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python-cheatsheet
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Pandas

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Jure Šorn 2 years ago
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  1. 11
      README.md
  2. 11
      index.html

11
README.md
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@ -3105,9 +3105,10 @@ if __name__ == '__main__':
Pandas
------
```python
# $ pip3 install pandas
# $ pip3 install pandas matplotlib
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
```
### Series
@ -3151,7 +3152,7 @@ Name: a, dtype: int64
```python
<Sr>.plot.line/area/bar/pie/hist() # Generates a Matplotlib plot.
matplotlib.pyplot.show() # Displays the plot. Also savefig(<path>).
plt.show() # Displays the plot. Also plt.savefig(<path>).
```
#### Series — Aggregate, Transform, Map:
@ -3310,12 +3311,12 @@ b 3 4
#### DataFrame — Plot, Encode, Decode:
```python
<DF>.plot.line/bar/hist/scatter() # Also: `x=column_key, y=column_key/s`.
import matplotlib.pyplot as plt; plt.show() # Displays the plot.
<DF>.plot.line/bar/hist/scatter/box() # Also: `x=column_key, y=column_key/s`.
plt.show() # Displays the plot. Also plt.savefig(<path>).
```
```python
<DF> = pd.read_json/html('<str/path/url>') # Run `$ pip3 install lxml` to read html.
<DF> = pd.read_json/html('<str/path/url>') # Run `$ pip3 install beautifulsoup4 lxml`.
<DF> = pd.read_csv/pickle/excel('<path/url>') # Use `sheet_name=None` to get all Excel sheets.
<DF> = pd.read_sql('<table/query>', <conn.>) # Accepts SQLite3 or SQLAlchemy connection.
<DF> = pd.read_clipboard() # Reads a copied table from the clipboard.

11
index.html
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@ -2538,9 +2538,10 @@ W, H, MAX_S = <span class="hljs-number">50</span>, <span class="hljs-number">50<
main()
</code></pre></div>
<div><h2 id="pandas"><a href="#pandas" name="pandas">#</a>Pandas</h2><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install pandas</span>
<div><h2 id="pandas"><a href="#pandas" name="pandas">#</a>Pandas</h2><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install pandas matplotlib</span>
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
<span class="hljs-keyword">from</span> pandas <span class="hljs-keyword">import</span> Series, DataFrame
<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
</code></pre></div>
<div><h3 id="series">Series</h3><p><strong>Ordered dictionary with a name.</strong></p><pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>Series([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>], index=[<span class="hljs-string">'x'</span>, <span class="hljs-string">'y'</span>], name=<span class="hljs-string">'a'</span>)
@ -2570,7 +2571,7 @@ Name: a, dtype: int64
&lt;Sr&gt;.update(&lt;Sr&gt;) <span class="hljs-comment"># Updates items that are already present.</span>
</code></pre>
<pre><code class="python language-python hljs">&lt;Sr&gt;.plot.line/area/bar/pie/hist() <span class="hljs-comment"># Generates a Matplotlib plot.</span>
matplotlib.pyplot.show() <span class="hljs-comment"># Displays the plot. Also savefig(&lt;path&gt;).</span>
plt.show() <span class="hljs-comment"># Displays the plot. Also plt.savefig(&lt;path&gt;).</span>
</code></pre>
<div><h4 id="seriesaggregatetransformmap">Series — Aggregate, Transform, Map:</h4><pre><code class="python language-python hljs">&lt;el&gt; = &lt;Sr&gt;.sum/max/mean/idxmax/all() <span class="hljs-comment"># Or: &lt;Sr&gt;.agg(lambda &lt;Sr&gt;: &lt;el&gt;)</span>
&lt;Sr&gt; = &lt;Sr&gt;.rank/diff/cumsum/ffill/interpl() <span class="hljs-comment"># Or: &lt;Sr&gt;.agg/transform(lambda &lt;Sr&gt;: &lt;Sr&gt;)</span>
@ -2701,11 +2702,11 @@ b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
<ul>
<li><strong>Use <code class="python hljs"><span class="hljs-string">'&lt;DF&gt;[col_key_1, col_key_2][row_key]'</span></code> to get the fifth result's values.</strong></li>
</ul>
<div><h4 id="dataframeplotencodedecode">DataFrame — Plot, Encode, Decode:</h4><pre><code class="python language-python hljs">&lt;DF&gt;.plot.line/bar/hist/scatter() <span class="hljs-comment"># Also: `x=column_key, y=column_key/s`.</span>
<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt; plt.show() <span class="hljs-comment"># Displays the plot.</span>
<div><h4 id="dataframeplotencodedecode">DataFrame — Plot, Encode, Decode:</h4><pre><code class="python language-python hljs">&lt;DF&gt;.plot.line/bar/hist/scatter/box() <span class="hljs-comment"># Also: `x=column_key, y=column_key/s`.</span>
plt.show() <span class="hljs-comment"># Displays the plot. Also plt.savefig(&lt;path&gt;).</span>
</code></pre></div>
<pre><code class="python language-python hljs">&lt;DF&gt; = pd.read_json/html(<span class="hljs-string">'&lt;str/path/url&gt;'</span>) <span class="hljs-comment"># Run `$ pip3 install lxml` to read html.</span>
<pre><code class="python language-python hljs">&lt;DF&gt; = pd.read_json/html(<span class="hljs-string">'&lt;str/path/url&gt;'</span>) <span class="hljs-comment"># Run `$ pip3 install beautifulsoup4 lxml`.</span>
&lt;DF&gt; = pd.read_csv/pickle/excel(<span class="hljs-string">'&lt;path/url&gt;'</span>) <span class="hljs-comment"># Use `sheet_name=None` to get all Excel sheets.</span>
&lt;DF&gt; = pd.read_sql(<span class="hljs-string">'&lt;table/query&gt;'</span>, &lt;conn.&gt;) <span class="hljs-comment"># Accepts SQLite3 or SQLAlchemy connection.</span>
&lt;DF&gt; = pd.read_clipboard() <span class="hljs-comment"># Reads a copied table from the clipboard.</span>

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