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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. 58
      README.md
  2. 60
      index.html

58
README.md
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@ -3114,7 +3114,7 @@ Name: a, dtype: int64
#### Apply, Aggregate, Transform:
```python
<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>.rank/diff/cumsum/pct_change() # Or: <Sr>.agg/transform(<trans_func>)
<Sr> = <Sr>.fillna(<el>) # Or: <Sr>.apply/agg/transform/map(<map_func>)
```
* **Also: `'ffill()'` and `'interpolate()'`.**
@ -3234,7 +3234,7 @@ c 6 7
#### Apply, Aggregate, Transform:
```python
<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>.rank/diff/cumsum/pct_change() # Or: <DF>.apply/agg/transform(<trans_func>)
<DF> = <DF>.fillna(<el>) # Or: <DF>.applymap(<map_func>)
```
* **Also: `'ffill()'` and `'interpolate()'`.**
@ -3268,7 +3268,7 @@ b 3 4
+-------------+---------------+---------------+---------------+
```
#### Encode:
#### Encode, Decode:
```python
<DF> = pd.read_json/html('<str/path/url>')
<DF> = pd.read_csv/pickle/excel('<path/url>')
@ -3276,7 +3276,6 @@ b 3 4
<DF> = pd.read_clipboard()
```
#### Decode:
```python
<dict> = <DF>.to_dict(['d/l/s/sp/r/i'])
<str> = <DF>.to_json/html/csv/markdown/latex([<path>])
@ -3295,7 +3294,7 @@ b 3 4
#### Apply, Aggregate, Transform:
```python
<DF> = <GB>.sum/max/mean/idxmax/all() # Or: <GB>.apply/agg(<agg_func>)
<DF> = <GB>.diff/cumsum/rank/ffill() # Or: <GB>.aggregate(<trans_func>)
<DF> = <GB>.rank/diff/cumsum/ffill() # Or: <GB>.aggregate(<trans_func>)
<DF> = <GB>.fillna(<el>) # Or: <GB>.transform(<map_func>)
```
@ -3309,37 +3308,20 @@ b 3 4
```
```python
+-------------+-------------+-------------+---------------+
| | 'sum' | ['sum'] | {'x': 'sum'} |
+-------------+-------------+-------------+---------------+
| gb.agg(…) | x y | x y | x |
| | z | sum sum | z |
| | 3 1 2 | z | 3 1 |
| | 6 11 13 | 3 1 2 | 6 11 |
| | | 6 11 13 | |
+-------------+-------------+-------------+---------------+
| gb.trans(…) | x y | | |
| | a 1 2 | | |
| | b 11 13 | | |
| | c 11 13 | | |
+-------------+-------------+-------------+---------------+
```
```python
+-------------+-------------+-------------+---------------+
| | 'rank' | ['rank'] | {'x': 'rank'} |
+-------------+-------------+-------------+---------------+
| gb.agg(…) | x y | x y | x |
| | a 1 1 | rank rank | a 1 |
| | b 1 1 | a 1 1 | b 1 |
| | c 2 2 | b 1 1 | c 2 |
| | | c 2 2 | |
+-------------+-------------+-------------+---------------+
| gb.trans(…) | x y | | |
| | a 1 1 | | |
| | b 1 1 | | |
| | 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.trans(…) | x y | x y | | |
| | a 1 2 | a 1 1 | | |
| | b 11 13 | b 1 1 | | |
| | c 11 13 | c 1 1 | | |
+-------------+-------------+-------------+-------------+---------------+
```
### Rolling
@ -3427,7 +3409,7 @@ import pandas, datetime
import plotly.graph_objects as go
def main():
display_data(mangle_data(*scrape_data()))
display_data(wrangle_data(*scrape_data()))
def scrape_data():
def scrape_yah(id_):
@ -3442,7 +3424,7 @@ def scrape_data():
dow_jones.name, gold.name, bitcoin.name = 'Dow Jones', 'Gold', 'Bitcoin'
return covid, dow_jones, gold, bitcoin
def mangle_data(covid, dow_jones, gold, bitcoin):
def wrangle_data(covid, dow_jones, gold, bitcoin):
out = pandas.concat([covid, dow_jones, gold, bitcoin], axis=1)
out = out.loc['2020-02-23':].iloc[:-2]
out = out.interpolate()

60
index.html
View File

@ -2640,7 +2640,7 @@ Name: a, dtype: int64
&lt;Sr&gt;.update(&lt;Sr&gt;) <span class="hljs-comment"># Updates items that are already present.</span>
</code></pre>
<div><h4 id="applyaggregatetransform">Apply, Aggregate, Transform:</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;.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;.rank/diff/cumsum/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></div>
@ -2737,7 +2737,7 @@ c <span class="hljs-number">6</span> <span class="hljs-number">7</span>
┗━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━┛
</code></pre>
<div><h4 id="applyaggregatetransform-1">Apply, Aggregate, Transform:</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;.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;.rank/diff/cumsum/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></div>
@ -2767,25 +2767,24 @@ b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
| | | b <span class="hljs-number">2</span> <span class="hljs-number">2</span> | |
+-------------+---------------+---------------+---------------+
</code></pre>
<div><h4 id="encode-2">Encode:</h4><pre><code class="python language-python hljs">&lt;DF&gt; = pd.read_json/html(<span class="hljs-string">'&lt;str/path/url&gt;'</span>)
<div><h4 id="encodedecode">Encode, Decode:</h4><pre><code class="python language-python hljs">&lt;DF&gt; = pd.read_json/html(<span class="hljs-string">'&lt;str/path/url&gt;'</span>)
&lt;DF&gt; = pd.read_csv/pickle/excel(<span class="hljs-string">'&lt;path/url&gt;'</span>)
&lt;DF&gt; = pd.read_sql(<span class="hljs-string">'&lt;query&gt;'</span>, &lt;connection&gt;)
&lt;DF&gt; = pd.read_clipboard()
</code></pre></div>
<div><h4 id="decode-3">Decode:</h4><pre><code class="python language-python hljs">&lt;dict&gt; = &lt;DF&gt;.to_dict([<span class="hljs-string">'d/l/s/sp/r/i'</span>])
<pre><code class="python language-python hljs">&lt;dict&gt; = &lt;DF&gt;.to_dict([<span class="hljs-string">'d/l/s/sp/r/i'</span>])
&lt;str&gt; = &lt;DF&gt;.to_json/html/csv/markdown/latex([&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;)
</code></pre></div>
</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>
</code></pre></div>
<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;.diff/cumsum/rank/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>
</code></pre></div>
@ -2796,35 +2795,20 @@ b <span class="hljs-number">3</span> <span class="hljs-number">4</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 language-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>} |
+-------------+-------------+-------------+---------------+
| gb.agg(…) | x y | x y | x |
| | z | sum sum | z |
| | <span class="hljs-number">3</span> <span class="hljs-number">1</span> <span class="hljs-number">2</span> | z | <span class="hljs-number">3</span> <span class="hljs-number">1</span> |
| | <span class="hljs-number">6</span> <span class="hljs-number">11</span> <span class="hljs-number">13</span> | <span class="hljs-number">3</span> <span class="hljs-number">1</span> <span class="hljs-number">2</span> | <span class="hljs-number">6</span> <span class="hljs-number">11</span> |
| | | <span class="hljs-number">6</span> <span class="hljs-number">11</span> <span class="hljs-number">13</span> | |
+-------------+-------------+-------------+---------------+
| gb.trans(…) | x y | | |
| | a <span class="hljs-number">1</span> <span class="hljs-number">2</span> | | |
| | b <span class="hljs-number">11</span> <span class="hljs-number">13</span> | | |
| | c <span class="hljs-number">11</span> <span class="hljs-number">13</span> | | |
+-------------+-------------+-------------+---------------+
</code></pre>
<pre><code class="python language-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>} |
+-------------+-------------+-------------+---------------+
| gb.agg(…) | x y | x y | x |
| | a <span class="hljs-number">1</span> <span class="hljs-number">1</span> | rank rank | a <span class="hljs-number">1</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> |
| | 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 | | |
| | a <span class="hljs-number">1</span> <span class="hljs-number">1</span> | | |
| | b <span class="hljs-number">1</span> <span class="hljs-number">1</span> | | |
| | c <span class="hljs-number">1</span> <span class="hljs-number">1</span> | | |
+-------------+-------------+-------------+---------------+
<pre><code class="python language-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> | | |
+-------------+-------------+-------------+-------------+---------------+
</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>
@ -2899,7 +2883,7 @@ plotly.express.line(summed, x=<span class="hljs-string">'Date'</span>, y=<span c
<span class="hljs-keyword">import</span> plotly.graph_objects <span class="hljs-keyword">as</span> go
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">main</span><span class="hljs-params">()</span>:</span>
display_data(mangle_data(*scrape_data()))
display_data(wrangle_data(*scrape_data()))
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">scrape_data</span><span class="hljs-params">()</span>:</span>
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">scrape_yah</span><span class="hljs-params">(id_)</span>:</span>
@ -2914,7 +2898,7 @@ plotly.express.line(summed, x=<span class="hljs-string">'Date'</span>, y=<span c
dow_jones.name, gold.name, bitcoin.name = <span class="hljs-string">'Dow Jones'</span>, <span class="hljs-string">'Gold'</span>, <span class="hljs-string">'Bitcoin'</span>
<span class="hljs-keyword">return</span> covid, dow_jones, gold, bitcoin
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">mangle_data</span><span class="hljs-params">(covid, dow_jones, gold, bitcoin)</span>:</span>
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">wrangle_data</span><span class="hljs-params">(covid, dow_jones, gold, bitcoin)</span>:</span>
out = pandas.concat([covid, dow_jones, gold, bitcoin], axis=<span class="hljs-number">1</span>)
out = out.loc[<span class="hljs-string">'2020-02-23'</span>:].iloc[:<span class="hljs-number">-2</span>]
out = out.interpolate()

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