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All LessonsFree Lessons (3)

Introduction

Before We Start Data Structures Series Introduction NaN Values in Series Similarities in pandas and NumPy Categorical Data in pandas Exercise: Series in pandas Solution: Series in pandas Summary Quiz: pandas Series

Series Deep Dive

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

Introduction to DataFrames

Similarities between Series and DataFrame

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

Manipulating Data

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

Appendix

Effective Data Manipulation with pandas/

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Indexing by Name

Indexing by Name

Learn about indexing by name in DataFrames.

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Summary

Let’s explore indexing by the name of index entries in a DataFrame. This is done by indexing on loc. If you’re confused between loc and iloc, just remember that iloc indexes on position and that computer programs generally use the variable i to represent an index position.

One thing to be aware of is the difference between iloc and loc when dealing with integer indexes. In particular, slicing has different behavior. Slicing with iloc follows the half-open interval (includes the first ...