      <td>2017-07-17 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286958</th>\n",
       "      <td>59f8dfca50b733300f29b507</td>\n",
       "      <td>2017-07-16T23:01:58.706Z</td>\n",
       "      <td>2017-07-16 23:01:58</td>\n",
       "      <td>2017-07-16 23:01:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286976</th>\n",
       "      <td>59f8e01550b733300f29f4d6</td>\n",
       "      <td>2017-07-16T23:25:35.935Z</td>\n",
       "      <td>2017-07-16 23:25:35</td>\n",
       "      <td>2017-07-16 23:25:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287533</th>\n",
       "      <td>59f8dff150b733300f29dc8a</td>\n",
       "      <td>2017-07-16T21:39:50.128Z</td>\n",
       "      <td>2017-07-16 21:39:50</td>\n",
       "      <td>2017-07-16 21:39:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288880</th>\n",
       "      <td>59f8dfca50b733300f29b5b5</td>\n",
       "      <td>2017-07-16T23:38:45.549Z</td>\n",
       "      <td>2017-07-16 23:38:45</td>\n",
       "      <td>2017-07-16 23:38:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               id           date_submission  \\\n",
       "2230012  59f8ded450b733300f28b67f  2017-07-17T00:00:00.000Z   \n",
       "2286958  59f8dfca50b733300f29b507  2017-07-16T23:01:58.706Z   \n",
       "2286976  59f8e01550b733300f29f4d6  2017-07-16T23:25:35.935Z   \n",
       "2287533  59f8dff150b733300f29dc8a  2017-07-16T21:39:50.128Z   \n",
       "2288880  59f8dfca50b733300f29b5b5  2017-07-16T23:38:45.549Z   \n",
       "\n",
       "                   date_sec            date_min  freq_time  \n",
       "2230012 2017-07-17 00:00:00 2017-07-17 00:00:00          1  \n",
       "2286958 2017-07-16 23:01:58 2017-07-16 23:01:00          1  \n",
       "2286976 2017-07-16 23:25:35 2017-07-16 23:25:00          2  \n",
       "2287533 2017-07-16 21:39:50 2017-07-16 21:39:00          1  \n",
       "2288880 2017-07-16 23:38:45 2017-07-16 23:38:00          4  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore the mask\n",
    "df.loc[mask].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#slice dataframe through the 24 hour mask\n",
    "df_timespan = df.loc[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(631076, 5)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#look at the shape of our dataframe\n",
    "df_timespan.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date_submission</th>\n",
       "      <th>date_sec</th>\n",
       "      <th>date_min</th>\n",
       "      <th>freq_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2230012</th>\n",
       "      <td>59f8ded450b733300f28b67f</td>\n",
       "      <td>2017-07-17T00:00:00.000Z</td>\n",
       "      <td>2017-07-17 00:00:00</td>\n",
       "      <td>2017-07-17 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286958</th>\n",
       "      <td>59f8dfca50b733300f29b507</td>\n",
       "      <td>2017-07-16T23:01:58.706Z</td>\n",
       "      <td>2017-07-16 23:01:58</td>\n",
       "      <td>2017-07-16 23:01:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286976</th>\n",
       "      <td>59f8e01550b733300f29f4d6</td>\n",
       "      <td>2017-07-16T23:25:35.935Z</td>\n",
       "      <td>2017-07-16 23:25:35</td>\n",
       "      <td>2017-07-16 23:25:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287533</th>\n",
       "      <td>59f8dff150b733300f29dc8a</td>\n",
       "      <td>2017-07-16T21:39:50.128Z</td>\n",
       "      <td>2017-07-16 21:39:50</td>\n",
       "      <td>2017-07-16 21:39:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288880</th>\n",
       "      <td>59f8dfca50b733300f29b5b5</td>\n",
       "      <td>2017-07-16T23:38:45.549Z</td>\n",
       "      <td>2017-07-16 23:38:45</td>\n",
       "      <td>2017-07-16 23:38:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               id           date_submission  \\\n",
       "2230012  59f8ded450b733300f28b67f  2017-07-17T00:00:00.000Z   \n",
       "2286958  59f8dfca50b733300f29b507  2017-07-16T23:01:58.706Z   \n",
       "2286976  59f8e01550b733300f29f4d6  2017-07-16T23:25:35.935Z   \n",
       "2287533  59f8dff150b733300f29dc8a  2017-07-16T21:39:50.128Z   \n",
       "2288880  59f8dfca50b733300f29b5b5  2017-07-16T23:38:45.549Z   \n",
       "\n",
       "                   date_sec            date_min  freq_time  \n",
       "2230012 2017-07-17 00:00:00 2017-07-17 00:00:00          1  \n",
       "2286958 2017-07-16 23:01:58 2017-07-16 23:01:00          1  \n",
       "2286976 2017-07-16 23:25:35 2017-07-16 23:25:00          2  \n",
       "2287533 2017-07-16 21:39:50 2017-07-16 21:39:00          1  \n",
       "2288880 2017-07-16 23:38:45 2017-07-16 23:38:00          4  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore the dataframe\n",
    "df_timespan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_dfs = df_timespan['date_sec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-07-16 23:01:58')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_dfs.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-07-17 23:16:41')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_dfs.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2230012   2017-07-17 00:00:00\n",
       "2286958   2017-07-16 23:01:58\n",
       "2286976   2017-07-16 23:25:35\n",
       "2287533   2017-07-16 21:39:50\n",
       "2288880   2017-07-16 23:38:45\n",
       "Name: date_sec, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_dfs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_s = df_timespan['date_sec'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-07-17 20:53:08    129286\n",
       "2017-07-17 20:39:21     90679\n",
       "2017-07-17 21:07:07     67025\n",
       "2017-07-17 21:02:51     66852\n",
       "2017-07-17 23:01:02     40426\n",
       "2017-07-17 23:16:41     14947\n",
       "2017-07-17 14:33:14     13433\n",
       "2017-07-17 20:04:55     12237\n",
       "2017-07-17 23:19:39      1345\n",
       "2017-07-17 18:40:41      1064\n",
       "2017-07-17 20:18:43       325\n",
       "2017-07-17 20:53:49        52\n",
       "2017-07-17 19:58:15        28\n",
       "2017-07-17 19:55:19        25\n",
       "2017-07-17 19:56:51        24\n",
       "Name: date_sec, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_s.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63731,)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_m = df_timespan['date_min'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-07-17 20:53:00    129719\n",
       "2017-07-17 20:39:00     91176\n",
       "2017-07-17 21:02:00     67349\n",
       "2017-07-17 21:07:00     67308\n",
       "2017-07-17 23:01:00     40972\n",
       "Name: date_min, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1588,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_s_time = pd.DataFrame(count_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_s_time = count_s_time.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e603a72710>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAKkCAYAAAC9J/aQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e603a53d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "plt.xlabel('Date of Submission')\n",
    "plt.ylabel('Submissions log10')\n",
    "plt.title('Net Neutrality Comment Submissions \\n Grouped by Second over 24 hrs')\n",
    "plt.annotate('129,719 submissions in the same second', xy=('2017-07-17 20:53:00', 128000), xytext=('2017-07-17 09:53:00', 60000),arrowprops=dict(facecolor='red', shrink=0.1))\n",
    "\n",
    "count_s_time['date_sec'].plot(x_compat=True, logy=True, figsize=(15,12))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_m_time = pd.DataFrame(count_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_m_time = count_m_time.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e60df879b0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAKkCAYAAAC9J/aQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAsh/ALjtTQJ/c0UAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e60df83080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "plt.xlabel('Date of Submission')\n",
    "plt.ylabel('Submissions log10')\n",
    "plt.title('Net Neutrality Comment Submissions \\n Grouped by Minute over 24hrs')\n",
    "\n",
    "plt.annotate('129,719 submissions in the same minute', xy=('2017-07-17 20:53:00', 128000), xytext=('2017-07-17 09:53:00', 60000),arrowprops=dict(facecolor='red', shrink=0.1))\n",
    "count_m_time['date_min'].plot(x_compat=True, logy=True, figsize=(15,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}