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Intermediate Data Manipulation and Analysis with Pandas

Packt via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

7 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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Advance your data analysis skills by exploring powerful pandas techniques for data extraction, text processing, hierarchical indexing, and group-based analysis. Unlock new ways to organize and summarize complex datasets. This course delves into intermediate data manipulation methods using pandas, enabling you to extract, filter, and transform data with greater precision. You will learn to work with text data, manage hierarchical (MultiIndex) structures, and perform group-based aggregations for insightful summaries. The course also covers merging DataFrames and handling diverse data sources, preparing you to tackle more sophisticated data analysis challenges. Building on foundational skills, this course uses practical demonstrations and real-world examples to deepen your understanding of pandas. Each module introduces new techniques and reinforces learning through progressively complex scenarios. This course is part two of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Data Analysis with Pandas and Python, by Boris Paskhaver. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

DataFrames III: Data Extraction
- 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.
Working with Text Data
- 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.
MultiIndex
- 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.
GroupBy
- 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.
Merging DataFrames
- 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.
Working with Dates and Times
- 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.
Input and Output
- 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.