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Udemy – Advanced Data Wrangling with Pandas 2024-8

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Dealing with Duplicates and Outliers

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Course Content

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Section 1: Introduction to Advanced Pandas4 videos

Course Overview

Refresher on Pandas Data Structures (Series, DataFrame)

27m

Importing and Exporting Data (CSV, Excel, Databases)

25m

High Performance Data Handling with Pandas

1h 7m

Section 2: String Manipulation and Text Processing4 videos

Working with String Data Types

35m

Text Preprocessing Techniques

38m

Regular Expressions for Advanced String Cleaning and Feature Engineering

43m

Vectorized String Operations with apply() and lambda functions

23m

Section 3: Working with Dates and Times4 videos

Creating and Working with Date Time Objects

41m

Datetime, Indexing and Selection

17m

Datetime manipulation

18m

Aggregating Time-series Data

Section 4: Hierachical Indexing and Multi-Indexing4 videos

Working with Levels in Multindex

12m

Multi-level Indexing (Hierachial Indexing)

28m

Stacking and Unstacking Data for Different Views

18m

Fancy Indexing with boolean masks and conditions

21m

Section 5: Advanced Data Cleaning and Handling Missing Values4 videos

Strategies for Handling Missing Values

15m

Detecting Missing Values

29m

Data Validation and Error Correction with Custom Functions

11m

Dealing with Duplicates and Outliers

33mNow Playing

Section 6: Advanced Merging and Joining Tecniques4 videos

Creating New Features and Columns with Custom Logic

12m

Vectorized Operations with apply(), map() and lambda functions

40m

Merging & Joining DataFrames (inner, outer, left, right)

34m

Concatenating DataFrames along rows & columns

10m

Section 7: Customizing and Extending Pandas Functionality3 videos

Lambda Functions and Applying Custom Logic

17m

User-Defined Functions (UDFs) for Data Transformations

36m

Integrating Pandas with other Data Science Libraries (NumPy, Scikit-learn)

36m

Section 8: Section 8 Performance Optimization and Best Practices4 videos

Profiling DataFrames to Identify Bottlenecks

13m

Memory Optimization Techniques (dtypes, memory usage)

20m

Best Practices for Efficient & Clean Pandas Code

10m

Vectorized Operations vs. Loops for Efficiency

17m

About this Lecture

Section: Advanced Data Cleaning and Handling Missing Values • Size: 72.26 MB • Duration: 33m

Course Summary

Advanced Data Wrangling with Pandas course. This course will take you to an expert level in data manipulation using the powerful Pandas library. If you are familiar with the basics of Pandas, this course is designed for you. The Advanced Data Manipulation with Pandas course is a comprehensive program that teaches you advanced techniques for solving complex data challenges. With structured sections and hands-on exercises, this course enables you to quickly implement your new knowledge in real-world scenarios. What you will learn: Mastering complex data manipulation techniques: Using Pandas advanced functions and methods, you will learn complex ways to manipulate data. Develop efficient strategies for managing and analyzing big data: You will learn how to work efficiently with large and complex data sets. Implement advanced data cleansing, transformation, and integration operations: Improve your skills in data cleansing, transformation, and integration. Creating an optimized reusable data processing pipeline using Pandas: You will learn how to create an efficient and reusable data processing pipeline. This course is suitable for people who: Data analysts, data scientists, and software developers who have experience with Pandas and want to take their skills to the next level. Professionals who work with large or complex data sets and need to perform complex data manipulation tasks efficiently.

Course Content

0 / 31 completed

Section 1: Introduction to Advanced Pandas4 videos

Course Overview

Refresher on Pandas Data Structures (Series, DataFrame)

27m

Importing and Exporting Data (CSV, Excel, Databases)

25m

High Performance Data Handling with Pandas

1h 7m

Section 2: String Manipulation and Text Processing4 videos

Working with String Data Types

35m

Text Preprocessing Techniques

38m

Regular Expressions for Advanced String Cleaning and Feature Engineering

43m

Vectorized String Operations with apply() and lambda functions

23m

Section 3: Working with Dates and Times4 videos

Creating and Working with Date Time Objects

41m

Datetime, Indexing and Selection

17m

Datetime manipulation

18m

Aggregating Time-series Data

Section 4: Hierachical Indexing and Multi-Indexing4 videos

Working with Levels in Multindex

12m

Multi-level Indexing (Hierachial Indexing)

28m

Stacking and Unstacking Data for Different Views

18m

Fancy Indexing with boolean masks and conditions

21m

Section 5: Advanced Data Cleaning and Handling Missing Values4 videos

Strategies for Handling Missing Values

15m

Detecting Missing Values

29m

Data Validation and Error Correction with Custom Functions

11m

Dealing with Duplicates and Outliers

33mNow Playing

Section 6: Advanced Merging and Joining Tecniques4 videos

Creating New Features and Columns with Custom Logic

12m

Vectorized Operations with apply(), map() and lambda functions

40m

Merging & Joining DataFrames (inner, outer, left, right)

34m

Concatenating DataFrames along rows & columns

10m

Section 7: Customizing and Extending Pandas Functionality3 videos

Lambda Functions and Applying Custom Logic

17m

User-Defined Functions (UDFs) for Data Transformations

36m

Integrating Pandas with other Data Science Libraries (NumPy, Scikit-learn)

36m

Section 8: Section 8 Performance Optimization and Best Practices4 videos

Profiling DataFrames to Identify Bottlenecks

13m

Memory Optimization Techniques (dtypes, memory usage)

20m

Best Practices for Efficient & Clean Pandas Code

10m

Vectorized Operations vs. Loops for Efficiency

17m