From 0db0c8a5417724975306770af3f6f7ed8637b19c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jure=20=C5=A0orn?= Date: Sat, 27 Jun 2020 02:22:55 +0200 Subject: [PATCH] Pandas and Plotly --- README.md | 50 +++++++++++++++++++++++++------------------------- index.html | 52 +++++++++++++++++++++++++++------------------------- parse.js | 4 ++-- 3 files changed, 54 insertions(+), 52 deletions(-) diff --git a/README.md b/README.md index f40a3c0..bab10e5 100644 --- a/README.md +++ b/README.md @@ -3123,14 +3123,13 @@ Name: a, dtype: int64 >>> sr = Series([1, 2], index=['x', 'y']) x 1 y 2 -dtype: int64 ``` ```text +-------------+-------------+-------------+---------------+ | | 'sum' | ['sum'] | {'s': 'sum'} | +-------------+-------------+-------------+---------------+ -| sr.apply(…) | 3 | sum 3 | s 3 | +| sr.apply(…) | 3 | sum 3 | s 3 | | sr.agg(…) | | | | +-------------+-------------+-------------+---------------+ ``` @@ -3144,6 +3143,7 @@ dtype: int64 | sr.trans(…) | y 2 | y 2 | y 2 | +-------------+-------------+-------------+---------------+ ``` +* **Last result has a hierarchical index. `'[, ]'` returns the value.** ### DataFrame **Table with labeled rows and columns.** @@ -3358,12 +3358,12 @@ covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv', continents = pd.read_csv('https://datahub.io/JohnSnowLabs/country-and-continent-codes-' + \ 'list/r/country-and-continent-codes-list-csv.csv', usecols=['Three_Letter_Country_Code', 'Continent_Name']) -merged = pd.merge(covid, continents, left_on='iso_code', right_on='Three_Letter_Country_Code') -summed = merged.groupby(['Continent_Name', 'date']).sum().reset_index() -summed['Total Deaths per Million'] = summed.total_deaths * 1e6 / summed.population -summed = summed[('2020-03-14' < summed.date) & (summed.date < '2020-06-25')] -summed = summed.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns') -plotly.express.line(summed, x='Date', y='Total Deaths per Million', color='Continent').show() +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 < '2020-06-25')] +df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns') +plotly.express.line(df, x='Date', y='Total Deaths per Million', color='Continent').show() ``` ### Confirmed Covid Cases, Dow Jones, Gold, and Bitcoin Price @@ -3379,7 +3379,7 @@ def main(): display_data(wrangle_data(*scrape_data())) def scrape_data(): - def scrape_yah(id_): + def scrape_yahoo(id_): BASE_URL = 'https://query1.finance.yahoo.com/v7/finance/download/' now = int(datetime.datetime.now().timestamp()) url = f'{BASE_URL}{id_}?period1=1579651200&period2={now}&interval=1d&events=history' @@ -3387,23 +3387,23 @@ def scrape_data(): covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv', usecols=['date', 'total_cases']) covid = covid.groupby('date').sum() - dow_jones, gold, bitcoin = scrape_yah('^DJI'), scrape_yah('GC=F'), scrape_yah('BTC-USD') - dow_jones.name, gold.name, bitcoin.name = 'Dow Jones', 'Gold', 'Bitcoin' - return covid, dow_jones, gold, bitcoin - -def wrangle_data(covid, dow_jones, gold, bitcoin): - out = pandas.concat([covid, dow_jones, gold, bitcoin], axis=1) - out = out.loc['2020-02-23':].iloc[:-2] - out = out.interpolate() - out.iloc[:, 1:] = out.rolling(10, min_periods=1, center=True).mean().iloc[:, 1:] - out.iloc[:, 1:] = out.iloc[:, 1:] / out.iloc[0, 1:] * 100 - return out - -def display_data(out): + 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 = pandas.concat([covid, dow, gold, bitcoin], axis=1) + df = df.loc['2020-02-23':].iloc[:-2] + df = df.interpolate() + df.iloc[:, 1:] = df.rolling(10, min_periods=1, center=True).mean().iloc[:, 1:] + df.iloc[:, 1:] = df.iloc[:, 1:] / df.iloc[0, 1:] * 100 + return df + +def display_data(df): def get_trace(col_name): - return go.Scatter(x=out.index, y=out[col_name], name=col_name, yaxis='y2') - traces = [get_trace(col_name) for col_name in out.columns[1:]] - traces.append(go.Scatter(x=out.index, y=out.total_cases, name='Total Cases', yaxis='y1')) + 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) figure.update_layout( diff --git a/index.html b/index.html index 7578fc6..24775a9 100644 --- a/index.html +++ b/index.html @@ -2660,12 +2660,11 @@ Name: a, dtype: int64 
>>> sr = Series([1, 2], index=['x', 'y'])
 x    1
 y    2
-dtype: int64
 
┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
 ┃             │    'sum'    │   ['sum']   │ {'s': 'sum'}  ┃
 ┠─────────────┼─────────────┼─────────────┼───────────────┨
-┃ sr.apply(…) │      3      │    sum 3    │      s  3     ┃
+┃ sr.apply(…) │      3      │    sum 3    │     s  3      ┃
 ┃ sr.agg(…)   │             │             │               ┃
 ┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
@@ -2677,6 +2676,9 @@ dtype: int64 ┃ sr.trans(…) │ y 2 │ y 2 │ y 2 ┃ ┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛ +
    +
  • Last result has a hierarchical index. '<Sr>[<key_1>, <key_2>]' returns the value.
    • +

DataFrame

Table with labeled rows and columns.

>>> DataFrame([[1, 2], [3, 4]], index=['a', 'b'], columns=['x', 'y'])
    x  y
 a  1  2
@@ -2839,12 +2841,12 @@ covid = pd.read_csv('https://covid.ourworldindata.org/
 continents = pd.read_csv('https://datahub.io/JohnSnowLabs/country-and-continent-codes-' + \
                          'list/r/country-and-continent-codes-list-csv.csv',
                          usecols=['Three_Letter_Country_Code', 'Continent_Name'])
-merged = pd.merge(covid, continents, left_on='iso_code', right_on='Three_Letter_Country_Code')
-summed = merged.groupby(['Continent_Name', 'date']).sum().reset_index()
-summed['Total Deaths per Million'] = summed.total_deaths * 1e6 / summed.population
-summed = summed[('2020-03-14' < summed.date) & (summed.date < '2020-06-25')]
-summed = summed.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns')
-plotly.express.line(summed, x='Date', y='Total Deaths per Million', color='Continent').show()
+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 < '2020-06-25')]
+df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns')
+plotly.express.line(df, x='Date', y='Total Deaths per Million', color='Continent').show()
@@ -2857,7 +2859,7 @@ plotly.express.line(summed, x='Date', y=def scrape_data(): - def scrape_yah(id_): + def scrape_yahoo(id_): BASE_URL = 'https://query1.finance.yahoo.com/v7/finance/download/' now = int(datetime.datetime.now().timestamp()) url = f'{BASE_URL}{id_}?period1=1579651200&period2={now}&interval=1d&events=history' @@ -2865,23 +2867,23 @@ plotly.express.line(summed, x='Date', y='https://covid.ourworldindata.org/data/owid-covid-data.csv', usecols=['date', 'total_cases']) covid = covid.groupby('date').sum() - dow_jones, gold, bitcoin = scrape_yah('^DJI'), scrape_yah('GC=F'), scrape_yah('BTC-USD') - dow_jones.name, gold.name, bitcoin.name = 'Dow Jones', 'Gold', 'Bitcoin' - return covid, dow_jones, gold, bitcoin - -def wrangle_data(covid, dow_jones, gold, bitcoin): - out = pandas.concat([covid, dow_jones, gold, bitcoin], axis=1) - out = out.loc['2020-02-23':].iloc[:-2] - out = out.interpolate() - out.iloc[:, 1:] = out.rolling(10, min_periods=1, center=True).mean().iloc[:, 1:] - out.iloc[:, 1:] = out.iloc[:, 1:] / out.iloc[0, 1:] * 100 - return out - -def display_data(out): + 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 = pandas.concat([covid, dow, gold, bitcoin], axis=1) + df = df.loc['2020-02-23':].iloc[:-2] + df = df.interpolate() + df.iloc[:, 1:] = df.rolling(10, min_periods=1, center=True).mean().iloc[:, 1:] + df.iloc[:, 1:] = df.iloc[:, 1:] / df.iloc[0, 1:] * 100 + return df + +def display_data(df): def get_trace(col_name): - return go.Scatter(x=out.index, y=out[col_name], name=col_name, yaxis='y2') - traces = [get_trace(col_name) for col_name in out.columns[1:]] - traces.append(go.Scatter(x=out.index, y=out.total_cases, name='Total Cases', yaxis='y1')) + 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) figure.update_layout( diff --git a/parse.js b/parse.js index 6eee291..86eaedd 100755 --- a/parse.js +++ b/parse.js @@ -275,13 +275,13 @@ const DIAGRAM_12_B = '┗━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━┷━━━━━━━━━━━━━┛\n'; const DIAGRAM_13_A = - '| sr.apply(…) | 3 | sum 3 | s 3 |'; + '| sr.apply(…) | 3 | sum 3 | s 3 |'; const DIAGRAM_13_B = "┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" + "┃ │ 'sum' │ ['sum'] │ {'s': 'sum'} ┃\n" + "┠─────────────┼─────────────┼─────────────┼───────────────┨\n" + - "┃ sr.apply(…) │ 3 │ sum 3 │ s 3 ┃\n" + + "┃ sr.apply(…) │ 3 │ sum 3 │ s 3 ┃\n" + "┃ sr.agg(…) │ │ │ ┃\n" + "┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";