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| MIT License | ||
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| Copyright (c) [2024] [Jakob Gabriel] | ||
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| 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: | ||
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| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
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| 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. |
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| # timeseries-shaper pypi | ||
| # timeseries-shaper | ||
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| Timeseries-Shaper is a Python library for efficiently filtering and preprocessing time series data using pandas. It provides a set of tools to handle various data transformations, making data preparation tasks easier and more intuitive. | ||
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| ## Features | ||
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| - **Filter Missing Values**: Quickly filter out or fill missing values in your time series data. | ||
| - **Boolean Filters**: Apply boolean logic to filter data based on specific conditions. | ||
| - **Integer and Double Filters**: Perform numeric operations and filters specific to integer and double data types. | ||
| - **String Filters**: Manipulate and filter data based on string operations. | ||
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| ## Installation | ||
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| Install timeseries-shaper using pip: | ||
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| ```bash | ||
| pip install timeseries-shaper | ||
| ``` | ||
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| ## Useage | ||
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| Here is a quick example to get you started: | ||
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| ```python | ||
| import pandas as pd | ||
| from timeseries_shaper.filters import IntegerFilter, StringFilter | ||
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| # Sample DataFrame | ||
| data = { | ||
| 'value_integer': [1, 2, None, 4, 5], | ||
| 'value_string': ['apple', 'banana', None, 'cherry', 'date'] | ||
| } | ||
| df = pd.DataFrame(data) | ||
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| # Initialize the filter object | ||
| integer_filter = IntegerFilter(df) | ||
| string_filter = StringFilter(df) | ||
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| # Apply filters | ||
| filtered_integers = integer_filter.filter_value_integer_not_match(2) | ||
| filtered_strings = string_filter.filter_value_string_not_match('banana') | ||
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| print(filtered_integers) | ||
| print(filtered_strings) | ||
| ``` | ||
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| ## Documentation | ||
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| For full documentation, visit GitHub Pages or check out the docstrings in the code. | ||
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| ## Contributing | ||
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| Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change. | ||
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| Please ensure to update tests as appropriate. | ||
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| ## License | ||
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| Distributed under the MIT License. See LICENSE for more information. | ||
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| ## Development | ||
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| - Generate new pdocs: `.\generate_docs.sh` | ||
| - Install package locally: `pip install -e .` | ||
| - Run tests locally with pytest: `pytest /tests` | ||
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| - Build package for upload: `python setup.py sdist bdist_wheel` | ||
| - Upload build package to pypi: `twine upload dist/* --verbose --skip-existing` |
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