Book Image

Data Wrangling with Python

By : Dr. Tirthajyoti Sarkar, Shubhadeep Roychowdhury
Book Image

Data Wrangling with Python

By: Dr. Tirthajyoti Sarkar, Shubhadeep Roychowdhury

Overview of this book

For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.
Table of Contents (12 chapters)
Data Wrangling with Python
Preface
Appendix

Activity 13: Data Wrangling Task – Cleaning GDP Data


The GDP data is available on https://data.worldbank.org/ and it is available on GitHub at https://github.com/TrainingByPackt/Data-Wrangling-with-Python/blob/master/Chapter09/Activity12-15/India_World_Bank_Info.csv.

In this activity, we will clean the GDP data.

  1. Create three DataFrames from the original DataFrame using filtering. Create the df_primary, df_secondary, and df_tertiary DataFrames for students enrolled in primary education, secondary education, and tertiary education in thousands, respectively.

  2. Plot bar charts of the enrollment of primary students in a low-income country like India and a higher-income country like the USA.

  3. Since there is missing data, use pandas imputation methods to impute these data points by simple linear interpolation between data points. To do that, create a DataFrame with missing values inserted and append a new DataFrame with missing values to the current DataFrame.

  4. (For India) Append the rows corresponding...