Make data analysis fast, reliable, and clean with Python, Pandas and Matplotlib. 


  • A detailed walk-through of the Pandas library's features with multiple examples.
  • Numerous graphical representations and reporting capabilities using popular Matplotlib.
  • A high-level overview of extracting data from including files, databases, and the web.


No matter how large or small your dataset is, the author 'Fabio Nelli' simply used this book to teach all the finest technical coaching on applying Pandas to conduct data analysis with zero worries.

Both newcomers and seasoned professionals will benefit from this book. It teaches you how to use the pandas library in just one week. Every day of the week, you'll learn and practise the features and data analysis exercises listed below:

Day 01: Get familiar with the fundamental data structures of pandas, including Declaration, data upload, indexing, and so on.

Day 02: Execute commands and operations related to data selection and extraction, including slicing, sorting, masking, iteration, and query execution.

Day 03: Advanced commands and operations such as grouping, multi-indexing, reshaping, cross-tabulations, and aggregations.

Day 04: Working with several data frames, including comparison, joins, concatenation, and merges.

Day 05: Cleaning, pre-processing, and numerous strategies for data extraction from external files, the web, databases, and other data sources.

Day 06: Working with missing data, interpolation, duplicate labels, boolean data types, text data, and time-series datasets.

Day 07: Introduction to Jupyter Notebooks, interactive data analysis, and analytical reporting with Matplotlib's stunning graphics.


  • Extract, cleanse, and process data from databases, text files, HTML pages, and JSON data.
  • Work with DataFrames and Series, and apply functions to scale data manipulations.
  • Graph your findings using charts typically used in modern business analytics.
  • Learn to use all of the pandas basic and advanced features independently.
  • Storing and manipulating labeled/columnar data efficiently.


If you're looking to expedite a data science or sophisticated data analysis project, you've come to the perfect place. Each data analysis topic is covered step-by-step with real-world examples. Python knowledge isn't required however, knowing a little bit helps.