{ "cells": [ { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2020-10-16T02:59:09.541327Z", "start_time": "2020-10-16T02:59:09.528362Z" } }, "source": [ "# \"Covid19 in Indonesia\"\n", "> \"My attempt to visualise Covid19 in Indonesia\"\n", "\n", "- toc: True\n", "- branch: master\n", "- badges: true\n", "- categories: [Data Viz]\n", "- hide: false\n", "- search_exclude: true\n", "- metadata_key1: metadata_value1\n", "- metadata_key2: metadata_value2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is my practise to do EDA (exploratory data analysis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I used to be interested on the daily covid data when covid hit Indonesia back in March and April. It had been a while, so let's have a look at them now" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data will be scrapped from:\n", "* https://github.com/CSSEGISandData/COVID-19\n", "* Our World in Data (for Indonesia): https://github.com/owid/covid-19-data/tree/master/public/data\n", "* Covid data for Indonesia from SINTA (via KawalCOVID): http://sinta.ristekbrin.go.id/covid/datasets\n", "* GEOjson for Indonesia: https://bitbucket.org/rifani/geojson-political-indonesia/src/master/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summary of this notebook:\n", "* Download Covid19 data for worldwide and Indonesia from several sources\n", "* Download GEOjson map for Indonesia\n", "* Worldwide: summary of latest data, worldwide map, death vs confirmed comparison\n", "* Indonesia: summary of latest data, summary plots (total cases, daily cases, positive rate and mortality rate) and other random stats that I am interested in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New skills I picked up and applied on this notebook:\n", "* First time using Git properly\n", "* Using Plotly Express\n", "* Extracting data from Google Sheet API\n", "* Cleaning data. The spreadsheet is messy. Table are stacked on other tables in the same spreadsheet tab\n", "* Extracted data is string. Not sure if there is a way to extract in a numeric format instead of converting it to float manually. For next time, maybe there is a way to just download from Google Sheet automatically and just pd.read_csv()\n", "* Working with GEOjson data format and plotting an interactive map\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Covid19 in Indonesia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import necessary python libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:41.658412Z", "start_time": "2020-12-14T08:53:38.803586Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#collapse\n", "# download python libraries\n", "from datetime import datetime, timedelta\n", "import os\n", "import glob\n", "import wget\n", "from bs4 import BeautifulSoup\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import geopandas as gpd\n", "import json\n", "import plotly.express as px\n", "import plotly.graph_objs as go\n", "\n", "# for offline ploting\n", "from plotly.offline import plot, iplot, init_notebook_mode\n", "init_notebook_mode(connected=True)\n", "\n", "from IPython.display import HTML" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last updated:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:41.674410Z", "start_time": "2020-12-14T08:53:41.659416Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "text/plain": [ "datetime.date(2020, 12, 14)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# collapse\n", "from datetime import date\n", "date.today()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:43.139389Z", "start_time": "2020-12-14T08:53:41.677410Z" }, "code_folding": [ 0 ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100% [............................................................................] 347677 / 347677" ] } ], "source": [ "#collapse\n", "# Download data from Github (daily)\n", "os.chdir(\"C:/Users/Riyan Aditya/Desktop/ML_learning/Project4_EDA_Covid_Indo/datasets\")\n", "\n", "os.remove('time_series_covid19_confirmed_global.csv')\n", "os.remove('time_series_covid19_deaths_global.csv')\n", "os.remove('time_series_covid19_recovered_global.csv')\n", "\n", "# urls of the files\n", "urls = ['https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv', \n", " 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv',\n", " 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv']\n", "\n", "# download files\n", "for url in urls:\n", " filename = wget.download(url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Clean & preprocess data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:43.250210Z", "start_time": "2020-12-14T08:53:43.140356Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "#collapse\n", "# convert csv to df\n", "confirmed_global = pd.read_csv('time_series_covid19_confirmed_global.csv')\n", "deaths_global = pd.read_csv('time_series_covid19_deaths_global.csv')\n", "recovered_global = pd.read_csv('time_series_covid19_recovered_global.csv')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:43.313175Z", "start_time": "2020-12-14T08:53:43.251176Z" }, "code_folding": [ 0 ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(88617, 6)\n", "(88617, 6)\n", "(83712, 6)\n" ] } ], "source": [ "#collapse\n", "# Melt DF => switch rows of dates into column for simpler DF\n", "dates = confirmed_global.columns[4:]\n", "\n", "confirmed_globalv2 = confirmed_global.melt(id_vars = ['Province/State', 'Country/Region', 'Lat', 'Long'],\n", " value_vars = dates, var_name ='Date', value_name = 'Confirmed')\n", "deaths_globalv2 = deaths_global.melt(id_vars = ['Province/State', 'Country/Region', 'Lat', 'Long'],\n", " value_vars = dates, var_name ='Date', value_name = 'Deaths')\n", "recovered_globalv2 = recovered_global.melt(id_vars = ['Province/State', 'Country/Region', 'Lat', 'Long'],\n", " value_vars = dates, var_name ='Date', value_name = 'Recovered')\n", "print(confirmed_globalv2.shape)\n", "print(deaths_globalv2.shape)\n", "print(recovered_globalv2.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Why are there differences in number of rows between confirmed (or death) & recovered?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This seems to suggest some countries are missing their data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:43.533382Z", "start_time": "2020-12-14T08:53:43.314176Z" }, "code_folding": [ 2 ] }, "outputs": [], "source": [ "#collapse\n", "# Combine df\n", "covid_global = confirmed_globalv2.merge(deaths_globalv2, how='left', on = \n", " ['Province/State', 'Country/Region', 'Lat', 'Long','Date']).merge(\n", " recovered_globalv2, how='left', on =\n", " ['Province/State', 'Country/Region', 'Lat', 'Long','Date'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:43.595754Z", "start_time": "2020-12-14T08:53:43.534176Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "#collapse\n", "# preprocessing\n", "covid_global['Date'] = pd.to_datetime(covid_global['Date'])\n", "\n", "#active cases\n", "covid_global['Active'] = covid_global['Confirmed'] - covid_global['Deaths'] - covid_global['Recovered']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grouby data by day" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:43.627373Z", "start_time": "2020-12-14T08:53:43.597713Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "#collapse\n", "# Data by day\n", "covid_global_daily = covid_global.groupby('Date')['Confirmed','Deaths','Recovered','Active'].sum().reset_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grouby data by country" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:43.801084Z", "start_time": "2020-12-14T08:53:43.629046Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "#collapse\n", "# Data by country\n", "temp = covid_global[covid_global['Date'] ==max(covid_global['Date'])].reset_index(drop=True).drop('Date', axis = 1)\n", "covid_global_percountry = temp.groupby('Country/Region')['Confirmed','Deaths','Recovered','Active'].sum().reset_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Worldwide Data Viz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Latest data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show latest data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:43.833047Z", "start_time": "2020-12-14T08:53:43.805053Z" }, "code_folding": [ 0 ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Date today 2020-12-13 00:00:00\n", "Total cases 72,252,541\n", "Active cases 23,312,234.0\n", "Recovered cases 46,849,822.0\n", "Deaths cases 1,612,362\n" ] } ], "source": [ "#collapse\n", "# latest data\n", "print('Date today',covid_global_daily['Date'].iloc[-1])\n", "print('Total cases','{:,}'.format(covid_global_daily['Confirmed'].iloc[-1]))\n", "print('Active cases','{:,}'.format(covid_global_daily['Active'].iloc[-1]))\n", "print('Recovered cases','{:,}'.format(covid_global_daily['Recovered'].iloc[-1]))\n", "print('Deaths cases','{:,}'.format(covid_global_daily['Deaths'].iloc[-1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have reached 1M global death =(" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:44.611086Z", "start_time": "2020-12-14T08:53:43.834048Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "#collapse\n", "# plot\n", "temp = covid_global_daily[['Date','Deaths','Recovered','Active']].tail(1)\n", "temp = temp.melt(id_vars='Date',value_vars = ['Active','Deaths','Recovered'])\n", "fig = px.treemap(temp, path=['variable'],values = 'value', height = 225)\n", "fig.data[0].textinfo = 'label+text+value'" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:44.627087Z", "start_time": "2020-12-14T08:53:44.612088Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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level_0indexlocationnew_tests_per_thousand
00178United Arab Emirates15.816
1112Bahrain5.914
2293Latvia5.112
3399Lithuania4.430
4443Cyprus4.265
55139Russia3.721
66149Serbia3.204
779Austria3.099
88130Panama3.082
9974Hungary3.036
101041Croatia2.946
111183Italy2.838
121281Ireland2.565
1313175Turkey2.445
141430Canada2.422
151544Czechia2.193
161687Kazakhstan2.156
1717181Uruguay1.838
181834Chile1.701
191966Greece1.662
2020138Romania1.634
21218Australia1.461
222225Bulgaria1.235
2323129Palestine1.221
2424104Maldives1.171
2525177Ukraine0.944
2626118Namibia0.792
2727157South Africa0.762
282836Colombia0.725
292976India0.669
3030161Sri Lanka0.647
313179Iran0.540
3232158South Korea0.527
3333115Morocco0.520
3434140Rwanda0.282
3535128Pakistan0.182
3636172Togo0.111
373713Bangladesh0.099
3838173Trinidad and Tobago0.096
3939116Mozambique0.067
404057Ethiopia0.050
4141124Nigeria0.036
4242167Taiwan0.018
\n", "
" ], "text/plain": [ " level_0 index location new_tests_per_thousand\n", "0 0 178 United Arab Emirates 15.816\n", "1 1 12 Bahrain 5.914\n", "2 2 93 Latvia 5.112\n", "3 3 99 Lithuania 4.430\n", "4 4 43 Cyprus 4.265\n", "5 5 139 Russia 3.721\n", "6 6 149 Serbia 3.204\n", "7 7 9 Austria 3.099\n", "8 8 130 Panama 3.082\n", "9 9 74 Hungary 3.036\n", "10 10 41 Croatia 2.946\n", "11 11 83 Italy 2.838\n", "12 12 81 Ireland 2.565\n", "13 13 175 Turkey 2.445\n", "14 14 30 Canada 2.422\n", "15 15 44 Czechia 2.193\n", "16 16 87 Kazakhstan 2.156\n", "17 17 181 Uruguay 1.838\n", "18 18 34 Chile 1.701\n", "19 19 66 Greece 1.662\n", "20 20 138 Romania 1.634\n", "21 21 8 Australia 1.461\n", "22 22 25 Bulgaria 1.235\n", "23 23 129 Palestine 1.221\n", "24 24 104 Maldives 1.171\n", "25 25 177 Ukraine 0.944\n", "26 26 118 Namibia 0.792\n", "27 27 157 South Africa 0.762\n", "28 28 36 Colombia 0.725\n", "29 29 76 India 0.669\n", "30 30 161 Sri Lanka 0.647\n", "31 31 79 Iran 0.540\n", "32 32 158 South Korea 0.527\n", "33 33 115 Morocco 0.520\n", "34 34 140 Rwanda 0.282\n", "35 35 128 Pakistan 0.182\n", "36 36 172 Togo 0.111\n", "37 37 13 Bangladesh 0.099\n", "38 38 173 Trinidad and Tobago 0.096\n", "39 39 116 Mozambique 0.067\n", "40 40 57 Ethiopia 0.050\n", "41 41 124 Nigeria 0.036\n", "42 42 167 Taiwan 0.018" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# collapse\n", "df3 = df3.reset_index()\n", "df3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And Indonesia:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:58.044873Z", "start_time": "2020-12-14T08:53:58.032853Z" } }, "outputs": [ { "data": { "text/html": [ "
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indexlocationnew_tests_per_thousand
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [index, location, new_tests_per_thousand]\n", "Index: []" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# collapse\n", "df3[df3['location']=='Indonesia']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we only tests 8% of our total population. Certainly much lower compared to other countries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Indonesia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data starts 18 Mar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import data" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:53:58.060816Z", "start_time": "2020-12-14T08:53:58.046850Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "#collapse\n", "# find how many days in between 18 Mar to today (so we know how many rows to download)\n", "from datetime import date\n", "\n", "d0_total = date(2020, 3, 18)\n", "d0_harian = date(2020,3,15)\n", "d0_aktif = date(2020,3,21)\n", "d0_sembuh = date(2020,3,21)\n", "d0_deaths = date(2020,3,18)\n", "d1 = date.today()\n", "\n", "delta_total = d1 - d0_total\n", "delta_harian = d1 - d0_harian\n", "delta_aktif = d1 - d0_aktif\n", "delta_sembuh = d1 - d0_sembuh\n", "delta_deaths = d1 - d0_deaths" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2020-10-08T03:30:10.961202Z", "start_time": "2020-10-08T03:30:10.954221Z" } }, "source": [ "All df imported are strings. need to convert to int. Also had to remove all the comma in the thousand separator." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: you need \"Credentials.json\". You can get one by following this example: https://developers.google.com/sheets/api/quickstart/python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Code to import data from KawalCOVID:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Update 16 Oct: Kawal covid changed their datetime format in their \"statistik harian\"" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:54:02.117548Z", "start_time": "2020-12-14T08:53:58.061816Z" }, "code_folding": [ 0 ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Timeline!A1:AZ\n", "Statistik Harian!A1:AL\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Riyan Aditya\\Anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py:837: PerformanceWarning:\n", "\n", "Non-vectorized DateOffset being applied to Series or DatetimeIndex\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Copy of Data PCR - Kemenkes!J2:M36\n" ] } ], "source": [ "#collapse\n", "# %load GoogleSheet from KawalCovid19\n", "from __future__ import print_function\n", "import pickle\n", "import os.path\n", "from googleapiclient.discovery import build\n", "from google_auth_oauthlib.flow import InstalledAppFlow\n", "from google.auth.transport.requests import Request\n", "\n", "# If modifying these scopes, delete the file token.pickle.\n", "SCOPES = ['https://www.googleapis.com/auth/spreadsheets.readonly']\n", "\n", "# The ID and range of a sample spreadsheet.\n", "SAMPLE_SPREADSHEET_ID = '1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw'\n", "\n", "\n", "def main():\n", " \"\"\"Shows basic usage of the Sheets API.\n", " Prints values from a sample spreadsheet.\n", " \"\"\"\n", " creds = None\n", " # The file token.pickle stores the user's access and refresh tokens, and is\n", " # created automatically when the authorization flow completes for the first\n", " # time.\n", " if os.path.exists('token.pickle'):\n", " with open('token.pickle', 'rb') as token:\n", " creds = pickle.load(token)\n", " # If there are no (valid) credentials available, let the user log in.\n", " if not creds or not creds.valid:\n", " if creds and creds.expired and creds.refresh_token:\n", " creds.refresh(Request())\n", " else:\n", " flow = InstalledAppFlow.from_client_secrets_file(\n", " 'credentials.json', SCOPES)\n", " creds = flow.run_local_server(port=0)\n", " # Save the credentials for the next run\n", " with open('token.pickle', 'wb') as token:\n", " pickle.dump(creds, token)\n", "\n", " service = build('sheets', 'v4', credentials=creds)\n", " \n", "\n", "\n", " # Call the Sheets API for total kasus ----------------------------------------------------------------\n", " sheet = service.spreadsheets()\n", " \n", " SAMPLE_RANGE_NAME = 'Timeline!A1:AZ'\n", " print(SAMPLE_RANGE_NAME)\n", " \n", " result = sheet.values().get(spreadsheetId=SAMPLE_SPREADSHEET_ID,\n", " range=SAMPLE_RANGE_NAME).execute()\n", " values = result.get('values', [])\n", " df1 = pd.DataFrame(values)\n", " \n", " # Call the Sheets API for Statistik harian ----------------------------------------------------------------\n", " sheet = service.spreadsheets()\n", " SAMPLE_RANGE_NAME = 'Statistik Harian!A1:AL'\n", " print(SAMPLE_RANGE_NAME)\n", " \n", " result = sheet.values().get(spreadsheetId=SAMPLE_SPREADSHEET_ID,\n", " range=SAMPLE_RANGE_NAME).execute()\n", " values = result.get('values', [])\n", " \n", " df = pd.DataFrame(values)\n", " headers = df.iloc[0]\n", " covid_id = pd.DataFrame(df.values[1:], columns=headers)\n", " covid_id.columns.values[0] = \"Dates\"\n", " covid_id = covid_id.replace(',','', regex=True).replace('-',' ', regex=True)\n", " covid_id = covid_id.replace('',0, regex=True)\n", " covid_id = covid_id.replace('#DIV/0!',0, regex=True)\n", " covid_id = covid_id.replace('#REF!',0, regex=True)\n", " covid_id = covid_id.set_index('Dates')\n", " covid_id = covid_id.replace('%','',regex=True).astype('float')/100\n", " covid_id = covid_id.astype('float')*100\n", " \n", " covid_id.index = pd.to_datetime(covid_id.index, format='%d %b')\n", " covid_id.index = covid_id.index + pd.DateOffset(year=2020)\n", " \n", " # Call the Sheets API for Population ----------------------------------------------------------------\n", " sheet = service.spreadsheets()\n", " \n", " SAMPLE_RANGE_NAME = 'Copy of Data PCR - Kemenkes!J2:M36'\n", " print(SAMPLE_RANGE_NAME)\n", " \n", " result = sheet.values().get(spreadsheetId=SAMPLE_SPREADSHEET_ID,\n", " range=SAMPLE_RANGE_NAME).execute()\n", " values = result.get('values', [])\n", " df2 = pd.DataFrame(values)\n", "\n", " # return DF --------------------------------------------------------------------------------------------------\n", " return df1, covid_id, df2\n", " \n", "\n", "indo_covid, covid_id, pop_id = main()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clean data" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:54:02.132986Z", "start_time": "2020-12-14T08:54:02.123950Z" } }, "outputs": [], "source": [ "#collapse\n", "indo_covid2 = indo_covid.copy()\n", "indo_covid2 = indo_covid2.iloc[:, :-1]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:54:02.147956Z", "start_time": "2020-12-14T08:54:02.134941Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "#collapse\n", "# find index of relevant tables\n", "index_tk = indo_covid2.loc[indo_covid2[indo_covid2.columns[0]] == 'Total Kasus'].index[0]\n", "index_ka = indo_covid2.loc[indo_covid2[indo_covid2.columns[0]] == 'Kasus Aktif'].index[0]\n", "index_ks = indo_covid2.loc[indo_covid2[indo_covid2.columns[0]] == 'Sembuh'].index[0]\n", "index_km = indo_covid2.loc[indo_covid2[indo_covid2.columns[0]] == 'Meninggal Dunia'].index[0]\n", "\n", "#index_tk, index_ka, index_ks, index_km" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:54:02.195465Z", "start_time": "2020-12-14T08:54:02.148939Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "#collapse\n", "#Split table into total cases, active cases, recovered cases and deaths\n", "#Then set dates as the index\n", "\n", "total_cases = indo_covid2[index_tk:index_tk+delta_total.days+1:].rename(\n", " columns=indo_covid2.iloc[0]).drop(indo_covid2.index[0]).replace('-',' ', regex=True).set_index('Total Kasus')\n", "active_cases = indo_covid2[index_ka:index_ka+delta_aktif.days+1:].rename(\n", " columns=indo_covid2.iloc[index_ka]).drop(indo_covid2.index[index_ka]).replace('-',' ', regex=True).set_index('Kasus Aktif')\n", "recovered_cases = indo_covid2[index_ks:index_ks+delta_sembuh.days+1:].rename(\n", " columns=indo_covid2.iloc[index_ks]).drop(indo_covid2.index[index_ks]).replace('-',' ', regex=True).set_index('Sembuh')\n", "deaths_cases = indo_covid2[index_km:index_km+delta_deaths.days+1:].rename(\n", " columns=indo_covid2.iloc[index_km]).drop(indo_covid2.index[index_km]).replace('-',' ', regex=True).set_index('Meninggal Dunia')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:54:02.466283Z", "start_time": "2020-12-14T08:54:02.196939Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "#collapse\n", "# clean df\n", "def clean_df(df):\n", " \n", " mask = df.applymap(lambda x: x is None)\n", " cols = df.columns[(mask).any()]\n", " for col in df[cols]:\n", " df.loc[mask[col], col] = 0\n", " \n", " df = df.replace(',','', regex=True).replace('',0, regex=True)\n", " df = df.astype('float64')\n", " df.index = pd.to_datetime(df.index, format='%d %b')\n", " df.index = df.index + pd.DateOffset(year=2020)\n", " \n", " return df\n", "\n", "\n", "total_cases = clean_df(total_cases) \n", "active_cases = clean_df(active_cases)\n", "recovered_cases = clean_df(recovered_cases)\n", "deaths_cases = clean_df(deaths_cases)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:54:02.497941Z", "start_time": "2020-12-14T08:54:02.471947Z" }, "code_folding": [] }, "outputs": [], "source": [ "#collapse\n", "# generate new cases, new recovered, new deaths\n", "new_cases = total_cases.diff()\n", "new_recovered = recovered_cases.diff()\n", "new_deaths = deaths_cases.diff()" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2020-10-07T04:39:00.646919Z", "start_time": "2020-10-07T04:39:00.636906Z" } }, "source": [ "## Latest Indonesian data" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:54:02.513960Z", "start_time": "2020-12-14T08:54:02.498945Z" }, "code_folding": [ 0 ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-12-14\n", "Total cases: 623,309.0\n", "New cases: 5,489.0\n", "Active cases: 93,396.0\n", "Total recovered: 510,957.0\n", "Total deaths: 18,956.0\n" ] } ], "source": [ "#collapse\n", "# display latest data\n", "print(d1)\n", "new_cases_temp = covid_id['Total kasus'][-1] - covid_id['Total kasus'][-2]\n", "print('Total cases:','{:,}'.format(covid_id['Total kasus'][-1].round(0)))\n", "print('New cases:','{:,}'.format(new_cases_temp.round(0)))\n", "print('Active cases:','{:,}'.format(covid_id['Kasus aktif'][-1].round(0)))\n", "print('Total recovered:','{:,}'.format(covid_id['Sembuh'][-1].round(0)))\n", "print('Total deaths:','{:,}'.format(covid_id['Meninggal\\nDunia'][-1].round(0)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Total cases plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Latest Indonesian Covid data:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:56:45.128007Z", "start_time": "2020-12-14T08:56:45.004983Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#collapse\n", "# codes\n", "import matplotlib.ticker as mtick\n", "from matplotlib.dates import DateFormatter\n", "from pandas.plotting import *\n", "register_matplotlib_converters() \n", "fig, ax = plt.subplots(1, 1, figsize=(15,5))\n", "\n", "ax.plot(covid_id.index,covid_id['Total kasus'],'-D',markersize = 3)\n", "\n", "plt.yticks(np.arange(0,800001,200000), fontsize=16)\n", "plt.xticks( fontsize=16)\n", "plt.ylabel('Total cases', fontsize=18)\n", "\n", "ax.get_yaxis().set_major_formatter(mtick.FuncFormatter(lambda x, p: format(int(x), ',')))\n", "myFmt = DateFormatter(\"%b\")\n", "ax.xaxis.set_major_formatter(myFmt)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Daily cases" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:57:02.194463Z", "start_time": "2020-12-14T08:57:01.630907Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#collapse\n", "# codes\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(15,5))\n", "ln1 = ax.plot(covid_id.index,covid_id['Kasus harian'],'-D',markersize = 3,label = 'Daily cases')\n", "ax2 = ax.twinx()\n", "ln2 = ax2.bar(covid_id.index,covid_id['Spesimen'],alpha = 0.2, color = 'black',label = 'Daily tests')\n", "\n", "ax.tick_params(axis='both', which='major', labelsize=16)\n", "ax2.tick_params(axis='both', which='major', labelsize=16)\n", "ax2.set_ylim([0,100000])\n", "ax.get_yaxis().set_major_formatter(mtick.FuncFormatter(lambda x, p: format(int(x), ',')))\n", "ax2.get_yaxis().set_major_formatter(mtick.FuncFormatter(lambda x, p: format(int(x), ',')))\n", "ax.set_ylabel('Daily cases',fontsize = 18)\n", "ax2.set_ylabel('Daily tests',fontsize = 18)\n", "\n", "myFmt = DateFormatter(\"%b\")\n", "ax.xaxis.set_major_formatter(myFmt)\n", "\n", "#legend\n", "fig.legend(bbox_to_anchor=(0.2,1), bbox_transform=ax.transAxes, fontsize = 16, frameon=False)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unsurprisingly, daily cases are highly correlated with number of daily tests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Positive rate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a 7 day rolling average of new cases divided by number of people that are tested.\n", "\n", "About 16% of the population tested returned a positive Covid test result." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:58:31.145838Z", "start_time": "2020-12-14T08:58:30.994370Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#collapse\n", "# codes\n", "import matplotlib.ticker as mtick\n", "from matplotlib.dates import DateFormatter\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(15,5))\n", "\n", "avg_pos_rate = covid_id['Positive rate harian'].mean()\n", "ax.plot(covid_id.index,covid_id['Tingkat positivitas mingguan'],'-D',markersize = 3)\n", "plt.axhline(y=avg_pos_rate, color='k', linestyle='--', alpha = 0.3)\n", "plt.text(pd.to_datetime(\"2020-05-01\"),avg_pos_rate+2,\"average positive rate: \"+str(round(avg_pos_rate,1))+\"%\",fontsize = 14)\n", "#plt.text(pd.to_datetime(\"2020-05-01\"),avg_pos_rate,'aaa',fontsize = 14)\n", "\n", "\n", "ax.set_ylim([0,40])\n", "plt.yticks(np.arange(0,41,10), fontsize=16)\n", "plt.xticks( fontsize=16)\n", "plt.ylabel('Weekly positive rate (%)', fontsize=18)\n", "\n", "ax.get_yaxis().set_major_formatter(mtick.FuncFormatter(lambda x, p: format(int(x), ',')))\n", "myFmt = DateFormatter(\"%b\")\n", "ax.xaxis.set_major_formatter(myFmt)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mortality rate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Average mortality rate is 5.5. However, this is likely affected by the data collected during March. This seems to indicate data was not collected (or the data collection is not fully functional yet). More accurate perhaps is the recent mortality rate, which is around 3.6." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:58:47.155645Z", "start_time": "2020-12-14T08:58:47.018607Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#collapse\n", "# codes\n", "\n", "#death_rate = df_indo['2020-03-1'::].new_deaths/df_indo['2020-03-1'::].new_cases*100\n", "avg_death_rate = covid_id['Tingkat kematian (seluruh kasus)'].mean()\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(15,5))\n", "\n", "ax.plot(covid_id.index, covid_id['Tingkat kematian (seluruh kasus)'] ,'-D',color ='b',markersize = 3,alpha = 0.5)\n", "plt.axhline(y=avg_death_rate, color='b', linestyle='--', alpha = 0.3)\n", "plt.text(pd.to_datetime(\"2020-08-01\"),avg_death_rate+0.5,\"average mortality rate: \"+str(round(avg_death_rate,1))+'%',fontsize = 14,color='blue')\n", "plt.axhline(y=3.6, color='g', linestyle='--', alpha = 0.3)\n", "plt.text(pd.to_datetime(\"2020-04-01\"),3.2-1.3,\"Recent mortality rate?: \"+str(3.6)+'%',fontsize = 14,color='g')\n", "\n", "plt.yticks(np.arange(0,15.1,5), fontsize=16)\n", "plt.xticks( fontsize=16)\n", "plt.ylabel('Mortality rate', fontsize=18)\n", "\n", "myFmt = DateFormatter(\"%b\")\n", "ax.xaxis.set_major_formatter(myFmt)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Province Map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: Kalimantan Utara has not been represented due to the limitation in the GEOjson that I extracted" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:58:50.916006Z", "start_time": "2020-12-14T08:58:50.786808Z" }, "code_folding": [ 2 ] }, "outputs": [], "source": [ "#collapse\n", "# Load GEOjson data\n", "with open('IDN_adm_1_province.json') as data_file:\n", " indo_map = json.load(data_file)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:58:50.931854Z", "start_time": "2020-12-14T08:58:50.916853Z" }, "code_folding": [ 3 ] }, "outputs": [], "source": [ "#collapse\n", "# temp\n", "a = []\n", "for x in range(len(indo_map['features'])):\n", " y = indo_map[\"features\"][x]['properties']['NAME_1']\n", " a.append(y)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:58:50.947807Z", "start_time": "2020-12-14T08:58:50.932837Z" }, "code_folding": [ 0, 7 ] }, "outputs": [], "source": [ "#collapse\n", "# transpose total cases per province\n", "indo_cases = total_cases.tail(1).T.reset_index()\n", "indo_cases.columns = ['Province','Total_cases']\n", "indo_cases = indo_cases[:-1]\n", "\n", "# rename Province based on JSON name\n", "indo_cases['Province'] = ['Aceh','Bali','Banten','Bangka-Belitung','Bengkulu','Yogyakarta','Jakarta Raya','Jambi',\n", " 'Jawa Barat','Jawa Tengah','Jawa Timur','Kalimantan Barat','Kalimantan Timur',\n", " 'Kalimantan Tengah','Kalimantan Selatan','Kalimantan Utara','Kepulauan Riau',\n", " 'Nusa Tenggara Barat','Sumatera Selatan','Sumatera Barat','Sulawesi Utara',\n", " 'Sumatera Utara','Sulawesi Tenggara','Sulawesi Selatan','Sulawesi Tengah','Lampung',\n", " 'Riau','Maluku Utara','Maluku','Irian Jaya Barat','Papua','Sulawesi Barat',\n", " 'Nusa Tenggara Timur','Gorontalo']\n", "\n", "# transpose new cases per province\n", "indo_cases2 = new_cases.tail(1).T.reset_index()\n", "indo_cases2.columns = ['Province','New_cases']\n", "indo_cases2 = indo_cases2[:-1]\n", "\n", "#combine DF\n", "indo_cases['New_cases'] = indo_cases2['New_cases']\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:58:52.363600Z", "start_time": "2020-12-14T08:58:50.948805Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# collapse\n", "# plot\n", "fig10 = px.scatter(indo_cases, \n", " y='Total_cases', x='Population', color='Total_cases', size='Total_cases', \n", " height=700, text='Province', log_x=True, log_y=True, \n", " title='Total cases vs Population (Scale is in log10)',\n", " hover_data={'Province':True,'Total_cases':':,','Population':':,'})\n", "fig10.update_traces(textposition='top center')\n", "fig10.update_layout(showlegend=False)\n", "HTML(fig10.to_html(include_plotlyjs='cdn'))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:58:54.203547Z", "start_time": "2020-12-14T08:58:54.047547Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# collapse\n", "# Plot LBF\n", "fig, ax = plt.subplots(1, 1, figsize=(15,5))\n", "\n", "from numpy.polynomial.polynomial import polyfit\n", "\n", "y,x = indo_cases.Total_cases,indo_cases.Population\n", "plt.plot(x,y,'o')\n", "\n", "b,m = polyfit(x, y, 1)\n", "plt.plot(x.sort_values(), m*x.sort_values() + b)\n", "\n", "jkt_data = indo_cases[indo_cases['Province']=='Jakarta Raya']\n", "\n", "plt.xlabel('Population',fontsize = 18)\n", "plt.ylabel('Total cases',fontsize = 18)\n", "plt.yticks(np.arange(0,80001,20000), fontsize=16)\n", "plt.xticks(np.arange(0,50000001,10000000), fontsize=16)\n", "\n", "ax.annotate('Jakarta', xy=(jkt_data['Population']+1000000, jkt_data['Total_cases']), xycoords='data',\n", " xytext=(0.38, 0.975), textcoords='axes fraction',\n", " arrowprops=dict(facecolor='black', shrink=0.05, width = 0.4, headwidth = 10),\n", " horizontalalignment='right', verticalalignment='top',fontsize = 16\n", " )\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's weird. Jakarta has a significantly higher total cases relative to its population (a proxy for density)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unless, if jakarta tests the proportion of their population much higher compared to other provinces?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately, I cannot find data regarding tests per province. However, there is an article below. Based on the data there, Jakarta ran 35k tests per 1M population. Three times as much as the second place, Bali (12k tests / 1M population). Hence it might be possible the high covid rate in Jakarta per population is due to the number of testing here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://www.thejakartapost.com/news/2020/07/22/testing-disparity-among-regions-a-challenge-for-covid-19-response.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Java vs non Java" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create java and non-java category." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:58:54.219549Z", "start_time": "2020-12-14T08:58:54.204548Z" }, "code_folding": [ 0 ] }, "outputs": [], "source": [ "# collapse\n", "# create java and non-java category\n", "indo_cases['Java'] = 'Non-Java'\n", "indo_cases.at[[2,5,6,8,9,10],'Java'] = indo_cases['Province']" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:58:54.315549Z", "start_time": "2020-12-14T08:58:54.220549Z" }, "code_folding": [ 0 ] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# collapse\n", "# plot\n", "from plotly.subplots import make_subplots\n", "\n", "fig11 = px.pie(indo_cases, values ='Total_cases', names = 'Java',color='Java', \n", " color_discrete_sequence=px.colors.sequential.Teal)\n", "\n", "fig12 = (px.pie(indo_cases, values ='New_cases', names = 'Java'\n", " ,title = 'Total cases'))\n", "trace1 = fig11['data'][0]\n", "trace2 = fig12['data'][0]\n", "\n", "fig13 = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n", "fig13.add_trace(trace1, row=1, col=1)\n", "fig13.add_trace(trace2, row=1, col=2)\n", "\n", "fig13.update_traces(textposition='inside', textinfo='percent+label')\n", "fig13.update_traces(hole =.4,hoverinfo=\"label+percent+name\")\n", "fig13.update_traces(textposition = \"outside\")\n", "fig13.update_layout(showlegend=False)\n", "fig13.update_layout(title_text=\"Total cases and new cases (\"+str(d1)+')'\n", " ,annotations =[dict(text='Total', x=0.18, y=0.5, font_size=20, showarrow=False),\n", " dict(text='New', x=0.82, y=0.5, font_size=20, showarrow=False)])\n", "HTML(fig13.to_html(include_plotlyjs='cdn'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It can be observed that 60% of the reported Covid19 cases in Indonesia is in Java. The trend still continues for the new cases reported daily" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Effect of PSBB?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PSBB is the Jakarta version of Lockdown. There is the full PSBB (stricter lockdown) or PSBB transisi (softer Lockdown). Those periods are:\n", "* Full PSBB: 10 April - 4 Juni\n", "* PSBB transisi: 5 Juni - 10 September\n", "* Full PSBB: 11 September - 12 Oktober\n", "* PSBB transisi: 12 Oktober - etc\n", "\n", "Let's see if PSBB worked or not, as well as whether softer lockdown increased the growth of new cases relative to the national data." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2020-12-14T08:59:32.030036Z", "start_time": "2020-12-14T08:59:31.862006Z" }, "code_folding": [ 0 ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Riyan Aditya\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py:853: RuntimeWarning:\n", "\n", "divide by zero encountered in log\n", "\n", "C:\\Users\\Riyan Aditya\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py:853: RuntimeWarning:\n", "\n", "invalid value encountered in log\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# collapse\n", "# plotting\n", "fig, ax = plt.subplots(1, 1, figsize=(15,5))\n", "\n", "np.log(covid_id['Kasus harian']).plot(label='National')\n", "np.log(new_cases['Jakarta']).plot(label='Jakarta')\n", "\n", "\n", "\n", "xposition = [pd.to_datetime('2020-04-10'), pd.to_datetime('2020-06-05'), pd.to_datetime('2020-09-11'), pd.to_datetime('2020-10-12')]\n", "for xc in xposition:\n", " ax.axvline(x=xc, color='k', linestyle='--', alpha = 0.5)\n", "\n", "plt.text(pd.to_datetime(\"2020-04-25\"),1.5,\"Full PSBB \\n(10 Apr - 4 Jun)\",fontsize = 14) \n", "plt.text(pd.to_datetime(\"2020-07-10\"),1.5,\"PSBB Transition\\n(5 Jun - 10 Sep)\",fontsize = 14) \n", "plt.text(pd.to_datetime(\"2020-09-17\"),1.5,\"Full PSBB \\n(11 Sep \\n- 12 Oct)\",fontsize = 14) \n", "plt.text(pd.to_datetime(\"2020-10-25\"),1.5,\"PSBB Transition \\n(12 Oct \\n- Current)\",fontsize = 14)\n", " \n", "plt.xticks(fontsize=16, rotation = 0)\n", "plt.yticks(np.arange(0,10.5, 5),fontsize=16)\n", "plt.ylabel('New cases (in log) \\n', fontsize=18)\n", "plt.xlabel('')\n", "\n", "ax.get_yaxis().set_major_formatter(mtick.FuncFormatter(lambda x, p: format(int(x), ',')))\n", "myFmt = DateFormatter(\"%b\")\n", "ax.xaxis.set_major_formatter(myFmt)\n", "\n", "plt.legend(frameon=False, fontsize = 16, loc = 'upper left')\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2020-10-14T03:19:38.482921Z", "start_time": "2020-10-14T03:19:13.794Z" } }, "source": [ "I do not think that this is a big indication whether PSBB was a success or not, but it seems that \"Full PSBB\" slowed down the detected new covid cases in Jakarta, relative to the national (gap between both curves widened during this Full PSBB). However, during the transition, the infection rate seems higher and closer to the national rate. \n", "\n", "However, it should be noted that the amount of covid testing also increases over time." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "307.2px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }