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High-performance, easy-to-use data structures and data analysis tools for the Python programming language

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 19.9K GitHub stars and 7.9K GitHub forks. Here’s a link to Pandas's open source repository on GitHub

Who uses Pandas?

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

46 developers use Pandas.

Pandas Integrations

Why developers like Pandas?

Here’s a list of reasons why companies and developers use Pandas

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

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