{ "cells": [ { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "Data from [European Centre for Disease Prevention and Control](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide)" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sql extension is already loaded. To reload it, use:\n", " %reload_ext sql\n" ] } ], "source": [ "import itertools\n", "import collections\n", "import json\n", "import pandas as pd\n", "import numpy as np\n", "from scipy.stats import gmean\n", "import datetime\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "%load_ext sql" ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "connection_string = 'postgresql://covid:3NbjJTkT63@localhost/covid'" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "Collapsed": "false" }, "outputs": [ { "data": { "text/plain": [ "'Connected: covid@covid'" ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%sql $connection_string" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "# DEATH_COUNT_THRESHOLD = 10\n", "COUNTRIES_CORE = tuple('IT DE UK ES IE FR BE'.split())\n", "# COUNTRIES_NORDIC = 'SE NO DK FI UK'.split()\n", "# COUNTRIES_FRIENDS = 'IT UK ES BE SI MX'.split()\n", "# # COUNTRIES_FRIENDS = 'IT UK ES BE SI PT'.split()\n", "\n", "# COUNTRIES_AMERICAS = ['AG', 'AR', 'AW', 'BS', 'BB', 'BZ', 'BM', 'BO', 'BR', 'VG', 'KY', # excluding Canada and USA\n", "# 'CL', 'CO', 'CR', 'CU', 'CW', 'DM', 'DO', 'EC', 'SV', 'GL', 'GD', 'GT',\n", "# 'GY', 'HT', 'HN', 'JM', 'MX', 'MS', 'NI', 'PA', 'PY', 'PE', 'PR', 'KN',\n", "# 'LC', 'VC', 'SX', 'SR', 'TT', 'TC', 'VI', 'UY', 'VE']\n", "# COUNTRIES_OF_INTEREST = list(set(COUNTRIES_CORE + COUNTRIES_FRIENDS))\n", "# COUNTRIES_ALL = list(set(COUNTRIES_CORE + COUNTRIES_FRIENDS + COUNTRIES_NORDIC + COUNTRIES_AMERICAS))" ] }, { "cell_type": "markdown", "metadata": { "Collapsed": "false" }, "source": [ "# Write results to summary file" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * postgresql://covid:***@localhost/covid\n", "1 rows affected.\n" ] }, { "data": { "text/plain": [ "datetime.date(2021, 1, 26)" ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" } ], "source": [ "last_uk_date = %sql select date from uk_data order by date desc limit 1\n", "last_uk_date = last_uk_date[0][0]\n", "last_uk_date" ] }, { "cell_type": "code", "execution_count": 197, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * postgresql://covid:***@localhost/covid\n", "1 rows affected.\n" ] }, { "data": { "text/plain": [ "datetime.date(2021, 1, 18)" ] }, "execution_count": 197, "metadata": {}, "output_type": "execute_result" } ], "source": [ "last_intl_date = %sql select report_date from weekly_cases order by report_date desc limit 1\n", "last_intl_date = last_intl_date[0][0]\n", "last_intl_date" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * postgresql://covid:***@localhost/covid\n", "390 rows affected.\n", "Returning data to local variable results\n" ] } ], "source": [ "%%sql results << select date, new_cases, new_deaths \n", "from uk_data \n", "order by date" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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new_casesnew_deaths
date
2021-01-1728875.0671
2021-01-1844732.0599
2021-01-1939311.01610
2021-01-2035015.01820
2021-01-2131430.01290
2021-01-2229094.01401
2021-01-2320495.01348
2021-01-2414266.0610
2021-01-254482.0592
2021-01-26NaN1631
\n", "
" ], "text/plain": [ " new_cases new_deaths\n", "date \n", "2021-01-17 28875.0 671\n", "2021-01-18 44732.0 599\n", "2021-01-19 39311.0 1610\n", "2021-01-20 35015.0 1820\n", "2021-01-21 31430.0 1290\n", "2021-01-22 29094.0 1401\n", "2021-01-23 20495.0 1348\n", "2021-01-24 14266.0 610\n", "2021-01-25 4482.0 592\n", "2021-01-26 NaN 1631" ] }, "execution_count": 199, "metadata": {}, "output_type": "execute_result" } ], "source": [ "uk_data = results.DataFrame()\n", "uk_data['date'] = uk_data.date.astype('datetime64[ns]')\n", "uk_data.set_index('date', inplace=True)\n", "uk_data.tail(10)" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * postgresql://covid:***@localhost/covid\n", "1 rows affected.\n" ] }, { "data": { "text/plain": [ "datetime.date(2021, 1, 26)" ] }, "execution_count": 200, "metadata": {}, "output_type": "execute_result" } ], "source": [ "most_recent_uk_date = %sql select max(date) from uk_data\n", "most_recent_uk_date = most_recent_uk_date[0][0]\n", "most_recent_uk_date" ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * postgresql://covid:***@localhost/covid\n", "7 rows affected.\n", "Returning data to local variable results\n" ] } ], "source": [ "%%sql results << select geo_id, country_name, culm_deaths \n", "from weekly_cases join countries using (geo_id)\n", "where geo_id in :COUNTRIES_CORE \n", " and (geo_id, report_date) in (select geo_id, max(report_date) from weekly_cases group by geo_id)\n", "order by geo_id" ] }, { "cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.date(2020, 12, 27)" ] }, "execution_count": 202, "metadata": {}, "output_type": "execute_result" } ], "source": [ "thirty_days_ago = most_recent_uk_date - datetime.timedelta(days=30)\n", "thirty_days_ago" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * postgresql://covid:***@localhost/covid\n", "1 rows affected.\n", " * postgresql://covid:***@localhost/covid\n", "1 rows affected.\n", " * postgresql://covid:***@localhost/covid\n", "1 rows affected.\n" ] }, { "data": { "text/plain": [ "(100184, 29366, 1283174)" ] }, "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# thirty_days_ago = most_recent_uk_date - datetime.interval(days=30)\n", "total_uk_deaths = %sql select sum(new_deaths) from uk_data\n", "total_uk_deaths = total_uk_deaths[0][0]\n", "deaths_in_past_month = %sql select sum(new_deaths) from uk_data where date > :thirty_days_ago\n", "deaths_in_past_month = deaths_in_past_month[0][0]\n", "cases_in_past_month = %sql select sum(new_cases) from uk_data where date > :thirty_days_ago\n", "cases_in_past_month = cases_in_past_month[0][0]\n", "total_uk_deaths, deaths_in_past_month, cases_in_past_month" ] }, { "cell_type": "code", "execution_count": 204, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "with open('covid_summary.md', 'w') as f:\n", " f.write('% Covid death data summary\\n')\n", " f.write('% Neil Smith\\n')\n", " f.write(f'% Created on {datetime.datetime.now().strftime(\"%Y-%m-%d\")}\\n')\n", " f.write('\\n') \n", " f.write(f'> Last UK data from {last_uk_date.strftime(\"%d %b %Y\")}. ')\n", " f.write(f' Last international data from {last_intl_date.strftime(\"%d %b %Y\")}.\\n')\n", " f.write('\\n') " ] }, { "cell_type": "code", "execution_count": 205, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "with open('covid_summary.md', 'a') as f:\n", " f.write('## Headlines (UK data)\\n')\n", " f.write('\\n')\n", " f.write('| []() | |\\n')\n", " f.write('|:---|---:|\\n')\n", " f.write(f'| Deaths reported so far | {total_uk_deaths} | \\n')\n", " f.write(f'| Deaths in last 30 days | {deaths_in_past_month} | \\n')\n", " f.write(f'| Cases in last 30 days | {cases_in_past_month} | \\n')\n", "# f.write(f'| Total Covid deaths to date (estimated) | {uk_deaths_to_date:.0f} |\\n')\n", " f.write('\\n')" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "with open('covid_summary.md', 'a') as f:\n", " f.write('## International comparison\\n')\n", " f.write('\\n')\n", " f.write(f'Based on weekly data. Last data from {last_intl_date.strftime(\"%d %b %Y\")}\\n')\n", " f.write('\\n')\n", " f.write('### Total deaths\\n')\n", " f.write('\\n')\n", " f.write('![Total deaths](covid_deaths_total_linear.png)\\n')\n", " f.write('\\n')\n", " f.write('| Country ID | Country name | Total deaths |\\n')\n", " f.write('|:-----------|:-------------|-------------:|\\n')\n", " for c_id, c_name, t_deaths in results:\n", " f.write(f'| {c_id} | {c_name} | {t_deaths} |\\n')\n", " f.write('\\n')" ] }, { "cell_type": "code", "execution_count": 207, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "# with open('covid_summary.md', 'a') as f:\n", "# f.write('## All-causes deaths, UK\\n')\n", "# f.write('\\n')\n", "# f.write('![All-causes deaths](deaths-radar.png)\\n')\n", "# f.write('\\n')\n", "# f.write('### True deaths\\n')\n", "# f.write('\\n')\n", "# f.write(f'The number of deaths reported in official statistics, {uk_covid_deaths}, is an underestimate '\n", "# 'of the true number of Covid deaths.\\n'\n", "# 'This is especially true early in the pandemic, approximately March to May 2020.\\n')\n", "# f.write('We can get a better understanding of the impact of Covid by looking at the number of deaths, '\n", "# 'over and above what would be expected at each week of the year.\\n')\n", "# f.write(f'The ONS (and other bodies in Scotland and Northern Ireland) have released data on the number of deaths '\n", "# f'up to {pd.to_datetime(excess_deaths_data[\"end_date\"]).strftime(\"%d %B %Y\")}.\\n\\n')\n", "# f.write('If, for each of those weeks, I take the largest of the excess deaths or the reported Covid deaths, ')\n", "# f.write(f'I estimate there have been **{uk_deaths_to_date}** total deaths so far.\\n')\n", "# f.write('\\n')" ] }, { "cell_type": "code", "execution_count": 208, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "with open('covid_summary.md', 'a') as f:\n", " f.write('### Deaths per week\\n')\n", " f.write('\\n')\n", " f.write('![Deaths per week](covid_deaths_per_week.png)\\n')\n", " f.write('\\n')\n", " f.write('![Deaths per week, last 6 weeks](deaths_by_date_last_6_weeks.png)\\n')\n", " f.write('\\n')" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [], "source": [ "with open('covid_summary.md', 'a') as f:\n", " f.write('## UK data\\n')\n", " f.write('\\n')\n", " f.write('### Total deaths\\n')\n", " f.write('\\n')\n", " f.write(f'Deaths reported up to {last_uk_date.strftime(\"%d %b %Y\")}: {total_uk_deaths}\\n')\n", " f.write('\\n') \n", " f.write('![Total deaths](cases_and_deaths.png)\\n')\n", " f.write('\\n') \n", " f.write('![Cases and deaths in last 60 days](cases_and_deaths_last_60_days.png)\\n')\n", " f.write('\\n')\n", " f.write('![Deaths compared to past five years](deaths-radar-2020.png)\\n')\n", " f.write('\\n')\n", " " ] }, { "cell_type": "code", "execution_count": 210, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "with open('hospital_normalisation_date.json') as f:\n", " hospital_normalisation_date_data = json.load(f)" ] }, { "cell_type": "code", "execution_count": 211, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "with open('covid_summary.md', 'a') as f:\n", " f.write('### Hospital care\\n')\n", " f.write(f'Based on a 7-day moving average\\n')\n", " f.write('\\n')\n", " f.write('![Cases, admissions, deaths](cases_admissions_deaths.png)\\n')\n", " f.write('\\n')\n", " f.write('Due to the large scale differences between the three '\n", " 'measures, they are all normalised to show changes ')\n", " f.write(f'since {pd.to_datetime(hospital_normalisation_date_data[\"hospital_normalisation_date\"]).strftime(\"%d %B %Y\")}.\\n')\n", " f.write('\\n')\n", " f.write('People in hospital, and on mechanical ventilators\\n')\n", " f.write('\\n')\n", " f.write('![People in hospital and on mechancial ventilators](people_in_hospital.png)\\n')\n", " f.write('\\n')" ] }, { "cell_type": "code", "execution_count": 212, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "with open('covid_summary.md', 'a') as f:\n", " f.write('### Testing effectiveness\\n')\n", " f.write('\\n')\n", " f.write('A question about testing is whether more detected cases is a result of more tests being '\n", " 'done or is because the number of cases is increasing. One way of telling the differeence '\n", " 'is by looking at the fraction of tests that are positive.\\n')\n", " f.write('\\n')\n", " f.write('![Positive tests and cases](tests_and_cases.png)\\n')\n", " f.write('\\n')\n", " f.write('Numbers of positive tests and cases, '\n", " '7-day moving average.\\n'\n", " 'Note the different y-axes\\n')\n", " f.write('\\n') \n", " f.write('![Fraction of tests with positive result](fraction_positive_tests.png)\\n')\n", " f.write('\\n')\n", " f.write('Fraction of tests with a positive result, both daily figures and '\n", " '7-day moving average.\\n')\n", " f.write('\\n') \n", " f.write('\\n')\n", " f.write('![Tests against fraction positive, trajectory](fraction_positive_tests_vs_tests.png)\\n')\n", " f.write('\\n')\n", " f.write('The trajectory of tests done vs fraction positive tests.\\n')\n", " f.write('\\n')\n", " f.write('Points higher indicate more tests; points to the right indicate more positive tests.'\n", " 'More tests being done with the same infection prevelance will move the point up '\n", " 'and to the left.\\n')\n", " f.write('\\n')\n", " f.write('\\n')\n", " f.write('![Tests against fraction positive, trajectory](tests_vs_fraction_positive_animation.png)\\n')\n", " f.write('\\n')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 213, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "with open('covid_summary.md', 'a') as f:\n", " f.write('# Data sources\\n')\n", " f.write('\\n')\n", " f.write('> Covid data from [European Centre for Disease Prevention and Control](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide)\\n')\n", " f.write('\\n') \n", " f.write(\"\"\"> Population data from:\n", "\n", "* [Office of National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales) (Endland and Wales) Weeks start on a Saturday.\n", "* [Northern Ireland Statistics and Research Agency](https://www.nisra.gov.uk/publications/weekly-deaths) (Northern Ireland). Weeks start on a Saturday. Note that the week numbers don't match the England and Wales data.\n", "* [National Records of Scotland](https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/general-publications/weekly-and-monthly-data-on-births-and-deaths/weekly-data-on-births-and-deaths) (Scotland). Note that Scotland uses ISO8601 week numbers, which start on a Monday.\"\"\")\n", " \n", " f.write('\\n\\n')\n", " f.write('> [Source code available](https://git.njae.me.uk/?p=covid19.git;a=tree)\\n')\n", " f.write('\\n') \n" ] }, { "cell_type": "code", "execution_count": 214, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "!pandoc --toc -s covid_summary.md > covid_summary.html" ] }, { "cell_type": "code", "execution_count": 215, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "covid_summary.html 100% 10KB 287.3KB/s 00:00 \n", "covid_deaths_total_linear.png 100% 45KB 1.8MB/s 00:00 \n", "cases_and_deaths.png 100% 62KB 5.3MB/s 00:00 \n", "cases_and_deaths_last_60_days.png 100% 62KB 8.4MB/s 00:00 \n", "deaths-radar-2020.png 100% 199KB 5.4MB/s 00:00 \n", "covid_deaths_per_week.png 100% 61KB 8.0MB/s 00:00 \n", "fraction_positive_tests.png 100% 59KB 2.8MB/s 00:00 \n", "tests_and_cases.png 100% 40KB 5.4MB/s 00:00 \n", "deaths_by_date_last_6_weeks.png 100% 33KB 5.4MB/s 00:00 \n", "fraction_positive_tests_vs_tests.png 100% 41KB 7.7MB/s 00:00 \n", "tests_vs_fraction_positive_animation.png 100% 1982KB 10.3MB/s 00:00 \n", "people_in_hospital.png 100% 42KB 8.0MB/s 00:00 \n", "cases_admissions_deaths.png 100% 44KB 2.3MB/s 00:00 \n" ] } ], "source": [ "!scp covid_summary.html neil@ogedei:/var/www/scripts.njae.me.uk/covid/index.html\n", "!scp covid_deaths_total_linear.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp cases_and_deaths.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp cases_and_deaths_last_60_days.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "# !scp deaths-radar.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp deaths-radar-2020.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp covid_deaths_per_week.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp fraction_positive_tests.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/ \n", "!scp tests_and_cases.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp deaths_by_date_last_6_weeks.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp fraction_positive_tests_vs_tests.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp tests_vs_fraction_positive_animation.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/ \n", "!scp people_in_hospital.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp cases_admissions_deaths.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/" ] }, { "cell_type": "code", "execution_count": 216, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "with open('uk_covid_deaths.js', 'w') as f:\n", " f.write(f\"document.write('{total_uk_deaths}');\")\n", " \n", "with open('uk_deaths_30_days.js', 'w') as f:\n", " f.write(f\"document.write('{deaths_in_past_month}');\")\n", "\n", "with open('uk_cases_30_days.js', 'w') as f:\n", " f.write(f\"document.write('{cases_in_past_month}');\") \n", " \n", "# with open('estimated_total_deaths.js', 'w') as f:\n", "# f.write(f\"document.write('{uk_deaths_to_date:.0f}');\")\n", "\n", "# edut = pd.to_datetime(excess_deaths_data[\"end_date\"]).strftime('%d %B %Y')\n", "# with open('excess_deaths_upto.js', 'w') as f:\n", "# f.write(f\"document.write('{edut}');\")\n", " \n", "with open('last_uk_date.js', 'w') as f:\n", " f.write(f\"document.write('{pd.to_datetime(last_uk_date).strftime('%d %B %Y')}');\")\n", "\n", "with open('last_intl_date.js', 'w') as f:\n", " f.write(f\"document.write('{pd.to_datetime(last_intl_date).strftime('%d %B %Y')}');\")\n" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [ "# pd.to_datetime(excess_deaths_upto).strftime('%d %B %Y')" ] }, { "cell_type": "code", "execution_count": 218, "metadata": { "Collapsed": "false" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uk_covid_deaths.js 100% 25 2.0KB/s 00:00 \n", "uk_deaths_30_days.js 100% 24 1.2KB/s 00:00 \n", "uk_cases_30_days.js 100% 26 21.3KB/s 00:00 \n", "last_uk_date.js 100% 34 2.4KB/s 00:00 \n", "last_intl_date.js 100% 34 3.8KB/s 00:00 \n", "hospital_normalisation_date.js 100% 33 29.1KB/s 00:00 \n" ] } ], "source": [ "!scp uk_covid_deaths.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp uk_deaths_30_days.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp uk_cases_30_days.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "# !scp estimated_total_deaths.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "# !scp excess_deaths_upto.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp last_uk_date.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp last_intl_date.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/\n", "!scp hospital_normalisation_date.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Collapsed": "false" }, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,md" }, "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }