Jure Šorn
4 years ago
5 changed files with 125 additions and 15 deletions
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web/covid_cases.js
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web/covid_deaths.js
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#!/usr/bin/env python3 |
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# |
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# Usage: ./update_plots.py |
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# Updates plots from the Plotly section so they show the latest data. |
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from pathlib import Path |
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from datetime import date, time, datetime, timedelta |
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import pandas as pd |
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from plotly.express import line |
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import plotly.graph_objects as go |
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def main(): |
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print('Updating covid deaths...') |
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update_covid_deaths() |
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print('Updating covid cases...') |
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update_confirmed_cases() |
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def update_covid_deaths(): |
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def update_readme(date_treshold): |
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lines = read_file('../README.md') |
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out = [re.sub("df.date < '\d{4}-\d{2}-\d{2}'", f"df.date < '{date_treshold}'", line) |
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for line in lines] |
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write_to_file('../README.md', out) |
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covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv', |
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usecols=['iso_code', 'date', 'total_deaths', 'population']) |
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continents = pd.read_csv('https://datahub.io/JohnSnowLabs/country-and-continent-codes-' + \ |
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'list/r/country-and-continent-codes-list-csv.csv', |
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usecols=['Three_Letter_Country_Code', 'Continent_Name']) |
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df = pd.merge(covid, continents, left_on='iso_code', right_on='Three_Letter_Country_Code') |
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df = df.groupby(['Continent_Name', 'date']).sum().reset_index() |
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df['Total Deaths per Million'] = df.total_deaths * 1e6 / df.population |
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date_treshold = str(date.today() - timedelta(days=2)) |
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df = df[('2020-03-14' < df.date) & (df.date < date_treshold)] |
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df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns') |
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f = line(df, x='Date', y='Total Deaths per Million', color='Continent') |
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f.update_layout(margin=dict(t=24, b=0), paper_bgcolor='rgba(0, 0, 0, 0)') |
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update_file('covid_deaths.js', f) |
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update_readme(date_treshold) |
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def update_confirmed_cases(): |
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def main(): |
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df = wrangle_data(*scrape_data()) |
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f = get_figure(df) |
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update_file('covid_cases.js', f) |
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def scrape_data(): |
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def scrape_yahoo(id_): |
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BASE_URL = 'https://query1.finance.yahoo.com/v7/finance/download/' |
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now = int(datetime.now().timestamp()) |
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url = f'{BASE_URL}{id_}?period1=1579651200&period2={now}&interval=1d&events=history' |
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return pd.read_csv(url, usecols=['Date', 'Close']).set_index('Date').Close |
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covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv', |
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usecols=['date', 'total_cases']) |
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covid = covid.groupby('date').sum() |
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dow, gold, bitcoin = [scrape_yahoo(id_) for id_ in ('^DJI', 'GC=F', 'BTC-USD')] |
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dow.name, gold.name, bitcoin.name = 'Dow Jones', 'Gold', 'Bitcoin' |
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return covid, dow, gold, bitcoin |
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def wrangle_data(covid, dow, gold, bitcoin): |
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df = pd.concat([dow, gold, bitcoin], axis=1) |
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df = df.sort_index().interpolate() |
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df = df.rolling(10, min_periods=1, center=True).mean() |
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df = df.loc['2020-02-23':].iloc[:-2] |
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df = (df / df.iloc[0]) * 100 |
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return pd.concat([covid, df], axis=1, join='inner') |
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def get_figure(df): |
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def get_trace(col_name): |
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return go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis='y2') |
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traces = [get_trace(col_name) for col_name in df.columns[1:]] |
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traces.append(go.Scatter(x=df.index, y=df.total_cases, name='Total Cases', yaxis='y1')) |
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figure = go.Figure() |
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figure.add_traces(traces) |
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figure.update_layout( |
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yaxis1=dict(title='Total Cases', rangemode='tozero'), |
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yaxis2=dict(title='%', rangemode='tozero', overlaying='y', side='right'), |
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legend=dict(x=1.1), |
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margin=dict(t=24, b=0), |
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paper_bgcolor='rgba(0, 0, 0, 0)' |
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) |
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return figure |
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main() |
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def update_file(filename, figure): |
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lines = read_file(filename) |
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out = lines[:6] + [f' {figure.to_json()}\n', ' )\n', '};\n'] |
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write_to_file(filename, out) |
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### |
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## UTIL |
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# |
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def read_file(filename): |
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with open(filename, encoding='utf-8') as file: |
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return file.readlines() |
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def write_to_file(filename, lines): |
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with open(filename, 'w', encoding='utf-8') as file: |
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file.writelines(lines) |
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if __name__ == '__main__': |
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main() |
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