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‎README.md

Lines changed: 19 additions & 21 deletions
Original file line numberDiff line numberDiff line change
@@ -3171,7 +3171,7 @@ b 3 4
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```
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```python
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<DF> = <DF> ><== <el/Sr/DF> # Returns DataFrame of bools.
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<DF> = <DF> ><== <el/Sr/DF> # Returns DF of bools. Sr is treated as a row.
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<DF> = <DF> +-*/ <el/Sr/DF> # Items with non-matching keys get value NaN.
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```
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@@ -3376,39 +3376,37 @@ line(df, x='Date', y='Total Deaths per Million', color='Continent').show()
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```python
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import pandas as pd
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import plotly.graph_objects as go
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import datetime
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def main():
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display_data(wrangle_data(*scrape_data()))
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33843383
def scrape_data():
3385-
def scrape_yahoo(id_):
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BASE_URL = 'https://query1.finance.yahoo.com/v7/finance/download/'
3387-
now = int(datetime.datetime.now().timestamp())
3388-
url = f'{BASE_URL}{id_}?period1=1579651200&period2={now}&interval=1d&events=history'
3389-
return pd.read_csv(url, usecols=['Date', 'Close']).set_index('Date').Close
3390-
covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv',
3391-
usecols=['location', 'date', 'total_cases'])
3392-
covid = covid[covid.location == 'World'].set_index('date').total_cases
3393-
dow, gold, bitcoin = [scrape_yahoo(id_) for id_ in ('^DJI', 'GC=F', 'BTC-USD')]
3394-
dow.name, gold.name, bitcoin.name = 'Dow Jones', 'Gold', 'Bitcoin'
3395-
return covid, dow, gold, bitcoin
3396-
3397-
def wrangle_data(covid, dow, gold, bitcoin):
3398-
df = pd.concat([dow, gold, bitcoin], axis=1)
3384+
def scrape_covid():
3385+
url = 'https://covid.ourworldindata.org/data/owid-covid-data.csv'
3386+
df = pd.read_csv(url, usecols=['location', 'date', 'total_cases'])
3387+
return df[df.location == 'World'].set_index('date').total_cases
3388+
def scrape_yahoo(slug):
3389+
url = f'https://query1.finance.yahoo.com/v7/finance/download/{slug}' + \
3390+
'?period1=1579651200&period2=1608850800&interval=1d&events=history'
3391+
df = pd.read_csv(url, usecols=['Date', 'Close'])
3392+
return df.set_index('Date').Close
3393+
return scrape_covid(), scrape_yahoo('BTC-USD'), scrape_yahoo('GC=F'), scrape_yahoo('^DJI')
3394+
3395+
def wrangle_data(covid, bitcoin, gold, dow):
3396+
df = pd.concat([bitcoin, gold, dow], axis=1)
33993397
df = df.sort_index().interpolate()
34003398
df = df.rolling(10, min_periods=1, center=True).mean()
34013399
df = df.loc['2020-02-23':'2020-11-25']
34023400
df = (df / df.iloc[0]) * 100
34033401
return pd.concat([covid, df], axis=1, join='inner')
34043402

34053403
def display_data(df):
3406-
def get_trace(col_name):
3407-
return go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis='y2')
3408-
traces = [get_trace(col_name) for col_name in df.columns[1:]]
3409-
traces.append(go.Scatter(x=df.index, y=df.total_cases, name='Total Cases', yaxis='y1'))
3404+
df.columns = ['Total Cases', 'Bitcoin', 'Gold', 'Dow Jones']
34103405
figure = go.Figure()
3411-
figure.add_traces(traces)
3406+
for col_name in df:
3407+
yaxis = 'y1' if col_name == 'Total Cases' else 'y2'
3408+
trace = go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis=yaxis)
3409+
figure.add_trace(trace)
34123410
figure.update_layout(
34133411
yaxis1=dict(title='Total Cases', rangemode='tozero'),
34143412
yaxis2=dict(title='%', rangemode='tozero', overlaying='y', side='right'),

‎index.html

Lines changed: 19 additions & 21 deletions
Original file line numberDiff line numberDiff line change
@@ -2717,7 +2717,7 @@
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&lt;DF&gt; = &lt;DF&gt;[row_bools] <span class="hljs-comment"># Keeps rows as specified by bools.</span>
27182718
&lt;DF&gt; = &lt;DF&gt;[&lt;DF_of_bools&gt;] <span class="hljs-comment"># Assigns NaN to False values.</span>
27192719
</code></pre>
2720-
<pre><code class="python language-python hljs">&lt;DF&gt; = &lt;DF&gt; &gt;&lt;== &lt;el/Sr/DF&gt; <span class="hljs-comment"># Returns DataFrame of bools.</span>
2720+
<pre><code class="python language-python hljs">&lt;DF&gt; = &lt;DF&gt; &gt;&lt;== &lt;el/Sr/DF&gt; <span class="hljs-comment"># Returns DF of bools. Sr is treated as a row.</span>
27212721
&lt;DF&gt; = &lt;DF&gt; +-*/ &lt;el/Sr/DF&gt; <span class="hljs-comment"># Items with non-matching keys get value NaN.</span>
27222722
</code></pre>
27232723
<pre><code class="python language-python hljs">&lt;DF&gt; = &lt;DF&gt;.set_index(column_key) <span class="hljs-comment"># Replaces row keys with values from a column.</span>
@@ -2879,39 +2879,37 @@
28792879

28802880
<div><h4 id="confirmedcovidcasesdowjonesgoldandbitcoinprice">Confirmed covid cases, Dow Jones, gold, and Bitcoin price:</h4><p></p><div id="e23ccacc-a456-478b-b467-7282a2165921" class="plotly-graph-div" style="height:315px; width:100%;"></div><pre><code class="python language-python hljs"><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
28812881
<span class="hljs-keyword">import</span> plotly.graph_objects <span class="hljs-keyword">as</span> go
2882-
<span class="hljs-keyword">import</span> datetime
28832882

28842883
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">main</span><span class="hljs-params">()</span>:</span>
28852884
display_data(wrangle_data(*scrape_data()))
28862885

28872886
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">scrape_data</span><span class="hljs-params">()</span>:</span>
2888-
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">scrape_yahoo</span><span class="hljs-params">(id_)</span>:</span>
2889-
BASE_URL = <span class="hljs-string">'https://query1.finance.yahoo.com/v7/finance/download/'</span>
2890-
now = int(datetime.datetime.now().timestamp())
2891-
url = <span class="hljs-string">f'<span class="hljs-subst">{BASE_URL}</span><span class="hljs-subst">{id_}</span>?period1=1579651200&amp;period2=<span class="hljs-subst">{now}</span>&amp;interval=1d&amp;events=history'</span>
2892-
<span class="hljs-keyword">return</span> pd.read_csv(url, usecols=[<span class="hljs-string">'Date'</span>, <span class="hljs-string">'Close'</span>]).set_index(<span class="hljs-string">'Date'</span>).Close
2893-
covid = pd.read_csv(<span class="hljs-string">'https://covid.ourworldindata.org/data/owid-covid-data.csv'</span>,
2894-
usecols=[<span class="hljs-string">'location'</span>, <span class="hljs-string">'date'</span>, <span class="hljs-string">'total_cases'</span>])
2895-
covid = covid[covid.location == <span class="hljs-string">'World'</span>].set_index(<span class="hljs-string">'date'</span>).total_cases
2896-
dow, gold, bitcoin = [scrape_yahoo(id_) <span class="hljs-keyword">for</span> id_ <span class="hljs-keyword">in</span> (<span class="hljs-string">'^DJI'</span>, <span class="hljs-string">'GC=F'</span>, <span class="hljs-string">'BTC-USD'</span>)]
2897-
dow.name, gold.name, bitcoin.name = <span class="hljs-string">'Dow Jones'</span>, <span class="hljs-string">'Gold'</span>, <span class="hljs-string">'Bitcoin'</span>
2898-
<span class="hljs-keyword">return</span> covid, dow, gold, bitcoin
2899-
2900-
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">wrangle_data</span><span class="hljs-params">(covid, dow, gold, bitcoin)</span>:</span>
2901-
df = pd.concat([dow, gold, bitcoin], axis=<span class="hljs-number">1</span>)
2887+
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">scrape_covid</span><span class="hljs-params">()</span>:</span>
2888+
url = <span class="hljs-string">'https://covid.ourworldindata.org/data/owid-covid-data.csv'</span>
2889+
df = pd.read_csv(url, usecols=[<span class="hljs-string">'location'</span>, <span class="hljs-string">'date'</span>, <span class="hljs-string">'total_cases'</span>])
2890+
<span class="hljs-keyword">return</span> df[df.location == <span class="hljs-string">'World'</span>].set_index(<span class="hljs-string">'date'</span>).total_cases
2891+
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">scrape_yahoo</span><span class="hljs-params">(slug)</span>:</span>
2892+
url = <span class="hljs-string">f'https://query1.finance.yahoo.com/v7/finance/download/<span class="hljs-subst">{slug}</span>'</span> + \
2893+
<span class="hljs-string">'?period1=1579651200&amp;period2=1608850800&amp;interval=1d&amp;events=history'</span>
2894+
df = pd.read_csv(url, usecols=[<span class="hljs-string">'Date'</span>, <span class="hljs-string">'Close'</span>])
2895+
<span class="hljs-keyword">return</span> df.set_index(<span class="hljs-string">'Date'</span>).Close
2896+
<span class="hljs-keyword">return</span> scrape_covid(), scrape_yahoo(<span class="hljs-string">'BTC-USD'</span>), scrape_yahoo(<span class="hljs-string">'GC=F'</span>), scrape_yahoo(<span class="hljs-string">'^DJI'</span>)
2897+
2898+
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">wrangle_data</span><span class="hljs-params">(covid, bitcoin, gold, dow)</span>:</span>
2899+
df = pd.concat([bitcoin, gold, dow], axis=<span class="hljs-number">1</span>)
29022900
df = df.sort_index().interpolate()
29032901
df = df.rolling(<span class="hljs-number">10</span>, min_periods=<span class="hljs-number">1</span>, center=<span class="hljs-keyword">True</span>).mean()
29042902
df = df.loc[<span class="hljs-string">'2020-02-23'</span>:<span class="hljs-string">'2020-11-25'</span>]
29052903
df = (df / df.iloc[<span class="hljs-number">0</span>]) * <span class="hljs-number">100</span>
29062904
<span class="hljs-keyword">return</span> pd.concat([covid, df], axis=<span class="hljs-number">1</span>, join=<span class="hljs-string">'inner'</span>)
29072905

29082906
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">display_data</span><span class="hljs-params">(df)</span>:</span>
2909-
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">get_trace</span><span class="hljs-params">(col_name)</span>:</span>
2910-
<span class="hljs-keyword">return</span> go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis=<span class="hljs-string">'y2'</span>)
2911-
traces = [get_trace(col_name) <span class="hljs-keyword">for</span> col_name <span class="hljs-keyword">in</span> df.columns[<span class="hljs-number">1</span>:]]
2912-
traces.append(go.Scatter(x=df.index, y=df.total_cases, name=<span class="hljs-string">'Total Cases'</span>, yaxis=<span class="hljs-string">'y1'</span>))
2907+
df.columns = [<span class="hljs-string">'Total Cases'</span>, <span class="hljs-string">'Bitcoin'</span>, <span class="hljs-string">'Gold'</span>, <span class="hljs-string">'Dow Jones'</span>]
29132908
figure = go.Figure()
2914-
figure.add_traces(traces)
2909+
<span class="hljs-keyword">for</span> col_name <span class="hljs-keyword">in</span> df:
2910+
yaxis = <span class="hljs-string">'y1'</span> <span class="hljs-keyword">if</span> col_name == <span class="hljs-string">'Total Cases'</span> <span class="hljs-keyword">else</span> <span class="hljs-string">'y2'</span>
2911+
trace = go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis=yaxis)
2912+
figure.add_trace(trace)
29152913
figure.update_layout(
29162914
yaxis1=dict(title=<span class="hljs-string">'Total Cases'</span>, rangemode=<span class="hljs-string">'tozero'</span>),
29172915
yaxis2=dict(title=<span class="hljs-string">'%'</span>, rangemode=<span class="hljs-string">'tozero'</span>, overlaying=<span class="hljs-string">'y'</span>, side=<span class="hljs-string">'right'</span>),

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