%global _empty_manifest_terminate_build 0 Name: python-mydatapreprocessing Version: 3.0.3 Release: 1 Summary: Library/framework for making predictions. License: mit URL: https://github.com/Malachov/mydatapreprocessing Source0: https://mirrors.nju.edu.cn/pypi/web/packages/63/b5/e4b0d97599501bed7d4b2a8340cff59de3caf288326fa39e9df8f1172ace/mydatapreprocessing-3.0.3.tar.gz BuildArch: noarch Requires: python3-mylogging Requires: python3-mypythontools Requires: python3-numpy Requires: python3-pandas Requires: python3-requests Requires: python3-scipy Requires: python3-sklearn Requires: python3-typing-extensions Requires: python3-wfdb Requires: python3-openpyxl Requires: python3-pyarrow Requires: python3-pyodbc Requires: python3-sqlalchemy Requires: python3-tables Requires: python3-xlrd Requires: python3-wfdb Requires: python3-openpyxl Requires: python3-pyarrow Requires: python3-pyodbc Requires: python3-sqlalchemy Requires: python3-tables Requires: python3-xlrd %description # mydatapreprocessing [![Python versions](https://img.shields.io/pypi/pyversions/mydatapreprocessing.svg)](https://pypi.python.org/pypi/mydatapreprocessing/) [![PyPI version](https://badge.fury.io/py/mydatapreprocessing.svg)](https://badge.fury.io/py/mydatapreprocessing) [![Downloads](https://pepy.tech/badge/mydatapreprocessing)](https://pepy.tech/project/mydatapreprocessing) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Malachov/mydatapreprocessing/HEAD?filepath=demo.ipynb) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Malachov/mydatapreprocessing.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Malachov/mydatapreprocessing/context:python) [![Documentation Status](https://readthedocs.org/projects/mydatapreprocessing/badge/?version=latest)](https://mydatapreprocessing.readthedocs.io/?badge=latest) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![codecov](https://codecov.io/gh/Malachov/mydatapreprocessing/branch/master/graph/badge.svg)](https://codecov.io/gh/Malachov/mydatapreprocessing) Load data from web link or local file (json, csv, Excel file, parquet, h5...), consolidate it (resample data, clean NaN values, do string embedding) derive new features via columns derivation and do preprocessing like standardization or smoothing. If you want to see how functions works, check it's docstrings - working examples with printed results are also in tests - visual.py. ## Links [Repo on GitHub](https://github.com/Malachov/mydatapreprocessing) [Official readthedocs documentation](https://mydatapreprocessing.readthedocs.io) ## Installation Python >=3.6 (Python 2 is not supported). Install just with ```console pip install mydatapreprocessing ``` There are some libraries that not every user will be using (for some specific data inputs for example). If you want to be sure to have all libraries, you can provide extras requirements like. ```console pip install mydatapreprocessing[datatypes] ``` Available extras are ["all", "datasets", "datatypes"] ## Examples You can use live [jupyter demo on binder](https://mybinder.org/v2/gh/Malachov/mydatapreprocessing/HEAD?filepath=demo.ipynb) ```python import mydatapreprocessing as mdp import pandas as pd import numpy as np ``` ### Load data You can use: - python formats (numpy.ndarray, pd.DataFrame, list, tuple, dict) - local files - web urls Supported path formats are: - csv - xlsx and xls - json - parquet - h5 You can load more data at once in list. Syntax is always the same. ```python data = mdp.load_data.load_data( "https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv", ) # data2 = mdp.load_data.load_data([PATH_TO_FILE.csv, PATH_TO_FILE2.csv]) ``` ### Consolidation If you want to use data for some machine learning models, you will probably want to remove Nan values, convert string columns to numeric if possible, do encoding or keep only numeric data and resample. Consolidation is working with pandas DataFrame as column names matters here. There are many functions, but there is main function pipelining other functions `consolidate_data` ```python consolidation_config = mdp.consolidation.consolidation_config.default_consolidation_config.do.copy() consolidation_config.datetime.datetime_column = 'Date' consolidation_config.resample.resample = 'M' consolidation_config.resample.resample_function = "mean" consolidation_config.dtype = 'float32' consolidated = mdp.consolidation.consolidate_data(data, consolidation_config) print(consolidated.head()) ``` ### Feature engineering Functions in `feature_engineering` and `preprocessing` expects that data are in form (*n_samples*, *n_features*). *n_samples* are usually much bigger and therefore transformed in `consolidate_data` if necessary. In config, you can use shorter update dict syntax as all values names are unique. ### Feature engineering Create new columns that can be for example used as another machine learning model input. ```python import mydatapreprocessing.feature_engineering as mdpf import mydatapreprocessing as mdp data = pd.DataFrame( [mdp.datasets.sin(n=30), mdp.datasets.ramp(n=30)] ).T extended = mdpf.add_derived_columns(data, differences=True, rolling_means=10) print(extended.columns) print(f"\nit has less rows then on input {len(extended)}") ``` Functions in `feature_engineering` and `preprocessing` expects that data are in form (n_samples, n_features). n_samples are usually much bigger and therefore transformed in `consolidate_data` if necessary. ### Preprocessing Preprocessing can be used on pandas DataFrame as well as on numpy array. Column names are not important as it's just matrix with defined dtype. There is many functions, but there is main function pipelining other functions `preprocess_data` Preprocessed data can be converted back with `preprocess_data_inverse` ```python from mydatapreprocessing import preprocessing as mdpp df = pd.DataFrame(np.array([range(5), range(20, 25), np.random.randn(5)]).astype("float32").T) df.iloc[2, 0] = 500 config = mdpp.preprocessing_config.default_preprocessing_config.do.copy() config.do.update({"remove_outliers": None, "difference_transform": True, "standardize": "standardize"}) data_preprocessed, inverse_config = mdpp.preprocess_data(df.values, config) inverse_config.difference_transform = df.iloc[0, 0] data_preprocessed_inverse = mdpp.preprocess_data_inverse( data_preprocessed[:, 0], inverse_config ) ``` %package -n python3-mydatapreprocessing Summary: Library/framework for making predictions. Provides: python-mydatapreprocessing BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-mydatapreprocessing # mydatapreprocessing [![Python versions](https://img.shields.io/pypi/pyversions/mydatapreprocessing.svg)](https://pypi.python.org/pypi/mydatapreprocessing/) [![PyPI version](https://badge.fury.io/py/mydatapreprocessing.svg)](https://badge.fury.io/py/mydatapreprocessing) [![Downloads](https://pepy.tech/badge/mydatapreprocessing)](https://pepy.tech/project/mydatapreprocessing) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Malachov/mydatapreprocessing/HEAD?filepath=demo.ipynb) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Malachov/mydatapreprocessing.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Malachov/mydatapreprocessing/context:python) [![Documentation Status](https://readthedocs.org/projects/mydatapreprocessing/badge/?version=latest)](https://mydatapreprocessing.readthedocs.io/?badge=latest) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![codecov](https://codecov.io/gh/Malachov/mydatapreprocessing/branch/master/graph/badge.svg)](https://codecov.io/gh/Malachov/mydatapreprocessing) Load data from web link or local file (json, csv, Excel file, parquet, h5...), consolidate it (resample data, clean NaN values, do string embedding) derive new features via columns derivation and do preprocessing like standardization or smoothing. If you want to see how functions works, check it's docstrings - working examples with printed results are also in tests - visual.py. ## Links [Repo on GitHub](https://github.com/Malachov/mydatapreprocessing) [Official readthedocs documentation](https://mydatapreprocessing.readthedocs.io) ## Installation Python >=3.6 (Python 2 is not supported). Install just with ```console pip install mydatapreprocessing ``` There are some libraries that not every user will be using (for some specific data inputs for example). If you want to be sure to have all libraries, you can provide extras requirements like. ```console pip install mydatapreprocessing[datatypes] ``` Available extras are ["all", "datasets", "datatypes"] ## Examples You can use live [jupyter demo on binder](https://mybinder.org/v2/gh/Malachov/mydatapreprocessing/HEAD?filepath=demo.ipynb) ```python import mydatapreprocessing as mdp import pandas as pd import numpy as np ``` ### Load data You can use: - python formats (numpy.ndarray, pd.DataFrame, list, tuple, dict) - local files - web urls Supported path formats are: - csv - xlsx and xls - json - parquet - h5 You can load more data at once in list. Syntax is always the same. ```python data = mdp.load_data.load_data( "https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv", ) # data2 = mdp.load_data.load_data([PATH_TO_FILE.csv, PATH_TO_FILE2.csv]) ``` ### Consolidation If you want to use data for some machine learning models, you will probably want to remove Nan values, convert string columns to numeric if possible, do encoding or keep only numeric data and resample. Consolidation is working with pandas DataFrame as column names matters here. There are many functions, but there is main function pipelining other functions `consolidate_data` ```python consolidation_config = mdp.consolidation.consolidation_config.default_consolidation_config.do.copy() consolidation_config.datetime.datetime_column = 'Date' consolidation_config.resample.resample = 'M' consolidation_config.resample.resample_function = "mean" consolidation_config.dtype = 'float32' consolidated = mdp.consolidation.consolidate_data(data, consolidation_config) print(consolidated.head()) ``` ### Feature engineering Functions in `feature_engineering` and `preprocessing` expects that data are in form (*n_samples*, *n_features*). *n_samples* are usually much bigger and therefore transformed in `consolidate_data` if necessary. In config, you can use shorter update dict syntax as all values names are unique. ### Feature engineering Create new columns that can be for example used as another machine learning model input. ```python import mydatapreprocessing.feature_engineering as mdpf import mydatapreprocessing as mdp data = pd.DataFrame( [mdp.datasets.sin(n=30), mdp.datasets.ramp(n=30)] ).T extended = mdpf.add_derived_columns(data, differences=True, rolling_means=10) print(extended.columns) print(f"\nit has less rows then on input {len(extended)}") ``` Functions in `feature_engineering` and `preprocessing` expects that data are in form (n_samples, n_features). n_samples are usually much bigger and therefore transformed in `consolidate_data` if necessary. ### Preprocessing Preprocessing can be used on pandas DataFrame as well as on numpy array. Column names are not important as it's just matrix with defined dtype. There is many functions, but there is main function pipelining other functions `preprocess_data` Preprocessed data can be converted back with `preprocess_data_inverse` ```python from mydatapreprocessing import preprocessing as mdpp df = pd.DataFrame(np.array([range(5), range(20, 25), np.random.randn(5)]).astype("float32").T) df.iloc[2, 0] = 500 config = mdpp.preprocessing_config.default_preprocessing_config.do.copy() config.do.update({"remove_outliers": None, "difference_transform": True, "standardize": "standardize"}) data_preprocessed, inverse_config = mdpp.preprocess_data(df.values, config) inverse_config.difference_transform = df.iloc[0, 0] data_preprocessed_inverse = mdpp.preprocess_data_inverse( data_preprocessed[:, 0], inverse_config ) ``` %package help Summary: Development documents and examples for mydatapreprocessing Provides: python3-mydatapreprocessing-doc %description help # mydatapreprocessing [![Python versions](https://img.shields.io/pypi/pyversions/mydatapreprocessing.svg)](https://pypi.python.org/pypi/mydatapreprocessing/) [![PyPI version](https://badge.fury.io/py/mydatapreprocessing.svg)](https://badge.fury.io/py/mydatapreprocessing) [![Downloads](https://pepy.tech/badge/mydatapreprocessing)](https://pepy.tech/project/mydatapreprocessing) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Malachov/mydatapreprocessing/HEAD?filepath=demo.ipynb) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Malachov/mydatapreprocessing.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Malachov/mydatapreprocessing/context:python) [![Documentation Status](https://readthedocs.org/projects/mydatapreprocessing/badge/?version=latest)](https://mydatapreprocessing.readthedocs.io/?badge=latest) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![codecov](https://codecov.io/gh/Malachov/mydatapreprocessing/branch/master/graph/badge.svg)](https://codecov.io/gh/Malachov/mydatapreprocessing) Load data from web link or local file (json, csv, Excel file, parquet, h5...), consolidate it (resample data, clean NaN values, do string embedding) derive new features via columns derivation and do preprocessing like standardization or smoothing. If you want to see how functions works, check it's docstrings - working examples with printed results are also in tests - visual.py. ## Links [Repo on GitHub](https://github.com/Malachov/mydatapreprocessing) [Official readthedocs documentation](https://mydatapreprocessing.readthedocs.io) ## Installation Python >=3.6 (Python 2 is not supported). Install just with ```console pip install mydatapreprocessing ``` There are some libraries that not every user will be using (for some specific data inputs for example). If you want to be sure to have all libraries, you can provide extras requirements like. ```console pip install mydatapreprocessing[datatypes] ``` Available extras are ["all", "datasets", "datatypes"] ## Examples You can use live [jupyter demo on binder](https://mybinder.org/v2/gh/Malachov/mydatapreprocessing/HEAD?filepath=demo.ipynb) ```python import mydatapreprocessing as mdp import pandas as pd import numpy as np ``` ### Load data You can use: - python formats (numpy.ndarray, pd.DataFrame, list, tuple, dict) - local files - web urls Supported path formats are: - csv - xlsx and xls - json - parquet - h5 You can load more data at once in list. Syntax is always the same. ```python data = mdp.load_data.load_data( "https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv", ) # data2 = mdp.load_data.load_data([PATH_TO_FILE.csv, PATH_TO_FILE2.csv]) ``` ### Consolidation If you want to use data for some machine learning models, you will probably want to remove Nan values, convert string columns to numeric if possible, do encoding or keep only numeric data and resample. Consolidation is working with pandas DataFrame as column names matters here. There are many functions, but there is main function pipelining other functions `consolidate_data` ```python consolidation_config = mdp.consolidation.consolidation_config.default_consolidation_config.do.copy() consolidation_config.datetime.datetime_column = 'Date' consolidation_config.resample.resample = 'M' consolidation_config.resample.resample_function = "mean" consolidation_config.dtype = 'float32' consolidated = mdp.consolidation.consolidate_data(data, consolidation_config) print(consolidated.head()) ``` ### Feature engineering Functions in `feature_engineering` and `preprocessing` expects that data are in form (*n_samples*, *n_features*). *n_samples* are usually much bigger and therefore transformed in `consolidate_data` if necessary. In config, you can use shorter update dict syntax as all values names are unique. ### Feature engineering Create new columns that can be for example used as another machine learning model input. ```python import mydatapreprocessing.feature_engineering as mdpf import mydatapreprocessing as mdp data = pd.DataFrame( [mdp.datasets.sin(n=30), mdp.datasets.ramp(n=30)] ).T extended = mdpf.add_derived_columns(data, differences=True, rolling_means=10) print(extended.columns) print(f"\nit has less rows then on input {len(extended)}") ``` Functions in `feature_engineering` and `preprocessing` expects that data are in form (n_samples, n_features). n_samples are usually much bigger and therefore transformed in `consolidate_data` if necessary. ### Preprocessing Preprocessing can be used on pandas DataFrame as well as on numpy array. Column names are not important as it's just matrix with defined dtype. There is many functions, but there is main function pipelining other functions `preprocess_data` Preprocessed data can be converted back with `preprocess_data_inverse` ```python from mydatapreprocessing import preprocessing as mdpp df = pd.DataFrame(np.array([range(5), range(20, 25), np.random.randn(5)]).astype("float32").T) df.iloc[2, 0] = 500 config = mdpp.preprocessing_config.default_preprocessing_config.do.copy() config.do.update({"remove_outliers": None, "difference_transform": True, "standardize": "standardize"}) data_preprocessed, inverse_config = mdpp.preprocess_data(df.values, config) inverse_config.difference_transform = df.iloc[0, 0] data_preprocessed_inverse = mdpp.preprocess_data_inverse( data_preprocessed[:, 0], inverse_config ) ``` %prep %autosetup -n mydatapreprocessing-3.0.3 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-mydatapreprocessing -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 15 2023 Python_Bot - 3.0.3-1 - Package Spec generated