# cat2cat
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### Unifying an inconsistently coded categorical variable in a panel/longtitudal dataset
There is offered the cat2cat procedure to map a categorical variable according to a mapping (transition) table between two different time points. The mapping (transition) table should to have a candidate for each category from the targeted for an update period. The main rule is to replicate the observation if it could be assigned to a few categories, then using simple frequencies or statistical methods to approximate probabilities of being assigned to each of them.
**This algorithm was invented and implemented in the paper by [(Nasinski, Majchrowska and Broniatowska (2020))](https://doi.org/10.24425/cejeme.2020.134747).**
**For more details please read the paper by [(Nasinski, Gajowniczek (2023))](https://doi.org/10.1016/j.softx.2023.101525).**
## Installation
```bash
$ pip install cat2cat
```
## Usage
For more examples and descriptions please vist [**the example notebook**](https://py-cat2cat.readthedocs.io/en/latest/example.html)
### load example data
```python
# cat2cat datasets
from cat2cat.datasets import load_trans, load_occup
trans = load_trans()
occup = load_occup()
```
### Low-level functions
```python
from cat2cat.mappings import get_mappings, get_freqs, cat_apply_freq
# convert the mapping table to two association lists
mappings = get_mappings(trans)
# get a variable levels freqencies
codes_new = occup.code[occup.year == 2010].values
freqs = get_freqs(codes_new)
# apply the frequencies to the (one) association list
mapp_new_p = cat_apply_freq(mappings["to_new"], freqs)
# mappings for a specific category
mappings["to_new"]['3481']
# probability mappings for a specific category
mapp_new_p['3481']
```
### cat2cat function
```python
from cat2cat import cat2cat
from cat2cat.dataclass import cat2cat_data, cat2cat_mappings, cat2cat_ml
from pandas import concat
# split the panel by the time variale
# here only two periods
o_old = occup.loc[occup.year == 2008, :].copy()
o_new = occup.loc[occup.year == 2010, :].copy()
# dataclasses, core arguments for the cat2cat function
data = cat2cat_data(
old = o_old,
new = o_new,
cat_var_old = "code",
cat_var_new = "code",
time_var = "year"
)
mappings = cat2cat_mappings(trans = trans, direction = "backward")
# apply the cat2cat procedure
c2c = cat2cat(data = data, mappings = mappings)
# pandas.concat used to bind per period datasets
data_final = concat([c2c["old"], c2c["new"]])
```
## Contributing
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
## License
`cat2cat` was created by Maciej Nasinski. It is licensed under the terms of the MIT license.
## Credits
`cat2cat` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
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"description": "# cat2cat \n\n<a href='https://github.com/polkas/py-cat2cat'>\n<img src='https://raw.githubusercontent.com/Polkas/cat2cat/master/man/figures/cat2cat_logo.png' style=\"display:block;margin-left:auto;margin-right:auto;width:200px;\" width=\"200px\" alt=\"cat2cat logo\"/>\n</a>\n\n<hr>\n\n<div>\n<a href=\"https://github.com/polkas/py-cat2cat/actions\">\n<img src=\"https://github.com/polkas/py-cat2cat/workflows/ci/badge.svg\" alt=\"Build Status\">\n</a>\n<a href=\"https://codecov.io/gh/Polkas/py-cat2cat\">\n<img src=\"https://codecov.io/gh/Polkas/py-cat2cat/branch/main/graph/badge.svg\" alt=\"codecov\">\n</a>\n<a href=\"https://pypi.org/project/cat2cat/\">\n<img src=\"https://img.shields.io/pypi/v/cat2cat.svg\" alt=\"pypi\">\n</a>\n<div>\n\n<br>\n\n### Unifying an inconsistently coded categorical variable in a panel/longtitudal dataset\n\nThere is offered the cat2cat procedure to map a categorical variable according to a mapping (transition) table between two different time points. The mapping (transition) table should to have a candidate for each category from the targeted for an update period. The main rule is to replicate the observation if it could be assigned to a few categories, then using simple frequencies or statistical methods to approximate probabilities of being assigned to each of them.\n\n**This algorithm was invented and implemented in the paper by [(Nasinski, Majchrowska and Broniatowska (2020))](https://doi.org/10.24425/cejeme.2020.134747).**\n\n**For more details please read the paper by [(Nasinski, Gajowniczek (2023))](https://doi.org/10.1016/j.softx.2023.101525).**\n\n## Installation\n\n```bash\n$ pip install cat2cat\n```\n\n## Usage\n\nFor more examples and descriptions please vist [**the example notebook**](https://py-cat2cat.readthedocs.io/en/latest/example.html)\n\n### load example data\n\n```python\n# cat2cat datasets\nfrom cat2cat.datasets import load_trans, load_occup\ntrans = load_trans()\noccup = load_occup()\n```\n\n### Low-level functions\n\n```python\nfrom cat2cat.mappings import get_mappings, get_freqs, cat_apply_freq\n\n# convert the mapping table to two association lists\nmappings = get_mappings(trans)\n# get a variable levels freqencies\ncodes_new = occup.code[occup.year == 2010].values\nfreqs = get_freqs(codes_new)\n# apply the frequencies to the (one) association list\nmapp_new_p = cat_apply_freq(mappings[\"to_new\"], freqs)\n\n# mappings for a specific category\nmappings[\"to_new\"]['3481']\n# probability mappings for a specific category\nmapp_new_p['3481']\n```\n\n### cat2cat function\n\n```python\nfrom cat2cat import cat2cat\nfrom cat2cat.dataclass import cat2cat_data, cat2cat_mappings, cat2cat_ml\n\nfrom pandas import concat\n\n# split the panel by the time variale\n# here only two periods\no_old = occup.loc[occup.year == 2008, :].copy()\no_new = occup.loc[occup.year == 2010, :].copy()\n\n# dataclasses, core arguments for the cat2cat function\ndata = cat2cat_data(\n old = o_old, \n new = o_new,\n cat_var_old = \"code\", \n cat_var_new = \"code\", \n time_var = \"year\"\n)\nmappings = cat2cat_mappings(trans = trans, direction = \"backward\")\n\n# apply the cat2cat procedure\nc2c = cat2cat(data = data, mappings = mappings)\n# pandas.concat used to bind per period datasets\ndata_final = concat([c2c[\"old\"], c2c[\"new\"]])\n```\n\n## Contributing\n\nInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.\n\n## License\n\n`cat2cat` was created by Maciej Nasinski. It is licensed under the terms of the MIT license.\n\n## Credits\n\n`cat2cat` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).\n",
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