Stacks666

Followers479

+ 1

Votes4

Pandas

Stacks895

Followers714

+ 1

Votes18

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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.

Pros of NumPy

Pros of Pandas

4
Great for data analysis

17
Easy data frame management
1
Extensive file format compatibility

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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.

What companies use NumPy?

What companies use Pandas?

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What tools integrate with NumPy?

What tools integrate with Pandas?

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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 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.

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.

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>

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

See all alternatives

Interest over time

News about NumPy

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News about Pandas

Pandas: Framing the Data

(www.activestate.com)

May 10, 2017

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