%global _empty_manifest_terminate_build 0 Name: python-dataiter Version: 0.43 Release: 1 Summary: Classes for data manipulation License: MIT URL: https://github.com/otsaloma/dataiter Source0: https://mirrors.aliyun.com/pypi/web/packages/38/25/e5498a00d5d8f07c11e98499bc4192b8080bc6c4d4e5b568f765b9122e38/dataiter-0.43.tar.gz BuildArch: noarch Requires: python3-attd Requires: python3-numpy Requires: python3-pandas %description [![Test](https://github.com/otsaloma/dataiter/workflows/Test/badge.svg)](https://github.com/otsaloma/dataiter/actions) [![Documentation Status](https://readthedocs.org/projects/dataiter/badge/?version=latest)](https://dataiter.readthedocs.io/en/latest/?badge=latest) [![PyPI](https://img.shields.io/pypi/v/dataiter.svg)](https://pypi.org/project/dataiter/) [![Downloads](https://pepy.tech/badge/dataiter/month)](https://pepy.tech/project/dataiter) Dataiter currently includes the following classes. **`DataFrame`** is a class for tabular data similar to R's `data.frame` or `pandas.DataFrame`. It is under the hood a dictionary of NumPy arrays and thus capable of fast vectorized operations. You can consider this to be a light-weight alternative to Pandas with a simple and consistent API. Performance-wise Dataiter relies on NumPy and Numba and is likely to be at best comparable to Pandas. **`ListOfDicts`** is a class useful for manipulating data from JSON APIs. It provides functionality similar to libraries such as Underscore.js, with manipulation functions that iterate over the data and return a shallow modified copy of the original. `attd.AttributeDict` is used to provide convenient access to dictionary keys. **`GeoJSON`** is a simple wrapper class that allows reading a GeoJSON file into a `DataFrame` and writing a data frame to a GeoJSON file. Any operations on the data are thus done with methods provided by the data frame class. Geometry is read as-is into the "geometry" column, but no special geometric operations are currently supported. ## Installation ```bash # Latest stable version pip install -U dataiter # Latest development version pip install -U git+https://github.com/otsaloma/dataiter # Numba (optional) pip install -U numba ``` Dataiter optionally uses **Numba** to speed up certain operations. If you have Numba installed and importing it succeeds, Dataiter will use it automatically. It's currently not a hard dependency, so you need to install it separately. ## Documentation https://dataiter.readthedocs.io/ If you're familiar with either dplyr (R) or Pandas (Python), the comparison table in the documentation will give you a quick overview of the differences and similarities. https://dataiter.readthedocs.io/en/latest/comparison.html %package -n python3-dataiter Summary: Classes for data manipulation Provides: python-dataiter BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dataiter [![Test](https://github.com/otsaloma/dataiter/workflows/Test/badge.svg)](https://github.com/otsaloma/dataiter/actions) [![Documentation Status](https://readthedocs.org/projects/dataiter/badge/?version=latest)](https://dataiter.readthedocs.io/en/latest/?badge=latest) [![PyPI](https://img.shields.io/pypi/v/dataiter.svg)](https://pypi.org/project/dataiter/) [![Downloads](https://pepy.tech/badge/dataiter/month)](https://pepy.tech/project/dataiter) Dataiter currently includes the following classes. **`DataFrame`** is a class for tabular data similar to R's `data.frame` or `pandas.DataFrame`. It is under the hood a dictionary of NumPy arrays and thus capable of fast vectorized operations. You can consider this to be a light-weight alternative to Pandas with a simple and consistent API. Performance-wise Dataiter relies on NumPy and Numba and is likely to be at best comparable to Pandas. **`ListOfDicts`** is a class useful for manipulating data from JSON APIs. It provides functionality similar to libraries such as Underscore.js, with manipulation functions that iterate over the data and return a shallow modified copy of the original. `attd.AttributeDict` is used to provide convenient access to dictionary keys. **`GeoJSON`** is a simple wrapper class that allows reading a GeoJSON file into a `DataFrame` and writing a data frame to a GeoJSON file. Any operations on the data are thus done with methods provided by the data frame class. Geometry is read as-is into the "geometry" column, but no special geometric operations are currently supported. ## Installation ```bash # Latest stable version pip install -U dataiter # Latest development version pip install -U git+https://github.com/otsaloma/dataiter # Numba (optional) pip install -U numba ``` Dataiter optionally uses **Numba** to speed up certain operations. If you have Numba installed and importing it succeeds, Dataiter will use it automatically. It's currently not a hard dependency, so you need to install it separately. ## Documentation https://dataiter.readthedocs.io/ If you're familiar with either dplyr (R) or Pandas (Python), the comparison table in the documentation will give you a quick overview of the differences and similarities. https://dataiter.readthedocs.io/en/latest/comparison.html %package help Summary: Development documents and examples for dataiter Provides: python3-dataiter-doc %description help [![Test](https://github.com/otsaloma/dataiter/workflows/Test/badge.svg)](https://github.com/otsaloma/dataiter/actions) [![Documentation Status](https://readthedocs.org/projects/dataiter/badge/?version=latest)](https://dataiter.readthedocs.io/en/latest/?badge=latest) [![PyPI](https://img.shields.io/pypi/v/dataiter.svg)](https://pypi.org/project/dataiter/) [![Downloads](https://pepy.tech/badge/dataiter/month)](https://pepy.tech/project/dataiter) Dataiter currently includes the following classes. **`DataFrame`** is a class for tabular data similar to R's `data.frame` or `pandas.DataFrame`. It is under the hood a dictionary of NumPy arrays and thus capable of fast vectorized operations. You can consider this to be a light-weight alternative to Pandas with a simple and consistent API. Performance-wise Dataiter relies on NumPy and Numba and is likely to be at best comparable to Pandas. **`ListOfDicts`** is a class useful for manipulating data from JSON APIs. It provides functionality similar to libraries such as Underscore.js, with manipulation functions that iterate over the data and return a shallow modified copy of the original. `attd.AttributeDict` is used to provide convenient access to dictionary keys. **`GeoJSON`** is a simple wrapper class that allows reading a GeoJSON file into a `DataFrame` and writing a data frame to a GeoJSON file. Any operations on the data are thus done with methods provided by the data frame class. Geometry is read as-is into the "geometry" column, but no special geometric operations are currently supported. ## Installation ```bash # Latest stable version pip install -U dataiter # Latest development version pip install -U git+https://github.com/otsaloma/dataiter # Numba (optional) pip install -U numba ``` Dataiter optionally uses **Numba** to speed up certain operations. If you have Numba installed and importing it succeeds, Dataiter will use it automatically. It's currently not a hard dependency, so you need to install it separately. ## Documentation https://dataiter.readthedocs.io/ If you're familiar with either dplyr (R) or Pandas (Python), the comparison table in the documentation will give you a quick overview of the differences and similarities. https://dataiter.readthedocs.io/en/latest/comparison.html %prep %autosetup -n dataiter-0.43 %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-dataiter -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Jun 09 2023 Python_Bot - 0.43-1 - Package Spec generated