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python-cheatsheet
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NumPy, Image, Pandas

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Jure Šorn 1 year ago
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  1. 20
      README.md
  2. 24
      index.html

20
README.md
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@ -2693,6 +2693,7 @@ import numpy as np
<1d/2d_a> = <2d_array>[<2d/1d_bools>] # 1d_bools must have size of a column.
```
* **Indexes should not be tuples because Python converts `'obj[i, j]'` to `'obj[(i, j)]'`!**
* **`':'` returns a slice of all dimension's indexes. Omitted dimensions default to `':'`.**
* **Any value that is broadcastable to the indexed shape can be assigned to the selection.**
### Broadcasting
@ -2738,9 +2739,7 @@ right = [[0.1, 0.6, 0.8], # Shape: (3, 3) <- !
[[ 0. , 0.5, 0.7],
[ 0.5, 0. , 0.2],
[ 0.7, 0.2, 0. ]]
>>> i = np.arange(3)
[0, 1, 2]
>>> distances[i, i] = np.inf
>>> distances[range(3), range(3)] = np.inf
[[ inf, 0.5, 0.7],
[ 0.5, inf, 0.2],
[ 0.7, 0.2, inf]]
@ -2765,11 +2764,11 @@ from PIL import Image, ImageFilter, ImageEnhance
```
```python
<int/tuple> = <Image>.getpixel((x, y)) # Returns a pixel.
<Image>.putpixel((x, y), <int/tuple>) # Writes a pixel to the image.
<ImagingCore> = <Image>.getdata() # Returns a flattened view of the pixels.
<Image>.putdata(<list/ImagingCore>) # Writes a flattened sequence of pixels.
<Image>.paste(<Image>, (x, y)) # Writes passed image to the image.
<int/tuple> = <Image>.getpixel((x, y)) # Returns pixel's color.
<Image>.putpixel((x, y), <int/tuple>) # Changes pixel's color.
<ImagingCore> = <Image>.getdata() # Returns a flattened view of all pixels.
<Image>.putdata(<list/ImagingCore>) # Updates pixels with a copy of the sequence.
<Image>.paste(<Image>, (x, y)) # Draws passed image at specified location.
```
```python
@ -3344,9 +3343,9 @@ plt.show() # Displays the plot. Also plt.sav
```python
<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_csv('<path/url>') # Also `names=<list>, parse_dates=False`.
<DF> = pd.read_pickle/excel('<path/url>') # Use `sheet_name=None` to get all Excel sheets.
<DF> = pd.read_sql('<table/query>', <conn.>) # SQLite3/SQLAlchemy connection (see #SQLite).
<DF> = pd.read_clipboard() # Reads a copied table from the clipboard.
```
```python
@ -3371,6 +3370,7 @@ c 7 8 6
<GB> = <DF>.groupby(column_key/s) # Splits DF into groups based on passed column.
<DF> = <GB>.apply(<func>) # Maps each group. Func can return DF, Sr or el.
<GB> = <GB>[column_key] # Single column GB. All operations return a Sr.
<Sr> = <GB>.size() # A Sr of group sizes. Keys are group "names".
```
#### GroupBy — Aggregate, Transform, Map:

24
index.html
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@ -54,7 +54,7 @@
<body>
<header>
<aside>October 16, 2023</aside>
<aside>November 3, 2023</aside>
<a href="https://gto76.github.io" rel="author">Jure Šorn</a>
</header>
@ -2198,6 +2198,7 @@ $ snakeviz test.prof
</code></pre>
<ul>
<li><strong>Indexes should not be tuples because Python converts <code class="python hljs"><span class="hljs-string">'obj[i, j]'</span></code> to <code class="python hljs"><span class="hljs-string">'obj[(i, j)]'</span></code>!</strong></li>
<li><strong><code class="python hljs"><span class="hljs-string">':'</span></code> returns a slice of all dimension's indexes. Omitted dimensions default to <code class="python hljs"><span class="hljs-string">':'</span></code>.</strong></li>
<li><strong>Any value that is broadcastable to the indexed shape can be assigned to the selection.</strong></li>
</ul>
<div><h3 id="broadcasting">Broadcasting</h3><p><strong>Set of rules by which NumPy functions operate on arrays of different sizes and/or dimensions.</strong></p><pre><code class="python language-python hljs">left = [[<span class="hljs-number">0.1</span>], [<span class="hljs-number">0.6</span>], [<span class="hljs-number">0.8</span>]] <span class="hljs-comment"># Shape: (3, 1)</span>
@ -2232,9 +2233,7 @@ right = [[<span class="hljs-number">0.1</span>, <span class="hljs-number">0.6</
[[ <span class="hljs-number">0.</span> , <span class="hljs-number">0.5</span>, <span class="hljs-number">0.7</span>],
[ <span class="hljs-number">0.5</span>, <span class="hljs-number">0.</span> , <span class="hljs-number">0.2</span>],
[ <span class="hljs-number">0.7</span>, <span class="hljs-number">0.2</span>, <span class="hljs-number">0.</span> ]]
<span class="hljs-meta">&gt;&gt;&gt; </span>i = np.arange(<span class="hljs-number">3</span>)
[<span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>distances[i, i] = np.inf
<span class="hljs-meta">&gt;&gt;&gt; </span>distances[range(<span class="hljs-number">3</span>), range(<span class="hljs-number">3</span>)] = np.inf
[[ inf, <span class="hljs-number">0.5</span>, <span class="hljs-number">0.7</span>],
[ <span class="hljs-number">0.5</span>, inf, <span class="hljs-number">0.2</span>],
[ <span class="hljs-number">0.7</span>, <span class="hljs-number">0.2</span>, inf]]
@ -2253,11 +2252,11 @@ right = [[<span class="hljs-number">0.1</span>, <span class="hljs-number">0.6</
&lt;Image&gt;.save(&lt;path&gt;) <span class="hljs-comment"># Selects format based on the path extension.</span>
&lt;Image&gt;.show() <span class="hljs-comment"># Opens image in the default preview app.</span>
</code></pre>
<pre><code class="python language-python hljs">&lt;int/tuple&gt; = &lt;Image&gt;.getpixel((x, y)) <span class="hljs-comment"># Returns a pixel.</span>
&lt;Image&gt;.putpixel((x, y), &lt;int/tuple&gt;) <span class="hljs-comment"># Writes a pixel to the image.</span>
&lt;ImagingCore&gt; = &lt;Image&gt;.getdata() <span class="hljs-comment"># Returns a flattened view of the pixels.</span>
&lt;Image&gt;.putdata(&lt;list/ImagingCore&gt;) <span class="hljs-comment"># Writes a flattened sequence of pixels.</span>
&lt;Image&gt;.paste(&lt;Image&gt;, (x, y)) <span class="hljs-comment"># Writes passed image to the image.</span>
<pre><code class="python language-python hljs">&lt;int/tuple&gt; = &lt;Image&gt;.getpixel((x, y)) <span class="hljs-comment"># Returns pixel's color.</span>
&lt;Image&gt;.putpixel((x, y), &lt;int/tuple&gt;) <span class="hljs-comment"># Changes pixel's color.</span>
&lt;ImagingCore&gt; = &lt;Image&gt;.getdata() <span class="hljs-comment"># Returns a flattened view of all pixels.</span>
&lt;Image&gt;.putdata(&lt;list/ImagingCore&gt;) <span class="hljs-comment"># Updates pixels with a copy of the sequence.</span>
&lt;Image&gt;.paste(&lt;Image&gt;, (x, y)) <span class="hljs-comment"># Draws passed image at specified location.</span>
</code></pre>
<pre><code class="python language-python hljs">&lt;Image&gt; = &lt;Image&gt;.filter(&lt;Filter&gt;) <span class="hljs-comment"># `&lt;Filter&gt; = ImageFilter.&lt;name&gt;([&lt;args&gt;])`</span>
&lt;Image&gt; = &lt;Enhance&gt;.enhance(&lt;float&gt;) <span class="hljs-comment"># `&lt;Enhance&gt; = ImageEnhance.&lt;name&gt;(&lt;Image&gt;)`</span>
@ -2727,9 +2726,9 @@ plt.show() <span class="hljs-comment"># Disp
</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 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_csv(<span class="hljs-string">'&lt;path/url&gt;'</span>) <span class="hljs-comment"># Also `names=&lt;list&gt;, parse_dates=False`.</span>
&lt;DF&gt; = pd.read_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"># SQLite3/SQLAlchemy connection (see #SQLite).</span>
&lt;DF&gt; = pd.read_clipboard() <span class="hljs-comment"># Reads a copied table from the clipboard.</span>
</code></pre>
<pre><code class="python language-python hljs">&lt;dict&gt; = &lt;DF&gt;.to_dict([<span class="hljs-string">'d/l/s/…'</span>]) <span class="hljs-comment"># Returns columns as dicts, lists or series.</span>
&lt;str&gt; = &lt;DF&gt;.to_json/html/csv([&lt;path&gt;]) <span class="hljs-comment"># Also to_markdown/latex([&lt;path&gt;]).</span>
@ -2747,6 +2746,7 @@ c <span class="hljs-number">7</span> <span class="hljs-number">8</span> <span
<pre><code class="python language-python hljs">&lt;GB&gt; = &lt;DF&gt;.groupby(column_key/s) <span class="hljs-comment"># Splits DF into groups based on passed column.</span>
&lt;DF&gt; = &lt;GB&gt;.apply(&lt;func&gt;) <span class="hljs-comment"># Maps each group. Func can return DF, Sr or el.</span>
&lt;GB&gt; = &lt;GB&gt;[column_key] <span class="hljs-comment"># Single column GB. All operations return a Sr.</span>
&lt;Sr&gt; = &lt;GB&gt;.size() <span class="hljs-comment"># A Sr of group sizes. Keys are group "names".</span>
</code></pre>
<div><h4 id="groupbyaggregatetransformmap">GroupBy — Aggregate, Transform, Map:</h4><pre><code class="python language-python hljs">&lt;DF&gt; = &lt;GB&gt;.sum/max/mean/idxmax/all() <span class="hljs-comment"># Or: &lt;GB&gt;.agg(lambda &lt;Sr&gt;: &lt;el&gt;)</span>
&lt;DF&gt; = &lt;GB&gt;.rank/diff/cumsum/ffill() <span class="hljs-comment"># Or: &lt;GB&gt;.transform(lambda &lt;Sr&gt;: &lt;Sr&gt;)</span>
@ -2925,7 +2925,7 @@ $ deactivate <span class="hljs-comment"># Deactivates the activ
<footer>
<aside>October 16, 2023</aside>
<aside>November 3, 2023</aside>
<a href="https://gto76.github.io" rel="author">Jure Šorn</a>
</footer>

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