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Working on Pandas

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Jure Šorn 4 years ago
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  1. 39
      README.md
  2. 41
      index.html

39
README.md
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@ -3076,7 +3076,7 @@ from pandas import Series, DataFrame
**Ordered dictionary with a name.**
```python
>>> sr = Series([1, 2], index=['x', 'y'], name='a')
>>> Series([1, 2], index=['x', 'y'], name='a')
x 1
y 2
Name: a, dtype: int64
@ -3107,15 +3107,24 @@ Name: a, dtype: int64
```python
<Sr> = <Sr>.append(<Sr>) # Or: pd.concat(<coll_of_Sr>)
<Sr> = <Sr>.combine_first(<Sr>) # Adds items that are not yet present (extends).
combine, update?
<Sr> = <Sr>.combine_first(<Sr>) # Adds items that are not yet present.
<Sr>.update(<Sr>) # Updates items that are already present.
```
#### Aggregations:
```python
<el> = <Sr>.sum/max/mean/idxmax/all()
<el> = <agg_func>(<Sr>)
<el> = <Sr>.apply/agg(<agg_func>) # Apply can only accept strings.
<el> = <Sr>.sum/max/mean/idxmax/all() # Or: <Sr>.aggregate(<agg_func>)
<Sr> = <Sr>.diff/cumsum/rank/pct_change() # Or: <Sr>.agg/transform(<trans_func>)
<Sr> = <Sr>.fillna(<el>) # Or: <Sr>.apply/agg/transform/map(<map_func>)
```
* **Also: `'ffill()'` and `'interpolate()'`.**
* **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.**
#### Apply, Aggregate, Transform:
```python
>>> sr = Series([1, 2], index=['x', 'y'], name='a')
x 1
y 2
Name: a, dtype: int64
```
```python
@ -3128,13 +3137,6 @@ combine, update?
+-------------+--------+-----------+---------------+
```
#### Transformations:
```python
<Sr> = <Sr>.diff/cumsum/rank/pct_change() # …/fillna/ffill/interpolate()
<Sr> = <Sr>.apply/agg/transform(<trans_func>)
map?
```
```python
+-------------+--------+-----------+---------------+
| | 'rank' | ['rank'] | {'r': 'rank'} |
@ -3186,12 +3188,11 @@ b 3 4
```
```python
<Sr> = <DF>.sum/max/mean/idxmax/all()
<Sr> = <DF>.apply/agg/transform(<agg_func>)
<DF> = <DF>.diff/cumsum/rank() # …/pct_change/fillna/ffill/interpolate()
<DF> = <DF>.apply/agg/transform(<trans_func>)
<DF> = <DF>.applymap(<func>) # Apply a function to a Dataframe elementwise.
<Sr> = <DF>.sum/max/mean/idxmax/all() # Or: <DF>.apply/agg/transform(<agg_func>)
<DF> = <DF>.diff/cumsum/rank/pct_change() # Or: <DF>.apply/agg/transform(<trans_func>)
<DF> = <DF>.fillna(<el>) # Or: <DF>.applymap(<map_func>)
```
* **Also: `'ffill()'` and `'interpolate()'`.**
* **All operations operate on columns by default. Use `'axis=1'` parameter to process the rows instead.**
#### Apply, Aggregate, Transform:

41
index.html
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@ -2613,7 +2613,7 @@ SIZE, MAX_SPEED = <span class="hljs-number">50</span>, P(<span class="hljs-numbe
<span class="hljs-keyword">from</span> pandas <span class="hljs-keyword">import</span> Series, DataFrame
</code></pre></div>
<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>)
<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>)
x <span class="hljs-number">1</span>
y <span class="hljs-number">2</span>
Name: a, dtype: int64
@ -2636,12 +2636,21 @@ Name: a, dtype: int64
&lt;Sr&gt; = &lt;Sr&gt; +-*/ &lt;el/Sr&gt; <span class="hljs-comment"># Non-matching keys get value NaN.</span>
</code></pre>
<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>
&lt;Sr&gt; = &lt;Sr&gt;.combine_first(&lt;Sr&gt;) <span class="hljs-comment"># Adds items that are not yet present (extends).</span>
combine, update?
&lt;Sr&gt; = &lt;Sr&gt;.combine_first(&lt;Sr&gt;) <span class="hljs-comment"># Adds items that are not yet present.</span>
&lt;Sr&gt;.update(&lt;Sr&gt;) <span class="hljs-comment"># Updates items that are already present.</span>
</code></pre>
<div><h4 id="aggregations">Aggregations:</h4><pre><code class="python language-python hljs">&lt;el&gt; = &lt;Sr&gt;.sum/max/mean/idxmax/all()
&lt;el&gt; = &lt;agg_func&gt;(&lt;Sr&gt;)
&lt;el&gt; = &lt;Sr&gt;.apply/agg(&lt;agg_func&gt;) <span class="hljs-comment"># Apply can only accept strings.</span>
<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>
&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>
&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>
</code></pre>
<ul>
<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>
<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>
</ul>
<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>)
x <span class="hljs-number">1</span>
y <span class="hljs-number">2</span>
Name: a, dtype: int64
</code></pre></div>
<pre><code class="python language-python hljs">+-------------+--------+-----------+---------------+
@ -2652,11 +2661,6 @@ combine, update?
| | | | |
+-------------+--------+-----------+---------------+
</code></pre>
<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>
&lt;Sr&gt; = &lt;Sr&gt;.apply/agg/transform(&lt;trans_func&gt;)
map?
</code></pre></div>
<pre><code class="python language-python hljs">+-------------+--------+-----------+---------------+
| | <span class="hljs-string">'rank'</span> | [<span class="hljs-string">'rank'</span>] | {<span class="hljs-string">'r'</span>: <span class="hljs-string">'rank'</span>} |
+-------------+--------+-----------+---------------+
@ -2692,16 +2696,15 @@ b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
&lt;DF&gt; = &lt;DF&gt;.transpose() <span class="hljs-comment"># Rotates the table.</span>
&lt;DF&gt; = &lt;DF&gt;.melt(id_vars=column_key/s) <span class="hljs-comment"># Melts on columns.</span>
</code></pre>
<pre><code class="python language-python hljs">&lt;Sr&gt; = &lt;DF&gt;.sum/max/mean/idxmax/all()
&lt;Sr&gt; = &lt;DF&gt;.apply/agg/transform(&lt;agg_func&gt;)
&lt;DF&gt; = &lt;DF&gt;.diff/cumsum/rank() <span class="hljs-comment"># …/pct_change/fillna/ffill/interpolate()</span>
&lt;DF&gt; = &lt;DF&gt;.apply/agg/transform(&lt;trans_func&gt;)
&lt;DF&gt; = &lt;DF&gt;.applymap(&lt;func&gt;) <span class="hljs-comment"># Apply a function to a Dataframe elementwise.</span>
<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>
&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>
&lt;DF&gt; = &lt;DF&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;DF&gt;.applymap(&lt;map_func&gt;)</span>
</code></pre>
<ul>
<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>
<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>
</ul>
<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>])
<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>])
x y
a <span class="hljs-number">1</span> <span class="hljs-number">2</span>
b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
@ -2783,7 +2786,7 @@ c <span class="hljs-number">6</span> <span class="hljs-number">7</span>
<ul>
<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>
</ul>
<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()
<div><h4 id="aggregations">Aggregations:</h4><pre><code class="python language-python hljs">&lt;DF&gt; = &lt;GB&gt;.sum/max/mean/idxmax/all()
&lt;DF&gt; = &lt;GB&gt;.apply/agg/transform(&lt;agg_func&gt;)
</code></pre></div>
@ -2807,7 +2810,7 @@ c <span class="hljs-number">6</span> <span class="hljs-number">7</span>
| | c <span class="hljs-number">11</span> <span class="hljs-number">13</span> | | |
+-------------+------------+-------------+---------------+
</code></pre>
<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>
<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>
&lt;DF&gt; = &lt;GB&gt;.agg/transform(&lt;trans_func&gt;)
</code></pre></div>

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