Window ops
================
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
This library is intended to be used as an alternative to
`pd.Series.rolling` and `pd.Series.expanding` to gain a speedup by using
numba optimized functions operating on numpy arrays. There are also
online classes for more efficient updates of window statistics.
## Install
`pip install window-ops`
## How to use
### Transformations
For a transformations `n_samples` -\> `n_samples` you can use
`{[seasonal_](rolling|expanding)}_{(mean|max|min|std)}` on an array.
#### Benchmarks
``` python
pd.__version__
```
'1.3.5'
``` python
n_samples = 10_000 # array size
window_size = 8 # for rolling operations
season_length = 7 # for seasonal operations
execute_times = 10 # number of times each function will be executed
```
Average times in milliseconds.
``` python
times.applymap('{:.2f}'.format)
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>window_ops</th>
<th>pandas</th>
</tr>
</thead>
<tbody>
<tr>
<th>rolling_mean</th>
<td>0.03</td>
<td>0.43</td>
</tr>
<tr>
<th>rolling_max</th>
<td>0.14</td>
<td>0.57</td>
</tr>
<tr>
<th>rolling_min</th>
<td>0.14</td>
<td>0.58</td>
</tr>
<tr>
<th>rolling_std</th>
<td>0.06</td>
<td>0.54</td>
</tr>
<tr>
<th>expanding_mean</th>
<td>0.03</td>
<td>0.31</td>
</tr>
<tr>
<th>expanding_max</th>
<td>0.05</td>
<td>0.76</td>
</tr>
<tr>
<th>expanding_min</th>
<td>0.05</td>
<td>0.47</td>
</tr>
<tr>
<th>expanding_std</th>
<td>0.09</td>
<td>0.41</td>
</tr>
<tr>
<th>seasonal_rolling_mean</th>
<td>0.05</td>
<td>3.89</td>
</tr>
<tr>
<th>seasonal_rolling_max</th>
<td>0.18</td>
<td>4.27</td>
</tr>
<tr>
<th>seasonal_rolling_min</th>
<td>0.18</td>
<td>3.75</td>
</tr>
<tr>
<th>seasonal_rolling_std</th>
<td>0.08</td>
<td>4.38</td>
</tr>
<tr>
<th>seasonal_expanding_mean</th>
<td>0.04</td>
<td>3.18</td>
</tr>
<tr>
<th>seasonal_expanding_max</th>
<td>0.06</td>
<td>3.29</td>
</tr>
<tr>
<th>seasonal_expanding_min</th>
<td>0.06</td>
<td>3.28</td>
</tr>
<tr>
<th>seasonal_expanding_std</th>
<td>0.12</td>
<td>3.89</td>
</tr>
</tbody>
</table>
</div>
``` python
speedups = times['pandas'] / times['window_ops']
speedups = speedups.to_frame('times faster')
speedups.applymap('{:.0f}'.format)
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>times faster</th>
</tr>
</thead>
<tbody>
<tr>
<th>rolling_mean</th>
<td>15</td>
</tr>
<tr>
<th>rolling_max</th>
<td>4</td>
</tr>
<tr>
<th>rolling_min</th>
<td>4</td>
</tr>
<tr>
<th>rolling_std</th>
<td>9</td>
</tr>
<tr>
<th>expanding_mean</th>
<td>12</td>
</tr>
<tr>
<th>expanding_max</th>
<td>15</td>
</tr>
<tr>
<th>expanding_min</th>
<td>9</td>
</tr>
<tr>
<th>expanding_std</th>
<td>4</td>
</tr>
<tr>
<th>seasonal_rolling_mean</th>
<td>77</td>
</tr>
<tr>
<th>seasonal_rolling_max</th>
<td>23</td>
</tr>
<tr>
<th>seasonal_rolling_min</th>
<td>21</td>
</tr>
<tr>
<th>seasonal_rolling_std</th>
<td>52</td>
</tr>
<tr>
<th>seasonal_expanding_mean</th>
<td>78</td>
</tr>
<tr>
<th>seasonal_expanding_max</th>
<td>52</td>
</tr>
<tr>
<th>seasonal_expanding_min</th>
<td>51</td>
</tr>
<tr>
<th>seasonal_expanding_std</th>
<td>33</td>
</tr>
</tbody>
</table>
</div>
### Online
If you have an array for which you want to compute a window statistic
and then keep updating it as more samples come in you can use the
classes in the `window_ops.online` module. They all have a
`fit_transform` method which take the array and return the
transformations defined above but also have an `update` method that take
a single value and return the new statistic.
#### Benchmarks
Average time in milliseconds it takes to transform the array and perform
100 updates.
``` python
times.to_frame().applymap('{:.2f}'.format)
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>average time (ms)</th>
</tr>
</thead>
<tbody>
<tr>
<th>RollingMean</th>
<td>0.12</td>
</tr>
<tr>
<th>RollingMax</th>
<td>0.23</td>
</tr>
<tr>
<th>RollingMin</th>
<td>0.22</td>
</tr>
<tr>
<th>RollingStd</th>
<td>0.32</td>
</tr>
<tr>
<th>ExpandingMean</th>
<td>0.10</td>
</tr>
<tr>
<th>ExpandingMax</th>
<td>0.07</td>
</tr>
<tr>
<th>ExpandingMin</th>
<td>0.07</td>
</tr>
<tr>
<th>ExpandingStd</th>
<td>0.17</td>
</tr>
<tr>
<th>SeasonalRollingMean</th>
<td>0.28</td>
</tr>
<tr>
<th>SeasonalRollingMax</th>
<td>0.35</td>
</tr>
<tr>
<th>SeasonalRollingMin</th>
<td>0.38</td>
</tr>
<tr>
<th>SeasonalRollingStd</th>
<td>0.42</td>
</tr>
<tr>
<th>SeasonalExpandingMean</th>
<td>0.17</td>
</tr>
<tr>
<th>SeasonalExpandingMax</th>
<td>0.14</td>
</tr>
<tr>
<th>SeasonalExpandingMin</th>
<td>0.15</td>
</tr>
<tr>
<th>SeasonalExpandingStd</th>
<td>0.23</td>
</tr>
</tbody>
</table>
</div>
Raw data
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"description": "Window ops\n================\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\nThis library is intended to be used as an alternative to\n`pd.Series.rolling` and `pd.Series.expanding` to gain a speedup by using\nnumba optimized functions operating on numpy arrays. There are also\nonline classes for more efficient updates of window statistics.\n\n## Install\n\n`pip install window-ops`\n\n## How to use\n\n### Transformations\n\nFor a transformations `n_samples` -\\> `n_samples` you can use\n`{[seasonal_](rolling|expanding)}_{(mean|max|min|std)}` on an array.\n\n#### Benchmarks\n\n``` python\npd.__version__\n```\n\n '1.3.5'\n\n``` python\nn_samples = 10_000 # array size\nwindow_size = 8 # for rolling operations\nseason_length = 7 # for seasonal operations\nexecute_times = 10 # number of times each function will be executed\n```\n\nAverage times in milliseconds.\n\n``` python\ntimes.applymap('{:.2f}'.format)\n```\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>window_ops</th>\n <th>pandas</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>rolling_mean</th>\n <td>0.03</td>\n <td>0.43</td>\n </tr>\n <tr>\n <th>rolling_max</th>\n <td>0.14</td>\n <td>0.57</td>\n </tr>\n <tr>\n <th>rolling_min</th>\n <td>0.14</td>\n <td>0.58</td>\n </tr>\n <tr>\n <th>rolling_std</th>\n <td>0.06</td>\n <td>0.54</td>\n </tr>\n <tr>\n <th>expanding_mean</th>\n <td>0.03</td>\n <td>0.31</td>\n </tr>\n <tr>\n <th>expanding_max</th>\n <td>0.05</td>\n <td>0.76</td>\n </tr>\n <tr>\n <th>expanding_min</th>\n <td>0.05</td>\n <td>0.47</td>\n </tr>\n <tr>\n <th>expanding_std</th>\n <td>0.09</td>\n <td>0.41</td>\n </tr>\n <tr>\n <th>seasonal_rolling_mean</th>\n <td>0.05</td>\n <td>3.89</td>\n </tr>\n <tr>\n <th>seasonal_rolling_max</th>\n <td>0.18</td>\n <td>4.27</td>\n </tr>\n <tr>\n <th>seasonal_rolling_min</th>\n <td>0.18</td>\n <td>3.75</td>\n </tr>\n <tr>\n <th>seasonal_rolling_std</th>\n <td>0.08</td>\n <td>4.38</td>\n </tr>\n <tr>\n <th>seasonal_expanding_mean</th>\n <td>0.04</td>\n <td>3.18</td>\n </tr>\n <tr>\n <th>seasonal_expanding_max</th>\n <td>0.06</td>\n <td>3.29</td>\n </tr>\n <tr>\n <th>seasonal_expanding_min</th>\n <td>0.06</td>\n <td>3.28</td>\n </tr>\n <tr>\n <th>seasonal_expanding_std</th>\n <td>0.12</td>\n <td>3.89</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n``` python\nspeedups = times['pandas'] / times['window_ops']\nspeedups = speedups.to_frame('times faster')\nspeedups.applymap('{:.0f}'.format)\n```\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>times faster</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>rolling_mean</th>\n <td>15</td>\n </tr>\n <tr>\n <th>rolling_max</th>\n <td>4</td>\n </tr>\n <tr>\n <th>rolling_min</th>\n <td>4</td>\n </tr>\n <tr>\n <th>rolling_std</th>\n <td>9</td>\n </tr>\n <tr>\n <th>expanding_mean</th>\n <td>12</td>\n </tr>\n <tr>\n <th>expanding_max</th>\n <td>15</td>\n </tr>\n <tr>\n <th>expanding_min</th>\n <td>9</td>\n </tr>\n <tr>\n <th>expanding_std</th>\n <td>4</td>\n </tr>\n <tr>\n <th>seasonal_rolling_mean</th>\n <td>77</td>\n </tr>\n <tr>\n <th>seasonal_rolling_max</th>\n <td>23</td>\n </tr>\n <tr>\n <th>seasonal_rolling_min</th>\n <td>21</td>\n </tr>\n <tr>\n <th>seasonal_rolling_std</th>\n <td>52</td>\n </tr>\n <tr>\n <th>seasonal_expanding_mean</th>\n <td>78</td>\n </tr>\n <tr>\n <th>seasonal_expanding_max</th>\n <td>52</td>\n </tr>\n <tr>\n <th>seasonal_expanding_min</th>\n <td>51</td>\n </tr>\n <tr>\n <th>seasonal_expanding_std</th>\n <td>33</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n### Online\n\nIf you have an array for which you want to compute a window statistic\nand then keep updating it as more samples come in you can use the\nclasses in the `window_ops.online` module. 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