Repository: loskost/pandas-categorical (language: python)

Issue:
Setup for publish



Discussion / hints:

Fix patch:
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000..e5266c5
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,37 @@
+name: Publish
+
+on:
+  release:
+    types:
+      - created
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.8.x
+      - name: Upgrade pip
+        run: pip install --upgrade --user pip
+      - name: Install Poetry
+        run: pip install poetry
+      - name: Cache virtual environment
+        uses: actions/cache@v2
+        with:
+          path: .venv
+          key: ${{ runner.os }}-python-${{ matrix.python-version }}-venv-${{ hashFiles('**/poetry.lock') }}
+      - name: Install dependencies
+        run: poetry install --no-interaction
+      - name: Build and validate
+        run: |
+          poetry build
+          poetry run pip install --quiet --disable-pip-version-check "twine>=1.12"
+          poetry run twine check --strict dist/*
+      - name: Publish
+        env:
+          # https://python-poetry.org/docs/repositories/#configuring-credentials
+          POETRY_PYPI_TOKEN_PYPI: ${{ secrets.POETRY_PYPI_TOKEN_PYPI }}
+        run: |
+          poetry publish
diff --git a/.gitignore b/.gitignore
index 8001e88..79f0987 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,8 @@
 .venv
 .*cache
 __pycache__
+*.parquet
+.DS_Store
+build/
+dist/
+.python-version
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..f205ebb
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Konstantin Losev (loskost) <loskost@ya.ru>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index e69de29..9c48148 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1,189 @@
+[![Conventional Commits](https://img.shields.io/badge/Conventional%20Commits-1.0.0-yellow.svg)](https://conventionalcommits.org)
+
+The package contains just a few features that make using pandas categorical types easier to use.
+The main purpose of using categorical types is to reduce RAM consumption when working with large datasets. Experience shows that on average, there is a decrease of 2 times (for datasets of several GB, this is very significant). The full justification of the reasons and examples will be given below.
+# Quickstart
+
+```
+pip install pandas-categorical
+```
+
+```python
+import pandas as pd
+import pandas-categorical as pdc
+```
+
+```python
+df.astype('category')     ->     pdc.cat_astype(df, ...)
+pd.concat()               ->     pdc.concat_categorical()
+pd.merge()                ->     pdc.merge_categorical()
+df.groupby(...)           ->     df.groupby(..., observed=True)
+```
+## cat_astype
+
+```python
+df = pd.read_csv("path_to_dataframe.csv")
+
+SUB_DTYPES = {
+	'cat_col_with_int_values': int,
+	'cat_col_with_string_values': 'string',
+	'ordered_cat_col_with_bool_values': bool,
+}
+pdc.cat_astype(
+	data=df,
+	cat_cols=SUB_DTYPES.keys(),
+	sub_dtypes=SUB_DTYPES,
+	ordered_cols=['ordered_cat_col_with_bool_values']
+)
+```
+
+## concat_categorical
+
+```python
+df_1 = ...  # dataset with some categorical columns
+df_2 = ...  # dataset with some categorical columns (categories are not equals)
+
+df_res = pdc.concat_categorical((df_1, df_2), axis=0, ignore_index=True)
+```
+
+## merge_categorical
+
+```python
+df_1 = ...  # dataset with some categorical columns
+df_2 = ...  # dataset with some categorical columns (categories are not equals)
+
+df_res = pdc.merge_categorical(df_1, df_2, on=['cat_col_1', 'cat_col_2'])
+```
+
+# A bit of theory
+
+The advantages are discussed in detail in the articles [here](https://towardsdatascience.com/staying-sane-while-adopting-pandas-categorical-datatypes-78dbd19dcd8a), [here](https://towardsdatascience.com/pandas-groupby-aggregate-transform-filter-c95ba3444bbb) and [here](https://pandas.pydata.org/docs/user_guide/categorical.html).
+
+The categorical type implies the presence of a certain set of unique values in this column, which are often repeated. By reducing the copying of identical values, it is possible to reduce the size of the column (the larger the dataset, the more likely repetitions are). By default, categories (unique values) have no order. That is, they are not comparable to each other. It is possible to make them ordered.
+
+Pandas already has everything for this (for example, `.astype(’category’)`). However, standard methods, in my opinion, require a high entry threshold and therefore are rarely used.
+
+Let's try to outline a number of problems and ways to solve them.
+
+## 1. Categorical types are easy to lose
+
+Suppose you want to connect two datasets into one using `pd.concat(..., axis=0)'. Datasets contain columns with categorical types.
+If the column categories of the source datasets are different, then `pandas` it does not combine multiple values, but simply resets their default type (for example, `object`, `int`, ...).
+In other words,
+$$\textcolor{red}{category1} + \textcolor{green}{category2} = object$$
+$$\textcolor{red}{category1} + \textcolor{red}{category1} = \textcolor{red}{category1}$$
+But we would like to observe a different behavior:
+$$\textcolor{red}{category1} + \textcolor{green}{category2} = \textcolor{blue}{category3}$$
+$$(\textcolor{blue}{category3} = \textcolor{red}{category1} \cup \textcolor{green}{category2})$$
+As a result, you need to monitor the reduction of categories before actions such as `merge` or `concat'.
+## 2.  Categories type control
+
+When you do a type conversion
+```python
+df['col_name'] = df['col_name'].astype('category')
+```
+the type of categories is equal to the type of the source column.
+But, if you want to change the type of categories, you probably want to write something like
+```python
+df['col_name'] = df['col_name'].astype('some_new_type').astype('category')
+
+```
+That is, you will temporarily lose the categorical type (respectively, and the advantages of memory).
+By the way, the usual way of control
+```python
+df.dtypes
+```
+does not display information about the type of categories themselves. You will only see only `category` next to the desired column.
+
+## 3. Unused values
+
+Suppose you have filtered the dataset. At the same time, the actual set of values of categorical columns could decrease, but the data type will not be changed.
+This can negatively affect, for example, when working with `groupby` on such a column. As a result, grouping will also occur by unused categories. To prevent this from happening, you need to specify the `observed=True` parameter.
+For example,
+```python
+df.groupby(['cat_col_1', 'cat_col_2'], observed=True).agg('mean')
+```
+
+## 4. Ordered categories
+
+There is no simple method to create ordered categorical columns.
+In addition to the name, you need to explicitly specify the categories themselves in the right order.
+To simplify, it is suggested to always use the same sort order.
+For example,
+```python
+df['ordered_col'] = pd.Categorical(
+	df['ordered_col'],
+	categories=np.sort(data['ordered_col'].dropna().unique()),
+	ordered=True,
+)
+```
+Note: Categories cannot contain `NaN` values, so `.dropna()` is required.
+
+## 5. Minimum copying
+
+To process large datasets, you need to minimize the creation of copies of even its parts. Therefore, the functions from this package do the transformations in place.
+
+
+## 6. Data storage in parquet format
+
+When using `pd.to_parquet(path, engine='pyarrow')` and `pd.read_parque(path, engine='pyarrow')`categorical types of some columns can be reset to normal. To solve this problem, you can use `engine='fast parquet'. 
+
+Note 1: `fastparquet` usually runs a little slower than `pyarrow'.
+
+Note 2: `pyarrow` and `fastparquet` cannot be used together (for example, save by one and read by the other). This can lead to data loss.
+
+```python
+import pandas as pd
+
+
+df = pd.DataFrame(
+	{
+		"Date": pd.date_range('2023-01-01', periods=10),
+		"Object": ["a"]*5+["b"]+["c"]*4,
+		"Int": [1, 1, 1, 2, 3, 1, 2, 4, 3, 2],
+		"Float": [1.1]*5+[2.2]*5,
+	}
+)
+
+print(df.dtypes)
+df = df.astype('category')
+print(df.dtypes)
+df.to_parquet('test.parquet', engine='pyarrow')
+df = pd.read_parquet('test.parquet', engine='pyarrow')
+print(df.dtypes)
+```
+Output:
+```
+Date      datetime64[ns]
+Object    object
+Int       int64
+Float     float64
+dtype: object
+
+Date      category
+Object    category
+Int       category
+Float     category
+dtype: object
+
+Date      datetime64[ns]
+Object    category
+Int       int64
+Float     float64
+dtype: object
+```
+
+# Examples
+
+[Jupiter notebook with examples](https://www.kaggle.com/code/loskost/problems-of-pandas-categorical-dtypes) of problems is posted on kaggle. A copy can be found in the `examples/` folder.
+# Remarks
+
+1. Processing of categorical indexes has not yet been implemented.
+2. In the future, the function `pdc.join_categorical()` will appear.
+3. The `cat_astype` function was designed so that the type information could be redundant (for example, it is specified for all possible column names in the project at once). In the future, it will be possible to set default values for this function.
+# Links
+
+1. [Official pandas documentation](https://pandas.pydata.org/docs/user_guide/categorical.html).
+2. https://towardsdatascience.com/staying-sane-while-adopting-pandas-categorical-datatypes-78dbd19dcd8a
+3. https://towardsdatascience.com/pandas-groupby-aggregate-transform-filter-c95ba3444bbb
+4. [The source of the idea](https://stackoverflow.com/questions/45639350/retaining-categorical-dtype-upon-dataframe-concatenation) that I wanted to develop.
diff --git a/examples/Problems of pandas categorical dtypes.ipynb b/examples/Problems of pandas categorical dtypes.ipynb
new file mode 100644
index 0000000..c659700
--- /dev/null
+++ b/examples/Problems of pandas categorical dtypes.ipynb
@@ -0,0 +1,1 @@
+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-11-06T08:12:17.110913Z","iopub.execute_input":"2023-11-06T08:12:17.111349Z","iopub.status.idle":"2023-11-06T08:12:17.533047Z","shell.execute_reply.started":"2023-11-06T08:12:17.111316Z","shell.execute_reply":"2023-11-06T08:12:17.531792Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# read dataframe","metadata":{}},{"cell_type":"code","source":"# optimal column types\nSUB_DTYPES = {\n    'Invoice/Item Number': \"string\",\n    'Date': 'datetime64[ns]',\n    'Store Number': \"uint16\",\n    'Store Name': \"string\",\n    'Address': 'string',\n    'City': \"string\",\n    'Zip Code': 'string',\n    'Store Location': 'string',\n    'County Number': \"Int16\",\n    'County': \"string\",\n    'Category': \"Int32\",\n    'Category Name': 'string',\n    'Vendor Number': \"Int16\",\n    'Vendor Name': 'string',\n    'Item Number': \"string\",\n    'Item Description': \"string\",\n    'Pack': \"uint16\",\n    'Bottle Volume (ml)': \"uint32\",\n    'State Bottle Cost': float,\n    'State Bottle Retail': float,\n    'Bottles Sold': int,\n    'Sale (Dollars)': float,\n    'Volume Sold (Liters)': float,\n    'Volume Sold (Gallons)': float,\n}","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:12:17.535222Z","iopub.execute_input":"2023-11-06T08:12:17.536122Z","iopub.status.idle":"2023-11-06T08:12:17.544675Z","shell.execute_reply.started":"2023-11-06T08:12:17.536081Z","shell.execute_reply":"2023-11-06T08:12:17.542226Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv(\n    \"/kaggle/input/big-sales-data/Sales_Data/Liquor_Sales.csv\",\n    engine='pyarrow',\n    dtype=SUB_DTYPES\n)","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:12:17.548354Z","iopub.execute_input":"2023-11-06T08:12:17.548834Z","iopub.status.idle":"2023-11-06T08:14:17.339883Z","shell.execute_reply.started":"2023-11-06T08:12:17.548793Z","shell.execute_reply":"2023-11-06T08:14:17.332817Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# Let's put a small part of the data aside\n(_, df), (_, df_1) = df.groupby(df.index < 1000)","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:14:17.346609Z","iopub.execute_input":"2023-11-06T08:14:17.347835Z","iopub.status.idle":"2023-11-06T08:14:30.334435Z","shell.execute_reply.started":"2023-11-06T08:14:17.347778Z","shell.execute_reply":"2023-11-06T08:14:30.332427Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Compression by categorization","metadata":{}},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:14:30.336575Z","iopub.execute_input":"2023-11-06T08:14:30.337069Z","iopub.status.idle":"2023-11-06T08:14:30.367843Z","shell.execute_reply.started":"2023-11-06T08:14:30.337024Z","shell.execute_reply":"2023-11-06T08:14:30.365524Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 19665763 entries, 1000 to 19666762\nData columns (total 24 columns):\n #   Column                 Dtype         \n---  ------                 -----         \n 0   Invoice/Item Number    string        \n 1   Date                   datetime64[ns]\n 2   Store Number           uint16        \n 3   Store Name             string        \n 4   Address                string        \n 5   City                   string        \n 6   Zip Code               string        \n 7   Store Location         string        \n 8   County Number          Int16         \n 9   County                 string        \n 10  Category               Int32         \n 11  Category Name          string        \n 12  Vendor Number          Int16         \n 13  Vendor Name            string        \n 14  Item Number            string        \n 15  Item Description       string        \n 16  Pack                   uint16        \n 17  Bottle Volume (ml)     uint32        \n 18  State Bottle Cost      float64       \n 19  State Bottle Retail    float64       \n 20  Bottles Sold           int64         \n 21  Sale (Dollars)         float64       \n 22  Volume Sold (Liters)   float64       \n 23  Volume Sold (Gallons)  float64       \ndtypes: Int16(2), Int32(1), datetime64[ns](1), float64(5), int64(1), string(11), uint16(2), uint32(1)\nmemory usage: 3.1 GB\n","output_type":"stream"}]},{"cell_type":"code","source":"CAT_COLS = [\n    'Date',\n    'Store Number',\n    'Store Name',\n    'Address',\n    'City',\n    'Zip Code',\n    'Store Location',\n    'County Number',\n    'County',\n    'Category',\n    'Category Name',\n    'Vendor Number',\n    'Vendor Name',\n    'Item Number',\n    'Item Description',\n    'Pack',\n    'Bottle Volume (ml)',\n]\n\nfor cat_col in CAT_COLS:\n    print(cat_col)\n    df[cat_col] = df[cat_col].astype(\"category\")","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:14:30.369104Z","iopub.execute_input":"2023-11-06T08:14:30.371714Z","iopub.status.idle":"2023-11-06T08:14:55.690888Z","shell.execute_reply.started":"2023-11-06T08:14:30.371558Z","shell.execute_reply":"2023-11-06T08:14:55.688995Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Date\nStore Number\nStore Name\nAddress\nCity\nZip Code\nStore Location\nCounty Number\nCounty\nCategory\nCategory Name\nVendor Number\nVendor Name\nItem Number\nItem Description\nPack\nBottle Volume (ml)\n","output_type":"stream"}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:14:55.693394Z","iopub.execute_input":"2023-11-06T08:14:55.695072Z","iopub.status.idle":"2023-11-06T08:14:55.727728Z","shell.execute_reply.started":"2023-11-06T08:14:55.694988Z","shell.execute_reply":"2023-11-06T08:14:55.726693Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 19665763 entries, 1000 to 19666762\nData columns (total 24 columns):\n #   Column                 Dtype   \n---  ------                 -----   \n 0   Invoice/Item Number    string  \n 1   Date                   category\n 2   Store Number           category\n 3   Store Name             category\n 4   Address                category\n 5   City                   category\n 6   Zip Code               category\n 7   Store Location         category\n 8   County Number          category\n 9   County                 category\n 10  Category               category\n 11  Category Name          category\n 12  Vendor Number          category\n 13  Vendor Name            category\n 14  Item Number            category\n 15  Item Description       category\n 16  Pack                   category\n 17  Bottle Volume (ml)     category\n 18  State Bottle Cost      float64 \n 19  State Bottle Retail    float64 \n 20  Bottles Sold           int64   \n 21  Sale (Dollars)         float64 \n 22  Volume Sold (Liters)   float64 \n 23  Volume Sold (Gallons)  float64 \ndtypes: category(17), float64(5), int64(1), string(1)\nmemory usage: 1.7 GB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Loss of categorical types in concat","metadata":{}},{"cell_type":"code","source":"df = pd.concat([df, df_1], ignore_index=True)","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:14:55.729261Z","iopub.execute_input":"2023-11-06T08:14:55.729907Z","iopub.status.idle":"2023-11-06T08:15:20.097158Z","shell.execute_reply.started":"2023-11-06T08:14:55.729876Z","shell.execute_reply":"2023-11-06T08:15:20.095899Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:15:20.098600Z","iopub.execute_input":"2023-11-06T08:15:20.098914Z","iopub.status.idle":"2023-11-06T08:15:20.115181Z","shell.execute_reply.started":"2023-11-06T08:15:20.098888Z","shell.execute_reply":"2023-11-06T08:15:20.113396Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 19666763 entries, 0 to 19666762\nData columns (total 24 columns):\n #   Column                 Dtype  \n---  ------                 -----  \n 0   Invoice/Item Number    string \n 1   Date                   object \n 2   Store Number           uint16 \n 3   Store Name             string \n 4   Address                string \n 5   City                   string \n 6   Zip Code               string \n 7   Store Location         string \n 8   County Number          Int16  \n 9   County                 string \n 10  Category               Int32  \n 11  Category Name          string \n 12  Vendor Number          Int16  \n 13  Vendor Name            string \n 14  Item Number            string \n 15  Item Description       string \n 16  Pack                   uint16 \n 17  Bottle Volume (ml)     uint32 \n 18  State Bottle Cost      float64\n 19  State Bottle Retail    float64\n 20  Bottles Sold           int64  \n 21  Sale (Dollars)         float64\n 22  Volume Sold (Liters)   float64\n 23  Volume Sold (Gallons)  float64\ndtypes: Int16(2), Int32(1), float64(5), int64(1), object(1), string(11), uint16(2), uint32(1)\nmemory usage: 3.0+ GB\n","output_type":"stream"}]},{"cell_type":"code","source":"for cat_col in CAT_COLS:\n    print(cat_col)\n    df[cat_col] = df[cat_col].astype(\"category\")","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:15:20.118821Z","iopub.execute_input":"2023-11-06T08:15:20.120870Z","iopub.status.idle":"2023-11-06T08:16:08.115295Z","shell.execute_reply.started":"2023-11-06T08:15:20.120775Z","shell.execute_reply":"2023-11-06T08:16:08.113853Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Date\nStore Number\nStore Name\nAddress\nCity\nZip Code\nStore Location\nCounty Number\nCounty\nCategory\nCategory Name\nVendor Number\nVendor Name\nItem Number\nItem Description\nPack\nBottle Volume (ml)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Loss of categorical types in merge","metadata":{}},{"cell_type":"code","source":"another_df = pd.read_csv(\"/kaggle/input/big-sales-data/Sales_Data/Sales_April_2019.csv\",\n                         usecols=['Order Date', 'Order ID'])\nanother_df = another_df[another_df['Order Date'] != \"Order Date\"]\nanother_df['Order Date'] = another_df['Order Date'].astype('datetime64[ns]').dt.normalize().astype('category')","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:16:08.116905Z","iopub.execute_input":"2023-11-06T08:16:08.117310Z","iopub.status.idle":"2023-11-06T08:16:09.463434Z","shell.execute_reply.started":"2023-11-06T08:16:08.117272Z","shell.execute_reply":"2023-11-06T08:16:09.461078Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_723/3700470845.py:4: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n  another_df['Order Date'] = another_df['Order Date'].astype('datetime64[ns]').dt.normalize().astype('category')\n","output_type":"stream"}]},{"cell_type":"code","source":"another_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:16:09.465471Z","iopub.execute_input":"2023-11-06T08:16:09.465834Z","iopub.status.idle":"2023-11-06T08:16:09.477815Z","shell.execute_reply.started":"2023-11-06T08:16:09.465805Z","shell.execute_reply":"2023-11-06T08:16:09.477008Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 18348 entries, 0 to 18382\nData columns (total 2 columns):\n #   Column      Non-Null Count  Dtype   \n---  ------      --------------  -----   \n 0   Order ID    18289 non-null  object  \n 1   Order Date  18289 non-null  category\ndtypes: category(1), object(1)\nmemory usage: 305.9+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"res = df.merge(another_df.head(1000), left_on='Date', right_on='Order Date', how='left')","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:18:46.955399Z","iopub.execute_input":"2023-11-06T08:18:46.955924Z","iopub.status.idle":"2023-11-06T08:19:56.050158Z","shell.execute_reply.started":"2023-11-06T08:18:46.955885Z","shell.execute_reply":"2023-11-06T08:19:56.048557Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"# categorical type of the Date column is lost\nres.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-11-06T08:19:56.532217Z","iopub.execute_input":"2023-11-06T08:19:56.532987Z","iopub.status.idle":"2023-11-06T08:19:56.541719Z","shell.execute_reply.started":"2023-11-06T08:19:56.532952Z","shell.execute_reply":"2023-11-06T08:19:56.540388Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"Invoice/Item Number      string[python]\nDate                     datetime64[ns]\nStore Number                   category\nStore Name                     category\nAddress                        category\nCity                           category\nZip Code                       category\nStore Location                 category\nCounty Number                  category\nCounty                         category\nCategory                       category\nCategory Name                  category\nVendor Number                  category\nVendor Name                    category\nItem Number                    category\nItem Description               category\nPack                           category\nBottle Volume (ml)             category\nState Bottle Cost               float64\nState Bottle Retail             float64\nBottles Sold                      int64\nSale (Dollars)                  float64\nVolume Sold (Liters)            float64\nVolume Sold (Gallons)           float64\nOrder ID                         object\nOrder Date                     category\ndtype: object"},"metadata":{}}]}]}
diff --git a/pandas_categorical/__init__.py b/pandas_categorical/__init__.py
index e69de29..b89f70b 100644
--- a/pandas_categorical/__init__.py
+++ b/pandas_categorical/__init__.py
@@ -0,0 +1,1 @@
+from pandas_categorical.utils import merge_categorical, concat_categorical, cat_astype
diff --git a/pandas_categorical/py.typed b/pandas_categorical/py.typed
new file mode 100644
index 0000000..e69de29
diff --git a/pandas_categorical/utils.py b/pandas_categorical/utils.py
index c1b33f1..c784c97 100644
--- a/pandas_categorical/utils.py
+++ b/pandas_categorical/utils.py
@@ -1,10 +1,11 @@
-import pandas as pd
+from typing import Mapping, Union, List, Optional, Iterable, Sequence
+
 import numpy as np
-from collections.abc import Iterable, Sequence
+import pandas as pd
 import pandas.core.common as com
 
 
-def concat_categorical(dfs: list[pd.DataFrame], *args, **kwargs) -> pd.DataFrame:
+def concat_categorical(dfs: Iterable[pd.DataFrame], *args, **kwargs) -> pd.DataFrame:
     """
     Concatenate while preserving categorical columns.
 
@@ -56,9 +57,9 @@ def merge_categorical(
 
 def cat_astype(
     data: pd.DataFrame,
-    cat_cols: Iterable[str] | None = None,
-    sub_dtypes: dict[str, type | str] | None = None,
-    ordered_cols: set[str] | None = None,
+    cat_cols: Optional[Iterable[str]] = None,
+    sub_dtypes: Optional[Mapping[str, Union[type, str]]] = None,
+    ordered_cols: Optional[Iterable[str]] = None,
     remove_unused_categories: bool = False,
 ) -> None:
     """
@@ -127,8 +128,8 @@ def _union_categories(dfs: Sequence[pd.DataFrame]) -> None:
 def _union_left_right_categories(
     left: pd.DataFrame,
     right: pd.DataFrame,
-    left_on: list[str],
-    right_on: list[str],
+    left_on: List[str],
+    right_on: List[str],
 ) -> None:
 
     left_cat_on = left.select_dtypes(include="category").columns.intersection(left_on)
diff --git a/poetry.lock b/poetry.lock
index 003a3df..0b2ade3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,254 +1,274 @@
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+    {file = "pandas-2.1.1.tar.gz", hash = "sha256:fecb198dc389429be557cde50a2d46da8434a17fe37d7d41ff102e3987fd947b"},
 ]
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.21.0", markers = "python_version >= \"3.10\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
 tzdata = ">=2022.1"
 
 [package.extras]
-all = ["PyQt5 (>=5.15.1)", "SQLAlchemy (>=1.4.16)", "beautifulsoup4 (>=4.9.3)", "bottleneck (>=1.3.2)", "brotlipy (>=0.7.0)", "fastparquet (>=0.6.3)", "fsspec (>=2021.07.0)", "gcsfs (>=2021.07.0)", "html5lib (>=1.1)", "hypothesis (>=6.34.2)", "jinja2 (>=3.0.0)", "lxml (>=4.6.3)", "matplotlib (>=3.6.1)", "numba (>=0.53.1)", "numexpr (>=2.7.3)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pandas-gbq (>=0.15.0)", "psycopg2 (>=2.8.6)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)", "python-snappy (>=0.6.0)", "pyxlsb (>=1.0.8)", "qtpy (>=2.2.0)", "s3fs (>=2021.08.0)", "scipy (>=1.7.1)", "tables (>=3.6.1)", "tabulate (>=0.8.9)", "xarray (>=0.21.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)", "zstandard (>=0.15.2)"]
-aws = ["s3fs (>=2021.08.0)"]
-clipboard = ["PyQt5 (>=5.15.1)", "qtpy (>=2.2.0)"]
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+aws = ["s3fs (>=2022.05.0)"]
+clipboard = ["PyQt5 (>=5.15.6)", "qtpy (>=2.2.0)"]
+compression = ["zstandard (>=0.17.0)"]
+computation = ["scipy (>=1.8.1)", "xarray (>=2022.03.0)"]
+consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
+excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pyxlsb (>=1.0.9)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)"]
 feather = ["pyarrow (>=7.0.0)"]
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-gcp = ["gcsfs (>=2021.07.0)", "pandas-gbq (>=0.15.0)"]
-hdf5 = ["tables (>=3.6.1)"]
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-mysql = ["SQLAlchemy (>=1.4.16)", "pymysql (>=1.0.2)"]
-output-formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"]
+fss = ["fsspec (>=2022.05.0)"]
+gcp = ["gcsfs (>=2022.05.0)", "pandas-gbq (>=0.17.5)"]
+hdf5 = ["tables (>=3.7.0)"]
+html = ["beautifulsoup4 (>=4.11.1)", "html5lib (>=1.1)", "lxml (>=4.8.0)"]
+mysql = ["SQLAlchemy (>=1.4.36)", "pymysql (>=1.0.2)"]
+output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.8.10)"]
 parquet = ["pyarrow (>=7.0.0)"]
-performance = ["bottleneck (>=1.3.2)", "numba (>=0.53.1)", "numexpr (>=2.7.1)"]
+performance = ["bottleneck (>=1.3.4)", "numba (>=0.55.2)", "numexpr (>=2.8.0)"]
 plot = ["matplotlib (>=3.6.1)"]
-postgresql = ["SQLAlchemy (>=1.4.16)", "psycopg2 (>=2.8.6)"]
-spss = ["pyreadstat (>=1.1.2)"]
-sql-other = ["SQLAlchemy (>=1.4.16)"]
-test = ["hypothesis (>=6.34.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
-xml = ["lxml (>=4.6.3)"]
-
-[[package]]
-name = "pathspec"
-version = "0.11.2"
-description = "Utility library for gitignore style pattern matching of file paths."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "pathspec-0.11.2-py3-none-any.whl", hash = "sha256:1d6ed233af05e679efb96b1851550ea95bbb64b7c490b0f5aa52996c11e92a20"},
-    {file = "pathspec-0.11.2.tar.gz", hash = "sha256:e0d8d0ac2f12da61956eb2306b69f9469b42f4deb0f3cb6ed47b9cce9996ced3"},
-]
-
-[[package]]
-name = "platformdirs"
-version = "3.10.0"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "platformdirs-3.10.0-py3-none-any.whl", hash = "sha256:d7c24979f292f916dc9cbf8648319032f551ea8c49a4c9bf2fb556a02070ec1d"},
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-[package.extras]
-docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
-
-[[package]]
-name = "pyright"
-version = "1.1.324"
-description = "Command line wrapper for pyright"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "pyright-1.1.324-py3-none-any.whl", hash = "sha256:0edb712afbbad474e347de12ca1bd9368aa85d3365a1c7b795012e48e6a65111"},
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-]
-
-[package.dependencies]
-nodeenv = ">=1.6.0"
-
-[package.extras]
-all = ["twine (>=3.4.1)"]
-dev = ["twine (>=3.4.1)"]
+postgresql = ["SQLAlchemy (>=1.4.36)", "psycopg2 (>=2.9.3)"]
+spss = ["pyreadstat (>=1.1.5)"]
+sql-other = ["SQLAlchemy (>=1.4.36)"]
+test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
+xml = ["lxml (>=4.8.0)"]
 
 [[package]]
 name = "python-dateutil"
@@ -266,57 +286,15 @@ six = ">=1.5"
 
 [[package]]
 name = "pytz"
-version = "2023.3"
+version = "2023.3.post1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
-    {file = "pytz-2023.3-py2.py3-none-any.whl", hash = "sha256:a151b3abb88eda1d4e34a9814df37de2a80e301e68ba0fd856fb9b46bfbbbffb"},
-    {file = "pytz-2023.3.tar.gz", hash = "sha256:1d8ce29db189191fb55338ee6d0387d82ab59f3d00eac103412d64e0ebd0c588"},
-]
-
-[[package]]
-name = "ruff"
-version = "0.0.286"
-description = "An extremely fast Python linter, written in Rust."
-optional = false
-python-versions = ">=3.7"
-files = [
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+    {file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"},
+    {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
 ]
 
-[[package]]
-name = "setuptools"
-version = "68.1.2"
-description = "Easily download, build, install, upgrade, and uninstall Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
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-    {file = "setuptools-68.1.2.tar.gz", hash = "sha256:3d4dfa6d95f1b101d695a6160a7626e15583af71a5f52176efa5d39a054d475d"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5,<=7.1.2)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
-testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -328,17 +306,6 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
-[[package]]
-name = "tomli"
-version = "2.0.1"
-description = "A lil' TOML parser"
-optional = false
-python-versions = ">=3.7"
-files = [
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-    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
-]
-
 [[package]]
 name = "tzdata"
 version = "2023.3"
@@ -352,5 +319,5 @@ files = [
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.10"
-content-hash = "230ab516e4f7ec197f9328d57117229b847ebc05004f9943c323c2411dfd0c00"
+python-versions = ">=3.7, <3.12"
+content-hash = "69dc3ff15083a4783fa2b61e104604a8844cd6d86135fa1b827c770cd7d9054f"
diff --git a/poetry.toml b/poetry.toml
new file mode 100644
index 0000000..53b35d3
--- /dev/null
+++ b/poetry.toml
@@ -0,0 +1,3 @@
+[virtualenvs]
+create = true
+in-project = true
diff --git a/pyproject.toml b/pyproject.toml
index f8e0579..7760298 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,20 +1,45 @@
 [tool.poetry]
 name = "pandas-categorical"
-version = "0.1.0"
+version = "1.0.0"
 description = "A set of functions for working with categorical columns in pandas"
 authors = ["KonstantinLosev <loskost@ya.ru>"]
+license = "MIT"
+homepage = "https://github.com/loskost/pandas-categorical"
+repository = "https://github.com/loskost/pandas-categorical"
 readme = "README.md"
 packages = [{include = "pandas_categorical"}]
+keywords = [
+    "data",
+    "datascience",
+    "pandas"
+]
+classifiers = [
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Operating System :: OS Independent",
+    "Topic :: Utilities",
+    "License :: OSI Approved :: MIT License",
+]
 
 [tool.poetry.dependencies]
-python = "^3.10"
-pandas = "^2.0.3"
-numpy = "^1.25.2"
-
-[tool.poetry.group.dev.dependencies]
-ruff = "^0.0.286"
-black = "^23.7.0"
-pyright = "^1.1.324"
+python = ">=3.7, <3.12"
+pandas = [
+    {version = ">=1.0.0, <1.4.0", python = "~3.7"},
+    {version = "^1.4.0", python = "~3.8"},
+    {version = "^2.0.0", python = ">=3.9, <3.12"},
+]
+numpy = [
+    {version = ">=1.19.0, <1.22.0", python = "~3.7"},
+    {version = ">=1.22.0, <1.25.0", python = "~3.8"},
+    {version = ">=1.25.0", python = ">=3.9, <3.12"},
+]
 
 [build-system]
 requires = ["poetry-core"]


Test patch:
diff --git a/.github/workflows/testing_package.yml b/.github/workflows/testing_package.yml
new file mode 100644
index 0000000..96f8f1c
--- /dev/null
+++ b/.github/workflows/testing_package.yml
@@ -0,0 +1,38 @@
+name: testing-multiple-python-versions
+
+on:
+  push:
+    branches: [ "main", "development", "github_actions"]
+  pull_request:
+    branches: ["main", "development"]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8
+        python -m pip install poetry==1.5.1
+        poetry install --only main
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude .venv
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude .venv
+    - name: Test with unittest
+      run: |
+        poetry run python -m unittest
diff --git a/tests/test_utils.py b/tests/test_utils.py
index e0bd24e..adcc16f 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,12 +1,17 @@
-import unittest
-import pandas as pd
+from unittest import TestCase
+from typing import Tuple
+
 import numpy as np
+import pandas as pd
 from pandas.testing import assert_frame_equal, assert_series_equal
-
-from pandas_categorical.utils import concat_categorical, merge_categorical, cat_astype
+from pandas_categorical.utils import (
+    cat_astype,
+    concat_categorical,
+    merge_categorical
+)
 
 
-class TestConcatCategirical(unittest.TestCase):
+class TestConcatCategirical(TestCase):
     def test_output_type(self):
         df_1 = pd.DataFrame()
         df_2 = pd.DataFrame()
@@ -190,9 +195,9 @@ def test_categorical_ordered_columns(self):
         assert_frame_equal(res, answer)
 
 
-class TestMergeCategirical(unittest.TestCase):
+class TestMergeCategirical(TestCase):
     @staticmethod
-    def get_artificial_data() -> tuple[pd.DataFrame, pd.DataFrame]:
+    def get_artificial_data() -> Tuple[pd.DataFrame, pd.DataFrame]:
         df_1 = pd.DataFrame({"a": ["key-1", "key-2"], "b": [1, 2]})
         df_2 = pd.DataFrame({"a": ["key-3", "key-2"], "c": [3, 4]})
         return df_1, df_2
@@ -275,7 +280,7 @@ def test_categorical_columns_4(self):
         assert_frame_equal(res, answer)
 
 
-class TestCatAstype(unittest.TestCase):
+class TestCatAstype(TestCase):
     def test_no_return(self):
         self.assertIs(cat_astype(pd.DataFrame()), None)