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Data Wrangling with Python

You're reading from Data Wrangling with Python Creating actionable data from raw sources

Product type Paperback

Published in Feb 2019

Publisher Packt

ISBN-13 9781789800111

Length 452 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Analysis

Authors (2):

Shubhadeep Roychowdhury

Dr. Tirthajyoti Sarkar

View More author details

Table of Contents (12) Chapters

Data Wrangling with Python

Preface

1. Introduction to Data Wrangling with Python FREE CHAPTER

2. Advanced Data Structures and File Handling

3. Introduction to NumPy, Pandas, and Matplotlib

4. A Deep Dive into Data Wrangling with Python

5. Getting Comfortable with Different Kinds of Data Sources

6. Learning the Hidden Secrets of Data Wrangling

7. Advanced Web Scraping and Data Gathering

8. RDBMS and SQL

9. Application of Data Wrangling in Real Life

Appendix

Summary

In this chapter, we started with the basics of NumPy arrays, including how to create them and their essential properties. We discussed and showed how a NumPy array is optimized for vectorized element-wise operations and differs from a regular Python list. Then, we moved on to practicing various operations on NumPy arrays such as indexing, slicing, filtering, and reshaping. We also covered special one-dimensional and two-dimensional arrays, such as zeros, ones, identity matrices, and random arrays.

In the second major topic of this chapter, we started with pandas series objects and quickly moved on to a critically important object – pandas DataFrames. It is analogous to Excel or MATLAB or a database tab, but with many useful properties for data wrangling. We demonstrated some basic operations on DataFrames, such as indexing, subsetting, row and column addition, and deletion.

Next, we covered the basics of plotting with matplotlib, the most widely used and popular Python library for...

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Authors (2)

Dr. Tirthajyoti Sarkar

Dr. Tirthajyoti Sarkar

Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in artificial intelligence and machine learning from Stanford and MIT.

See other products by Dr. Tirthajyoti Sarkar

Shubhadeep Roychowdhury

Shubhadeep Roychowdhury

Shubhadeep Roychowdhury holds a master's degree in computer science from West Bengal University of Technology and certifications in machine learning from Stanford. He works as a senior software engineer at a Paris-based cybersecurity startup, where he is applying state-of-the-art computer vision and data engineering algorithms and tools to develop cutting-edge products. He often writes about algorithm implementation in Python and similar topics.

See other products by Shubhadeep Roychowdhury

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