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Working on Pandas
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‎README.md

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### GroupBy
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**Object that groups together rows of a dataframe based on the value of passed column.**
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```python
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>>> df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 6]], index=list('abc'), columns=list('xyz'))
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>>> df.groupby('z').get_group(3)
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x y
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a 1 2
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>>> df.groupby('z').get_group(6)
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x y
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b 4 5
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c 7 8
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```
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```python
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<GB> = <DF>.groupby(column_key/s) # DF is split into groups based on passed column.
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<DF> = <GB>.get_group(group_key) # Selects a group by value of grouping column.
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```
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```python
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>>> df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 6]], index=list('abc'), columns=list('xyz'))
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>>> gb = df.groupby('z')
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x y z
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3: a 1 2 3
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```
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### Rolling
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**Object for rolling window calculations.**
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```python
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<Rl_S/D/G> = <Sr/DF/GB>.rolling(window_size) # Also: `min_periods=None, center=False`.
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<Rl_S/D> = <Rl_D/G>[column_key/s] # Or: <Rl>.column_key
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<Sr/DF/DF> = <Rl_S/D/G>.sum/max/mean()
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<Sr/DF/DF> = <Rl_S/D/G>.apply(<agg_func>) # Invokes function on every window.
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<Sr/DF/DF> = <Rl_S/D/G>.aggregate(<func/str>) # Invokes function on every window.
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<R_Sr/R_DF/R_GB> = <Sr/DF/GB>.rolling(window_size) # Also: `min_periods=None, center=False`.
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<R_Sr/R_DF> = <R_DF/R_GB>[column_key/s] # Or: <R>.column_key
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<Sr/DF/DF> = <R_Sr/R_DF/R_GB>.sum/max/mean() # Or: <R>.apply/agg(<agg_func/str>)
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```
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‎index.html

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&lt;DF&gt;.to_pickle/excel(&lt;path&gt;)
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&lt;DF&gt;.to_sql(<span class="hljs-string">'&lt;table_name&gt;'</span>, &lt;connection&gt;)
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</code></pre>
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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 passed column.</strong></p><pre><code class="python language-python hljs">&lt;GB&gt; = &lt;DF&gt;.groupby(column_key/s) <span class="hljs-comment"># DF is split into groups based on passed column.</span>
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&lt;DF&gt; = &lt;GB&gt;.get_group(group_key) <span class="hljs-comment"># Selects a group by value of grouping column.</span>
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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 passed column.</strong></p><pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>df = 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>, <span class="hljs-number">5</span>, <span class="hljs-number">6</span>], [<span class="hljs-number">7</span>, <span class="hljs-number">8</span>, <span class="hljs-number">6</span>]], index=list(<span class="hljs-string">'abc'</span>), columns=list(<span class="hljs-string">'xyz'</span>))
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<span class="hljs-meta">&gt;&gt;&gt; </span>df.groupby(<span class="hljs-string">'z'</span>).get_group(<span class="hljs-number">3</span>)
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x y
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a <span class="hljs-number">1</span> <span class="hljs-number">2</span>
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<span class="hljs-meta">&gt;&gt;&gt; </span>df.groupby(<span class="hljs-string">'z'</span>).get_group(<span class="hljs-number">6</span>)
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x y
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b <span class="hljs-number">4</span> <span class="hljs-number">5</span>
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c <span class="hljs-number">7</span> <span class="hljs-number">8</span>
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</code></pre></div>
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<pre><code class="python language-python hljs">&lt;GB&gt; = &lt;DF&gt;.groupby(column_key/s) <span class="hljs-comment"># DF is split into groups based on passed column.</span>
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&lt;DF&gt; = &lt;GB&gt;.get_group(group_key) <span class="hljs-comment"># Selects a group by value of grouping column.</span>
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</code></pre>
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<div><h4 id="applyaggregatetransform-2">Apply, Aggregate, Transform:</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;.apply/agg(&lt;agg_func&gt;)</span>
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&lt;DF&gt; = &lt;GB&gt;.rank/diff/cumsum/ffill() <span class="hljs-comment"># Or: &lt;GB&gt;.aggregate(&lt;trans_func&gt;) </span>
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&lt;DF&gt; = &lt;GB&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;GB&gt;.transform(&lt;map_func&gt;)</span>
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</code></pre></div>
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<pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>df = 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>, <span class="hljs-number">5</span>, <span class="hljs-number">6</span>], [<span class="hljs-number">7</span>, <span class="hljs-number">8</span>, <span class="hljs-number">6</span>]], index=list(<span class="hljs-string">'abc'</span>), columns=list(<span class="hljs-string">'xyz'</span>))
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<span class="hljs-meta">&gt;&gt;&gt; </span>gb = df.groupby(<span class="hljs-string">'z'</span>)
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<pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>gb = df.groupby(<span class="hljs-string">'z'</span>)
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x y z
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<span class="hljs-number">3</span>: a <span class="hljs-number">1</span> <span class="hljs-number">2</span> <span class="hljs-number">3</span>
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<span class="hljs-number">6</span>: b <span class="hljs-number">4</span> <span class="hljs-number">5</span> <span class="hljs-number">6</span>
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| | c <span class="hljs-number">11</span> <span class="hljs-number">13</span> | c <span class="hljs-number">1</span> <span class="hljs-number">1</span> | | |
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+-------------+-------------+-------------+-------------+---------------+
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</code></pre>
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<div><h3 id="rolling">Rolling</h3><pre><code class="python language-python hljs">&lt;Rl_S/D/G&gt; = &lt;Sr/DF/GB&gt;.rolling(window_size) <span class="hljs-comment"># Also: `min_periods=None, center=False`.</span>
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&lt;Rl_S/D&gt; = &lt;Rl_D/G&gt;[column_key/s] <span class="hljs-comment"># Or: &lt;Rl&gt;.column_key</span>
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&lt;Sr/DF/DF&gt; = &lt;Rl_S/D/G&gt;.sum/max/mean()
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&lt;Sr/DF/DF&gt; = &lt;Rl_S/D/G&gt;.apply(&lt;agg_func&gt;) <span class="hljs-comment"># Invokes function on every window.</span>
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&lt;Sr/DF/DF&gt; = &lt;Rl_S/D/G&gt;.aggregate(&lt;func/str&gt;) <span class="hljs-comment"># Invokes function on every window.</span>
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<div><h3 id="rolling">Rolling</h3><p><strong>Object for rolling window calculations.</strong></p><pre><code class="python language-python hljs">&lt;R_Sr/R_DF/R_GB&gt; = &lt;Sr/DF/GB&gt;.rolling(window_size) <span class="hljs-comment"># Also: `min_periods=None, center=False`.</span>
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&lt;R_Sr/R_DF&gt; = &lt;R_DF/R_GB&gt;[column_key/s] <span class="hljs-comment"># Or: &lt;R&gt;.column_key</span>
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&lt;Sr/DF/DF&gt; = &lt;R_Sr/R_DF/R_GB&gt;.sum/max/mean() <span class="hljs-comment"># Or: &lt;R&gt;.apply/agg(&lt;agg_func/str&gt;)</span>
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</code></pre></div>
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<div><h2 id="plotly"><a href="#plotly" name="plotly">#</a>Plotly</h2><div><h3 id="top10countriesbypercentageofpopulationwithconfirmedcovid19infection">Top 10 Countries by Percentage of Population With Confirmed COVID-19 Infection</h3><pre><code class="text language-text">|
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