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Working on Pandas
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‎README.md

Lines changed: 20 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -3076,7 +3076,7 @@ from pandas import Series, DataFrame
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**Ordered dictionary with a name.**
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```python
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>>> sr = Series([1, 2], index=['x', 'y'], name='a')
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>>> Series([1, 2], index=['x', 'y'], name='a')
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x 1
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y 2
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Name: a, dtype: int64
@@ -3107,15 +3107,24 @@ Name: a, dtype: int64
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```python
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<Sr> = <Sr>.append(<Sr>) # Or: pd.concat(<coll_of_Sr>)
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<Sr> = <Sr>.combine_first(<Sr>) # Adds items that are not yet present (extends).
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combine, update?
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<Sr> = <Sr>.combine_first(<Sr>) # Adds items that are not yet present.
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<Sr>.update(<Sr>) # Updates items that are already present.
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```
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#### Aggregations:
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```python
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<el> = <Sr>.sum/max/mean/idxmax/all()
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<el> = <agg_func>(<Sr>)
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<el> = <Sr>.apply/agg(<agg_func>) # Apply can only accept strings.
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<el> = <Sr>.sum/max/mean/idxmax/all() # Or: <Sr>.aggregate(<agg_func>)
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<Sr> = <Sr>.diff/cumsum/rank/pct_change() # Or: <Sr>.agg/transform(<trans_func>)
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<Sr> = <Sr>.fillna(<el>) # Or: <Sr>.apply/agg/transform/map(<map_func>)
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```
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* **Also: `'ffill()'` and `'interpolate()'`.**
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* **The way `'aggregate()'` and `'transform()'` find out whether a function accepts an element or the whole Series is by passing it a single value at first and if it raises an error, then they pass it the whole Series.**
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#### Apply, Aggregate, Transform:
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```python
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>>> sr = Series([1, 2], index=['x', 'y'], name='a')
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x 1
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y 2
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Name: a, dtype: int64
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```
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```python
@@ -3128,13 +3137,6 @@ combine, update?
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+-------------+--------+-----------+---------------+
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```
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#### Transformations:
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```python
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<Sr> = <Sr>.diff/cumsum/rank/pct_change() # …/fillna/ffill/interpolate()
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<Sr> = <Sr>.apply/agg/transform(<trans_func>)
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map?
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```
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```python
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+-------------+--------+-----------+---------------+
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| | 'rank' | ['rank'] | {'r': 'rank'} |
@@ -3186,12 +3188,11 @@ b 3 4
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```
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```python
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<Sr> = <DF>.sum/max/mean/idxmax/all()
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<Sr> = <DF>.apply/agg/transform(<agg_func>)
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<DF> = <DF>.diff/cumsum/rank() # …/pct_change/fillna/ffill/interpolate()
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<DF> = <DF>.apply/agg/transform(<trans_func>)
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<DF> = <DF>.applymap(<func>) # Apply a function to a Dataframe elementwise.
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<Sr> = <DF>.sum/max/mean/idxmax/all() # Or: <DF>.apply/agg/transform(<agg_func>)
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<DF> = <DF>.diff/cumsum/rank/pct_change() # Or: <DF>.apply/agg/transform(<trans_func>)
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<DF> = <DF>.fillna(<el>) # Or: <DF>.applymap(<map_func>)
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```
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* **Also: `'ffill()'` and `'interpolate()'`.**
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* **All operations operate on columns by default. Use `'axis=1'` parameter to process the rows instead.**
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#### Apply, Aggregate, Transform:

‎index.html

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Original file line numberDiff line numberDiff line change
@@ -2613,7 +2613,7 @@
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<span class="hljs-keyword">from</span> pandas <span class="hljs-keyword">import</span> Series, DataFrame
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</code></pre></div>
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<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>sr = 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>)
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<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>)
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x <span class="hljs-number">1</span>
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y <span class="hljs-number">2</span>
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Name: a, dtype: int64
@@ -2636,12 +2636,21 @@
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&lt;Sr&gt; = &lt;Sr&gt; +-*/ &lt;el/Sr&gt; <span class="hljs-comment"># Non-matching keys get value NaN.</span>
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</code></pre>
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<pre><code class="python language-python hljs">&lt;Sr&gt; = &lt;Sr&gt;.append(&lt;Sr&gt;) <span class="hljs-comment"># Or: pd.concat(&lt;coll_of_Sr&gt;)</span>
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&lt;Sr&gt; = &lt;Sr&gt;.combine_first(&lt;Sr&gt;) <span class="hljs-comment"># Adds items that are not yet present (extends).</span>
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combine, update?
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&lt;Sr&gt; = &lt;Sr&gt;.combine_first(&lt;Sr&gt;) <span class="hljs-comment"># Adds items that are not yet present.</span>
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&lt;Sr&gt;.update(&lt;Sr&gt;) <span class="hljs-comment"># Updates items that are already present.</span>
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</code></pre>
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<div><h4 id="aggregations">Aggregations:</h4><pre><code class="python language-python hljs">&lt;el&gt; = &lt;Sr&gt;.sum/max/mean/idxmax/all()
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&lt;el&gt; = &lt;agg_func&gt;(&lt;Sr&gt;)
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&lt;el&gt; = &lt;Sr&gt;.apply/agg(&lt;agg_func&gt;) <span class="hljs-comment"># Apply can only accept strings.</span>
2642+
<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;.aggregate(&lt;agg_func&gt;)</span>
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&lt;Sr&gt; = &lt;Sr&gt;.diff/cumsum/rank/pct_change() <span class="hljs-comment"># Or: &lt;Sr&gt;.agg/transform(&lt;trans_func&gt;)</span>
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&lt;Sr&gt; = &lt;Sr&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;Sr&gt;.apply/agg/transform/map(&lt;map_func&gt;)</span>
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</code></pre>
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<ul>
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<li><strong>Also: <code class="python hljs"><span class="hljs-string">'ffill()'</span></code> and <code class="python hljs"><span class="hljs-string">'interpolate()'</span></code>.</strong></li>
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<li><strong>The way <code class="python hljs"><span class="hljs-string">'aggregate()'</span></code> and <code class="python hljs"><span class="hljs-string">'transform()'</span></code> find out whether a function accepts an element or the whole Series is by passing it a single value at first and if it raises an error, then they pass it the whole Series.</strong></li>
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</ul>
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<div><h4 id="applyaggregatetransform">Apply, Aggregate, Transform:</h4><pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>sr = 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>)
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x <span class="hljs-number">1</span>
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y <span class="hljs-number">2</span>
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Name: a, dtype: int64
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</code></pre></div>
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<pre><code class="python language-python hljs">+-------------+--------+-----------+---------------+
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| | | | |
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+-------------+--------+-----------+---------------+
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</code></pre>
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<div><h4 id="transformations">Transformations:</h4><pre><code class="python language-python hljs">&lt;Sr&gt; = &lt;Sr&gt;.diff/cumsum/rank/pct_change() <span class="hljs-comment"># …/fillna/ffill/interpolate()</span>
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&lt;Sr&gt; = &lt;Sr&gt;.apply/agg/transform(&lt;trans_func&gt;)
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map?
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</code></pre></div>
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<pre><code class="python language-python hljs">+-------------+--------+-----------+---------------+
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| | <span class="hljs-string">'rank'</span> | [<span class="hljs-string">'rank'</span>] | {<span class="hljs-string">'r'</span>: <span class="hljs-string">'rank'</span>} |
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+-------------+--------+-----------+---------------+
@@ -2692,16 +2696,15 @@
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&lt;DF&gt; = &lt;DF&gt;.transpose() <span class="hljs-comment"># Rotates the table.</span>
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&lt;DF&gt; = &lt;DF&gt;.melt(id_vars=column_key/s) <span class="hljs-comment"># Melts on columns.</span>
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</code></pre>
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<pre><code class="python language-python hljs">&lt;Sr&gt; = &lt;DF&gt;.sum/max/mean/idxmax/all()
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&lt;Sr&gt; = &lt;DF&gt;.apply/agg/transform(&lt;agg_func&gt;)
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&lt;DF&gt; = &lt;DF&gt;.diff/cumsum/rank() <span class="hljs-comment"># …/pct_change/fillna/ffill/interpolate()</span>
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&lt;DF&gt; = &lt;DF&gt;.apply/agg/transform(&lt;trans_func&gt;)
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&lt;DF&gt; = &lt;DF&gt;.applymap(&lt;func&gt;) <span class="hljs-comment"># Apply a function to a Dataframe elementwise.</span>
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<pre><code class="python language-python hljs">&lt;Sr&gt; = &lt;DF&gt;.sum/max/mean/idxmax/all() <span class="hljs-comment"># Or: &lt;DF&gt;.apply/agg/transform(&lt;agg_func&gt;)</span>
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&lt;DF&gt; = &lt;DF&gt;.diff/cumsum/rank/pct_change() <span class="hljs-comment"># Or: &lt;DF&gt;.apply/agg/transform(&lt;trans_func&gt;)</span>
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&lt;DF&gt; = &lt;DF&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;DF&gt;.applymap(&lt;map_func&gt;)</span>
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</code></pre>
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<ul>
2704+
<li><strong>Also: <code class="python hljs"><span class="hljs-string">'ffill()'</span></code> and <code class="python hljs"><span class="hljs-string">'interpolate()'</span></code>.</strong></li>
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<li><strong>All operations operate on columns by default. Use <code class="python hljs"><span class="hljs-string">'axis=1'</span></code> parameter to process the rows instead.</strong> </li>
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</ul>
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<div><h4 id="applyaggregatetransform">Apply, Aggregate, Transform:</h4><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>]], index=[<span class="hljs-string">'a'</span>, <span class="hljs-string">'b'</span>], columns=[<span class="hljs-string">'x'</span>, <span class="hljs-string">'y'</span>])
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<div><h4 id="applyaggregatetransform-1">Apply, Aggregate, Transform:</h4><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>]], index=[<span class="hljs-string">'a'</span>, <span class="hljs-string">'b'</span>], columns=[<span class="hljs-string">'x'</span>, <span class="hljs-string">'y'</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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b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
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<ul>
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<li><strong>Result of an operation is a dataframe with index made up of group keys. Use <code class="python hljs"><span class="hljs-string">'&lt;DF&gt;.reset_index()'</span></code> to move the index back into it's own column.</strong></li>
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</ul>
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<div><h4 id="aggregations-1">Aggregations:</h4><pre><code class="python language-python hljs">&lt;DF&gt; = &lt;GB&gt;.sum/max/mean/idxmax/all()
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<div><h4 id="aggregations">Aggregations:</h4><pre><code class="python language-python hljs">&lt;DF&gt; = &lt;GB&gt;.sum/max/mean/idxmax/all()
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&lt;DF&gt; = &lt;GB&gt;.apply/agg/transform(&lt;agg_func&gt;)
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</code></pre></div>
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| | c <span class="hljs-number">11</span> <span class="hljs-number">13</span> | | |
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+-------------+------------+-------------+---------------+
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</code></pre>
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<div><h4 id="transformations-1">Transformations:</h4><pre><code class="python language-python hljs">&lt;DF&gt; = &lt;GB&gt;.diff/cumsum/rank() <span class="hljs-comment"># …/pct_change/fillna/ffill()</span>
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<div><h4 id="transformations">Transformations:</h4><pre><code class="python language-python hljs">&lt;DF&gt; = &lt;GB&gt;.diff/cumsum/rank() <span class="hljs-comment"># …/pct_change/fillna/ffill()</span>
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&lt;DF&gt; = &lt;GB&gt;.agg/transform(&lt;trans_func&gt;)
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</code></pre></div>
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