Packt

Intermediate Data Manipulation and Analysis with Pandas

Packt

Intermediate Data Manipulation and Analysis with Pandas

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Set up Python and Jupyter Lab for data analysis projects

  • Perform data filtering, sorting, and aggregation

  • Work with text, dates, and times using pandas methods

Details to know

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Recently updated!

September 2026

Assessments

8 assignments

Taught in English

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This course is part of the Data Analysis with Pandas and Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 7 modules in this course

This module delves into advanced techniques for working with pandas DataFrames, focusing on data extraction, manipulation, and transformation. Learners will gain hands-on experience with methods like set_index, reset_index, loc, iloc, and apply, as well as learn how to rename, delete, and sample data effectively. By the end, students will be able to efficiently manage and extract specific data from structured datasets.

What's included

11 videos1 reading1 assignment

This module covers essential techniques for working with text data in pandas, including string methods, filtering, splitting, and transforming data. Learners will gain hands-on experience in manipulating and cleaning textual information efficiently. The content focuses on practical applications of pandas' string operations and data reshaping methods.

What's included

7 videos1 assignment

This module explores advanced techniques for working with multi-indexed DataFrames in pandas, including extracting, reshaping, and manipulating data with complex indexing structures. Learners will gain hands-on skills in using methods like xs, swaplevel, transpose, stack, and unstack to manage and analyze hierarchical data. By the end, students will be able to effectively handle structured data for more sophisticated data analysis tasks.

What's included

11 videos1 assignment

This module covers the fundamentals of working with GroupBy objects in pandas, including retrieving groups, applying aggregation methods, and performing advanced data manipulation techniques. Learners will gain hands-on skills in data grouping and analysis, enabling them to handle complex datasets efficiently.

What's included

6 videos1 assignment

This module covers essential techniques for merging and combining DataFrames in pandas, including concatenation, various join types, and parameter usage. Learners will gain skills in handling complex data integration tasks, such as merging on indexes and multiple columns, and will understand how to apply these methods effectively in real-world data analysis scenarios.

What's included

10 videos1 assignment

This module covers essential techniques for working with date and time data using pandas. Learners will gain skills in extracting, manipulating, and analyzing time-based data through functions like date_range, Timestamp, and DateOffset. The content emphasizes practical applications for data analysis and time-based operations.

What's included

8 videos1 assignment

This module equips learners with essential skills for handling data input and output operations using pandas. It covers importing and exporting data from various file formats, including CSV and Excel, and explores customization options for data handling.

What's included

4 videos1 reading2 assignments

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