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python-cheatsheet
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Working on Pandas

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Jure Šorn 4 years ago
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  1. 22
      README.md
  2. 25
      index.html

22
README.md
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@ -3286,6 +3286,17 @@ b 3 4
### GroupBy ### GroupBy
**Object that groups together rows of a dataframe based on the value of passed column.** **Object that groups together rows of a dataframe based on the value of passed column.**
```python
>>> df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 6]], index=list('abc'), columns=list('xyz'))
>>> df.groupby('z').get_group(3)
x y
a 1 2
>>> df.groupby('z').get_group(6)
x y
b 4 5
c 7 8
```
```python ```python
<GB> = <DF>.groupby(column_key/s) # DF is split into groups based on passed column. <GB> = <DF>.groupby(column_key/s) # DF is split into groups based on passed column.
<DF> = <GB>.get_group(group_key) # Selects a group by value of grouping column. <DF> = <GB>.get_group(group_key) # Selects a group by value of grouping column.
@ -3299,7 +3310,6 @@ b 3 4
``` ```
```python ```python
>>> df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 6]], index=list('abc'), columns=list('xyz'))
>>> gb = df.groupby('z') >>> gb = df.groupby('z')
x y z x y z
3: a 1 2 3 3: a 1 2 3
@ -3325,12 +3335,12 @@ b 3 4
``` ```
### Rolling ### Rolling
**Object for rolling window calculations.**
```python ```python
<Rl_S/D/G> = <Sr/DF/GB>.rolling(window_size) # Also: `min_periods=None, center=False`.
<Rl_S/D> = <Rl_D/G>[column_key/s] # Or: <Rl>.column_key
<Sr/DF/DF> = <Rl_S/D/G>.sum/max/mean()
<Sr/DF/DF> = <Rl_S/D/G>.apply(<agg_func>) # Invokes function on every window.
<Sr/DF/DF> = <Rl_S/D/G>.aggregate(<func/str>) # Invokes function on every window.
<R_Sr/R_DF/R_GB> = <Sr/DF/GB>.rolling(window_size) # Also: `min_periods=None, center=False`.
<R_Sr/R_DF> = <R_DF/R_GB>[column_key/s] # Or: <R>.column_key
<Sr/DF/DF> = <R_Sr/R_DF/R_GB>.sum/max/mean() # Or: <R>.apply/agg(<agg_func/str>)
``` ```

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@ -2778,18 +2778,26 @@ b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
&lt;DF&gt;.to_pickle/excel(&lt;path&gt;) &lt;DF&gt;.to_pickle/excel(&lt;path&gt;)
&lt;DF&gt;.to_sql(<span class="hljs-string">'&lt;table_name&gt;'</span>, &lt;connection&gt;) &lt;DF&gt;.to_sql(<span class="hljs-string">'&lt;table_name&gt;'</span>, &lt;connection&gt;)
</code></pre> </code></pre>
<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>
&lt;DF&gt; = &lt;GB&gt;.get_group(group_key) <span class="hljs-comment"># Selects a group by value of grouping column.</span>
<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>))
<span class="hljs-meta">&gt;&gt;&gt; </span>df.groupby(<span class="hljs-string">'z'</span>).get_group(<span class="hljs-number">3</span>)
x y
a <span class="hljs-number">1</span> <span class="hljs-number">2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>df.groupby(<span class="hljs-string">'z'</span>).get_group(<span class="hljs-number">6</span>)
x y
b <span class="hljs-number">4</span> <span class="hljs-number">5</span>
c <span class="hljs-number">7</span> <span class="hljs-number">8</span>
</code></pre></div> </code></pre></div>
<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>
&lt;DF&gt; = &lt;GB&gt;.get_group(group_key) <span class="hljs-comment"># Selects a group by value of grouping column.</span>
</code></pre>
<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> <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>
&lt;DF&gt; = &lt;GB&gt;.rank/diff/cumsum/ffill() <span class="hljs-comment"># Or: &lt;GB&gt;.aggregate(&lt;trans_func&gt;) </span> &lt;DF&gt; = &lt;GB&gt;.rank/diff/cumsum/ffill() <span class="hljs-comment"># Or: &lt;GB&gt;.aggregate(&lt;trans_func&gt;) </span>
&lt;DF&gt; = &lt;GB&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;GB&gt;.transform(&lt;map_func&gt;)</span> &lt;DF&gt; = &lt;GB&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;GB&gt;.transform(&lt;map_func&gt;)</span>
</code></pre></div> </code></pre></div>
<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>))
<span class="hljs-meta">&gt;&gt;&gt; </span>gb = df.groupby(<span class="hljs-string">'z'</span>)
<pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>gb = df.groupby(<span class="hljs-string">'z'</span>)
x y z x y z
<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> <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>
<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> <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>
@ -2810,13 +2818,12 @@ b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
| | 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> | | | | | 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> | | |
+-------------+-------------+-------------+-------------+---------------+ +-------------+-------------+-------------+-------------+---------------+
</code></pre> </code></pre>
<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>
&lt;Rl_S/D&gt; = &lt;Rl_D/G&gt;[column_key/s] <span class="hljs-comment"># Or: &lt;Rl&gt;.column_key</span>
&lt;Sr/DF/DF&gt; = &lt;Rl_S/D/G&gt;.sum/max/mean()
&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>
&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>
<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>
&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>
&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>
</code></pre></div> </code></pre></div>
<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">| <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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