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Advanced pandas—Going Beyond the Basics

Explore advanced pandas techniques for reshaping, manipulating, and handling data. Gain insights into using unconventional features and extended libraries to streamline workflows and enhance data analysis.

Intermediate

94 Lessons

16h

Certificate of Completion

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584 Playgrounds

10 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

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Course Overview

Pandas is an easy-to-use and performant Python library for data manipulation and analysis. It is an important toolkit in a world where the volume of data created and captured is growing exponentially. In this course, you’ll discover an in-depth exploration of the pandas library. The course covers advanced techniques to reshape data, manipulate data, and handle various data types. You will also learn how to use less conventional pandas features to streamline workflows, while also leveraging extended pandas...Show More

Pandas is an easy-to-use and performant Python library for data manipulation and analysis. It is an important toolkit in a world...Show More

TAKEAWAY SKILLS

Python

Data Manipulation

Data Plotting

Data Extraction

Data Statistics

Data Science

Analysis Tools

Python 3

Data Collection

Data Cleaning

Data Visualisation

Data Analysis

Data types

Python Programming

Polars

Time Series Analysis

Data Storytelling

What You'll Learn

Understanding of pandas’ extensive capabilities in reading data from different sources

Working knowledge of how to effectively handle different types of data in pandas

Hands-on experience using data reshaping and manipulation techniques in pandas

Ability to leverage extended pandas libraries for advanced capabilities in handling and analyzing data

Familiarity with less conventional yet useful features of pandas

Knowledge of efficiency boosting techniques

Understanding of pandas’ extensive capabilities in reading data from different sources

Course Content

Before We Begin

1 Lessons

Get familiar with advanced pandas for data manipulation, transformation, and visualization skills.

Course Introduction

Reading Data into pandas

8 Lessons

Get started with reading diverse data formats into pandas for efficient analysis.

Importance of Data Ingestion

Read Data from the Web

Read Data from HTML Files

Read Data from XML Files

Read Data from Statistical Software Files

Read Data from SQL Databases

Read Data from Binary Files (Feather, HDF5, ORC, Parquet)

Quiz

Combining Data

7 Lessons

Examine methods for combining and merging pandas DataFrames to enhance data manipulation.

Importance of Combining Data

Concatenate

Merging—Types of Joins

Merge 2—Implementation

Merging—Scenarios

Combine and Update

Quiz

Reshaping and Manipulating Data

13 Lessons

Enhance your skills in reshaping and manipulating data for effective analysis in pandas.

Importance of Reshaping and Manipulating Data

GroupBy—The groupby() method

GroupBy—Aggregate

GroupBy—Transform and Filter

GroupBy—Further Useful Features

Pivot—Long and Wide Formats

Pivot—The pivot() method

Unpivot

Stack and Unstack

Further Reshaping Capabilities

Advanced Indexing 1

Advanced Indexing 2

Quiz

Encoding Data Types

2 Lessons

Take a closer look at inspecting, converting, and encoding data types in pandas.

Inspect Data Types

Convert and Encode Data Types

Handling Numerical Data

10 Lessons

Focus on advanced arithmetic, comparison, filtering, and statistical methods using pandas.

Arithmetic Operations 1

Arithmetic Operations 2

Comparison Operations

Filtering

Ordered Numerical Data

Discretize and Clip Data

Statistical Methods

Cumulative Operations

Apply Custom Functions

Quiz

Handling Categorical Data

6 Lessons

Master handling and transforming categorical data for efficient and meaningful analysis using pandas.

Encode Categorical Data

Modify Categorical Data

Comparison and Other Common Operations

Factorize and Cross-Tabulate

Other Features and Properties

Quiz

Handling Text Data

7 Lessons

Learn how to use pandas methods for transforming and managing complex text data efficiently.

Text Data Types

String Methods—Basic Transformations and Checks

String Methods—Slice, Split, and Partition

String Methods—Concatenate and Match

String Methods—Find, Extract, and Replace

String Methods—Leading and Trailing Characters

Quiz

Handling Time Series Data

17 Lessons

Unpack the core of advanced pandas techniques for time series analysis, manipulation, and resampling.

Time Series Data Types

Time Series Indexes—Creating Indexes

Time Series Indexes—Slice and Index

Time Series Indexes—Attributes

Time Series Indexes—Methods

Arithmetic Operations with Timedelta and DateOffset

Closer Look at Time Deltas

Business Dates and Holidays

Business Hours

Time Zones

Timeseries Merging

Resampling—Upsample and Downsample

Resampling—Aggregation and Other Parameters

Shifting and Frequency Conversion

Window Operations—Rolling

Window Operations—Weighted, Expanding, and Exponential Moving

Quiz

10.

Handling Sparse Data Structures

2 Lessons

Break apart the techniques for handling and optimizing sparse data structures using pandas.

Sparse Arrays

Accessors and Operations

11.

Handling Missing Data

4 Lessons

Grasp the fundamentals of representing, detecting, and managing missing data in pandas.

Missing Data Representation

Missing Data Detection and Calculations

Managing Missing Data

Quiz

12.

Leveraging Further Features of pandas

6 Lessons

Solve problems in advanced pandas features like function chaining, assertions, styling, settings, and efficiency.

Function Chaining with pipe()

Assertion Functions

DataFrame Table Styles

Option Settings

Efficiency Boosts

Quiz

13.

Utilizing Extended Libraries

5 Lessons

Follow the process of leveraging advanced libraries to enhance data analysis with pandas.

14.

Wrap Up

1 Lessons

Build on essential pandas skills for advanced data handling and real-world applications.

Conclusion

15.

Appendix

5 Lessons

Learn how to use setups, prerequisites, and updates for advanced pandas functionalities.

What's New in pandas 2.0

Setup Requirements

Overview of Statistical Software and Binary File Formats

Prerequisite Setup for Reading Data into pandas

Prerequisite Setup for Utilizing Extended Libraries

Course Author

Kenneth Leung

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