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 15: Data Wrangling Task – Connecting the New Data to the Database


The steps to connect the data to the database is as follows:

  1. Import the sqlite3 module of Python and use the connect function to connect to the database. The main database engine is embedded. But for a different database like Postgresql or MySQL, we will need to connect to them using those credentials. We designate Year as the PRIMARY KEY of this table.

  2. Then, run a loop with the dataset rows one by one to insert them into the table.

  3. If we look at the current folder, we should see a file called Education_GDP.db, and if we examine that using a database viewer program, we can see the data transferred there.

Note

The solution for this activity can be found on page 347.

In this notebook, we examined a complete data wrangling flow, including reading data from the web and local drive, filtering, cleaning, quick visualization, imputation, indexing, merging, and writing back to a database table. We also wrote custom functions to...