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Example of scatter plot based on median values by hour from datetimes
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Simple Timeseries Plots in `pandas`\n", | |
| "\n", | |
| "Using a dummy dataset..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "First, let's create a function to generate a random datetime between two datetimes:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "datetime.datetime(2019, 1, 29, 20, 40, 17)" | |
| ] | |
| }, | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "#https://stackoverflow.com/a/553448/454773\n", | |
| "from random import randrange\n", | |
| "from datetime import timedelta\n", | |
| "from datetime import datetime\n", | |
| "\n", | |
| "def random_datetime(start=datetime.strptime('1/1/2019 1:30 PM','%d/%m/%Y %I:%M %p'),\n", | |
| " end=datetime.strptime('31/1/2019 4:50 AM', '%d/%m/%Y %I:%M %p')):\n", | |
| " \"\"\"\n", | |
| " This function will return a random datetime between two datetime \n", | |
| " objects.\n", | |
| " \"\"\"\n", | |
| " delta = end - start\n", | |
| " int_delta = (delta.days * 24 * 60 * 60) + delta.seconds\n", | |
| " random_second = randrange(int_delta)\n", | |
| " return start + timedelta(seconds=random_second)\n", | |
| "\n", | |
| "random_datetime()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now we can createa dummy dataframe with a couple of columns of random data:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 63, | |
| "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>val1</th>\n", | |
| " <th>datetime</th>\n", | |
| " <th>val2</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>0.231440</td>\n", | |
| " <td>2019-01-02 07:55:19</td>\n", | |
| " <td>0.291342</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>0.266321</td>\n", | |
| " <td>2019-01-22 01:14:58</td>\n", | |
| " <td>0.009075</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>0.952032</td>\n", | |
| " <td>2019-01-09 08:51:14</td>\n", | |
| " <td>1.428048</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>0.643093</td>\n", | |
| " <td>2019-01-12 09:13:53</td>\n", | |
| " <td>1.097828</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4</td>\n", | |
| " <td>0.354115</td>\n", | |
| " <td>2019-01-07 10:05:28</td>\n", | |
| " <td>0.660788</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>995</td>\n", | |
| " <td>0.042920</td>\n", | |
| " <td>2019-01-17 18:38:57</td>\n", | |
| " <td>0.042920</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>996</td>\n", | |
| " <td>0.208727</td>\n", | |
| " <td>2019-01-30 04:19:08</td>\n", | |
| " <td>0.104364</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>997</td>\n", | |
| " <td>0.249041</td>\n", | |
| " <td>2019-01-02 03:52:03</td>\n", | |
| " <td>0.072942</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>998</td>\n", | |
| " <td>0.484270</td>\n", | |
| " <td>2019-01-07 17:22:07</td>\n", | |
| " <td>0.609609</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>999</td>\n", | |
| " <td>0.765707</td>\n", | |
| " <td>2019-01-03 03:24:35</td>\n", | |
| " <td>0.224270</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1000 rows × 3 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " val1 datetime val2\n", | |
| "0 0.231440 2019-01-02 07:55:19 0.291342\n", | |
| "1 0.266321 2019-01-22 01:14:58 0.009075\n", | |
| "2 0.952032 2019-01-09 08:51:14 1.428048\n", | |
| "3 0.643093 2019-01-12 09:13:53 1.097828\n", | |
| "4 0.354115 2019-01-07 10:05:28 0.660788\n", | |
| ".. ... ... ...\n", | |
| "995 0.042920 2019-01-17 18:38:57 0.042920\n", | |
| "996 0.208727 2019-01-30 04:19:08 0.104364\n", | |
| "997 0.249041 2019-01-02 03:52:03 0.072942\n", | |
| "998 0.484270 2019-01-07 17:22:07 0.609609\n", | |
| "999 0.765707 2019-01-03 03:24:35 0.224270\n", | |
| "\n", | |
| "[1000 rows x 3 columns]" | |
| ] | |
| }, | |
| "execution_count": 63, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "import numpy as np\n", | |
| "\n", | |
| "numsamples = 1000\n", | |
| "\n", | |
| "df=pd.DataFrame({'val1':np.random.rand(numsamples)})\n", | |
| "df['datetime']= [random_datetime() for i in range(numsamples)]\n", | |
| "#Add some periodicity to the second column of random numbers...\n", | |
| "df['val2']=df.apply(lambda x: x['val1']*(1+np.sin(2*np.pi*(x['datetime'].hour-6)/24)), axis=1)\n", | |
| "df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 64, | |
| "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>val1</th>\n", | |
| " <th>val2</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>datetime</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>0.401714</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>0.611881</td>\n", | |
| " <td>0.020849</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>0.552599</td>\n", | |
| " <td>0.074034</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>0.539660</td>\n", | |
| " <td>0.158063</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4</td>\n", | |
| " <td>0.441285</td>\n", | |
| " <td>0.220642</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5</td>\n", | |
| " <td>0.557000</td>\n", | |
| " <td>0.412838</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>6</td>\n", | |
| " <td>0.588485</td>\n", | |
| " <td>0.588485</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>7</td>\n", | |
| " <td>0.471580</td>\n", | |
| " <td>0.593633</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>8</td>\n", | |
| " <td>0.509826</td>\n", | |
| " <td>0.764738</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>9</td>\n", | |
| " <td>0.498654</td>\n", | |
| " <td>0.851255</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>10</td>\n", | |
| " <td>0.366607</td>\n", | |
| " <td>0.684097</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>11</td>\n", | |
| " <td>0.431500</td>\n", | |
| " <td>0.848297</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>12</td>\n", | |
| " <td>0.379588</td>\n", | |
| " <td>0.759176</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>13</td>\n", | |
| " <td>0.523362</td>\n", | |
| " <td>1.028892</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>14</td>\n", | |
| " <td>0.436434</td>\n", | |
| " <td>0.814397</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>15</td>\n", | |
| " <td>0.413217</td>\n", | |
| " <td>0.705405</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>16</td>\n", | |
| " <td>0.329830</td>\n", | |
| " <td>0.494745</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>17</td>\n", | |
| " <td>0.686149</td>\n", | |
| " <td>0.863737</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>18</td>\n", | |
| " <td>0.514521</td>\n", | |
| " <td>0.514521</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>19</td>\n", | |
| " <td>0.485645</td>\n", | |
| " <td>0.359951</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>20</td>\n", | |
| " <td>0.472008</td>\n", | |
| " <td>0.236004</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>21</td>\n", | |
| " <td>0.496440</td>\n", | |
| " <td>0.145404</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>22</td>\n", | |
| " <td>0.566665</td>\n", | |
| " <td>0.075919</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>23</td>\n", | |
| " <td>0.535262</td>\n", | |
| " <td>0.018239</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " val1 val2\n", | |
| "datetime \n", | |
| "0 0.401714 0.000000\n", | |
| "1 0.611881 0.020849\n", | |
| "2 0.552599 0.074034\n", | |
| "3 0.539660 0.158063\n", | |
| "4 0.441285 0.220642\n", | |
| "5 0.557000 0.412838\n", | |
| "6 0.588485 0.588485\n", | |
| "7 0.471580 0.593633\n", | |
| "8 0.509826 0.764738\n", | |
| "9 0.498654 0.851255\n", | |
| "10 0.366607 0.684097\n", | |
| "11 0.431500 0.848297\n", | |
| "12 0.379588 0.759176\n", | |
| "13 0.523362 1.028892\n", | |
| "14 0.436434 0.814397\n", | |
| "15 0.413217 0.705405\n", | |
| "16 0.329830 0.494745\n", | |
| "17 0.686149 0.863737\n", | |
| "18 0.514521 0.514521\n", | |
| "19 0.485645 0.359951\n", | |
| "20 0.472008 0.236004\n", | |
| "21 0.496440 0.145404\n", | |
| "22 0.566665 0.075919\n", | |
| "23 0.535262 0.018239" | |
| ] | |
| }, | |
| "execution_count": 64, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# median value by hour\n", | |
| "df.groupby([df['datetime'].dt.hour]).median()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 67, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x11ff5d890>" | |
| ] | |
| }, | |
| "execution_count": 67, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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Jlcel6tLVZe0psmvoCFmdomnr833TzgXeAHxJ0iGy5yKPSirkMZhuClsnfHbvEbbe9iDXfexhtt72IKN7j5z9l8xKVmSLYAzYLGkTWQK4Fvj56Tcj4iiwdvq1pC8Bv1bkrCE3ha1Z0XfqLv1g3aKwRBARJyXdANxPNn30jojYL+kWYDwiRov67Pm4KWxQzgwyj0tZtyh0jCAi7gPum7Hv5jmOvbLIWMymlXWn7nGpDmg0PAmiBa41ZMkpq26Qx6VKNjKSrZrfvj3bjox0OqKu4VpDlpwy79Q9LlWSRiOrnzU5eXrV/NBQNk3aLYOzcovAklP2nfpinqNrLZoundJsunSKnZVbBJYk36nXjEuntMUtAkuW79RbU8lnGLh0SlvcIjCzBat04UaXTmmZE4GZLUhXLJBz6ZSWuGvIzBakyo/rtPY4EZjZgniBXH05EZjZgniBXH15jMDMFszTbuvJicDMFsWFG+vHXUNmZolzIjAzS5wTgZlZ4pwIzMwS50RgZpY4JwIzs8Q5EZiZJc6JwMwscU4EZmaJcyIwM0ucE4GZpa3RgLGxbJsoJwIzS9fICFxwAWzfnm1HRjodUUc4EZhZmhoNGBqCyUk4ejTbDg0l2TJwIqgDN23NFu/QIVi58sx9K1Zk+xPjRNDt3LQ1a01/P5w4cea+qalsf2KcCLqZm7Zmrevrg+Fh6O2FNWuy7fBwtj8xfjBNN5tu2k42PTx8ummb4JfZbNEGB2Hbtuz/mf7+ZP+/KbRFIGmHpMclHZR00yzv/6qkxyTtk/RFSRcUGU/tuGlr1r6+PrjssmSTABSYCCT1ALuBq4AtwKCkLTMO+wYwEBGXAPcAHy4qnlpy09bMlkCRXUOXAwcj4gkASXcBO4HHpg+IiIeajv86cF2B8dSTm7Zm1qYiE8E64Kmm1xPAFfMcPwR8frY3JO0CdgFs3LhxqeKrj74+JwAza1klZg1Jug4YAD4y2/sRsSciBiJioM8XPDOzJVVki+AIsKHp9fp83xkkbQN+A/j3EXG8wHjMzGwWRbYIxoDNkjZJWglcC4w2HyDpjcCfAldHxDMFxmJmZnMoLBFExEngBuB+4NvA3RGxX9Itkq7OD/sIsBr4tKS9kkbn+HNmZlaQQheURcR9wH0z9t3c9PO2Ij+/KzUangFkZqWqxGCx5Vw3yMw6wImgKlw3yKx71KzirxNBVbgkrll3qGHL3YmgKlw3yKz6atpydyKoCtcNMqu+mrbcXYa6Slw3yKzaatpyd4ugalwS16y6atpyd4vAzGwxathydyIwM1usmlX8ddeQmVninAjMzBLnRGBmljgnAjOzxDkRmJklzonAzCxxTgRFqlmFQjNrQ4WvB04ERalhhUIza1HFrweKiE7HsCgDAwMxPj7e6TDm12hk/7EnJ0/v6+2Fw4drtQjFzBagItcDSY9ExMBs77lFUISaVig0sxZ0wfXAiaAINa1QaGYt6ILrgRNBEWpaodDMWtAF1wOPERSp0ahVhUIza0OHrwfzjRG4+miRalah0MzaUOHrgbuGzMwS50RgZpY4J4KFqvCqQDOzdjgRLETFVwWaWU2VdAPqRHA2jQYMDWWrAo8ezbZDQ24ZmFmxSrwBdSI4my5YFWhmNVPyDWihiUDSDkmPSzoo6aZZ3l8l6VP5+w9L6i8yHmDxTa0uWBVoZjVT8g1oYYlAUg+wG7gK2AIMStoy47Ah4IWIuBD4KHBbUfEArTW1umBVoJnVTMk3oEW2CC4HDkbEExFxArgL2DnjmJ3Ax/Of7wF+WpIKiaadptbgYFYp8IEHsu3gYCEhmpkBpd+AFrmyeB3wVNPrCeCKuY6JiJOSjgLnA882HyRpF7ALYOPGja1FM93Uai4FO93UWsjJrfCqQDOrocFB2LatlLIUXVFiIiL2AHsgqzXU0h9xX7+ZdZuSbkCL7Bo6Amxoer0+3zfrMZKWA68CniskGvf1m5nNqsgWwRiwWdImsgv+tcDPzzhmFHgn8DXg7cCDUWQ51BKbWmZm3aKwRJD3+d8A3A/0AHdExH5JtwDjETEKDAOfkHQQeJ4sWRTLff1mZmcodIwgIu4D7pux7+amn18CfrbIGMzMbH5eWWxmljgnAjOzxDkRmJklzonAzCxxTgRmZolTkdP2iyCpARxu88+sZUYZi0T5PPgcgM8BpHEOLoiIWefOd10iWAqSxiNioNNxdJrPg88B+ByAz4G7hszMEudEYGaWuFQTwZ5OB1ARPg8+B+BzAImfgyTHCMzM7LRUWwRmZpZzIjAzS1xyiUDSDkmPSzoo6aZOx9MJkg5J+qakvZLGOx1PWSTdIekZSd9q2neepL+S9J18+5pOxli0Oc7Bb0s6kn8f9kp6aydjLJqkDZIekvSYpP2Sfjnfn9R3oVlSiUBSD7AbuArYAgxK2tLZqDrmJyPi0sTmTt8J7Jix7ybgixGxGfhi/rrO7uSV5wDgo/n34dK8fHydnQRujIgtwJuB6/PrQGrfhX+RVCIALgcORsQTEXECuAvY2eGYrCQR8WWyByA12wl8PP/548A1pQZVsjnOQVIi4umI+Jv852PAt4F1JPZdaJZaIlgHPNX0eiLfl5oAviDpEUm7Oh1Mh/1QRDyd//yPwA91MpgOukHSvrzrKJkuEUn9wBuBh0n4u5BaIrDMj0fEm8i6yK6X9BOdDqgK8udlpzif+o+B1wOXAk8Dt3c2nHJIWg3cC/xKRHy/+b3UvgupJYIjwIam1+vzfUmJiCP59hngM2RdZqn6rqTXAeTbZzocT+ki4rsR8XJEnAL+jAS+D5JWkCWBP4+Iv8h3J/tdSC0RjAGbJW2StBK4FhjtcEylkvSDks6d/hl4C/Ct+X+r1kaBd+Y/vxP4bAdj6Yjpi1/uP1Dz74MkAcPAtyPi95reSva7kNzK4nxq3O8DPcAdEfHfOxxSqST9K7JWAMBy4JOpnANJI8CVZCWHvwv8FvCXwN3ARrLy5u+IiNoOps5xDq4k6xYK4BDwn5v6ymtH0o8DXwG+CZzKd/862ThBMt+FZsklAjMzO1NqXUNmZjaDE4GZWeKcCMzMEudEYGaWOCcCM7PEORFY8vLqm782z/vXLKQ44czjJN0iadtSxWlWFCcCs7O7hqxa7aKOi4ibI+KBwqIyWyJOBJYkSb8h6YCk/wv863zfuyWNSXpU0r2SfkDSjwFXAx/Ja/W/Pv/3f/KifV+R9G/mOO5OSW/P//YhSR+afgaEpDdJul/S30n6paa43pPHsE/SBztwaixByzsdgFnZJP0oWXmRS8n+H/gb4BHgLyLiz/JjfgcYioj/IWkU+FxE3JO/90XglyLiO5KuAP4oIn5qluNmfvTfR8Slkj5K9lyArcA5ZCUd/kTSW4DNZLV+BIxK+om8dLRZYZwILEX/DvhMRPwzQH4BB3hDngBeDawG7p/5i3nFyh8DPt10oV+1wM+d/pxvAqvzWvjHJB2X9Gqyuk9vAb6RH7eaLDE4EVihnAjMTrsTuCYiHpX0LrIaPDMtA74XEZe28PeP59tTTT9Pv15O1gr4UET8aQt/26xlHiOwFH0ZuEZSb16J9Wfy/ecCT+clin+h6fhj+XvkdeuflPSzkFWylPQjM49r0f3Af8pbHUhaJ+m1bfw9swVxIrDk5I8p/BTwKPB5svLkAB8gq0D5VeBvm37lLuA9kr4h6fVkSWJI0qPAfk4/7nTmcYuN6wvAJ4GvSfomcA/tJRazBXH1UTOzxLlFYGaWOCcCM7PEORGYmSXOicDMLHFOBGZmiXMiMDNLnBOBmVni/j8UgldFMoynwQAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "ax = df.groupby([df['datetime'].dt.hour]).median().reset_index().plot(kind='scatter',\n", | |
| " x='datetime',y='val1')\n", | |
| "\n", | |
| "df.groupby([df['datetime'].dt.hour]).median().reset_index().plot(kind='scatter',\n", | |
| " x='datetime',y='val2', color='red', ax=ax)" | |
| ] | |
| } | |
| ], | |
| "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": "3.7.5" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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