pdpatch
================
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
`pdpatch` adds methods to [pandas](https://pandas.pydata.org/)’
`DataFrame` and `Series` for a faster data science pipeline. It also
defines drop-in replacements for `seaborn` and `plotly.express` that
automatically label axes with nicer titles. We use
[nbdev](https://nbdev.fast.ai/) to build this project.
## Install
`pip install pdpatch`
## How to use
``` python
from pdpatch.all import *
```
### Interactive Method `.less()`
### Automatically Rename snake_case columns in `plotly.express` and `seaborn`
``` python
import pandas as pd
from pdpatch.express import *
df = pd.DataFrame({'time__s__': range(10), 'position__m__': [i**1.3 for i in range(10)], 'speed__m/s__': 10*[1]})
#df = pd.DataFrame({'time__s__': range(10), 'position__m__': range(10)})
px.scatter(df, x='time__s__', y='position__m__').show('png')
```
``` python
from pdpatch.seaborn import sns
sns.scatterplot(data=df, x='time__s__', y='position__m__');
```
### Add Altair-like Operation to plotly Figures
``` python
fig = px.scatter(df,x='time__s__', y='time__s__') | px.scatter(df,x='time__s__', y=['position__m__', 'speed__m/s__'])
fig.show('png')
```
``` python
fig = px.scatter(df,x='time__s__', y='time__s__') / px.scatter(df,x='time__s__', y=['position__m__', 'speed__m/s__'])
fig.show('png')
```
``` python
fig = px.scatter(df,x='time__s__', y='time__s__') | px.scatter(df,x='time__s__', y=['position__m__', 'speed__m/s__'])
(fig / fig).show('png')
```
### Shorter methods
`df.rename(columns={'col_1': 'new_name'})`-\>`df.renamec('col_1', 'new_name')`
``` python
df = dummydf()
df.renamec('col_1', 'new_name').to_html()
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>
</th>
<th>
new_name
</th>
<th>
col_2
</th>
</tr>
</thead>
<tbody>
<tr>
<th>
0
</th>
<td>
100
</td>
<td>
a
</td>
</tr>
<tr>
<th>
1
</th>
<td>
101
</td>
<td>
b
</td>
</tr>
<tr>
<th>
2
</th>
<td>
102
</td>
<td>
c
</td>
</tr>
<tr>
<th>
3
</th>
<td>
103
</td>
<td>
d
</td>
</tr>
<tr>
<th>
4
</th>
<td>
104
</td>
<td>
e
</td>
</tr>
</tbody>
</table>
### Functions as methods
``` python
df.len()
```
5
### New methods
``` python
df.col_1.minmax
```
(100, 104)
### Utility functions
``` python
df = dummydf()
df.to_html()
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>
</th>
<th>
col_1
</th>
<th>
col_2
</th>
</tr>
</thead>
<tbody>
<tr>
<th>
0
</th>
<td>
100
</td>
<td>
a
</td>
</tr>
<tr>
<th>
1
</th>
<td>
101
</td>
<td>
b
</td>
</tr>
<tr>
<th>
2
</th>
<td>
102
</td>
<td>
c
</td>
</tr>
<tr>
<th>
3
</th>
<td>
103
</td>
<td>
d
</td>
</tr>
<tr>
<th>
4
</th>
<td>
104
</td>
<td>
e
</td>
</tr>
</tbody>
</table>
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"description": "pdpatch\n================\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n`pdpatch` adds methods to [pandas](https://pandas.pydata.org/)\u2019\n`DataFrame` and `Series` for a faster data science pipeline. It also\ndefines drop-in replacements for `seaborn` and `plotly.express` that\nautomatically label axes with nicer titles. We use\n[nbdev](https://nbdev.fast.ai/) to build this project.\n\n## Install\n\n`pip install pdpatch`\n\n## How to use\n\n``` python\nfrom pdpatch.all import *\n```\n\n### Interactive Method `.less()`\n\n\n\n### Automatically Rename snake_case columns in `plotly.express` and `seaborn`\n\n``` python\nimport pandas as pd\nfrom pdpatch.express import *\ndf = pd.DataFrame({'time__s__': range(10), 'position__m__': [i**1.3 for i in range(10)], 'speed__m/s__': 10*[1]})\n#df = pd.DataFrame({'time__s__': range(10), 'position__m__': range(10)})\npx.scatter(df, x='time__s__', y='position__m__').show('png')\n```\n\n\n\n``` python\nfrom pdpatch.seaborn import sns\nsns.scatterplot(data=df, x='time__s__', y='position__m__');\n```\n\n\n\n### Add Altair-like Operation to plotly Figures\n\n``` python\nfig = px.scatter(df,x='time__s__', y='time__s__') | px.scatter(df,x='time__s__', y=['position__m__', 'speed__m/s__'])\nfig.show('png')\n```\n\n\n\n``` python\nfig = px.scatter(df,x='time__s__', y='time__s__') / px.scatter(df,x='time__s__', y=['position__m__', 'speed__m/s__'])\nfig.show('png')\n```\n\n\n\n``` python\nfig = px.scatter(df,x='time__s__', y='time__s__') | px.scatter(df,x='time__s__', y=['position__m__', 'speed__m/s__'])\n(fig / fig).show('png')\n```\n\n\n\n### Shorter methods\n\n`df.rename(columns={'col_1': 'new_name'})`-\\>`df.renamec('col_1', 'new_name')`\n\n``` python\ndf = dummydf()\ndf.renamec('col_1', 'new_name').to_html()\n```\n\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th>\n</th>\n<th>\nnew_name\n</th>\n<th>\ncol_2\n</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<th>\n0\n</th>\n<td>\n100\n</td>\n<td>\na\n</td>\n</tr>\n<tr>\n<th>\n1\n</th>\n<td>\n101\n</td>\n<td>\nb\n</td>\n</tr>\n<tr>\n<th>\n2\n</th>\n<td>\n102\n</td>\n<td>\nc\n</td>\n</tr>\n<tr>\n<th>\n3\n</th>\n<td>\n103\n</td>\n<td>\nd\n</td>\n</tr>\n<tr>\n<th>\n4\n</th>\n<td>\n104\n</td>\n<td>\ne\n</td>\n</tr>\n</tbody>\n</table>\n\n### Functions as methods\n\n``` python\ndf.len()\n```\n\n 5\n\n### New methods\n\n``` python\ndf.col_1.minmax\n```\n\n (100, 104)\n\n### Utility functions\n\n``` python\ndf = dummydf()\ndf.to_html()\n```\n\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th>\n</th>\n<th>\ncol_1\n</th>\n<th>\ncol_2\n</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<th>\n0\n</th>\n<td>\n100\n</td>\n<td>\na\n</td>\n</tr>\n<tr>\n<th>\n1\n</th>\n<td>\n101\n</td>\n<td>\nb\n</td>\n</tr>\n<tr>\n<th>\n2\n</th>\n<td>\n102\n</td>\n<td>\nc\n</td>\n</tr>\n<tr>\n<th>\n3\n</th>\n<td>\n103\n</td>\n<td>\nd\n</td>\n</tr>\n<tr>\n<th>\n4\n</th>\n<td>\n104\n</td>\n<td>\ne\n</td>\n</tr>\n</tbody>\n</table>\n",
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