Pandas with Python: Analyze, Transform & Export Data

This course is part of Data Analysis with NumPy and Pandas Specialization

Instructor: EDUCBA

Access provided by Santander Open Academy

4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Filter, group, aggregate, and reshape datasets using Pandas.
Handle missing values, manage indexes, and analyze time series.
Create pivot tables, crosstabs, and export data to CSV/Excel.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Assessments

16 assignments

Taught in English

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This course is part of the Data Analysis with NumPy and Pandas Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
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There are 4 modules in this course

Learners will gain the ability to manipulate, analyze, and visualize data effectively using Python’s Pandas library. By the end of this course, they will be able to filter and transform datasets, apply grouping and aggregation, handle missing values, manage indexes, and reshape data for advanced analytics. They will also master techniques for working with time series, pivot tables, crosstabs, and exporting data to CSV and Excel.

This course is designed for aspiring data analysts, Python enthusiasts, and professionals looking to strengthen their data manipulation skills. With hands-on lessons and quizzes, learners will build confidence in handling real-world datasets while applying best practices for efficiency and readability. What makes this course unique is its structured progression—from foundational Pandas operations to advanced techniques—combined with practical exercises and applied projects. Learners won’t just watch tutorials; they will actively practice data handling in Jupyter Notebooks, ensuring they are job-ready for data science and analytics roles.

This module introduces learners to the Pandas library, its installation, and the Jupyter environment for hands-on coding. It covers Pandas’ core data structures, including Series and DataFrames, and explores fundamental operations for working with rows and columns. Learners build a strong foundation for effective data handling.

What's included

9 videos4 assignments

9 videosTotal 46 minutes

Introduction to Pandas with Python5 minutes
Understanding Jupiter Environment4 minutes
Reading the Data Set9 minutes
Series and Data Frame4 minutes
Operations in Data Set4 minutes
More on Panda Functions6 minutes
Column Names and Operation9 minutes
Removing Columns and Rows3 minutes
Sorting Data Frame3 minutes

4 assignmentsTotal 60 minutes

Graded-Getting Started with Pandas30 minutes
Introduction and Setup10 minutes
Core Data Structures10 minutes
Working with Columns and Rows10 minutes

This module focuses on advanced filtering, selection, and transformation of data. Learners explore indexing by labels and positions, handle data types, apply string methods, and group data for aggregation. It also emphasizes working with Series, plotting, and handling null values.

What's included

12 videos4 assignments

12 videosTotal 50 minutes

Filtering Data7 minutes
Filter Multiple Criteria6 minutes
Selective Columns and Rows3 minutes
Data Frame and Series2 minutes
Axis Parameter3 minutes
String Methods in Pandas4 minutes
Changing the Data Types4 minutes
Example of Data Type Change4 minutes
Group by Functions4 minutes
Functions on Series4 minutes
Plotting series in Pandas4 minutes
Dealing with Null Values5 minutes

4 assignmentsTotal 60 minutes

Graded-Data Selection and Transformation30 minutes
Filtering and Selection Techniques10 minutes
Data Types and Grouping10 minutes
Working with Series and Null Values10 minutes

This module introduces indexing concepts and parameters that enhance data manipulation. Learners explore memory management, sampling strategies, dummy coding, handling duplicates, working with date/time functions, and avoiding common pitfalls like copy warnings.

What's included

16 videos4 assignments

16 videosTotal 106 minutes

Uses of Index6 minutes
Column in Index6 minutes
Output of Data8 minutes
Functions of iX Method9 minutes
InPlace Parameter4 minutes
Inspecting the Space5 minutes
Reducing the Space10 minutes
Using in Country Series4 minutes
Creating Manual Data Frame7 minutes
Random Sampling with Pandas4 minutes
Concept of Dummy Coding12 minutes
Creating Dummified Values4 minutes
Duplicates in Data Frame9 minutes
Functions for Date and Time9 minutes
Setting with Copy Warning5 minutes
Example on Copy Warning5 minutes

4 assignmentsTotal 60 minutes

Graded-Indexing, Sampling, and Advanced Functions30 minutes
Exploring Index and Parameters10 minutes
Space, Sampling, and Dummy Coding10 minutes
Handling Duplicates, Dates, and Warnings10 minutes

This module covers advanced reshaping, merging, and exporting functionalities in Pandas. Learners gain expertise in display options, formatting, working with pivot tables, crosstab functions, and exporting data to external formats like CSV and Excel for practical applications.