Pandas Tutorials

What is pandas?

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. pandas' data analysis and modeling features enable users to carry out their entire data analysis workflow in Python.

Language: Python

Industry: Business & Industry Applications, Higher Education Research & Teaching, Government

Features:

• Data Wrangling
• Modeling
• Visualization
• High Performance Computing
• Big Data
• Statistical Computing
• Numerical Computing
• Data Mining
• Text Processing
• Computing Language
• Educational Outreach
• Computational thinking

Due to its status as a foundational data wrangling tool, pandas is used in virtually every large company in tech and finance (e.g. Athena Capital Research, Two Sigma) and at every major university. It is used in national research labs such as the Program for Climate Model Diagnosis and Intercomparison (PCMDI).

Pandas Installation :

The release can be installed with conda from conda-forge or the default channel :

conda install pandas

Or via PyPI:

python3 -m pip install --upgrade pandas

Version:
See the latest pandas version from here.

pandas Library:

• A fast and efficient DataFrame object for data manipulation with integrated indexing;
• Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
• 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;
• Flexible reshaping and pivoting of data sets;
• Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
• Columns can be inserted and deleted from data structures for size mutability;
• Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
• High performance merging and joining of data sets;
• Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
• Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
• Highly optimized for performance, with critical code paths written in Cythonor C.
• Python with pandas is in use in a wide variety of academic and commercialdomains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.

Pandas Cheat Sheet:

Download the Pandas DataFrame Notebooks from here.