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python-cheatsheet
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Jure Šorn 4 years ago
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4 changed files with 14 additions and 14 deletions
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  1. 12
      README.md
  2. 12
      index.html
  3. 2
      pdf/index_for_pdf.html
  4. 2
      pdf/index_for_pdf_print.html

12
README.md
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@ -3143,7 +3143,7 @@ y 2
| sr.trans(…) | y 2 | y 2 | y 2 |
+-------------+-------------+-------------+---------------+
```
* **Last result has a hierarchical index. `'<Sr>[<key_1>, <key_2>]'` returns the value.**
* **Last result has a hierarchical index. Use `'<Sr>[<key_1>, <key_2>]'` to get the value.**
### DataFrame
**Table with labeled rows and columns.**
@ -3387,12 +3387,12 @@ 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, 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
dow, gold, btc = [scrape_yahoo(id_) for id_ in ('^DJI', 'GC=F', 'BTC-USD')]
dow.name, gold.name, btc.name = 'Dow Jones', 'Gold', 'Bitcoin'
return covid, dow, gold, btc
def wrangle_data(covid, dow, gold, bitcoin):
df = pandas.concat([covid, dow, gold, bitcoin], axis=1)
def wrangle_data(covid, dow, gold, btc):
df = pandas.concat([covid, dow, gold, btc], 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:]

12
index.html
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@ -2681,7 +2681,7 @@ y <span class="hljs-number">2</span>
┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
</code></pre>
<ul>
<li><strong>Last result has a hierarchical index. <code class="python hljs"><span class="hljs-string">'&lt;Sr&gt;[&lt;key_1&gt;, &lt;key_2&gt;]'</span></code> returns the value.</strong></li>
<li><strong>Last result has a hierarchical index. Use <code class="python hljs"><span class="hljs-string">'&lt;Sr&gt;[&lt;key_1&gt;, &lt;key_2&gt;]'</span></code> to get the value.</strong></li>
</ul>
<div><h3 id="dataframe">DataFrame</h3><p><strong>Table with labeled rows and columns.</strong></p><pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>DataFrame([[<span class="hljs-number">1</span>, <span class="hljs-number">2</span>], [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>]], index=[<span class="hljs-string">'a'</span>, <span class="hljs-string">'b'</span>], columns=[<span class="hljs-string">'x'</span>, <span class="hljs-string">'y'</span>])
x y
@ -2871,12 +2871,12 @@ plotly.express.line(df, x=<span class="hljs-string">'Date'</span>, y=<span class
covid = pd.read_csv(<span class="hljs-string">'https://covid.ourworldindata.org/data/owid-covid-data.csv'</span>,
usecols=[<span class="hljs-string">'date'</span>, <span class="hljs-string">'total_cases'</span>])
covid = covid.groupby(<span class="hljs-string">'date'</span>).sum()
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>)]
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>
<span class="hljs-keyword">return</span> covid, dow, gold, bitcoin
dow, gold, btc = [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>)]
dow.name, gold.name, btc.name = <span class="hljs-string">'Dow Jones'</span>, <span class="hljs-string">'Gold'</span>, <span class="hljs-string">'Bitcoin'</span>
<span class="hljs-keyword">return</span> covid, dow, gold, btc
<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>
df = pandas.concat([covid, dow, gold, bitcoin], axis=<span class="hljs-number">1</span>)
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">wrangle_data</span><span class="hljs-params">(covid, dow, gold, btc)</span>:</span>
df = pandas.concat([covid, dow, gold, btc], axis=<span class="hljs-number">1</span>)
df = df.loc[<span class="hljs-string">'2020-02-23'</span>:].iloc[:<span class="hljs-number">-2</span>]
df = df.interpolate()
df.iloc[:, <span class="hljs-number">1</span>:] = df.rolling(<span class="hljs-number">10</span>, min_periods=<span class="hljs-number">1</span>, center=<span class="hljs-keyword">True</span>).mean().iloc[:, <span class="hljs-number">1</span>:]

2
pdf/index_for_pdf.html
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@ -121,7 +121,7 @@
<strong>regular expressions, <a href="#regex">5</a>-<a href="#specialsequences">6</a></strong><br>
<strong>requests library, <a href="#scraping">35</a>, <a href="#test">36</a></strong> </p>
<h3 id="s">S</h3>
<p><strong>scraping, <a href="#scraping">35</a>, <a href="#basicmariobrothersexample">43</a>, <a href="#plotly">47</a>, <a href="#confirmedcovidcasesdowjonesgoldandbitcoinprice">48</a></strong><br>
<p><strong>scraping, <a href="#scraping">35</a>, <a href="#basicmariobrothersexample">43</a>, <a href="#encodedecode">46</a>, <a href="#plotly">47</a>, <a href="#confirmedcovidcasesdowjonesgoldandbitcoinprice">48</a></strong><br>
<strong>sequence, <a href="#type">4</a>, <a href="#sequence">18</a>, <a href="#abcsequence">19</a></strong><br>
<strong>sets, <a href="#set">2</a>, <a href="#otheruses">11</a></strong><br>
<strong>shell commands, <a href="#shellcommands">25</a></strong><br>

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pdf/index_for_pdf_print.html
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@ -121,7 +121,7 @@
<strong>regular expressions, 5-6</strong><br>
<strong>requests library, 35, 36</strong> </p>
<h3 id="s">S</h3>
<p><strong>scraping, 35, 43, 47, 48</strong><br>
<p><strong>scraping, 35, 43, 46, 47, 48</strong><br>
<strong>sequence, 4, 18, 19</strong><br>
<strong>sets, 2, 11</strong><br>
<strong>shell commands, 25</strong><br>

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