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Pandas

1.4K
1.1K
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22
SciPy

345
155
+ 1
0
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Pandas vs SciPy: What are the differences?

Pandas: High-performance, easy-to-use data structures and data analysis tools for the Python programming language. Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more; SciPy: Scientific Computing Tools for Python. Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Pandas and SciPy belong to "Data Science Tools" category of the tech stack.

Pandas and SciPy are both open source tools. It seems that Pandas with 20.2K GitHub stars and 8K forks on GitHub has more adoption than SciPy with 6.01K GitHub stars and 2.85K GitHub forks.

According to the StackShare community, Pandas has a broader approval, being mentioned in 73 company stacks & 49 developers stacks; compared to SciPy, which is listed in 12 company stacks and 4 developer stacks.

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Pros of Pandas
Pros of SciPy
  • 21
    Easy data frame management
  • 1
    Extensive file format compatibility
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    - No public GitHub repository available -

    What is 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.

    What is SciPy?

    Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

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    Jobs that mention Pandas and SciPy as a desired skillset
    CBRE
    Philippines National Capital Region Makati City
    CBRE
    United Kingdom of Great Britain and Northern Ireland England London
    CBRE
    Philippines National Capital Region Makati City
    What companies use Pandas?
    What companies use SciPy?
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    What are some alternatives to Pandas and SciPy?
    Panda
    Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br>
    NumPy
    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
    R Language
    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    PySpark
    It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
    See all alternatives