%global _empty_manifest_terminate_build 0 Name: python-pyoptimus Version: 23.5.0b0 Release: 1 Summary: Optimus is the missing framework for cleaning and pre-processing data in a distributed fashion. License: APACHE URL: https://github.com/hi-primus/optimus/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4e/74/09cc31d0598263899d35084eafd5804de42f9bfbb516647f754433ed1006/pyoptimus-23.5.0b0.tar.gz BuildArch: noarch Requires: python3-pandas Requires: python3-numpy Requires: python3-nltk Requires: python3-requests Requires: python3-fastnumbers Requires: python3-setuptools Requires: python3-Jinja2 Requires: python3-humanize Requires: python3-deepdiff Requires: python3-glom Requires: python3-statsmodels Requires: python3-fast-histogram Requires: python3-rply Requires: python3-tabulate Requires: python3-matplotlib Requires: python3-seaborn Requires: python3-jellyfish Requires: python3-Metaphone Requires: python3-num2words Requires: python3-hi-dateinfer Requires: python3-hi-urlparser Requires: python3-yellowbrick Requires: python3-openpyxl Requires: python3-xlrd Requires: python3-fastavro Requires: python3-pandavro Requires: python3-pybigquery Requires: python3-snappy Requires: python3-s3fs Requires: python3-aiobotocore[boto3] Requires: python3-tensorflow Requires: python3-keras Requires: python3-nltk Requires: python3-pyspark Requires: python3-findspark Requires: python3-koalas Requires: python3-pandas Requires: python3-dask[complete] Requires: python3-distributed Requires: python3-dask-ml Requires: python3-pyarrow Requires: python3-coiled Requires: python3-psutil Requires: python3-vaex Requires: python3-gputil Requires: python3-numpy Requires: python3-tensorflow Requires: python3-keras Requires: python3-nltk Requires: python3-sqlalchemy Requires: python3-flask Requires: python3-flask Requires: python3-gputil Requires: python3-numpy Requires: python3-distributed Requires: python3-dask[complete] Requires: python3-dask[complete] Requires: python3-distributed Requires: python3-dask-ml Requires: python3-pyarrow Requires: python3-coiled Requires: python3-psutil Requires: python3-sqlalchemy Requires: python3-sphinx Requires: python3-pytest Requires: python3-mock Requires: python3-nose Requires: python3-pep8 Requires: python3-pyflakes Requires: python3-pandas Requires: python3-pyspark Requires: python3-findspark Requires: python3-koalas Requires: python3-pytest Requires: python3-mock Requires: python3-nose Requires: python3-vaex %description # Optimus [![Logo Optimus](https://raw.githubusercontent.com/hi-primus/optimus/develop-23.5/images/optimus-logo.png)](https://hi-optimus.com) [![Tests](https://github.com/hi-primus/optimus/actions/workflows/main.yml/badge.svg)](https://github.com/hi-primus/optimus/actions/workflows/main.yml) [![Docker image updated](https://github.com/hi-primus/optimus/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/r/hiprimus/optimus) [![PyPI Latest Release](https://img.shields.io/pypi/v/pyoptimus.svg)](https://pypi.org/project/pyoptimus/) [![GitHub release](https://img.shields.io/github/release/hi-primus/optimus.svg?include_prereleases)](https://github.com/hi-primus/optimus/releases) [![CalVer](https://img.shields.io/badge/calver-YY.MM.MICRO-22bfda.svg)](http://calver.org) [![Downloads](https://pepy.tech/badge/pyoptimus)](https://pepy.tech/project/pyoptimus) [![Downloads](https://pepy.tech/badge/pyoptimus/month)](https://pepy.tech/project/pyoptimus/month) [![Downloads](https://pepy.tech/badge/pyoptimus/week)](https://pepy.tech/project/pyoptimus/week) [![Mentioned in Awesome Data Science](https://awesome.re/mentioned-badge.svg)](https://github.com/bulutyazilim/awesome-datascience) [![Slack](https://img.shields.io/badge/chat-slack-red.svg?logo=slack&color=36c5f0)](https://communityinviter.com/apps/hi-bumblebee/welcome) # Overview Optimus is an opinionated python library to easily load, process, plot and create ML models that run over pandas, Dask, cuDF, dask-cuDF, Vaex or Spark. Some amazing things Optimus can do for you: * Process using a simple API, making it easy to use for newcomers. * More than 100 functions to handle strings, process dates, urls and emails. * Easily plot data from any size. * Out of box functions to explore and fix data quality. * Use the same code to process your data in your laptop or in a remote cluster of GPUs. [See Documentation](https://docs.hi-optimus.com/en/latest/) ## Try Optimus To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/hi-primus/optimus/develop-23.5?filepath=https%3A%2F%2Fraw.githubusercontent.com%2Fhi-primus%2Foptimus%2Fdevelop-23.5%2Fexamples%2F10_min_to_optimus.ipynb) [![Colab](https://img.shields.io/badge/launch-colab-yellow.svg?logo=googlecolab&color=e6a210)](https://colab.research.google.com/github/hi-primus/optimus/blob/master/examples/10_min_to_optimus_colab.ipynb) ## Installation (pip): In your terminal just type: ``` pip install pyoptimus ``` By default Optimus install Pandas as the default engine, to install other engines you can use the following commands: | Engine | Command | |-----------|----------------------------------------| | Dask | ```pip install pyoptimus[dask]``` | | cuDF | ```pip install pyoptimus[cudf]``` | | Dask-cuDF | ```pip install pyoptimus[dask-cudf]``` | | Vaex | ```pip install pyoptimus[vaex]``` | | Spark | ```pip install pyoptimus[spark]``` | To install from the repo: ``` pip install git+https://github.com/hi-primus/optimus.git@develop-23.5 ``` To install other engines: ``` pip install git+https://github.com/hi-primus/optimus.git@develop-23.5#egg=pyoptimus[dask] ``` ### Requirements * Python 3.7 or 3.8 ## Examples You can go to [10 minutes to Optimus](https://github.com/hi-primus/optimus/blob/develop-23.5/examples/10_min_to_optimus.ipynb) where you can find the basics to start working in a notebook. Also you can go to the [Examples](https://github.com/hi-primus/optimus/tree/develop-23.5/examples/examples.md) section and find specific notebooks about data cleaning, data munging, profiling, data enrichment and how to create ML and DL models. Here's a handy [Cheat Sheet](https://htmlpreview.github.io/?https://github.com/hi-primus/optimus/blob/develop-23.5/docs/cheatsheet/optimus_cheat_sheet.html) with the most common Optimus' operations. ## Start Optimus Start Optimus using ```"pandas"```, ```"dask"```, ```"cudf"```,```"dask_cudf"```,```"vaex"``` or ```"spark"```. ```python from optimus import Optimus op = Optimus("pandas") ``` ## Loading data Now Optimus can load data in csv, json, parquet, avro and excel formats from a local file or from a URL. ```python #csv df = op.load.csv("../examples/data/foo.csv") #json df = op.load.json("../examples/data/foo.json") # using a url df = op.load.json("https://raw.githubusercontent.com/hi-primus/optimus/develop-23.5/examples/data/foo.json") # parquet df = op.load.parquet("../examples/data/foo.parquet") # ...or anything else df = op.load.file("../examples/data/titanic3.xls") ``` Also, you can load data from Oracle, Redshift, MySQL and Postgres databases. ## Saving Data ```python #csv df.save.csv("data/foo.csv") # json df.save.json("data/foo.json") # parquet df.save.parquet("data/foo.parquet") ``` You can also save data to oracle, redshift, mysql and postgres. ## Create dataframes Also, you can create a dataframe from scratch ```python df = op.create.dataframe({ 'A': ['a', 'b', 'c', 'd'], 'B': [1, 3, 5, 7], 'C': [2, 4, 6, None], 'D': ['1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10'] }) ``` Using `display` you have a beautiful way to show your data with extra information like column number, column data type and marked white spaces. ```python display(df) ``` ![](https://github.com/hi-primus/optimus/tree/develop-23.5/readme/images/table.png) ## Cleaning and Processing Optimus was created to make data cleaning a breeze. The API was designed to be super easy to newcomers and very familiar for people that comes from Pandas. Optimus expands the standard DataFrame functionality adding `.rows` and `.cols` accessors. For example you can load data from a url, transform and apply some predefined cleaning functions: ```python new_df = df\ .rows.sort("rank", "desc")\ .cols.lower(["names", "function"])\ .cols.date_format("date arrival", "yyyy/MM/dd", "dd-MM-YYYY")\ .cols.years_between("date arrival", "dd-MM-YYYY", output_cols="from arrival")\ .cols.normalize_chars("names")\ .cols.remove_special_chars("names")\ .rows.drop(df["rank"]>8)\ .cols.rename("*", str.lower)\ .cols.trim("*")\ .cols.unnest("japanese name", output_cols="other names")\ .cols.unnest("last position seen", separator=",", output_cols="pos")\ .cols.drop(["last position seen", "japanese name", "date arrival", "cybertronian", "nulltype"]) ``` # Need help? 🛠️ ## Feedback Feedback is what drive Optimus future, so please take a couple of minutes to help shape the Optimus' Roadmap: http://bit.ly/optimus_survey Also if you want to a suggestion or feature request use https://github.com/hi-primus/optimus/issues ## Troubleshooting If you have issues, see our [Troubleshooting Guide](https://github.com/hi-primus/optimus/tree/develop-23.5/troubleshooting.md) # Contributing to Optimus 💡 Contributions go far beyond pull requests and commits. We are very happy to receive any kind of contributions including: * [Documentation](https://docs.hi-optimus.com/en/latest/) updates, enhancements, designs, or bugfixes. * Spelling or grammar fixes. * README.md corrections or redesigns. * Adding unit, or functional [tests](https://github.com/hi-primus/optimus/tree/develop-23.5/tests) * Triaging GitHub issues -- especially determining whether an issue still persists or is reproducible. * [Blogging, speaking about, or creating tutorials](https://hioptimus.com/category/blog/) about Optimus and its many features. * Helping others on our official chats # Backers and Sponsors Become a [backer](https://opencollective.com/optimus#backer) or a [sponsor](https://opencollective.com/optimus#sponsor) and get your image on our README on Github with a link to your site. [![OpenCollective](https://opencollective.com/optimus/backers/badge.svg)](#backers) [![OpenCollective](https://opencollective.com/optimus/sponsors/badge.svg)](#sponsors) %package -n python3-pyoptimus Summary: Optimus is the missing framework for cleaning and pre-processing data in a distributed fashion. Provides: python-pyoptimus BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pyoptimus # Optimus [![Logo Optimus](https://raw.githubusercontent.com/hi-primus/optimus/develop-23.5/images/optimus-logo.png)](https://hi-optimus.com) [![Tests](https://github.com/hi-primus/optimus/actions/workflows/main.yml/badge.svg)](https://github.com/hi-primus/optimus/actions/workflows/main.yml) [![Docker image updated](https://github.com/hi-primus/optimus/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/r/hiprimus/optimus) [![PyPI Latest Release](https://img.shields.io/pypi/v/pyoptimus.svg)](https://pypi.org/project/pyoptimus/) [![GitHub release](https://img.shields.io/github/release/hi-primus/optimus.svg?include_prereleases)](https://github.com/hi-primus/optimus/releases) [![CalVer](https://img.shields.io/badge/calver-YY.MM.MICRO-22bfda.svg)](http://calver.org) [![Downloads](https://pepy.tech/badge/pyoptimus)](https://pepy.tech/project/pyoptimus) [![Downloads](https://pepy.tech/badge/pyoptimus/month)](https://pepy.tech/project/pyoptimus/month) [![Downloads](https://pepy.tech/badge/pyoptimus/week)](https://pepy.tech/project/pyoptimus/week) [![Mentioned in Awesome Data Science](https://awesome.re/mentioned-badge.svg)](https://github.com/bulutyazilim/awesome-datascience) [![Slack](https://img.shields.io/badge/chat-slack-red.svg?logo=slack&color=36c5f0)](https://communityinviter.com/apps/hi-bumblebee/welcome) # Overview Optimus is an opinionated python library to easily load, process, plot and create ML models that run over pandas, Dask, cuDF, dask-cuDF, Vaex or Spark. Some amazing things Optimus can do for you: * Process using a simple API, making it easy to use for newcomers. * More than 100 functions to handle strings, process dates, urls and emails. * Easily plot data from any size. * Out of box functions to explore and fix data quality. * Use the same code to process your data in your laptop or in a remote cluster of GPUs. [See Documentation](https://docs.hi-optimus.com/en/latest/) ## Try Optimus To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/hi-primus/optimus/develop-23.5?filepath=https%3A%2F%2Fraw.githubusercontent.com%2Fhi-primus%2Foptimus%2Fdevelop-23.5%2Fexamples%2F10_min_to_optimus.ipynb) [![Colab](https://img.shields.io/badge/launch-colab-yellow.svg?logo=googlecolab&color=e6a210)](https://colab.research.google.com/github/hi-primus/optimus/blob/master/examples/10_min_to_optimus_colab.ipynb) ## Installation (pip): In your terminal just type: ``` pip install pyoptimus ``` By default Optimus install Pandas as the default engine, to install other engines you can use the following commands: | Engine | Command | |-----------|----------------------------------------| | Dask | ```pip install pyoptimus[dask]``` | | cuDF | ```pip install pyoptimus[cudf]``` | | Dask-cuDF | ```pip install pyoptimus[dask-cudf]``` | | Vaex | ```pip install pyoptimus[vaex]``` | | Spark | ```pip install pyoptimus[spark]``` | To install from the repo: ``` pip install git+https://github.com/hi-primus/optimus.git@develop-23.5 ``` To install other engines: ``` pip install git+https://github.com/hi-primus/optimus.git@develop-23.5#egg=pyoptimus[dask] ``` ### Requirements * Python 3.7 or 3.8 ## Examples You can go to [10 minutes to Optimus](https://github.com/hi-primus/optimus/blob/develop-23.5/examples/10_min_to_optimus.ipynb) where you can find the basics to start working in a notebook. Also you can go to the [Examples](https://github.com/hi-primus/optimus/tree/develop-23.5/examples/examples.md) section and find specific notebooks about data cleaning, data munging, profiling, data enrichment and how to create ML and DL models. Here's a handy [Cheat Sheet](https://htmlpreview.github.io/?https://github.com/hi-primus/optimus/blob/develop-23.5/docs/cheatsheet/optimus_cheat_sheet.html) with the most common Optimus' operations. ## Start Optimus Start Optimus using ```"pandas"```, ```"dask"```, ```"cudf"```,```"dask_cudf"```,```"vaex"``` or ```"spark"```. ```python from optimus import Optimus op = Optimus("pandas") ``` ## Loading data Now Optimus can load data in csv, json, parquet, avro and excel formats from a local file or from a URL. ```python #csv df = op.load.csv("../examples/data/foo.csv") #json df = op.load.json("../examples/data/foo.json") # using a url df = op.load.json("https://raw.githubusercontent.com/hi-primus/optimus/develop-23.5/examples/data/foo.json") # parquet df = op.load.parquet("../examples/data/foo.parquet") # ...or anything else df = op.load.file("../examples/data/titanic3.xls") ``` Also, you can load data from Oracle, Redshift, MySQL and Postgres databases. ## Saving Data ```python #csv df.save.csv("data/foo.csv") # json df.save.json("data/foo.json") # parquet df.save.parquet("data/foo.parquet") ``` You can also save data to oracle, redshift, mysql and postgres. ## Create dataframes Also, you can create a dataframe from scratch ```python df = op.create.dataframe({ 'A': ['a', 'b', 'c', 'd'], 'B': [1, 3, 5, 7], 'C': [2, 4, 6, None], 'D': ['1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10'] }) ``` Using `display` you have a beautiful way to show your data with extra information like column number, column data type and marked white spaces. ```python display(df) ``` ![](https://github.com/hi-primus/optimus/tree/develop-23.5/readme/images/table.png) ## Cleaning and Processing Optimus was created to make data cleaning a breeze. The API was designed to be super easy to newcomers and very familiar for people that comes from Pandas. Optimus expands the standard DataFrame functionality adding `.rows` and `.cols` accessors. For example you can load data from a url, transform and apply some predefined cleaning functions: ```python new_df = df\ .rows.sort("rank", "desc")\ .cols.lower(["names", "function"])\ .cols.date_format("date arrival", "yyyy/MM/dd", "dd-MM-YYYY")\ .cols.years_between("date arrival", "dd-MM-YYYY", output_cols="from arrival")\ .cols.normalize_chars("names")\ .cols.remove_special_chars("names")\ .rows.drop(df["rank"]>8)\ .cols.rename("*", str.lower)\ .cols.trim("*")\ .cols.unnest("japanese name", output_cols="other names")\ .cols.unnest("last position seen", separator=",", output_cols="pos")\ .cols.drop(["last position seen", "japanese name", "date arrival", "cybertronian", "nulltype"]) ``` # Need help? 🛠️ ## Feedback Feedback is what drive Optimus future, so please take a couple of minutes to help shape the Optimus' Roadmap: http://bit.ly/optimus_survey Also if you want to a suggestion or feature request use https://github.com/hi-primus/optimus/issues ## Troubleshooting If you have issues, see our [Troubleshooting Guide](https://github.com/hi-primus/optimus/tree/develop-23.5/troubleshooting.md) # Contributing to Optimus 💡 Contributions go far beyond pull requests and commits. We are very happy to receive any kind of contributions including: * [Documentation](https://docs.hi-optimus.com/en/latest/) updates, enhancements, designs, or bugfixes. * Spelling or grammar fixes. * README.md corrections or redesigns. * Adding unit, or functional [tests](https://github.com/hi-primus/optimus/tree/develop-23.5/tests) * Triaging GitHub issues -- especially determining whether an issue still persists or is reproducible. * [Blogging, speaking about, or creating tutorials](https://hioptimus.com/category/blog/) about Optimus and its many features. * Helping others on our official chats # Backers and Sponsors Become a [backer](https://opencollective.com/optimus#backer) or a [sponsor](https://opencollective.com/optimus#sponsor) and get your image on our README on Github with a link to your site. [![OpenCollective](https://opencollective.com/optimus/backers/badge.svg)](#backers) [![OpenCollective](https://opencollective.com/optimus/sponsors/badge.svg)](#sponsors) %package help Summary: Development documents and examples for pyoptimus Provides: python3-pyoptimus-doc %description help # Optimus [![Logo Optimus](https://raw.githubusercontent.com/hi-primus/optimus/develop-23.5/images/optimus-logo.png)](https://hi-optimus.com) [![Tests](https://github.com/hi-primus/optimus/actions/workflows/main.yml/badge.svg)](https://github.com/hi-primus/optimus/actions/workflows/main.yml) [![Docker image updated](https://github.com/hi-primus/optimus/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/r/hiprimus/optimus) [![PyPI Latest Release](https://img.shields.io/pypi/v/pyoptimus.svg)](https://pypi.org/project/pyoptimus/) [![GitHub release](https://img.shields.io/github/release/hi-primus/optimus.svg?include_prereleases)](https://github.com/hi-primus/optimus/releases) [![CalVer](https://img.shields.io/badge/calver-YY.MM.MICRO-22bfda.svg)](http://calver.org) [![Downloads](https://pepy.tech/badge/pyoptimus)](https://pepy.tech/project/pyoptimus) [![Downloads](https://pepy.tech/badge/pyoptimus/month)](https://pepy.tech/project/pyoptimus/month) [![Downloads](https://pepy.tech/badge/pyoptimus/week)](https://pepy.tech/project/pyoptimus/week) [![Mentioned in Awesome Data Science](https://awesome.re/mentioned-badge.svg)](https://github.com/bulutyazilim/awesome-datascience) [![Slack](https://img.shields.io/badge/chat-slack-red.svg?logo=slack&color=36c5f0)](https://communityinviter.com/apps/hi-bumblebee/welcome) # Overview Optimus is an opinionated python library to easily load, process, plot and create ML models that run over pandas, Dask, cuDF, dask-cuDF, Vaex or Spark. Some amazing things Optimus can do for you: * Process using a simple API, making it easy to use for newcomers. * More than 100 functions to handle strings, process dates, urls and emails. * Easily plot data from any size. * Out of box functions to explore and fix data quality. * Use the same code to process your data in your laptop or in a remote cluster of GPUs. [See Documentation](https://docs.hi-optimus.com/en/latest/) ## Try Optimus To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/hi-primus/optimus/develop-23.5?filepath=https%3A%2F%2Fraw.githubusercontent.com%2Fhi-primus%2Foptimus%2Fdevelop-23.5%2Fexamples%2F10_min_to_optimus.ipynb) [![Colab](https://img.shields.io/badge/launch-colab-yellow.svg?logo=googlecolab&color=e6a210)](https://colab.research.google.com/github/hi-primus/optimus/blob/master/examples/10_min_to_optimus_colab.ipynb) ## Installation (pip): In your terminal just type: ``` pip install pyoptimus ``` By default Optimus install Pandas as the default engine, to install other engines you can use the following commands: | Engine | Command | |-----------|----------------------------------------| | Dask | ```pip install pyoptimus[dask]``` | | cuDF | ```pip install pyoptimus[cudf]``` | | Dask-cuDF | ```pip install pyoptimus[dask-cudf]``` | | Vaex | ```pip install pyoptimus[vaex]``` | | Spark | ```pip install pyoptimus[spark]``` | To install from the repo: ``` pip install git+https://github.com/hi-primus/optimus.git@develop-23.5 ``` To install other engines: ``` pip install git+https://github.com/hi-primus/optimus.git@develop-23.5#egg=pyoptimus[dask] ``` ### Requirements * Python 3.7 or 3.8 ## Examples You can go to [10 minutes to Optimus](https://github.com/hi-primus/optimus/blob/develop-23.5/examples/10_min_to_optimus.ipynb) where you can find the basics to start working in a notebook. Also you can go to the [Examples](https://github.com/hi-primus/optimus/tree/develop-23.5/examples/examples.md) section and find specific notebooks about data cleaning, data munging, profiling, data enrichment and how to create ML and DL models. Here's a handy [Cheat Sheet](https://htmlpreview.github.io/?https://github.com/hi-primus/optimus/blob/develop-23.5/docs/cheatsheet/optimus_cheat_sheet.html) with the most common Optimus' operations. ## Start Optimus Start Optimus using ```"pandas"```, ```"dask"```, ```"cudf"```,```"dask_cudf"```,```"vaex"``` or ```"spark"```. ```python from optimus import Optimus op = Optimus("pandas") ``` ## Loading data Now Optimus can load data in csv, json, parquet, avro and excel formats from a local file or from a URL. ```python #csv df = op.load.csv("../examples/data/foo.csv") #json df = op.load.json("../examples/data/foo.json") # using a url df = op.load.json("https://raw.githubusercontent.com/hi-primus/optimus/develop-23.5/examples/data/foo.json") # parquet df = op.load.parquet("../examples/data/foo.parquet") # ...or anything else df = op.load.file("../examples/data/titanic3.xls") ``` Also, you can load data from Oracle, Redshift, MySQL and Postgres databases. ## Saving Data ```python #csv df.save.csv("data/foo.csv") # json df.save.json("data/foo.json") # parquet df.save.parquet("data/foo.parquet") ``` You can also save data to oracle, redshift, mysql and postgres. ## Create dataframes Also, you can create a dataframe from scratch ```python df = op.create.dataframe({ 'A': ['a', 'b', 'c', 'd'], 'B': [1, 3, 5, 7], 'C': [2, 4, 6, None], 'D': ['1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10'] }) ``` Using `display` you have a beautiful way to show your data with extra information like column number, column data type and marked white spaces. ```python display(df) ``` ![](https://github.com/hi-primus/optimus/tree/develop-23.5/readme/images/table.png) ## Cleaning and Processing Optimus was created to make data cleaning a breeze. The API was designed to be super easy to newcomers and very familiar for people that comes from Pandas. Optimus expands the standard DataFrame functionality adding `.rows` and `.cols` accessors. For example you can load data from a url, transform and apply some predefined cleaning functions: ```python new_df = df\ .rows.sort("rank", "desc")\ .cols.lower(["names", "function"])\ .cols.date_format("date arrival", "yyyy/MM/dd", "dd-MM-YYYY")\ .cols.years_between("date arrival", "dd-MM-YYYY", output_cols="from arrival")\ .cols.normalize_chars("names")\ .cols.remove_special_chars("names")\ .rows.drop(df["rank"]>8)\ .cols.rename("*", str.lower)\ .cols.trim("*")\ .cols.unnest("japanese name", output_cols="other names")\ .cols.unnest("last position seen", separator=",", output_cols="pos")\ .cols.drop(["last position seen", "japanese name", "date arrival", "cybertronian", "nulltype"]) ``` # Need help? 🛠️ ## Feedback Feedback is what drive Optimus future, so please take a couple of minutes to help shape the Optimus' Roadmap: http://bit.ly/optimus_survey Also if you want to a suggestion or feature request use https://github.com/hi-primus/optimus/issues ## Troubleshooting If you have issues, see our [Troubleshooting Guide](https://github.com/hi-primus/optimus/tree/develop-23.5/troubleshooting.md) # Contributing to Optimus 💡 Contributions go far beyond pull requests and commits. We are very happy to receive any kind of contributions including: * [Documentation](https://docs.hi-optimus.com/en/latest/) updates, enhancements, designs, or bugfixes. * Spelling or grammar fixes. * README.md corrections or redesigns. * Adding unit, or functional [tests](https://github.com/hi-primus/optimus/tree/develop-23.5/tests) * Triaging GitHub issues -- especially determining whether an issue still persists or is reproducible. * [Blogging, speaking about, or creating tutorials](https://hioptimus.com/category/blog/) about Optimus and its many features. * Helping others on our official chats # Backers and Sponsors Become a [backer](https://opencollective.com/optimus#backer) or a [sponsor](https://opencollective.com/optimus#sponsor) and get your image on our README on Github with a link to your site. [![OpenCollective](https://opencollective.com/optimus/backers/badge.svg)](#backers) [![OpenCollective](https://opencollective.com/optimus/sponsors/badge.svg)](#sponsors) %prep %autosetup -n pyoptimus-23.5.0b0 %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-pyoptimus -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 31 2023 Python_Bot - 23.5.0b0-1 - Package Spec generated