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Data Analysis with pandas

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Pandas Data Structures

Pandas Series Pandas DataFrame

Creating a Data Frame

Reading Data from a File Create a DataFrame

Exploring a Data Frame

Size of a DataFrame Data Types of Columns Different Values in a Column Measures of Central Tendency Challenge: Find the Most Frequent Values in a Column Quiz Yourself: Exploring a DataFrame

Filtering a Data Frame

Why Do We Need Filtering?Filtering with "loc" and "iloc" Methods Filtering by Selecting a Subset of Columns Filtering by Condition Filtering Rows with the Query Function Challenge: Filter Out the Values Below Average Quiz Yourself: Filtering a DataFrame

String Manipulation with pandas

Slicing and Indexing on Strings Splitting and Combining Strings Converting Strings to Upper and Lower Case Challenge: Create City and State Columns from the Address Replacing Characters in a String Chained Operations Challenge: Make the Salary Column Proper Quiz Yourself: String Manipulation with Pandas

Date Manipulation with pandas

Pandas Date and Time Data Types Extracting Information from Dates The dt Accessor Manipulating Dates by Adding Time Intervals Challenge: The Age of Employees Quiz Yourself: Date Manipulation

Handling Missing Values with pandas

Missing Value Types and Representation Finding the Missing Values Dropping Rows and Columns with Missing Values Replacing the Missing Values Challenge: Filling the Missing Values Quiz Yourself: Handling Missing Values

Data Analysis with pandas

The groupby Function Enhancing the groupby Function Challenge: Find the Weekly Sales Quantities The Pivot Table Function The Cut and Qcut Functions Challenge: Categorize Groceries Based on Price The "where" Function Quiz Yourself: Data Analysis with Pandas

Data Visualization with pandas

Histogram Line Plot Scatter Plot Quiz Yourself: Data Visualization with Pandas

Combining DataFrames with pandas

The Concat Function The Merge Function Quiz Yourself: Combining DataFrames with Pandas

Conclusion

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Final Remarks

Final Remarks

Here’s a quick summary of what we learned from the “Data Analysis with Pandas” module.

Congratulations! You have successfully completed the “Data Analysis with Pandas” module.

Summary

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