# pandasmore
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
The full documentation site is
[here](https://ionmihai.github.io/pandasmore/), and the GitHub page is
[here](https://github.com/ionmihai/pandasmore).
Here is a short description of some of the main functions (more details
below and in the
[documentation](https://ionmihai.github.io/pandasmore/core.html)):
- [`setup_tseries`](https://ionmihai.github.io/pandasmore/core.html#setup_tseries):
cleans up dates and sets them as the index
- [`setup_panel`](https://ionmihai.github.io/pandasmore/core.html#setup_panel):
cleans up dates and panel id’s and sets them as the index (panel id,
period date)
- [`lag`](https://ionmihai.github.io/pandasmore/core.html#lag): robust
lagging that accounts for panel structure, unsorted or duplicate
dates, or gaps in the time-series
## Install
``` sh
pip install pandasmore
```
## How to use
First, we set up an example dataset to showcase the functions in this
module.
``` python
import pandas as pd
import numpy as np
import pandasmore as pdm
```
``` python
raw = pd.DataFrame(np.random.rand(15,2),
columns=list('AB'),
index=pd.MultiIndex.from_product(
[[1,2, np.nan],[np.nan,'2010-01','2010-02','2010-02','2010-04']],
names = ['firm_id','date'])
).reset_index()
raw
```
<div>
| | firm_id | date | A | B |
|-----|---------|---------|----------|----------|
| 0 | 1.0 | NaN | 0.249370 | 0.926335 |
| 1 | 1.0 | 2010-01 | 0.282501 | 0.513859 |
| 2 | 1.0 | 2010-02 | 0.804278 | 0.307171 |
| 3 | 1.0 | 2010-02 | 0.828895 | 0.746789 |
| 4 | 1.0 | 2010-04 | 0.569099 | 0.331814 |
| 5 | 2.0 | NaN | 0.533977 | 0.823457 |
| 6 | 2.0 | 2010-01 | 0.207558 | 0.401378 |
| 7 | 2.0 | 2010-02 | 0.086001 | 0.959371 |
| 8 | 2.0 | 2010-02 | 0.054230 | 0.993980 |
| 9 | 2.0 | 2010-04 | 0.062525 | 0.200272 |
| 10 | NaN | NaN | 0.091012 | 0.635409 |
| 11 | NaN | 2010-01 | 0.866369 | 0.972394 |
| 12 | NaN | 2010-02 | 0.432087 | 0.837597 |
| 13 | NaN | 2010-02 | 0.878219 | 0.148009 |
| 14 | NaN | 2010-04 | 0.820386 | 0.834821 |
</div>
``` python
df = pdm.setup_tseries(raw.query('firm_id==1'),
time_var='date', time_var_format="%Y-%m",
freq='M')
df
```
<div>
| | date | dtdate | firm_id | A | B |
|---------|---------|------------|---------|----------|----------|
| Mdate | | | | | |
| 2010-01 | 2010-01 | 2010-01-01 | 1.0 | 0.282501 | 0.513859 |
| 2010-02 | 2010-02 | 2010-02-01 | 1.0 | 0.828895 | 0.746789 |
| 2010-04 | 2010-04 | 2010-04-01 | 1.0 | 0.569099 | 0.331814 |
</div>
``` python
df = pdm.setup_panel(raw,
panel_ids='firm_id',
time_var='date', time_var_format="%Y-%m",
freq='M')
df
```
<div>
| | | date | dtdate | A | B |
|---------|---------|---------|------------|----------|----------|
| firm_id | Mdate | | | | |
| 1 | 2010-01 | 2010-01 | 2010-01-01 | 0.282501 | 0.513859 |
| | 2010-02 | 2010-02 | 2010-02-01 | 0.828895 | 0.746789 |
| | 2010-04 | 2010-04 | 2010-04-01 | 0.569099 | 0.331814 |
| 2 | 2010-01 | 2010-01 | 2010-01-01 | 0.207558 | 0.401378 |
| | 2010-02 | 2010-02 | 2010-02-01 | 0.054230 | 0.993980 |
| | 2010-04 | 2010-04 | 2010-04-01 | 0.062525 | 0.200272 |
</div>
``` python
pdm.lag(df['A'])
```
firm_id Mdate
1 2010-01 NaN
2010-02 0.282501
2010-04 NaN
2 2010-01 NaN
2010-02 0.207558
2010-04 NaN
Name: A_lag1, dtype: float64
Raw data
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"description": "# pandasmore\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\nThe full documentation site is\n[here](https://ionmihai.github.io/pandasmore/), and the GitHub page is\n[here](https://github.com/ionmihai/pandasmore).\n\nHere is a short description of some of the main functions (more details\nbelow and in the\n[documentation](https://ionmihai.github.io/pandasmore/core.html)):\n\n- [`setup_tseries`](https://ionmihai.github.io/pandasmore/core.html#setup_tseries):\n cleans up dates and sets them as the index\n- [`setup_panel`](https://ionmihai.github.io/pandasmore/core.html#setup_panel):\n cleans up dates and panel id\u2019s and sets them as the index (panel id,\n period date)\n- [`lag`](https://ionmihai.github.io/pandasmore/core.html#lag): robust\n lagging that accounts for panel structure, unsorted or duplicate\n dates, or gaps in the time-series\n\n## Install\n\n``` sh\npip install pandasmore\n```\n\n## How to use\n\nFirst, we set up an example dataset to showcase the functions in this\nmodule.\n\n``` python\nimport pandas as pd\nimport numpy as np\nimport pandasmore as pdm\n```\n\n``` python\nraw = pd.DataFrame(np.random.rand(15,2), \n columns=list('AB'), \n index=pd.MultiIndex.from_product(\n [[1,2, np.nan],[np.nan,'2010-01','2010-02','2010-02','2010-04']],\n names = ['firm_id','date'])\n ).reset_index()\nraw\n```\n\n<div>\n\n\n| | firm_id | date | A | B |\n|-----|---------|---------|----------|----------|\n| 0 | 1.0 | NaN | 0.249370 | 0.926335 |\n| 1 | 1.0 | 2010-01 | 0.282501 | 0.513859 |\n| 2 | 1.0 | 2010-02 | 0.804278 | 0.307171 |\n| 3 | 1.0 | 2010-02 | 0.828895 | 0.746789 |\n| 4 | 1.0 | 2010-04 | 0.569099 | 0.331814 |\n| 5 | 2.0 | NaN | 0.533977 | 0.823457 |\n| 6 | 2.0 | 2010-01 | 0.207558 | 0.401378 |\n| 7 | 2.0 | 2010-02 | 0.086001 | 0.959371 |\n| 8 | 2.0 | 2010-02 | 0.054230 | 0.993980 |\n| 9 | 2.0 | 2010-04 | 0.062525 | 0.200272 |\n| 10 | NaN | NaN | 0.091012 | 0.635409 |\n| 11 | NaN | 2010-01 | 0.866369 | 0.972394 |\n| 12 | NaN | 2010-02 | 0.432087 | 0.837597 |\n| 13 | NaN | 2010-02 | 0.878219 | 0.148009 |\n| 14 | NaN | 2010-04 | 0.820386 | 0.834821 |\n\n</div>\n\n``` python\ndf = pdm.setup_tseries(raw.query('firm_id==1'),\n time_var='date', time_var_format=\"%Y-%m\",\n freq='M')\ndf\n```\n\n<div>\n\n\n| | date | dtdate | firm_id | A | B |\n|---------|---------|------------|---------|----------|----------|\n| Mdate | | | | | |\n| 2010-01 | 2010-01 | 2010-01-01 | 1.0 | 0.282501 | 0.513859 |\n| 2010-02 | 2010-02 | 2010-02-01 | 1.0 | 0.828895 | 0.746789 |\n| 2010-04 | 2010-04 | 2010-04-01 | 1.0 | 0.569099 | 0.331814 |\n\n</div>\n\n``` python\ndf = pdm.setup_panel(raw,\n panel_ids='firm_id',\n time_var='date', time_var_format=\"%Y-%m\",\n freq='M')\ndf\n```\n\n<div>\n\n\n| | | date | dtdate | A | B |\n|---------|---------|---------|------------|----------|----------|\n| firm_id | Mdate | | | | |\n| 1 | 2010-01 | 2010-01 | 2010-01-01 | 0.282501 | 0.513859 |\n| | 2010-02 | 2010-02 | 2010-02-01 | 0.828895 | 0.746789 |\n| | 2010-04 | 2010-04 | 2010-04-01 | 0.569099 | 0.331814 |\n| 2 | 2010-01 | 2010-01 | 2010-01-01 | 0.207558 | 0.401378 |\n| | 2010-02 | 2010-02 | 2010-02-01 | 0.054230 | 0.993980 |\n| | 2010-04 | 2010-04 | 2010-04-01 | 0.062525 | 0.200272 |\n\n</div>\n\n``` python\npdm.lag(df['A'])\n```\n\n firm_id Mdate \n 1 2010-01 NaN\n 2010-02 0.282501\n 2010-04 NaN\n 2 2010-01 NaN\n 2010-02 0.207558\n 2010-04 NaN\n Name: A_lag1, dtype: float64\n",
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