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‎README.md‎

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#### DataFrame — Encode, Decode:
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```python
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<DF> = pd.read_json/html('<str/path/url>') # Run `$ pip3 install beautifulsoup4 lxml`.
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<DF> = pd.read_csv('<path/url>') # `header/index_col/dtype/usecols/…=<obj>`.
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<DF> = pd.read_pickle/excel('<path/url>') # Use `sheet_name=None` to get all Excel sheets.
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<DF> = pd.read_sql('<table/query>', <conn.>) # SQLite3/SQLAlchemy connection (see #SQLite).
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<DF> = pd.read_json/pickle(<path/url/file>) # Also accepts io.StringIO/BytesIO(<str/bytes>).
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<DF> = pd.read_csv(<path/url/file>) # `header/index_col/dtype/usecols/…=<obj>`.
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<DF> = pd.read_excel(<path/url/file>) # `sheet_name=None` returns dict of all sheets.
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<DF> = pd.read_sql('<table/query>', <conn>) # SQLite3/SQLAlchemy connection (see #SQLite).
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<list> = pd.read_html(<path/url/file>) # Run `$ pip3 install beautifulsoup4 lxml`.
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```
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```python
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<dict> = <DF>.to_dict('d/l/s/…') # Returns columns as dicts, lists or series.
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<str> = <DF>.to_json/html/csv/latex() # Saves output to a file if path is passed.
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<str> = <DF>.to_json/csv/html/latex() # Saves output to a file if path is passed.
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<DF>.to_pickle/excel(<path>) # Run `$ pip3 install "pandas[excel]" odfpy`.
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<DF>.to_sql('<table_name>', <connection>) # Also `if_exists='fail/replace/append'`.
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```
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```python
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<GB> = <DF>.groupby(col_key/s) # Splits DF into groups based on passed column.
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<DF> = <GB>.apply(<func>) # Maps each group. Func can return DF, S or el.
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<DF> = <GB>.filter(<func>) # Drops a group if function returns False.
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<DF> = <GB>.apply/filter(<func>) # Filter drops a group if func returns False.
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<DF> = <GB>.get_group(<el>) # Selects a group by grouping column's value.
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<S> = <GB>.size() # S of group sizes. Same keys as get_group().
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<GB> = <GB>[col_key] # Single column GB. All operations return S.

‎index.html‎

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<body>
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<header>
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<aside>December 19, 2024</aside>
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<aside>December 20, 2024</aside>
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<a href="https://gto76.github.io" rel="author">Jure Šorn</a>
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</header>
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&lt;DF&gt; = &lt;DF&gt;.pivot_table(index=col_key/s) <span class="hljs-comment"># `columns=key/s, values=key/s, aggfunc='mean'`.</span>
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</code></pre></div>
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<div><h4 id="dataframeencodedecode">DataFrame — Encode, Decode:</h4><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>
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&lt;DF&gt; = pd.read_csv(<span class="hljs-string">'&lt;path/url&gt;'</span>) <span class="hljs-comment"># `header/index_col/dtype/usecols/…=&lt;obj&gt;`.</span>
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&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>
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&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>
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<div><h4 id="dataframeencodedecode">DataFrame — Encode, Decode:</h4><pre><code class="python language-python hljs">&lt;DF&gt; = pd.read_json/pickle(&lt;path/url/file&gt;) <span class="hljs-comment"># Also accepts io.StringIO/BytesIO(&lt;str/bytes&gt;).</span>
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&lt;DF&gt; = pd.read_csv(&lt;path/url/file&gt;) <span class="hljs-comment"># `header/index_col/dtype/usecols/…=&lt;obj&gt;`.</span>
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&lt;DF&gt; = pd.read_excel(&lt;path/url/file&gt;) <span class="hljs-comment"># `sheet_name=None` returns dict of all sheets.</span>
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&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>
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&lt;list&gt; = pd.read_html(&lt;path/url/file&gt;) <span class="hljs-comment"># Run `$ pip3 install beautifulsoup4 lxml`.</span>
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</code></pre></div>
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<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>
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&lt;str&gt; = &lt;DF&gt;.to_json/html/csv/latex() <span class="hljs-comment"># Saves output to a file if path is passed.</span>
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&lt;str&gt; = &lt;DF&gt;.to_json/csv/html/latex() <span class="hljs-comment"># Saves output to a file if path is passed.</span>
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&lt;DF&gt;.to_pickle/excel(&lt;path&gt;) <span class="hljs-comment"># Run `$ pip3 install "pandas[excel]" odfpy`.</span>
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&lt;DF&gt;.to_sql(<span class="hljs-string">'&lt;table_name&gt;'</span>, &lt;connection&gt;) <span class="hljs-comment"># Also `if_exists='fail/replace/append'`.</span>
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</code></pre>
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<li><strong>To get specific attributes from a series of Timestamps use <code class="python hljs"><span class="hljs-string">'&lt;S&gt;.dt.year/date/…'</span></code>.</strong></li>
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</ul>
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<div><h3 id="groupby">GroupBy</h3><p><strong>Object that groups together rows of a dataframe based on the value of the passed column.</strong></p><pre><code class="python language-python hljs">&lt;GB&gt; = &lt;DF&gt;.groupby(col_key/s) <span class="hljs-comment"># Splits DF into groups based on passed column.</span>
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&lt;DF&gt; = &lt;GB&gt;.apply(&lt;func&gt;) <span class="hljs-comment"># Maps each group. Func can return DF, S or el.</span>
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&lt;DF&gt; = &lt;GB&gt;.filter(&lt;func&gt;) <span class="hljs-comment"># Drops a group if function returns False.</span>
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&lt;DF&gt; = &lt;GB&gt;.apply/filter(&lt;func&gt;) <span class="hljs-comment"># Filter drops a group if func returns False.</span>
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&lt;DF&gt; = &lt;GB&gt;.get_group(&lt;el&gt;) <span class="hljs-comment"># Selects a group by grouping column's value.</span>
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&lt;S&gt; = &lt;GB&gt;.size() <span class="hljs-comment"># S of group sizes. Same keys as get_group().</span>
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&lt;GB&gt; = &lt;GB&gt;[col_key] <span class="hljs-comment"># Single column GB. All operations return S.</span>
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<footer>
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<aside>December 19, 2024</aside>
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<aside>December 20, 2024</aside>
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<a href="https://gto76.github.io" rel="author">Jure Šorn</a>
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</footer>
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