In One Minute : Pandas

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

    Pandas is a Python library for PANel DAta manipulation and analysis, example: multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance. Pandas is implemented primarily using NumPy and Cython; it is intended to be able to integrate very easily with NumPy-based scientific libraries, such as statsmodels.

  • sphinx

    The Sphinx documentation generator

    Rich User Documentation, using Sphinx

  • InfluxDB

    Build time-series-based applications quickly and at scale.. InfluxDB is the Time Series Platform where developers build real-time applications for analytics, IoT and cloud-native services. Easy to start, it is available in the cloud or on-premises.

  • CPython

    The Python programming language

    Pandas is a Python library for PANel DAta manipulation and analysis, example: multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance. Pandas is implemented primarily using NumPy and Cython; it is intended to be able to integrate very easily with NumPy-based scientific libraries, such as statsmodels.

  • cheatsheets

    Official Matplotlib cheat sheets (by matplotlib)

    Simple Matplotlib integration for plotting and graphing

  • Cython

    The most widely used Python to C compiler

    Pandas is a Python library for PANel DAta manipulation and analysis, example: multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance. Pandas is implemented primarily using NumPy and Cython; it is intended to be able to integrate very easily with NumPy-based scientific libraries, such as statsmodels.

  • Sonar

    Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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