Pandas logo

Pandas

High-performance, easy-to-use data structures and data analysis tools for the Python programming language
815
689
+ 1
18

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 25.1K GitHub stars and 10.2K GitHub forks. Here鈥檚 a link to Pandas's open source repository on GitHub

Who uses Pandas?

Companies
142 companies reportedly use Pandas in their tech stacks, including Instacart, Ruangguru, and Delivery Hero.

Developers
641 developers on StackShare have stated that they use Pandas.

Pandas Integrations

Python, Jupyter, Ludwig, Dask, and Streamlit are some of the popular tools that integrate with Pandas. Here's a list of all 7 tools that integrate with Pandas.
Private Decisions at about Pandas

Here are some stack decisions, common use cases and reviews by members of with Pandas in their tech stack.

Shared insights
on
PandasPandas

Data wrangling, analysis and pre-processing Pandas

See more
Shared insights
on
PandasPandas

Great data manipulation tool Pandas

See more
Guillaume Simler
Guillaume Simler
at Velchanos.io | 1 upvotes 6.4K views
Shared insights
on
PythonPythonFlaskFlaskPandasPandas

Python Flask Pandas

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

See more
Guillaume Simler
Guillaume Simler
at Velchanos.io | 4 upvotes 180.2K 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

See more
Public Decisions about Pandas

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 Velchanos.io | 4 upvotes 180.2K 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

See more
Guillaume Simler
Guillaume Simler
at Velchanos.io | 1 upvotes 6.4K views
Shared insights
on
PythonPythonFlaskFlaskPandasPandas

Python Flask Pandas

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

See more

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
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

Pandas's Followers
689 developers follow Pandas to keep up with related blogs and decisions.
gs-findev-osx
Charles Achilefu
Devesh Tarasia
Carlos Irigoyen
Ramesh Borukati
Bhuvana sagi
rushabh-v
Anton Golosnichenko
Jonathan McDermott
Brian Stewart