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High-performance, easy-to-use data structures and data analysis tools for the Python programming language
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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.
Pandas is a tool in the Data Science Tools category of a tech stack.
Pandas is an open source tool with 22.7K GitHub stars and 9K GitHub forks. Here鈥檚 a link to Pandas's open source repository on GitHub

Who uses Pandas?

121 companies reportedly use Pandas in their tech stacks, including Instacart, SendGrid, and Sighten.

393 developers on StackShare have stated that they use Pandas.

Pandas Integrations

Python, PyXLL, Dask, Faust, and Ludwig are some of the popular tools that integrate with Pandas. Here's a list of all 6 tools that integrate with Pandas.

Why developers like Pandas?

Here鈥檚 a list of reasons why companies and developers use Pandas
Pandas Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Pandas in their tech stack.

Guillaume Simler
Guillaume Simler
at | 4 upvotes 21.6K views

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

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Guillaume Simler
Guillaume Simler
at | 1 upvotes 2K views

Python Flask Pandas

It is light-weight and straightforward: it is fast for prototyping, especially if you don't have a CS degree.

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Pandas's 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
  • Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
  • Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
  • Intuitive merging and joining data sets
  • Flexible reshaping and pivoting of data sets
  • Hierarchical labeling of axes (possible to have multiple labels per tick)
  • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format
  • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.

Pandas Alternatives & Comparisons

What are some alternatives to Pandas?
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>
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 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.
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.
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.
See all alternatives

Pandas's Followers
418 developers follow Pandas to keep up with related blogs and decisions.
Pumrapee Poomka
fabiano filho
Moe S3k
Mario Burbano
Ryan McCall
Stephan Zimmer
Mathieu Kudla
ali alladin
Jesse Khorasanee