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  1. 111
      README.md
  2. 119
      index.html
  3. 94
      parse.js

111
README.md
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@ -3142,11 +3142,11 @@ Name: a, dtype: int64
#### Aggregate, Transform, Map:
```python
<el> = <Sr>.sum/max/mean/idxmax/all() # Or: <Sr>.aggregate(<agg_func>)
<Sr> = <Sr>.rank/diff/cumsum/ffill/interpl() # Or: <Sr>.agg/transform(<trans_func>)
<Sr> = <Sr>.fillna(<el>) # Or: <Sr>.apply/agg/transform/map(<map_func>)
<el> = <Sr>.sum/max/mean/idxmax/all() # Or: <Sr>.agg(lambda <Sr>: <el>)
<Sr> = <Sr>.rank/diff/cumsum/ffill/interpl() # Or: <Sr>.agg/transform(lambda <Sr>: <Sr>)
<Sr> = <Sr>.fillna(<el>) # Or: <Sr>.agg/transform/map(lambda <el>: <el>)
```
* **The way `'aggregate()'` and `'transform()'` find out whether the passed 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.**
* **The way `'agg()'` and `'transform()'` find out whether the passed 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. `'agg()'` only accepts Attribute/Type/ValueError.**
```python
>>> sr = Series([1, 2], index=['x', 'y'])
@ -3155,22 +3155,22 @@ y 2
```
```text
+-------------+-------------+-------------+---------------+
| | 'sum' | ['sum'] | {'s': 'sum'} |
+-------------+-------------+-------------+---------------+
| sr.apply(…) | 3 | sum 3 | s 3 |
| sr.agg(…) | | | |
+-------------+-------------+-------------+---------------+
+-----------------+-------------+-------------+---------------+
| | 'sum' | ['sum'] | {'s': 'sum'} |
+-----------------+-------------+-------------+---------------+
| sr.apply(…) | 3 | sum 3 | s 3 |
| sr.agg(…) | | | |
+-----------------+-------------+-------------+---------------+
```
```text
+-------------+-------------+-------------+---------------+
| | 'rank' | ['rank'] | {'r': 'rank'} |
+-------------+-------------+-------------+---------------+
| sr.apply(…) | | rank | |
| sr.agg(…) | x 1 | x 1 | r x 1 |
| sr.trans(…) | y 2 | y 2 | y 2 |
+-------------+-------------+-------------+---------------+
+-----------------+-------------+-------------+---------------+
| | 'rank' | ['rank'] | {'r': 'rank'} |
+-----------------+-------------+-------------+---------------+
| sr.apply(…) | | rank | |
| sr.agg(…) | x 1 | x 1 | r x 1 |
| sr.transform(…) | y 2 | y 2 | y 2 |
+-----------------+-------------+-------------+---------------+
```
* **Last result has a hierarchical index. Use `'<Sr>[key_1, key_2]'` to get its values.**
@ -3260,11 +3260,11 @@ c 6 7
#### Aggregate, Transform, Map:
```python
<Sr> = <DF>.sum/max/mean/idxmax/all() # Or: <DF>.apply/agg/transform(<agg_func>)
<DF> = <DF>.rank/diff/cumsum/ffill/interpl() # Or: <DF>.apply/agg/transform(<trans_func>)
<DF> = <DF>.fillna(<el>) # Or: <DF>.applymap(<map_func>)
<Sr> = <DF>.sum/max/mean/idxmax/all() # Or: <DF>.apply/agg(lambda <Sr>: <el>)
<DF> = <DF>.rank/diff/cumsum/ffill/interpl() # Or: <DF>.apply/agg/transform(lambda <Sr>: <Sr>)
<DF> = <DF>.fillna(<el>) # Or: <DF>.applymap(lambda <el>: <el>)
```
* **All operations operate on columns by default. Use `'axis=1'` parameter to process the rows instead.**
* **All operations operate on columns by default. Use `'axis=1'` parameter to process the rows instead. Transform passes DF to a function if it rases an error after receiving a Sr.**
```python
>>> df = DataFrame([[1, 2], [3, 4]], index=['a', 'b'], columns=['x', 'y'])
@ -3274,24 +3274,24 @@ b 3 4
```
```text
+-------------+-------------+-------------+---------------+
| | 'sum' | ['sum'] | {'x': 'sum'} |
+-------------+-------------+-------------+---------------+
| df.apply(…) | | x y | |
| df.agg(…) | x 4 | sum 4 6 | x 4 |
| | y 6 | | |
+-------------+-------------+-------------+---------------+
+-----------------+-------------+-------------+---------------+
| | 'sum' | ['sum'] | {'x': 'sum'} |
+-----------------+-------------+-------------+---------------+
| df.apply(…) | | x y | |
| df.agg(…) | x 4 | sum 4 6 | x 4 |
| | y 6 | | |
+-----------------+-------------+-------------+---------------+
```
```text
+-------------+-------------+-------------+---------------+
| | 'rank' | ['rank'] | {'x': 'rank'} |
+-------------+-------------+-------------+---------------+
| df.apply(…) | x y | x y | x |
| df.agg(…) | a 1 1 | rank rank | a 1 |
| df.trans(…) | b 2 2 | a 1 1 | b 2 |
| | | b 2 2 | |
+-------------+-------------+-------------+---------------+
+-----------------+-------------+-------------+---------------+
| | 'rank' | ['rank'] | {'x': 'rank'} |
+-----------------+-------------+-------------+---------------+
| df.apply(…) | x y | x y | x |
| df.agg(…) | a 1 1 | rank rank | a 1 |
| df.transform(…) | b 2 2 | a 1 1 | b 2 |
| | | b 2 2 | |
+-----------------+-------------+-------------+---------------+
```
* **Use `'<DF>[col_key_1, col_key_2][row_key]'` to get the fifth result's values.**
@ -3315,9 +3315,6 @@ b 3 4
```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
@ -3327,13 +3324,15 @@ c 7 8
```python
<GB> = <DF>.groupby(column_key/s) # DF is split into groups based on passed column.
<DF> = <GB>.get_group(group_key/s) # Selects a group by value of grouping column.
<DF> = <GB>.apply(<func>) # Maps each group. Func can return DF, Sr or el.
<GB> = <GB>[column_key] # A single column GB. All operations return a Sr.
```
#### Aggregate, Transform, Map:
```python
<DF> = <GB>.sum/max/mean/idxmax/all() # Or: <GB>.apply/agg(<agg_func>)
<DF> = <GB>.rank/diff/cumsum/ffill() # Or: <GB>.aggregate(<trans_func>)
<DF> = <GB>.fillna(<el>) # Or: <GB>.transform(<map_func>)
<DF> = <GB>.sum/max/mean/idxmax/all() # Or: <GB>.agg(lambda <Sr>: <el>)
<DF> = <GB>.rank/diff/cumsum/ffill() # Or: <GB>.transform(lambda <Sr>: <Sr>)
<DF> = <GB>.fillna(<el>) # Or: <GB>.transform(lambda <Sr>: <Sr>)
```
```python
@ -3345,20 +3344,20 @@ c 7 8
```
```text
+-------------+-------------+-------------+-------------+---------------+
| | 'sum' | 'rank' | ['rank'] | {'x': 'rank'} |
+-------------+-------------+-------------+-------------+---------------+
| gb.agg(…) | x y | x y | x y | x |
| | z | a 1 1 | rank rank | a 1 |
| | 3 1 2 | b 1 1 | a 1 1 | b 1 |
| | 6 11 13 | c 2 2 | b 1 1 | c 2 |
| | | | c 2 2 | |
+-------------+-------------+-------------+-------------+---------------+
| gb.trans(…) | x y | x y | | |
| | a 1 2 | a 1 1 | | |
| | b 11 13 | b 1 1 | | |
| | c 11 13 | c 1 1 | | |
+-------------+-------------+-------------+-------------+---------------+
+-----------------+-------------+-------------+-------------+---------------+
| | 'sum' | 'rank' | ['rank'] | {'x': 'rank'} |
+-----------------+-------------+-------------+-------------+---------------+
| gb.agg(…) | x y | x y | x y | x |
| | z | a 1 1 | rank rank | a 1 |
| | 3 1 2 | b 1 1 | a 1 1 | b 1 |
| | 6 11 13 | c 2 2 | b 1 1 | c 2 |
| | | | c 2 2 | |
+-----------------+-------------+-------------+-------------+---------------+
| gb.transform(…) | x y | x y | | |
| | a 1 2 | a 1 1 | | |
| | b 11 13 | b 1 1 | | |
| | c 11 13 | c 2 2 | | |
+-----------------+-------------+-------------+-------------+---------------+
```
### Rolling

119
index.html
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@ -54,7 +54,7 @@
<body>
<header>
<aside>January 29, 2022</aside>
<aside>February 4, 2022</aside>
<a href="https://gto76.github.io" rel="author">Jure Šorn</a>
</header>
@ -2549,32 +2549,32 @@ Name: a, dtype: int64
&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="aggregatetransformmap">Aggregate, Transform, Map:</h4><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;.rank/diff/cumsum/ffill/interpl() <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>
<div><h4 id="aggregatetransformmap">Aggregate, Transform, Map:</h4><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;.agg(lambda &lt;Sr&gt;: &lt;el&gt;)</span>
&lt;Sr&gt; = &lt;Sr&gt;.rank/diff/cumsum/ffill/interpl() <span class="hljs-comment"># Or: &lt;Sr&gt;.agg/transform(lambda &lt;Sr&gt;: &lt;Sr&gt;)</span>
&lt;Sr&gt; = &lt;Sr&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;Sr&gt;.agg/transform/map(lambda &lt;el&gt;: &lt;el&gt;)</span>
</code></pre></div>
<ul>
<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 the passed 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>
<li><strong>The way <code class="python hljs"><span class="hljs-string">'agg()'</span></code> and <code class="python hljs"><span class="hljs-string">'transform()'</span></code> find out whether the passed 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. <code class="python hljs"><span class="hljs-string">'agg()'</span></code> only accepts Attribute/Type/ValueError.</strong></li>
</ul>
<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>])
x <span class="hljs-number">1</span>
y <span class="hljs-number">2</span>
</code></pre>
<pre><code class="python hljs">┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'sum'</span> │ [<span class="hljs-string">'sum'</span>] │ {<span class="hljs-string">'s'</span>: <span class="hljs-string">'sum'</span>} ┃
┠─────────────┼─────────────┼─────────────┼───────────────┨
┃ sr.apply(…) │ <span class="hljs-number">3</span> │ sum <span class="hljs-number">3</span> │ s <span class="hljs-number">3</span> ┃
┃ sr.agg(…) │ │ │ ┃
┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
</code></pre>
<pre><code class="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>} ┃
┠─────────────┼─────────────┼─────────────┼───────────────┨
┃ sr.apply(…) │ │ rank │ ┃
┃ sr.agg(…) │ x <span class="hljs-number">1</span> │ x <span class="hljs-number">1</span> │ r x <span class="hljs-number">1</span> ┃
┃ sr.trans(…) │ y <span class="hljs-number">2</span> │ y <span class="hljs-number">2</span> │ y <span class="hljs-number">2</span> ┃
┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
<pre><code class="python hljs">┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'sum'</span> │ [<span class="hljs-string">'sum'</span>] │ {<span class="hljs-string">'s'</span>: <span class="hljs-string">'sum'</span>} ┃
┠─────────────────┼─────────────┼─────────────┼───────────────┨
┃ sr.apply(…) │ <span class="hljs-number">3</span> │ sum <span class="hljs-number">3</span> │ s <span class="hljs-number">3</span> ┃
┃ sr.agg(…) │ │ │ ┃
┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
</code></pre>
<pre><code class="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>} ┃
┠─────────────────┼─────────────┼─────────────┼───────────────┨
┃ sr.apply(…) │ │ rank │ ┃
┃ sr.agg(…) │ x <span class="hljs-number">1</span> │ x <span class="hljs-number">1</span> │ r x <span class="hljs-number">1</span> ┃
┃ sr.transform(…) │ y <span class="hljs-number">2</span> │ y <span class="hljs-number">2</span> │ y <span class="hljs-number">2</span> ┃
┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
</code></pre>
<ul>
<li><strong>Last result has a hierarchical index. Use <code class="python hljs"><span class="hljs-string">'&lt;Sr&gt;[key_1, key_2]'</span></code> to get its values.</strong></li>
@ -2646,35 +2646,35 @@ c <span class="hljs-number">6</span> <span class="hljs-number">7</span>
┃ │ c . <span class="hljs-number">6</span> <span class="hljs-number">7</span> │ │ │ R must be a DataFrame. ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━┛
</code></pre>
<div><h4 id="aggregatetransformmap-1">Aggregate, Transform, Map:</h4><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;.rank/diff/cumsum/ffill/interpl() <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>
<div><h4 id="aggregatetransformmap-1">Aggregate, Transform, Map:</h4><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(lambda &lt;Sr&gt;: &lt;el&gt;)</span>
&lt;DF&gt; = &lt;DF&gt;.rank/diff/cumsum/ffill/interpl() <span class="hljs-comment"># Or: &lt;DF&gt;.apply/agg/transform(lambda &lt;Sr&gt;: &lt;Sr&gt;)</span>
&lt;DF&gt; = &lt;DF&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;DF&gt;.applymap(lambda &lt;el&gt;: &lt;el&gt;)</span>
</code></pre></div>
<ul>
<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>
<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. Transform passes DF to a function if it rases an error after receiving a Sr.</strong></li>
</ul>
<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>
</code></pre>
<pre><code class="python hljs">┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'sum'</span> │ [<span class="hljs-string">'sum'</span>] │ {<span class="hljs-string">'x'</span>: <span class="hljs-string">'sum'</span>} ┃
┠─────────────┼─────────────┼─────────────┼───────────────┨
┃ df.apply(…) │ │ x y │ ┃
┃ df.agg(…) │ x <span class="hljs-number">4</span> │ sum <span class="hljs-number">4</span> <span class="hljs-number">6</span> │ x <span class="hljs-number">4</span> ┃
┃ │ y <span class="hljs-number">6</span> │ │ ┃
┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
</code></pre>
<pre><code class="python hljs">┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'rank'</span> │ [<span class="hljs-string">'rank'</span>] │ {<span class="hljs-string">'x'</span>: <span class="hljs-string">'rank'</span>} ┃
┠─────────────┼─────────────┼─────────────┼───────────────┨
┃ df.apply(…) │ x y │ x y │ x ┃
┃ df.agg(…) │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ rank rank │ a <span class="hljs-number">1</span> ┃
┃ df.trans(…) │ b <span class="hljs-number">2</span> <span class="hljs-number">2</span> │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ b <span class="hljs-number">2</span> ┃
┃ │ │ b <span class="hljs-number">2</span> <span class="hljs-number">2</span> │ ┃
┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
<pre><code class="python hljs">┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'sum'</span> │ [<span class="hljs-string">'sum'</span>] │ {<span class="hljs-string">'x'</span>: <span class="hljs-string">'sum'</span>} ┃
┠─────────────────┼─────────────┼─────────────┼───────────────┨
┃ df.apply(…) │ │ x y │ ┃
┃ df.agg(…) │ x <span class="hljs-number">4</span> │ sum <span class="hljs-number">4</span> <span class="hljs-number">6</span> │ x <span class="hljs-number">4</span> ┃
┃ │ y <span class="hljs-number">6</span> │ │ ┃
┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
</code></pre>
<pre><code class="python hljs">┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'rank'</span> │ [<span class="hljs-string">'rank'</span>] │ {<span class="hljs-string">'x'</span>: <span class="hljs-string">'rank'</span>} ┃
┠─────────────────┼─────────────┼─────────────┼───────────────┨
┃ df.apply(…) │ x y │ x y │ x ┃
┃ df.agg(…) │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ rank rank │ a <span class="hljs-number">1</span> ┃
┃ df.transform(…) │ b <span class="hljs-number">2</span> <span class="hljs-number">2</span> │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ b <span class="hljs-number">2</span> ┃
┃ │ │ b <span class="hljs-number">2</span> <span class="hljs-number">2</span> │ ┃
┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
</code></pre>
<ul>
<li><strong>Use <code class="python hljs"><span class="hljs-string">'&lt;DF&gt;[col_key_1, col_key_2][row_key]'</span></code> to get the fifth result's values.</strong></li>
@ -2691,9 +2691,6 @@ b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
&lt;DF&gt;.to_sql(<span class="hljs-string">'&lt;table_name&gt;'</span>, &lt;connection&gt;)
</code></pre>
<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"><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>
@ -2703,10 +2700,12 @@ c <span class="hljs-number">7</span> <span class="hljs-number">8</span>
<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/s) <span class="hljs-comment"># Selects a group by value of grouping column.</span>
&lt;DF&gt; = &lt;GB&gt;.apply(&lt;func&gt;) <span class="hljs-comment"># Maps each group. Func can return DF, Sr or el.</span>
&lt;GB&gt; = &lt;GB&gt;[column_key] <span class="hljs-comment"># A single column GB. All operations return a Sr.</span>
</code></pre>
<div><h4 id="aggregatetransformmap-2">Aggregate, Transform, Map:</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;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;GB&gt;.transform(&lt;map_func&gt;)</span>
<div><h4 id="aggregatetransformmap-2">Aggregate, Transform, Map:</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;.agg(lambda &lt;Sr&gt;: &lt;el&gt;)</span>
&lt;DF&gt; = &lt;GB&gt;.rank/diff/cumsum/ffill() <span class="hljs-comment"># Or: &lt;GB&gt;.transform(lambda &lt;Sr&gt;: &lt;Sr&gt;)</span>
&lt;DF&gt; = &lt;GB&gt;.fillna(&lt;el&gt;) <span class="hljs-comment"># Or: &lt;GB&gt;.transform(lambda &lt;Sr&gt;: &lt;Sr&gt;)</span>
</code></pre></div>
<pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>gb = df.groupby(<span class="hljs-string">'z'</span>)
@ -2715,20 +2714,20 @@ c <span class="hljs-number">7</span> <span class="hljs-number">8</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>
c <span class="hljs-number">7</span> <span class="hljs-number">8</span> <span class="hljs-number">6</span>
</code></pre>
<pre><code class="python hljs">┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'sum'</span> │ <span class="hljs-string">'rank'</span> │ [<span class="hljs-string">'rank'</span>] │ {<span class="hljs-string">'x'</span>: <span class="hljs-string">'rank'</span>} ┃
┠─────────────┼─────────────┼─────────────┼─────────────┼───────────────┨
┃ gb.agg(…) │ x y │ x y │ x y │ x ┃
┃ │ z │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ rank rank │ a <span class="hljs-number">1</span> ┃
┃ │ <span class="hljs-number">3</span> <span class="hljs-number">1</span> <span class="hljs-number">2</span> │ b <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ b <span class="hljs-number">1</span> ┃
┃ │ <span class="hljs-number">6</span> <span class="hljs-number">11</span> <span class="hljs-number">13</span> │ c <span class="hljs-number">2</span> <span class="hljs-number">2</span> │ b <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ c <span class="hljs-number">2</span> ┃
┃ │ │ │ c <span class="hljs-number">2</span> <span class="hljs-number">2</span> │ ┃
┠─────────────┼─────────────┼─────────────┼─────────────┼───────────────┨
┃ gb.trans(…) │ x y │ x y │ │ ┃
┃ │ a <span class="hljs-number">1</span> <span class="hljs-number">2</span> │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ │ ┃
┃ │ b <span class="hljs-number">11</span> <span class="hljs-number">13</span> │ b <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> │ │ ┃
┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
<pre><code class="python hljs">┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓
┃ │ <span class="hljs-string">'sum'</span> │ <span class="hljs-string">'rank'</span> │ [<span class="hljs-string">'rank'</span>] │ {<span class="hljs-string">'x'</span>: <span class="hljs-string">'rank'</span>} ┃
┠─────────────────┼─────────────┼─────────────┼─────────────┼───────────────┨
┃ gb.agg(…) │ x y │ x y │ x y │ x ┃
┃ │ z │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ rank rank │ a <span class="hljs-number">1</span> ┃
┃ │ <span class="hljs-number">3</span> <span class="hljs-number">1</span> <span class="hljs-number">2</span> │ b <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ b <span class="hljs-number">1</span> ┃
┃ │ <span class="hljs-number">6</span> <span class="hljs-number">11</span> <span class="hljs-number">13</span> │ c <span class="hljs-number">2</span> <span class="hljs-number">2</span> │ b <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ c <span class="hljs-number">2</span> ┃
┃ │ │ │ c <span class="hljs-number">2</span> <span class="hljs-number">2</span> │ ┃
┠─────────────────┼─────────────┼─────────────┼─────────────┼───────────────┨
┃ gb.transform(…) │ x y │ x y │ │ ┃
┃ │ a <span class="hljs-number">1</span> <span class="hljs-number">2</span> │ a <span class="hljs-number">1</span> <span class="hljs-number">1</span> │ │ ┃
┃ │ b <span class="hljs-number">11</span> <span class="hljs-number">13</span> │ b <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">2</span> <span class="hljs-number">2</span> │ │ ┃
┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛
</code></pre>
<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>
@ -2882,7 +2881,7 @@ $ pyinstaller script.py --add-data '&lt;path&gt;:.' <span class="hljs-comment">
<footer>
<aside>January 29, 2022</aside>
<aside>February 4, 2022</aside>
<a href="https://gto76.github.io" rel="author">Jure Šorn</a>
</footer>

94
parse.js
View File

@ -308,27 +308,27 @@ const DIAGRAM_12_B =
'┗━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━┷━━━━━━━━━━━━━┛\n';
const DIAGRAM_13_A =
'| sr.apply(…) | 3 | sum 3 | s 3 |';
'| sr.apply(…) | 3 | sum 3 | s 3 |';
const DIAGRAM_13_B =
"┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'sum' │ ['sum'] │ {'s': 'sum'} ┃\n" +
"┠─────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ sr.apply(…) │ 3 │ sum 3 │ s 3 ┃\n" +
"┃ sr.agg(…) │ │ │ ┃\n" +
"┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
"┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'sum' │ ['sum'] │ {'s': 'sum'} ┃\n" +
"┠─────────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ sr.apply(…) │ 3 │ sum 3 │ s 3 ┃\n" +
"┃ sr.agg(…) │ │ │ ┃\n" +
"┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
const DIAGRAM_14_A =
'| sr.apply(…) | | rank | |';
'| sr.apply(…) | | rank | |';
const DIAGRAM_14_B =
"┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'rank' │ ['rank'] │ {'r': 'rank'} ┃\n" +
"┠─────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ sr.apply(…) │ │ rank │ ┃\n" +
"┃ sr.agg(…) │ x 1 │ x 1 │ r x 1 ┃\n" +
"┃ sr.trans(…) │ y 2 │ y 2 │ y 2 ┃\n" +
"┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
"┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'rank' │ ['rank'] │ {'r': 'rank'} ┃\n" +
"┠─────────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ sr.apply(…) │ │ rank │ ┃\n" +
"┃ sr.agg(…) │ x 1 │ x 1 │ r x 1 ┃\n" +
"┃ sr.transform(…) │ y 2 │ y 2 │ y 2 ┃\n" +
"┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
const DIAGRAM_15_A =
'+------------------------+---------------+------------+------------+--------------------------+';
@ -365,48 +365,48 @@ const DIAGRAM_15_B =
"┗━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━┛\n";
const DIAGRAM_16_A =
'| df.apply(…) | | x y | |';
'| df.apply(…) | | x y | |';
const DIAGRAM_16_B =
"┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'sum' │ ['sum'] │ {'x': 'sum'} ┃\n" +
"┠─────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ df.apply(…) │ │ x y │ ┃\n" +
"┃ df.agg(…) │ x 4 │ sum 4 6 │ x 4 ┃\n" +
"┃ │ y 6 │ │ ┃\n" +
"┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
"┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'sum' │ ['sum'] │ {'x': 'sum'} ┃\n" +
"┠─────────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ df.apply(…) │ │ x y │ ┃\n" +
"┃ df.agg(…) │ x 4 │ sum 4 6 │ x 4 ┃\n" +
"┃ │ y 6 │ │ ┃\n" +
"┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
const DIAGRAM_17_A =
'| df.apply(…) | x y | x y | x |';
'| df.apply(…) | x y | x y | x |';
const DIAGRAM_17_B =
"┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'rank' │ ['rank'] │ {'x': 'rank'} ┃\n" +
"┠─────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ df.apply(…) │ x y │ x y │ x ┃\n" +
"┃ df.agg(…) │ a 1 1 │ rank rank │ a 1 ┃\n" +
"┃ df.trans(…) │ b 2 2 │ a 1 1 │ b 2 ┃\n" +
"┃ │ │ b 2 2 │ ┃\n" +
"┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
"┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'rank' │ ['rank'] │ {'x': 'rank'} ┃\n" +
"┠─────────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ df.apply(…) │ x y │ x y │ x ┃\n" +
"┃ df.agg(…) │ a 1 1 │ rank rank │ a 1 ┃\n" +
"┃ df.transform(…) │ b 2 2 │ a 1 1 │ b 2 ┃\n" +
"┃ │ │ b 2 2 │ ┃\n" +
"┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
const DIAGRAM_18_A =
'| gb.agg(…) | x y | x y | x y | x |';
'| gb.agg(…) | x y | x y | x y | x |';
const DIAGRAM_18_B =
"┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'sum' │ 'rank' │ ['rank'] │ {'x': 'rank'} ┃\n" +
"┠─────────────┼─────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ gb.agg(…) │ x y │ x y │ x y │ x ┃\n" +
"┃ │ z │ a 1 1 │ rank rank │ a 1 ┃\n" +
"┃ │ 3 1 2 │ b 1 1 │ a 1 1 │ b 1 ┃\n" +
"┃ │ 6 11 13 │ c 2 2 │ b 1 1 │ c 2 ┃\n" +
"┃ │ │ │ c 2 2 │ ┃\n" +
"┠─────────────┼─────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ gb.trans(…) │ x y │ x y │ │ ┃\n" +
"┃ │ a 1 2 │ a 1 1 │ │ ┃\n" +
"┃ │ b 11 13 │ b 1 1 │ │ ┃\n" +
"┃ │ c 11 13 │ c 1 1 │ │ ┃\n" +
"┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
"┏━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓\n" +
"┃ │ 'sum' │ 'rank' │ ['rank'] │ {'x': 'rank'} ┃\n" +
"┠─────────────────┼─────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ gb.agg(…) │ x y │ x y │ x y │ x ┃\n" +
"┃ │ z │ a 1 1 │ rank rank │ a 1 ┃\n" +
"┃ │ 3 1 2 │ b 1 1 │ a 1 1 │ b 1 ┃\n" +
"┃ │ 6 11 13 │ c 2 2 │ b 1 1 │ c 2 ┃\n" +
"┃ │ │ │ c 2 2 │ ┃\n" +
"┠─────────────────┼─────────────┼─────────────┼─────────────┼───────────────┨\n" +
"┃ gb.transform(…) │ x y │ x y │ │ ┃\n" +
"┃ │ a 1 2 │ a 1 1 │ │ ┃\n" +
"┃ │ b 11 13 │ b 1 1 │ │ ┃\n" +
"┃ │ c 11 13 │ c 2 2 │ │ ┃\n" +
"┗━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛\n";
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