Fundamentals of Pandas

Gain proficiency using the Python Pandas library for data analysis.​

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool.

Pandas provides a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modeling, and visualization. Fields with widespread use of Pandas include: data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering.

In this course, you'll learn how to use the pandas library and tools for data analysis and data structuring. Students will learn about DataFrames, basic plotting, indexing, and groupby. To help you learn how to work with data more effectively, 

By the end of this course, students should have a good understanding of Pandas and gain proficiency using the Python Pandas library for data analysis.

Library Highlights

• 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, 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 Cython or C.