diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 925c15c..23d2ecb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,13 +1,17 @@ name: ci -on: [push, pull_request] +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] jobs: ci: strategy: matrix: os: [ubuntu-latest] - python: ["3.8", "3.9", "3.10"] + python: ["3.8", "3.9", "3.10", "3.11"] # Set up operating system runs-on: ${{ matrix.os }} diff --git a/docs/conf.py b/docs/conf.py index b6f5cc5..81953b9 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,9 +6,9 @@ # -- Project information ----------------------------------------------------- -project = u"cat2cat" -copyright = u"2022, Maciej Nasinski" -author = u"Maciej Nasinski" +project = "cat2cat" +copyright = "2022, Maciej Nasinski" +author = "Maciej Nasinski" # -- General configuration --------------------------------------------------- @@ -16,7 +16,7 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - "myst_nb", + "myst_parser", "autoapi.extension", "sphinx.ext.napoleon", "sphinx.ext.viewcode", diff --git a/docs/example.ipynb b/docs/example.ipynb deleted file mode 100644 index 3e70056..0000000 --- a/docs/example.ipynb +++ /dev/null @@ -1,391 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# cat2cat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## About\n", - "\n", - "Unifying an inconsistent coded categorical variable in a panel/longtitudal dataset\n", - "\n", - "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.\n", - "\n", - "This algorithm was invented and implemented in the paper by (Nasinski, Majchrowska and Broniatowska (2020) doi:10.24425/cejeme.2020.134747)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Graph - cat2cat procedure\n", - "\n", - "The graphs present how the `cat2cat` function (and the underlying procedure) works, in this case under a panel dataset without the unique identifiers and only two periods.\n", - "\n", - "![Backward Mapping](https://raw.githubusercontent.com/Polkas/cat2cat/master/man/figures/back_nom.png)\n", - "\n", - "![Forward Mapping](https://raw.githubusercontent.com/Polkas/cat2cat/master/man/figures/for_nom.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example usage\n", - "\n", - "To use `cat2cat` in a project:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load example data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# cat2cat datasets\n", - "from cat2cat.datasets import load_trans, load_occup, load_verticals\n", - "from numpy.random import seed\n", - "\n", - "seed(1234)\n", - "\n", - "trans = load_trans()\n", - "occup = load_occup()\n", - "verticals = load_verticals()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Low-level functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from cat2cat.mappings import get_mappings, get_freqs, cat_apply_freq\n", - "\n", - "# convert the mapping table to two association lists\n", - "mappings = get_mappings(trans)\n", - "# get a variable levels freqencies\n", - "codes_new = occup.code[occup.year == 2010].values\n", - "freqs = get_freqs(codes_new)\n", - "# apply the frequencies to the (one) association list\n", - "mapp_new_p = cat_apply_freq(mappings[\"to_new\"], freqs)\n", - "\n", - "# mappings for a specific category\n", - "print(mappings[\"to_new\"]['3481'])\n", - "# probability mappings for a specific category\n", - "print(mapp_new_p['3481'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### cat2cat procedure - one iteration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from cat2cat import cat2cat\n", - "from cat2cat.dataclass import cat2cat_data, cat2cat_mappings, cat2cat_ml\n", - "\n", - "from pandas import concat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# split the panel by the time variale\n", - "# here only two periods\n", - "o_old = occup.loc[occup.year == 2008, :].copy()\n", - "o_new = occup.loc[occup.year == 2010, :].copy()\n", - "\n", - "# dataclasses, two core arguments for the cat2cat function\n", - "data = 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", - ")\n", - "mappings = cat2cat_mappings(trans = trans, direction = \"backward\")\n", - "\n", - "# apply the cat2cat procedure\n", - "c2c = cat2cat(data = data, mappings = mappings)\n", - "# pandas.concat used to bind per period datasets\n", - "data_final = concat([c2c[\"old\"], c2c[\"new\"]])\n", - "\n", - "sub_cols = [\"id\", \"edu\", \"code\", \"year\", \"index_c2c\", \"g_new_c2c\", \"rep_c2c\", \"wei_naive_c2c\", \"wei_freq_c2c\"]\n", - "data_final.groupby([\"year\"]).sample(5).loc[:, sub_cols]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### With ML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.neighbors import KNeighborsClassifier\n", - "\n", - "# ml dataclass, one of the arguments for the cat2cat function\n", - "ml = cat2cat_ml(\n", - " data = o_new, \n", - " cat_var = \"code\", \n", - " features = [\"salary\", \"age\", \"edu\"], \n", - " models = [KNeighborsClassifier()]\n", - ")\n", - "\n", - "# apply the cat2cat procedure\n", - "c2c = cat2cat(data = data, mappings = mappings, ml = ml)\n", - "# pandas.concat used to bind per period datasets\n", - "data_final = concat([c2c[\"old\"], c2c[\"new\"]])\n", - "\n", - "sub_cols = [\"id\", \"year\", \"wei_naive_c2c\", \"wei_freq_c2c\", \"wei_KNeighborsClassifier_c2c\"]\n", - "data_final.groupby([\"year\"]).sample(3).loc[:, sub_cols]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With 4 periods, one mapping table and backward direction:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from cat2cat.cat2cat_utils import dummy_c2c\n", - "\n", - "# split the panel by the time variale\n", - "# here four periods\n", - "o_2006 = occup.loc[occup.year == 2006, :].copy()\n", - "o_2008 = occup.loc[occup.year == 2008, :].copy()\n", - "o_2010 = occup.loc[occup.year == 2010, :].copy()\n", - "o_2012 = occup.loc[occup.year == 2012, :].copy()\n", - "\n", - "# dataclasses, two core arguments for the cat2cat function\n", - "data = cat2cat_data(\n", - " old = o_2008, \n", - " new = o_2010, \n", - " cat_var_old = \"code\", \n", - " cat_var_new = \"code\", \n", - " time_var = \"year\"\n", - ")\n", - "mappings = cat2cat_mappings(trans = trans, direction = \"backward\")\n", - "\n", - "# apply the cat2cat procedure\n", - "occup_back_2008_2010 = cat2cat(data = data, mappings = mappings)\n", - "\n", - "# updated for the next iteration data cat2cat argument\n", - "data = cat2cat_data(\n", - " old = o_2006, \n", - " new = occup_back_2008_2010[\"old\"], \n", - " cat_var_old = \"code\", \n", - " cat_var_new = \"g_new_c2c\", \n", - " time_var = \"year\"\n", - ")\n", - "\n", - "# apply the cat2cat procedure\n", - "occup_back_2006_2008 = cat2cat(data = data, mappings = mappings)\n", - "\n", - "# gather the datasets for each period\n", - "o_2006_n = occup_back_2006_2008[\"old\"]\n", - "o_2008_n = occup_back_2006_2008[\"new\"] # or occup_back_2008_2010[\"old\"]\n", - "o_2010_n = occup_back_2008_2010[\"new\"]\n", - "o_2012_n = dummy_c2c(o_2012, \"code\")\n", - "\n", - "# pandas.concat used to bind per period datasets\n", - "data_final = concat([o_2006_n, o_2008_n, o_2010_n, o_2012_n])\n", - "\n", - "sub_cols = [\"id\", \"edu\", \"code\", \"year\", \"index_c2c\",\n", - " \"g_new_c2c\", \"rep_c2c\", \"wei_naive_c2c\", \"wei_freq_c2c\"]\n", - "data_final.groupby([\"year\"]).sample(2).loc[:, sub_cols]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prune - prune_c2c" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pruning which could be useful after the mapping process, the custom prune_fun is provided by the end user.\n", - "The prune_fun is a function to process a 1D-array of weights (float) and return a 1D-array of boolean of the same length. The weighs will be reweighted automatically to still to sum to one per each original observation.\n", - "\n", - "- non-zero - lambda x: x > 0\n", - "- highest1 - lambda x: arange(len(x)) == argmax(x)\n", - "- highest - lambda x: x == max(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from cat2cat.cat2cat_utils import prune_c2c\n", - "from numpy import arange, argmax\n", - "\n", - "# prune_c2c\n", - "# highest1 leave only one observation with the highest probability for each orginal one\n", - "(o_2006_n.shape[0], \n", - " prune_c2c(o_2006_n, lambda x: arange(len(x)) == argmax(x)).shape[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Direct match" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is important to set the `id_var` argument as then we merging categories 1 to 1\n", - "for this identifier which exists in both periods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# split the panel by the time variale\n", - "vert_old = verticals.loc[verticals[\"v_date\"] == \"2020-04-01\", :]\n", - "vert_new = verticals.loc[verticals[\"v_date\"] == \"2020-05-01\", :]\n", - "\n", - "## extract mapping (transition) table from data using identifier\n", - "trans_v = vert_old.merge(vert_new, on = \"ean\", how = \"inner\")\\\n", - ".loc[:, [\"vertical_x\", \"vertical_y\"]]\\\n", - ".drop_duplicates()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# dataclasses, two core arguments for the cat2cat function\n", - "data = cat2cat_data(\n", - " old = vert_old, \n", - " new = vert_new, \n", - " id_var = \"ean\", \n", - " cat_var_old = \"vertical\", \n", - " cat_var_new = \"vertical\", \n", - " time_var = \"v_date\"\n", - ")\n", - "mappings = cat2cat_mappings(trans = trans_v, direction = \"backward\")\n", - "\n", - "# apply the cat2cat procedure\n", - "verts = cat2cat(\n", - " data = data,\n", - " mappings = mappings\n", - ")\n", - "\n", - "# pandas.concat used to bind per period datasets\n", - "data_final = concat([verts[\"old\"], verts[\"new\"]])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Direct match with ML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ml dataclass, one of the arguments for the cat2cat function\n", - "ml = cat2cat_ml(\n", - " data = vert_old, \n", - " cat_var = \"vertical\", \n", - " features = [\"sales\"], \n", - " models = [KNeighborsClassifier()]\n", - ")\n", - "\n", - "# apply the cat2cat procedure\n", - "verts_ml = cat2cat(\n", - " data = data,\n", - " mappings = mappings,\n", - " ml = ml\n", - ")\n", - "\n", - "# pandas.concat used to bind per period datasets\n", - "data_final = concat([verts_ml[\"old\"], verts_ml[\"new\"]])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 ('.venv': poetry)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "vscode": { - "interpreter": { - "hash": "4d03c0596c3b959c81b29ef0685c029c97abeebc025b647cb9a87b20e91929f4" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/example.md b/docs/example.md new file mode 100644 index 0000000..ad2bcf6 --- /dev/null +++ b/docs/example.md @@ -0,0 +1,245 @@ +# cat2cat + +## About + +Unifying an inconsistent 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).** + + +## Graph - cat2cat procedure + +The graphs present how the `cat2cat` function (and the underlying procedure) works, in this case under a panel dataset without the unique identifiers and only two periods. + +![Backward Mapping](https://raw.githubusercontent.com/Polkas/cat2cat/master/man/figures/back_nom.png) + +![Forward Mapping](https://raw.githubusercontent.com/Polkas/cat2cat/master/man/figures/for_nom.png) + + +## Example usage + +To use `cat2cat` in a project: + +### Load example data + +```python +# cat2cat datasets +from cat2cat.datasets import load_trans, load_occup, load_verticals +from numpy.random import seed + +seed(1234) + +trans = load_trans() +occup = load_occup() +verticals = load_verticals() +``` + +### 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 +print(mappings["to_new"]['3481']) +# probability mappings for a specific category +print(mapp_new_p['3481']) +``` + +### cat2cat procedure - one iteration + +```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, two 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"]]) + +sub_cols = ["id", "edu", "code", "year", "index_c2c", "g_new_c2c", "rep_c2c", "wei_naive_c2c", "wei_freq_c2c"] +data_final.groupby(["year"]).sample(5).loc[:, sub_cols] +``` + +### With ML + +```python +from sklearn.neighbors import KNeighborsClassifier + +# ml dataclass, one of the arguments for the cat2cat function +ml = cat2cat_ml( + data = o_new, + cat_var = "code", + features = ["salary", "age", "edu"], + models = [KNeighborsClassifier()] +) + +# apply the cat2cat procedure +c2c = cat2cat(data = data, mappings = mappings, ml = ml) +# pandas.concat used to bind per period datasets +data_final = concat([c2c["old"], c2c["new"]]) + +sub_cols = ["id", "year", "wei_naive_c2c", "wei_freq_c2c", "wei_KNeighborsClassifier_c2c"] +data_final.groupby(["year"]).sample(3).loc[:, sub_cols] +``` + +With 4 periods, one mapping table and backward direction: + +```python +from cat2cat.cat2cat_utils import dummy_c2c + +# split the panel by the time variale +# here four periods +o_2006 = occup.loc[occup.year == 2006, :].copy() +o_2008 = occup.loc[occup.year == 2008, :].copy() +o_2010 = occup.loc[occup.year == 2010, :].copy() +o_2012 = occup.loc[occup.year == 2012, :].copy() + +# dataclasses, two core arguments for the cat2cat function +data = cat2cat_data( + old = o_2008, + new = o_2010, + cat_var_old = "code", + cat_var_new = "code", + time_var = "year" +) +mappings = cat2cat_mappings(trans = trans, direction = "backward") + +# apply the cat2cat procedure +occup_back_2008_2010 = cat2cat(data = data, mappings = mappings) + +# updated for the next iteration data cat2cat argument +data = cat2cat_data( + old = o_2006, + new = occup_back_2008_2010["old"], + cat_var_old = "code", + cat_var_new = "g_new_c2c", + time_var = "year" +) + +# apply the cat2cat procedure +occup_back_2006_2008 = cat2cat(data = data, mappings = mappings) + +# gather the datasets for each period +o_2006_n = occup_back_2006_2008["old"] +o_2008_n = occup_back_2006_2008["new"] # or occup_back_2008_2010["old"] +o_2010_n = occup_back_2008_2010["new"] +o_2012_n = dummy_c2c(o_2012, "code") + +# pandas.concat used to bind per period datasets +data_final = concat([o_2006_n, o_2008_n, o_2010_n, o_2012_n]) + +sub_cols = ["id", "edu", "code", "year", "index_c2c", + "g_new_c2c", "rep_c2c", "wei_naive_c2c", "wei_freq_c2c"] +data_final.groupby(["year"]).sample(2).loc[:, sub_cols] +``` + +### Prune - prune_c2c + + +Pruning which could be useful after the mapping process, the custom prune_fun is provided by the end user. +The prune_fun is a function to process a 1D-array of weights (float) and return a 1D-array of boolean of the same length. The weighs will be reweighted automatically to still to sum to one per each original observation. + +- non-zero - lambda x: x > 0 +- highest1 - lambda x: arange(len(x)) == argmax(x) +- highest - lambda x: x == max(x) + +```python +from cat2cat.cat2cat_utils import prune_c2c +from numpy import arange, argmax + +# prune_c2c +# highest1 leave only one observation with the highest probability for each orginal one +(o_2006_n.shape[0], + prune_c2c(o_2006_n, lambda x: arange(len(x)) == argmax(x)).shape[0]) +``` + +### Direct match + + +It is important to set the `id_var` argument as then we merging categories 1 to 1 +for this identifier which exists in both periods. + +```python +# split the panel by the time variable +vert_old = verticals.loc[verticals["v_date"] == "2020-04-01", :] +vert_new = verticals.loc[verticals["v_date"] == "2020-05-01", :] + +## extract mapping (transition) table from data using identifier +trans_v = vert_old.merge(vert_new, on = "ean", how = "inner")\ +.loc[:, ["vertical_x", "vertical_y"]]\ +.drop_duplicates() +``` + +```python +# dataclasses, two core arguments for the cat2cat function +data = cat2cat_data( + old = vert_old, + new = vert_new, + id_var = "ean", + cat_var_old = "vertical", + cat_var_new = "vertical", + time_var = "v_date" +) +mappings = cat2cat_mappings(trans = trans_v, direction = "backward") + +# apply the cat2cat procedure +verts = cat2cat( + data = data, + mappings = mappings +) + +# pandas.concat used to bind per period datasets +data_final = concat([verts["old"], verts["new"]]) +``` + +### Direct match with ML + +```python +# ml dataclass, one of the arguments for the cat2cat function +ml = cat2cat_ml( + data = vert_old, + cat_var = "vertical", + features = ["sales"], + models = [KNeighborsClassifier()] +) + +# apply the cat2cat procedure +verts_ml = cat2cat( + data = data, + mappings = mappings, + ml = ml +) + +# pandas.concat used to bind per period datasets +data_final = concat([verts_ml["old"], verts_ml["new"]]) +``` + diff --git a/docs/index.md b/docs/index.md index c3a6f27..1fd0898 100755 --- a/docs/index.md +++ b/docs/index.md @@ -2,10 +2,12 @@ ``` ```{toctree} -:maxdepth: 1 +:maxdepth: 2 :hidden: -example.ipynb +example.md changelog.md autoapi/index +conduct.md +contributing.md ``` \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt index db98d20..0032b1e 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,7 +1,8 @@ -myst-nb==0.16.0 -sphinx-autoapi==1.9.0 -sphinx-rtd-theme==1.0.0 +sphinx +sphinx-autoapi +sphinx-rtd-theme pandas numpy -sklearn +myst-parser +scikit-learn importlib_resources \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 3ed0497..0f4f90a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,86 +1,78 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + [[package]] name = "alabaster" -version = "0.7.12" +version = "0.7.13" description = "A configurable sidebar-enabled Sphinx theme" -category = "dev" optional = false -python-versions = "*" +python-versions = ">=3.6" +files = [ + {file = "alabaster-0.7.13-py3-none-any.whl", hash = "sha256:1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3"}, + {file = "alabaster-0.7.13.tar.gz", hash = "sha256:a27a4a084d5e690e16e01e03ad2b2e552c61a65469419b907243193de1a84ae2"}, +] [[package]] -name = "appnope" -version = "0.1.3" -description = "Disable App Nap on macOS >= 10.9" -category = "dev" +name = "anyascii" +version = "0.3.2" +description = "Unicode to ASCII transliteration" optional = false -python-versions = "*" +python-versions = ">=3.3" +files = [ + {file = "anyascii-0.3.2-py3-none-any.whl", hash = "sha256:3b3beef6fc43d9036d3b0529050b0c48bfad8bc960e9e562d7223cfb94fe45d4"}, + {file = "anyascii-0.3.2.tar.gz", hash = "sha256:9d5d32ef844fe225b8bc7cba7f950534fae4da27a9bf3a6bea2cb0ea46ce4730"}, +] [[package]] name = "astroid" -version = "2.12.12" +version = "3.0.1" description = "An abstract syntax tree for Python with inference support." -category = "dev" -optional = false -python-versions = ">=3.7.2" - 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inconsistently coded categorical variable in a panel/longtitudal dataset." authors = ["Maciej Nasinski"] license = "MIT" @@ -10,27 +10,37 @@ repository = "https://github.com/polkas/py-cat2cat" documentation = "https://py-cat2cat.readthedocs.io/en/latest/" keywords = ["panel", "categorical", "longtitudal", "inconsistent", "cat2cat"] include = ["CHANGELOG.md"] +classifiers = [ +"Development Status :: 3 - Alpha", +"Programming Language :: Python", +"Programming Language :: Python :: 3", +"Programming Language :: Python :: 3.8", +"Programming Language :: Python :: 3.9", +"Programming Language :: Python :: 3.10", +"Programming Language :: Python :: 3.11", +"Programming Language :: Python :: Implementation :: PyPy", +] [build-system] requires = ["poetry_core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry.dependencies] -python = ">=3.8,<3.11" +python = ">=3.8,<3.12" numpy = "^1.23.1" pandas = "^1.4.3" scikit-learn = "^1.1.2" importlib-resources = "^5.9.0" 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