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Effective Data Manipulation with pandas
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Delve into effective data manipulation with pandas. Discover filtering, merging, grouping, and reshaping techniques through hands-on examples, best practices, and performance tips for real-world analysis.

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The course is a comprehensive guide to using pandas for data analysis. It covers a wide range of topics related to data manipulation, including filtering, merging, grouping, pivoting, and reshaping data. In this course, you’ll learn about the best practices and efficient techniques for working with data. You’ll be provided with numerous examples of real-world data analysis problems and gain hands-on experience with the pandas library to solve them effectively. This course will also cover performance considerations and provide insights on common pitfalls to avoid when using pandas. The course is suitable for both beginners and experienced users of pandas. It includes detailed explanations of pandas concepts and functions, as well as tips and tricks for optimizing your code. By the time you’re done with this course, you’ll be able to effectively handle data using pandas.

The course is a comprehensive guide to using pandas for data analysis. It covers a wide range of topics related to data manipula...Show More

WHAT YOU'LL LEARN

An understanding of efficient data manipulation techniques for working with data in pandas

Working knowledge of different techniques for handling missing data in pandas

The ability to reshape and pivot data

Hands-on experience of the best practices for working with pandas

An understanding of efficient data manipulation techniques for working with data in pandas

Content

89 Lessons17 Quizzes4 Challenges

Introduction

10 Lessons

Get familiar with pandas data structures for efficient data manipulation in Python.

Similarities in pandas and NumPy

Categorical Data in pandas

Exercise: Series in pandas

Solution: Series in pandas

Summary

Quiz: pandas Series

Series Deep Dive

27 Lessons

Unpack the core of pandas Series manipulation, covering attributes, operations, and data cleaning.

Data Loading and Series Attributes

Operators and Dunder Methods

Aggregate Methods

Exercise: Aggregate Methods

Solution: Aggregate Methods

Conversion Methods

Manipulation Methods

Quiz: Methods in Series

Conditional Methods

Handling Missing Data

Other Manipulation Methods

Data Cleaning

Quiz: Duplicate values and NaN

Indexing Operations

Indexing Operations (.iloc)

Exercise: Data Manipulation and Indexing

Solution: Data Manipulation and Indexing

Quiz: Indexing, Manipulation Methods, and Data Cleaning

String Manipulation

Optimizing Manipulations Using Cython

Date and Time Manipulation

Dates in the Index

Quiz: String Manipulation, Date-Time Manipulation, and DT Indexing

Plotting with a Series: Histogram, Box Plot, and Kernel Density

Plotting with a Series: Line, Bar, and Pie Plots

Categorical Manipulation

Quiz: Plotting and Categorical Data

DataFrames

29 Lessons

Examine building, manipulating, and visualizing DataFrames in pandas for effective data analysis.

Introduction to DataFrames

Math Methods on DataFrames

Quiz: DataFrames and Math Methods on DataFrames

Exercise: DataFrames in pandas

Solution: DataFrames in pandas

Looping and Aggregation

The apply() Method

Exercise: Looping, Aggregation and apply() Method

Solution: Looping, Aggregation, and apply() Method

Columns Types, .assign, and Memory Usage

Creating and Updating Columns

Dealing with Missing and Duplicated Data

Quiz: Missing and Duplicated Data

Sorting Columns and Indexes

Filtering and Indexing Operations

Indexing by Position

Indexing by Name

Filtering with Function and .loc

Line and Bar Plots

Scatter, Area, and Stacked Bar Plots

Exercise: Sorting, Columns, Filtering, and Indexing Operations.

Solution: Sorting, Columns, Filtering, and Indexing Operations

Exercise: Plotting

Solution: Plotting

Quiz: Sorting Columns, Filtering, and Indexing Operations.

Reshaping DataFrames with Dummies

Reshaping by Pivoting and Grouping

Aggregations

Manipulating Data

21 Lessons

Grasp the fundamentals of manipulating and reshaping data, handling time series, joins, and exporting formats.

Grouping

More Aggregations

Cross-tabulation Deep Dive

Exercise: Reshaping/Aggregations

Solution: Reshaping/Aggregations

Quiz: Reshaping, Pivot Tables, Aggregations

Melting and Transposing Data

Stacking and Flattening Data

Quiz: Melting, Transposing, Stacking, and Unstacking Data

Working with Time Series

Adding Rows and Columns in a DataFrame

Joins in DataFrames

Joining Real-World Datasets

Quiz: Joining in pandas

Exporting Data to CSV and Excel

Exporting Data to Feather, SQL, and JSON

Styling DataFrames

Checking if DataFrames are Equal

Debugging Chains

Debugging apply (and friends)

Debugging Memory Usage and Timing Information

Wrapping Up

1 Lessons

Explore continuous improvement in data analysis, from understanding data to engaging in real-world projects.

Conclusion

Appendix

1 Lessons

Follow the process of installing and setting up pandas for Python projects.

Installation

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

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