Pandas with Python: Analyze, Transform & Export Data
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Pandas with Python: Analyze, Transform & Export Data
This course is part of Data Analysis with NumPy and Pandas Specialization
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13 reviews
13 reviews
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
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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 videos•Total 46 minutes
- Introduction to Pandas with Python•5 minutes
- Understanding Jupiter Environment•4 minutes
- Reading the Data Set•9 minutes
- Series and Data Frame•4 minutes
- Operations in Data Set•4 minutes
- More on Panda Functions•6 minutes
- Column Names and Operation•9 minutes
- Removing Columns and Rows•3 minutes
- Sorting Data Frame•3 minutes
4 assignments•Total 60 minutes
- Graded-Getting Started with Pandas•30 minutes
- Introduction and Setup•10 minutes
- Core Data Structures•10 minutes
- Working with Columns and Rows•10 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 videos•Total 50 minutes
- Filtering Data•7 minutes
- Filter Multiple Criteria•6 minutes
- Selective Columns and Rows•3 minutes
- Data Frame and Series•2 minutes
- Axis Parameter•3 minutes
- String Methods in Pandas•4 minutes
- Changing the Data Types•4 minutes
- Example of Data Type Change•4 minutes
- Group by Functions•4 minutes
- Functions on Series•4 minutes
- Plotting series in Pandas•4 minutes
- Dealing with Null Values•5 minutes
4 assignments•Total 60 minutes
- Graded-Data Selection and Transformation•30 minutes
- Filtering and Selection Techniques•10 minutes
- Data Types and Grouping•10 minutes
- Working with Series and Null Values•10 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 videos•Total 106 minutes
- Uses of Index•6 minutes
- Column in Index•6 minutes
- Output of Data•8 minutes
- Functions of iX Method•9 minutes
- InPlace Parameter•4 minutes
- Inspecting the Space•5 minutes
- Reducing the Space•10 minutes
- Using in Country Series•4 minutes
- Creating Manual Data Frame•7 minutes
- Random Sampling with Pandas•4 minutes
- Concept of Dummy Coding•12 minutes
- Creating Dummified Values•4 minutes
- Duplicates in Data Frame•9 minutes
- Functions for Date and Time•9 minutes
- Setting with Copy Warning•5 minutes
- Example on Copy Warning•5 minutes
4 assignments•Total 60 minutes
- Graded-Indexing, Sampling, and Advanced Functions•30 minutes
- Exploring Index and Parameters•10 minutes
- Space, Sampling, and Dummy Coding•10 minutes
- Handling Duplicates, Dates, and Warnings•10 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.
What's included
22 videos4 assignments
22 videos•Total 152 minutes
- Changing the Display Option•4 minutes
- Formatting the Data•7 minutes
- Tricks for Display Options•7 minutes
- Data with Rows and Columns•9 minutes
- Converting Data Frame•4 minutes
- Introduction to Azure Data Lake•7 minutes
- Merging Data Frames•9 minutes
- Shaping a Data Frame•8 minutes
- Filling NA Values•3 minutes
- Importing Time Series Data•8 minutes
- Working with Interpolate Method•10 minutes
- Stacking and Unstacking•10 minutes
- Stacking and Unstacking for 3 Levels•4 minutes
- Concept of Crosstab•9 minutes
- More on Crosstab•8 minutes
- More Options with Crosstab•10 minutes
- Functions of Pivot•4 minutes
- Pivot Table Method•6 minutes
- Example on Pivot Table•10 minutes
- Data Frame to CSV File•7 minutes
- Using Excel Functions•8 minutes
- Summary on Pandas•3 minutes
4 assignments•Total 60 minutes
- Graded-Advanced Data Operations and Export•30 minutes
- Display Options and Formatting•10 minutes
- Merging, Reshaping, and Time Series•10 minutes
- Stacking, Pivoting, and Exporting•10 minutes
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Showing 3 of 13
Reviewed on Dec 18, 2025
The instructor explained everything clearly, especially indexing and data reshaping. These
concepts used to confuse me, but now I feel comfortable using them.
Reviewed on Dec 8, 2025
I loved how the course started with the basics and slowly moved into advanced topics like pivot
tables and time series. It felt very beginner-friendly.
Reviewed on Apr 3, 2026
The step-by-step approach made learning Pandas simple. I now feel confident working with datasets in Python and handling missing values.
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