pandas – Python Data Analysis Library

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

pandas needs NumPy, python-dateutil, and pytz.

pandas is well suited for many different kinds of data:

  • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet.
  • Ordered and unordered (not necessarily fixed-frequency) time series data.
  • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels.
  • Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure.

Features include:

  • Provides a DataFrame object – a 2-dimensional labelled data structure with columns of potentially different types. DataFrame accepts many different kinds of input:
    • Dict of 1D ndarrays, lists, dicts, or Series;
    • 2-D numpy.ndarray;
    • Structured or record ndarray;
    • A Series;
    • Another DataFrame.
  • Simple API for plotting DataFrames.
  • Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form.
  • 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.
  • Makes 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 (with the option of multiple labels per tick).
  • Tools for reading and writing data between in-memory data structures and different formats for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultra-fast HDF5 format.
  • Offers sophisticated statistical visualization tools.
  • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.
  • Keeps matplotlib as its backend but provides a new API.
  • Highly optimized for performance, with critical code paths written in Cython or C.

Python with pandas is used in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.

Support: Documentation, GitHub
Developer: PyData Development Team
License: 3-clause (“Simplified” or “New”) BSD license


pandas is written in Python. Learn Python with our recommended free books and free tutorials.

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