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Data Wrangling with Python

Achieve proficiency in the data analysis process in no time!

(DATA-WRGLG-PYTHON.AJ1) / ISBN : 978-1-64459-302-8
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About This Course

This Data Wrangling with Python course is your access point to polishing your data cleaning and manipulation skills. You’ll learn how to handle advanced data structures, perform file operations, and leverage powerful libraries such as NumPy, Pandas, and Matplotlib. In hands-on labs, you’ll transform raw data into valuable insights. Ideal for data scientists and analysts, this course covers everything from basic concepts to advanced web scraping and SQL. 

Skills You’ll Get

Learn Python for data analysis data wrangling with Pandas & NumPy techniques to streamline your data analysis operations  Implement data cleaning to prepare datasets for analysis  Use Python Libraries like NumPy, Pandas, and Matplotlib  Perform data manipulation with advanced data structures  Conduct file operations for data handling and storage  Leverage SQL for database interactions and data retrieval  Apply web scraping methods to gather data from online sources  Execute data analytic functions and create visualizations  Develop problem-solving skills with real-life data-wrangling tasks  Enhance data preprocessing capabilities for machine learning (ML)
Download Course Outline

1

Introduction

  • About the Course
  • Learning Objectives
  • Approach
  • Audience
  • Minimum Hardware Requirements
  • Software Requirements
  • Conventions
  • Installation and Setup
2

Introduction to Data Wrangling with Python

  • Introduction
  • Python for Data Wrangling
  • Lists, Sets, Strings, Tuples, and Dictionaries
  • Summary
3

Advanced Data Structures and File Handling

  • Introduction
  • Advanced Data Structures
  • Basic File Operations in Python
  • Summary
4

Introduction to NumPy, Pandas, and Matplotlib

  • Introduction
  • NumPy Arrays
  • Pandas DataFrames
  • Statistics and Visualization with NumPy and Pandas
  • Summary
5

A Deep Dive into Data Wrangling with Python

  • Introduction
  • Subsetting, Filtering, and Grouping
  • Detecting Outliers and Handling Missing Values
  • Concatenating, Merging, and Joining
  • Useful Methods of Pandas
  • Summary
6

Getting Comfortable with Different Kinds of Data Sources

  • Introduction
  • Reading Data from Different Text-Based (and Non-Text-Based) Sources
  • Introduction to Beautiful Soup 4 and Web Page Parsing
  • Summary
7

Learning the Hidden Secrets of Data Wrangling

  • Introduction
  • Advanced List Comprehension and the zip Function
  • Data Formatting
  • Identify and Clean Outliers
  • Summary
8

Advanced Web Scraping and Data Gathering

  • Introduction
  • The Basics of Web Scraping and the Beautiful Soup Library
  • Reading Data from XML
  • Reading Data from an API
  • Fundamentals of Regular Expressions (RegEx)
  • Summary
9

RDBMS and SQL

  • Introduction
  • Refresher of RDBMS and SQL
  • Using an RDBMS (MySQL/PostgreSQL/SQLite)
  • Reading Data from a Database in SQLite
  • Summary
10

Application of Data Wrangling in Real Life

  • Introduction
  • Applying Your Knowledge to a Real-life Data Wrangling Task
  • An Extension to Data Wrangling
  • Summary

1

Introduction to Data Wrangling with Python

  • Sorting a List
  • Generating a List
  • Deleting a Value from a Dictionary
  • Accessing and Setting Values in a Dictionary
  • Slicing a String
2

Advanced Data Structures and File Handling

  • Implementing a Queue
  • Splitting a String
  • Implementing Multi-Element Membership Checking
  • Implementing a Stack
  • Opening a File and Printing its Content
3

Introduction to NumPy, Pandas, and Matplotlib

  • Generating Arrays Using arange and linspace
  • Multiplying Two Arrays
  • Adding Two NumPy Arrays
  • Creating a NumPy Array
  • Filtering Elements from a Matrix
  • Stacking Arrays
4

A Deep Dive into Data Wrangling with Python

  • Subsetting a DataFrame
  • Grouping a DataFrame
  • Dropping the Missing Values
  • Replacing Missing Values in a DataFrame
  • Joining DataFrames
  • Concatenating Data Frames
  • Counting Values
5

Getting Comfortable with Different Kinds of Data Sources

  • Bypassing the Headers of a CSV File
  • Reading Data from a CSV File
  • Stacking URLs from a Document Using bs4
  • Counting Tags
6

Learning the Hidden Secrets of Data Wrangling

  • Using the zip Function
  • Using a One-Liner Generator Expression
  • Using a Generator Expression
  • Using the format Function
  • Using a Box Plot
7

Advanced Web Scraping and Data Gathering

  • Checking the Status of the Web Request
  • Extracting Text from a Section
  • Traversing an XML Tree
  • Checking Whether the Input String Begins with a Specific Word
  • Matching Pattern
  • Finding the Number of Words in a List That End with ing
8

RDBMS and SQL

  • Deleting the Data
  • Using Joins
  • Using the Foreign Key
  • Updating Data
  • Using the ORDER BY Clause
  • Using the SELECT Statement
  • Using the SELECT Statement
9

Application of Data Wrangling in Real Life

  • Skipping the First Row of the Data Set

Any questions?
Check out the FAQs

Still have questions? Find out more about our data wrangling and analysis with the Python course.

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Data cleaning and wrangling in Python involves removing or correcting data anomalies. This can be done using the Pandas library, which provides functions for handling missing values, correcting data types, and removing duplicates to prepare raw data for transformation into meaningful insights.

Yes, having prior experience, especially in Python, is beneficial for taking this data wrangling course.

The top Python libraries for data wrangling include:

Pandas: For data manipulation and analysis

NumPy: For numerical operations

Matplotlib and Seaborn: For data visualization

PyJanitor: For extended data cleaning functions

Data Cleaning is the process of identifying and correcting errors in the data.

Data Wrangling is a broader process that includes data cleaning, transforming, and mapping raw data into a more useful format for analysis.

Common data wrangling techniques in Python include:

Data Merging: Combining multiple data sources into one dataset.

Data Transformation: Changing the format or structure of the data.

Data Subsetting: Selecting specific rows or columns of interest.

Handling Outliers: Identifying and correcting outliers in the data.

Data Aggregation: Summarizing data by grouping and calculating statistics.

NumPy provides support for numerical operations on large, multi-dimensional arrays and matrices, which are essential for efficient data manipulation.

Pandas offers data structures and functions designed to make data manipulation and analysis easy, such as DataFrames for handling tabular data. 

Career opportunities after completing our Python for data wrangling course include roles such as: 

  • Data Analyst 
  • Data Scientist
  • Data Engineer
  • Business Analyst
  • ML Engineer

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