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NumPy
NumPy

406
310
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2
Pandas
Pandas

544
417
+ 1
18
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NumPy vs Pandas: What are the differences?

Developers describe NumPy as "Fundamental package for scientific computing with Python". 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. On the other hand, Pandas is detailed as "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.

NumPy and Pandas can be primarily classified as "Data Science" tools.

Some of the features offered by NumPy are:

  • a powerful N-dimensional array object
  • sophisticated (broadcasting) functions
  • tools for integrating C/C++ and Fortran code

On the other hand, Pandas provides the following key features:

  • Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
  • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
  • Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations

NumPy and Pandas are both open source tools. It seems that Pandas with 20K GitHub stars and 7.92K forks on GitHub has more adoption than NumPy with 10.9K GitHub stars and 3.64K GitHub forks.

Instacart, SendGrid, and Sighten are some of the popular companies that use Pandas, whereas NumPy is used by Instacart, SendGrid, and SweepSouth. Pandas has a broader approval, being mentioned in 73 company stacks & 46 developers stacks; compared to NumPy, which is listed in 62 company stacks and 32 developer stacks.

What is 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.

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.
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      What are some alternatives to NumPy and Pandas?
      MATLAB
      Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
      R
      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.
      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.
      Anaconda
      A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
      Pentaho Data Integration
      It enable users to ingest, blend, cleanse and prepare diverse data from any source. With visual tools to eliminate coding and complexity, It puts the best quality data at the fingertips of IT and the business.
      See all alternatives
      Decisions about NumPy and Pandas
      Guillaume Simler
      Guillaume Simler
      at Velchanos.io | 4 upvotes 20.6K views
      Jupyter
      Jupyter
      Anaconda
      Anaconda
      Pandas
      Pandas
      IPython
      IPython

      Jupyter Anaconda Pandas IPython

      A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

      The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

      See more
      Interest over time
      Reviews of NumPy and Pandas
      No reviews found
      How developers use NumPy and Pandas
      Avatar of Vital Labs, Inc.
      Vital Labs, Inc. uses NumPyNumPy

      We utilize NumPy, SciPy, Pandas, and iPython Notebooks to power our analysis and analytics tools.

      Avatar of Eliana Abraham
      Eliana Abraham uses NumPyNumPy

      EECS 445 and All of Linear Algebra

      Nuff said

      Avatar of Eliana Abraham
      Eliana Abraham uses PandasPandas

      I used this a lot more than I used Jupyter.

      Avatar of Morris Clay
      Morris Clay uses PandasPandas

      Data wrangling, analysis and pre-processing

      Avatar of GadgetSteve
      GadgetSteve uses PandasPandas

      Great data manipulation tool

      Avatar of GadgetSteve
      GadgetSteve uses NumPyNumPy

      Fast Numeric Processing

      Avatar of Nough You
      Nough You uses NumPyNumPy

      Fast array operations.

      Avatar of BobStein
      BobStein uses NumPyNumPy

      big data analysis

      How much does NumPy cost?
      How much does Pandas cost?
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