diff --git a/README.md b/README.md index b93dc2a..0ca6cce 100644 --- a/README.md +++ b/README.md @@ -598,12 +598,11 @@ from dateutil.tz import UTC, tzlocal, gettz, datetime_exists, resolve_imaginary = date(year, month, day) = time(hour=0, minute=0, second=0, microsecond=0, tzinfo=None, fold=0)
= datetime(year, month, day, hour=0, minute=0, second=0, ...) - = timedelta(days=0, seconds=0, microseconds=0, milliseconds=0, - minutes=0, hours=0, weeks=0) + = timedelta(weeks=0, days=0, hours=0, minutes=0, seconds=0, ...) ``` * **Use `'.weekday()'` to get the day of the week (Mon == 0).** * **`'fold=1'` means the second pass in case of time jumping back for one hour.** -* **`' = resolve_imaginary()'` fixes DTs that fall into the missing hour.** +* **TD converts and normalizes args to ±days, seconds (< 86,400) and microseconds (< 1M).** ### Now ```python @@ -3390,7 +3389,7 @@ continents = pd.read_csv('https://gist.githubusercontent.com/stevewithington/20a df = pd.merge(covid, continents, left_on='iso_code', right_on='Three_Letter_Country_Code') df = df.groupby(['Continent_Name', 'date']).sum().reset_index() df['Total Deaths per Million'] = df.total_deaths * 1e6 / df.population -df = df[('2020-03-14' < df.date) & (df.date < '2020-11-25')] +df = df[df.date > '2020-03-14'] df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns') line(df, x='Date', y='Total Deaths per Million', color='Continent').show() ``` @@ -3414,23 +3413,23 @@ def scrape_data(): return df[df.location == 'World'].set_index('date').total_cases def scrape_yahoo(slug): url = f'https://query1.finance.yahoo.com/v7/finance/download/{slug}' + \ - '?period1=1579651200&period2=1608850800&interval=1d&events=history' + '?period1=1579651200&period2=9999999999&interval=1d&events=history' df = pd.read_csv(url, usecols=['Date', 'Close']) return df.set_index('Date').Close - return scrape_covid(), scrape_yahoo('BTC-USD'), scrape_yahoo('GC=F'), scrape_yahoo('^DJI') + out = scrape_covid(), scrape_yahoo('BTC-USD'), scrape_yahoo('GC=F'), scrape_yahoo('^DJI') + return map(pd.Series.rename, out, ['Total Cases', 'Bitcoin', 'Gold', 'Dow Jones']) def wrangle_data(covid, bitcoin, gold, dow): - df = pd.concat([bitcoin, gold, dow], axis=1) - df = df.sort_index().interpolate() - df = df.rolling(10, min_periods=1, center=True).mean() - df = df.loc['2020-02-23':'2020-11-25'] - df = (df / df.iloc[0]) * 100 - return pd.concat([covid, df], axis=1, join='inner') + df = pd.concat([bitcoin, gold, dow], axis=1) # Joins columns on dates. + df = df.sort_index().interpolate() # Sorts by date and interpolates NaN-s. + df = df.loc['2020-02-23':] # Discards rows before '2020-02-23'. + df = (df / df.iloc[0]) * 100 # Calculates percentages relative to day 1. + df = df.join(covid) # Adds column with covid cases. + return df.sort_values(df.index[-1], axis=1) # Sorts columns by last day's value. def display_data(df): - df.columns = ['Total Cases', 'Bitcoin', 'Gold', 'Dow Jones'] figure = go.Figure() - for col_name in df: + for col_name in reversed(df.columns): yaxis = 'y1' if col_name == 'Total Cases' else 'y2' trace = go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis=yaxis) figure.add_trace(trace) diff --git a/index.html b/index.html index 81d952e..38732fd 100644 --- a/index.html +++ b/index.html @@ -50,7 +50,7 @@
- +
@@ -524,14 +524,13 @@ to_exclusive = <range>.stop

Constructors

<D>  = date(year, month, day)
 <T>  = time(hour=0, minute=0, second=0, microsecond=0, tzinfo=None, fold=0)
 <DT> = datetime(year, month, day, hour=0, minute=0, second=0, ...)
-<TD> = timedelta(days=0, seconds=0, microseconds=0, milliseconds=0,
-                 minutes=0, hours=0, weeks=0)
+<TD> = timedelta(weeks=0, days=0, hours=0, minutes=0, seconds=0, ...)
  • Use '<D/DT>.weekday()' to get the day of the week (Mon == 0).
  • 'fold=1' means the second pass in case of time jumping back for one hour.
    • -
  • '<DTa> = resolve_imaginary(<DTa>)' fixes DTs that fall into the missing hour.
    • +
  • TD converts and normalizes args to ±days, seconds (< 86,400) and microseconds (< 1M).

Now

<D/DTn>  = D/DT.today()                     # Current local date or naive datetime.
 <DTn>    = DT.utcnow()                      # Naive datetime from current UTC time.
@@ -2740,7 +2739,7 @@ continents = pd.read_csv('https://gist.githubuserconte
 df = pd.merge(covid, continents, left_on='iso_code', right_on='Three_Letter_Country_Code')
 df = df.groupby(['Continent_Name', 'date']).sum().reset_index()
 df['Total Deaths per Million'] = df.total_deaths * 1e6 / df.population
-df = df[('2020-03-14' < df.date) & (df.date < '2020-11-25')]
+df = df[df.date > '2020-03-14']
 df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns')
 line(df, x='Date', y='Total Deaths per Million', color='Continent').show()
@@ -2760,23 +2759,23 @@ line(df, x='Date', y= return df[df.location == 'World'].set_index('date').total_cases def scrape_yahoo(slug): url = f'https://query1.finance.yahoo.com/v7/finance/download/{slug}' + \ - '?period1=1579651200&period2=1608850800&interval=1d&events=history' + '?period1=1579651200&period2=9999999999&interval=1d&events=history' df = pd.read_csv(url, usecols=['Date', 'Close']) return df.set_index('Date').Close - return scrape_covid(), scrape_yahoo('BTC-USD'), scrape_yahoo('GC=F'), scrape_yahoo('^DJI') + out = scrape_covid(), scrape_yahoo('BTC-USD'), scrape_yahoo('GC=F'), scrape_yahoo('^DJI') + return map(pd.Series.rename, out, ['Total Cases', 'Bitcoin', 'Gold', 'Dow Jones']) def wrangle_data(covid, bitcoin, gold, dow): - df = pd.concat([bitcoin, gold, dow], axis=1) - df = df.sort_index().interpolate() - df = df.rolling(10, min_periods=1, center=True).mean() - df = df.loc['2020-02-23':'2020-11-25'] - df = (df / df.iloc[0]) * 100 - return pd.concat([covid, df], axis=1, join='inner') + df = pd.concat([bitcoin, gold, dow], axis=1) # Joins columns on dates. + df = df.sort_index().interpolate() # Sorts by date and interpolates NaN-s. + df = df.loc['2020-02-23':] # Discards rows before '2020-02-23'. + df = (df / df.iloc[0]) * 100 # Calculates percentages relative to day 1. + df = df.join(covid) # Adds column with covid cases. + return df.sort_values(df.index[-1], axis=1) # Sorts columns by last day's value. def display_data(df): - df.columns = ['Total Cases', 'Bitcoin', 'Gold', 'Dow Jones'] figure = go.Figure() - for col_name in df: + for col_name in reversed(df.columns): yaxis = 'y1' if col_name == 'Total Cases' else 'y2' trace = go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis=yaxis) figure.add_trace(trace) @@ -2870,7 +2869,7 @@ $ pyinstaller script.py --add-data '<path>:.'
- +
diff --git a/web/covid_cases.js b/web/covid_cases.js index 3182690..78f6cf2 100644 --- a/web/covid_cases.js +++ b/web/covid_cases.js @@ -7,8 +7,10 @@ if (document.getElementById("e23ccacc-a456-478b-b467-7282a2165921")) { { "data": [ { + "line": { + "color": "red" + }, "name": "Total Cases", - "type": "scatter", "x": [ "2020-02-23", "2020-02-24", @@ -286,292 +288,1075 @@ if (document.getElementById("e23ccacc-a456-478b-b467-7282a2165921")) { "2020-11-22", "2020-11-23", "2020-11-24", - "2020-11-25" + "2020-11-25", + "2020-11-26", + "2020-11-27", + "2020-11-28", + "2020-11-29", + "2020-11-30", + "2020-12-01", + "2020-12-02", + "2020-12-03", + "2020-12-04", + "2020-12-05", + "2020-12-06", + "2020-12-07", + "2020-12-08", + "2020-12-09", + "2020-12-10", + "2020-12-11", + "2020-12-12", + "2020-12-13", + "2020-12-14", + "2020-12-15", + "2020-12-16", + "2020-12-17", + "2020-12-18", + "2020-12-19", + "2020-12-20", + "2020-12-21", + "2020-12-22", + "2020-12-23", + "2020-12-24", + "2020-12-25", + "2020-12-26", + "2020-12-27", + "2020-12-28", + "2020-12-29", + "2020-12-30", + "2020-12-31", + "2021-01-01", + "2021-01-02", + "2021-01-03", + "2021-01-04", + "2021-01-05", + "2021-01-06", + "2021-01-07", + "2021-01-08", + "2021-01-09", + "2021-01-10", + "2021-01-11", + "2021-01-12", + "2021-01-13", + "2021-01-14", + "2021-01-15", + "2021-01-16", + "2021-01-17", + "2021-01-18", + "2021-01-19", + "2021-01-20", + "2021-01-21", + "2021-01-22", + "2021-01-23", + "2021-01-24", + "2021-01-25", + "2021-01-26", + "2021-01-27", + "2021-01-28", + "2021-01-29", + "2021-01-30", + "2021-01-31", + "2021-02-01", + "2021-02-02", + "2021-02-03", + "2021-02-04", + "2021-02-05", + "2021-02-06", + "2021-02-07", + "2021-02-08", + "2021-02-09", + "2021-02-10", + "2021-02-11", + "2021-02-12", + "2021-02-13", + "2021-02-14", + "2021-02-15", + "2021-02-16", + "2021-02-17", + "2021-02-18", + "2021-02-19", + "2021-02-20", + "2021-02-21", + "2021-02-22", + "2021-02-23", + "2021-02-24", + "2021-02-25", + "2021-02-26", + "2021-02-27", + "2021-02-28", + "2021-03-01", + "2021-03-02", + "2021-03-03", + "2021-03-04", + "2021-03-05", + "2021-03-06", + "2021-03-07", + "2021-03-08", + "2021-03-09", + "2021-03-10", + "2021-03-11", + "2021-03-12", + "2021-03-13", + "2021-03-14", + "2021-03-15", + "2021-03-16", + "2021-03-17", + "2021-03-18", + "2021-03-19", + "2021-03-20", + "2021-03-21", + "2021-03-22", + "2021-03-23", + "2021-03-24", + "2021-03-25", + "2021-03-26", + "2021-03-27", + "2021-03-28", + "2021-03-29", + "2021-03-30", + "2021-03-31", + "2021-04-01", + "2021-04-02", + "2021-04-03", + "2021-04-04", + "2021-04-05", + "2021-04-06", + "2021-04-07", + "2021-04-08", + "2021-04-09", + "2021-04-10", + "2021-04-11", + "2021-04-12", + "2021-04-13", + "2021-04-14", + "2021-04-15", + "2021-04-16", + "2021-04-17", + "2021-04-18", + "2021-04-19", + "2021-04-20", + "2021-04-21", + "2021-04-22", + "2021-04-23", + "2021-04-24", + "2021-04-25", + "2021-04-26", + "2021-04-27", + "2021-04-28", + "2021-04-29", + "2021-04-30", + "2021-05-01", + "2021-05-02", + "2021-05-03", + "2021-05-04", + "2021-05-05", + "2021-05-06", + "2021-05-07", + "2021-05-08", + "2021-05-09", + "2021-05-10", + "2021-05-11", + "2021-05-12", + "2021-05-13", + "2021-05-14", + "2021-05-15", + "2021-05-16", + "2021-05-17", + "2021-05-18", + "2021-05-19", + "2021-05-20", + "2021-05-21", + "2021-05-22", + "2021-05-23", + "2021-05-24", + "2021-05-25", + "2021-05-26", + "2021-05-27", + "2021-05-28", + "2021-05-29", + "2021-05-30", + "2021-05-31", + "2021-06-01", + "2021-06-02", + "2021-06-03", + "2021-06-04", + "2021-06-05", + "2021-06-06", + "2021-06-07", + "2021-06-08", + "2021-06-09", + "2021-06-10", + "2021-06-11", + "2021-06-12", + "2021-06-13", + "2021-06-14", + "2021-06-15", + "2021-06-16", + "2021-06-17", + "2021-06-18", + "2021-06-19", + "2021-06-20", + "2021-06-21", + "2021-06-22", + "2021-06-23", + "2021-06-24", + "2021-06-25", + "2021-06-26", + "2021-06-27", + "2021-06-28", + "2021-06-29", + "2021-06-30", + "2021-07-01", + "2021-07-02", + "2021-07-03", + "2021-07-04", + "2021-07-05", + "2021-07-06", + "2021-07-07", + "2021-07-08", + "2021-07-09", + "2021-07-10", + "2021-07-11", + "2021-07-12", + "2021-07-13", + "2021-07-14", + "2021-07-15", + "2021-07-16", + "2021-07-17", + "2021-07-18", + "2021-07-19", + "2021-07-20", + "2021-07-21", + "2021-07-22", + "2021-07-23", + "2021-07-24", + "2021-07-25", + "2021-07-26", + "2021-07-27", + "2021-07-28", + "2021-07-29", + "2021-07-30", + "2021-07-31", + "2021-08-01", + "2021-08-02", + "2021-08-03", + "2021-08-04", + "2021-08-05", + "2021-08-06", + "2021-08-07", + "2021-08-08", + "2021-08-09", + "2021-08-10", + "2021-08-11", + "2021-08-12", + "2021-08-13", + "2021-08-14", + "2021-08-15", + "2021-08-16", + "2021-08-17", + "2021-08-18", + "2021-08-19", + "2021-08-20", + "2021-08-21", + "2021-08-22", + "2021-08-23", + "2021-08-24", + "2021-08-25", + "2021-08-26", + "2021-08-27", + "2021-08-28", + "2021-08-29", + "2021-08-30", + 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"paper_bgcolor": "rgba(0, 0, 0, 0)", "template": { "data": { "bar": [ @@ -3201,6 +8152,11 @@ if (document.getElementById("2a950764-39fc-416d-97fe-0a6226a3095f")) { "line": { "color": "#E5ECF6", "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 } }, "type": "bar" @@ -3212,6 +8168,11 @@ if (document.getElementById("2a950764-39fc-416d-97fe-0a6226a3095f")) { "line": { "color": "#E5ECF6", "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 } }, "type": "barpolar" @@ -3410,9 +8371,10 @@ if (document.getElementById("2a950764-39fc-416d-97fe-0a6226a3095f")) { "histogram": [ { "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 } }, "type": "histogram" @@ -3730,6 +8692,7 @@ if (document.getElementById("2a950764-39fc-416d-97fe-0a6226a3095f")) { "arrowhead": 0, "arrowwidth": 1 }, + "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, @@ -4011,7 +8974,18 @@ if (document.getElementById("2a950764-39fc-416d-97fe-0a6226a3095f")) { "title": { "text": "Total Deaths per Million" } - } + }, + "legend": { + "title": { + "text": "Continent" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 24, + "b": 0 + }, + "paper_bgcolor": "rgba(0, 0, 0, 0)" } } ) diff --git a/web/covid_deaths.png b/web/covid_deaths.png index bd36904..b522c54 100644 Binary files a/web/covid_deaths.png and b/web/covid_deaths.png differ diff --git a/web/update_plots.py b/web/update_plots.py index 3b946ed..3916b7b 100755 --- a/web/update_plots.py +++ b/web/update_plots.py @@ -4,7 +4,7 @@ # Updates plots from the Plotly section so they show the latest data. from pathlib import Path -from datetime import date, time, datetime, timedelta +import datetime import pandas as pd from plotly.express import line import plotly.graph_objects as go @@ -19,26 +19,23 @@ def main(): def update_covid_deaths(): - def update_readme(date_treshold): - lines = read_file(Path('..') / 'README.md') - out = [re.sub("df.date < '\d{4}-\d{2}-\d{2}'", f"df.date < '{date_treshold}'", line) - for line in lines] - write_to_file(Path('..') / 'README.md', out) covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv', usecols=['iso_code', 'date', 'total_deaths', 'population']) - continents = pd.read_csv('https://datahub.io/JohnSnowLabs/country-and-continent-codes-' + \ - 'list/r/country-and-continent-codes-list-csv.csv', + continents = pd.read_csv('https://gist.githubusercontent.com/stevewithington/20a69c0b6d2ff' + '846ea5d35e5fc47f26c/raw/country-and-continent-codes-list-csv.csv', usecols=['Three_Letter_Country_Code', 'Continent_Name']) df = pd.merge(covid, continents, left_on='iso_code', right_on='Three_Letter_Country_Code') df = df.groupby(['Continent_Name', 'date']).sum().reset_index() - df['Total Deaths per Million'] = df.total_deaths * 1e6 / df.population - date_treshold = str(date.today() - timedelta(days=2)) # '2020-11-25' - df = df[('2020-03-14' < df.date) & (df.date < date_treshold)] + df['Total Deaths per Million'] = round(df.total_deaths * 1e6 / df.population) + today = str(datetime.date.today()) + df = df[('2020-02-22' < df.date) & (df.date < today)] df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns') + gb = df.groupby('Continent') + df['Max Total Deaths'] = gb[['Total Deaths per Million']].transform('max') + df = df.sort_values(['Max Total Deaths', 'Date'], ascending=[False, True]) f = line(df, x='Date', y='Total Deaths per Million', color='Continent') f.update_layout(margin=dict(t=24, b=0), paper_bgcolor='rgba(0, 0, 0, 0)') update_file('covid_deaths.js', f) - update_readme(date_treshold) write_to_png_file('covid_deaths.png', f, width=960, height=340) @@ -50,34 +47,36 @@ def update_confirmed_cases(): write_to_png_file('covid_cases.png', f, width=960, height=315) def scrape_data(): - def scrape_yahoo(id_): - BASE_URL = 'https://query1.finance.yahoo.com/v7/finance/download/' - now = int(datetime.now().timestamp()) - url = f'{BASE_URL}{id_}?period1=1579651200&period2={now}&interval=1d&' + \ - 'events=history' - return pd.read_csv(url, usecols=['Date', 'Close']).set_index('Date').Close - covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv', - usecols=['location', 'date', 'total_cases']) - covid = covid[covid.location == 'World'].set_index('date').total_cases - dow, gold, bitcoin = [scrape_yahoo(id_) for id_ in ('^DJI', 'GC=F', 'BTC-USD')] - dow.name, gold.name, bitcoin.name = 'Dow Jones', 'Gold', 'Bitcoin' - return covid, dow, gold, bitcoin - - def wrangle_data(covid, dow, gold, bitcoin): - df = pd.concat([dow, gold, bitcoin], axis=1) - df = df.sort_index().interpolate() - df = df.rolling(10, min_periods=1, center=True).mean() - df = df.loc['2020-02-23':].iloc[:-2] - df = (df / df.iloc[0]) * 100 - return pd.concat([covid, df], axis=1, join='inner') + def scrape_covid(): + url = 'https://covid.ourworldindata.org/data/owid-covid-data.csv' + df = pd.read_csv(url, usecols=['location', 'date', 'total_cases']) + return df[df.location == 'World'].set_index('date').total_cases + def scrape_yahoo(slug): + url = f'https://query1.finance.yahoo.com/v7/finance/download/{slug}' + \ + '?period1=1579651200&period2=9999999999&interval=1d&events=history' + df = pd.read_csv(url, usecols=['Date', 'Close']) + return df.set_index('Date').Close + out = scrape_covid(), scrape_yahoo('BTC-USD'), scrape_yahoo('GC=F'), scrape_yahoo('^DJI') + return map(pd.Series.rename, out, ['Total Cases', 'Bitcoin', 'Gold', 'Dow Jones']) + + def wrangle_data(covid, bitcoin, gold, dow): + df = pd.concat([dow, gold, bitcoin], axis=1) # Joins columns on dates. + df = df.sort_index().interpolate() # Sorts by date and interpolates NaN-s. + yesterday = str(datetime.date.today() - datetime.timedelta(1)) + df = df.loc['2020-02-23':yesterday] # Discards rows before '2020-02-23'. + df = round((df / df.iloc[0]) * 100, 2) # Calculates percentages relative to day 1. + df = df.join(covid) # Adds column with covid cases. + return df.sort_values(df.index[-1], axis=1) # Sorts columns by last day's value. def get_figure(df): - def get_trace(col_name): - return go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis='y2') - traces = [get_trace(col_name) for col_name in df.columns[1:]] - traces.append(go.Scatter(x=df.index, y=df.total_cases, name='Total Cases', yaxis='y1')) figure = go.Figure() - figure.add_traces(traces) + for col_name in reversed(df.columns): + yaxis = 'y1' if col_name == 'Total Cases' else 'y2' + colors = {'Total Cases': 'red', 'Bitcoin': 'blue', 'Gold': 'goldenrod', + 'Dow Jones': 'green'} + trace = go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis=yaxis, + line=dict(color=colors[col_name])) + figure.add_trace(trace) figure.update_layout( yaxis1=dict(title='Total Cases', rangemode='tozero'), yaxis2=dict(title='%', rangemode='tozero', overlaying='y', side='right'), @@ -90,6 +89,10 @@ def update_confirmed_cases(): main() +### +## UTIL +# + def update_file(filename, figure): lines = read_file(filename) f_json = figure.to_json(pretty=True).replace('\n', '\n ') @@ -97,10 +100,6 @@ def update_file(filename, figure): write_to_file(filename, out) -### -## UTIL -# - def read_file(filename): p = Path(__file__).resolve().parent / filename with open(p, encoding='utf-8') as file: