Advanced Data Wrangling With Pandas

Mastering Advanced Techniques for Efficient Data Manipulation, Cleaning, and Analysis with Python's Pandas Library

What you'll learn

Master complex data manipulation techniques using Pandas advanced functions and methods.

Develop efficient strategies for handling and analyzing large-scale datasets.

Implement advanced data cleaning, transformation, and merging operations.

Create reusable and optimized data processing pipelines using Pandas.

Requirements

Basic knowledge of Python programming

Basic understanding of Pandas library and its core functionalities

Familiarity with fundamental data analysis concepts

Experience working with datasets in various formats (CSV, JSON, Excel, etc.)

Description

Pandas is a Python library used by data analysts and data scientists to clean, transform, and analyze data. If you have basic knowledge of pandas, then this course is for you.Advanced-Data Wrangling with Pandas is an intensive course designed to elevate your data manipulation skills to the expert level. This comprehensive program dives deep into the powerful Pandas library, equipping you with advanced techniques to tackle complex data challenges efficiently.Throughout nine carefully structured sections, you'll master a wide array of advanced topics. Starting with a refresher on Pandas fundamentals, you'll quickly progress to advanced string manipulation, DateTime handling, and multi-indexing techniques. The course covers crucial skills such as managing missing data, outlier detection, and sophisticated merging and joining operations.You'll learn to optimize your code for performance, work with large datasets, and integrate Pandas with other data science libraries. Each section combines theoretical lectures with hands-on exercises, ensuring you can immediately apply your new knowledge to real-world scenarios.Highlights include mastering regular expressions for text cleaning, advanced time-series analysis, and creating custom functions to extend Pandas' functionality. You'll also dive into memory optimization techniques and best practices for writing efficient Pandas code.By the end of this course, you'll have transformed into a Pandas expert, capable of handling any data manipulation challenge with confidence and efficiency.

Overview

Section 1: Introduction to Advanced Pandas

Lecture 1 Course Overview

Lecture 2 Refresher on Pandas Data Structures (Series, DataFrame)

Lecture 3 Importing and Exporting Data (CSV, Excel, Databases)

Lecture 4 High Performance Data Handling with Pandas

Section 2: String Manipulation and Text Processing

Lecture 5 Working with String Data Types

Lecture 6 Regular Expressions for Advanced String Cleaning and Feature Engineering

Lecture 7 Text Preprocessing Techniques

Lecture 8 Vectorized String Operations with apply() and lambda functions

Section 3: Working with Dates and Times

Lecture 9 Creating and Working with Date Time Objects

Lecture 10 Datetime, Indexing and Selection

Lecture 11 Datetime manipulation

Lecture 12 Aggregating Time-series Data

Section 4: Hierachical Indexing and Multi-Indexing

Lecture 13 Multi-level Indexing (Hierachial Indexing)

Lecture 14 Working with Levels in Multindex

Lecture 15 Stacking and Unstacking Data for Different Views

Lecture 16 Fancy Indexing with boolean masks and conditions

Section 5: Advanced Data Cleaning and Handling Missing Values

Lecture 17 Detecting Missing Values

Lecture 18 Strategies for Handling Missing Values

Lecture 19 Dealing with Duplicates and Outliers

Lecture 20 Data Validation and Error Correction with Custom Functions

Section 6: Advanced Merging and Joining Tecniques.

Lecture 21 Vectorized Operations with apply(), map() and lambda functions

Lecture 22 Creating New Features and Columns with Custom Logic

Lecture 23 Merging & Joining DataFrames (inner, outer, left, right)

Lecture 24 Concatenating DataFrames along rows & columns

Section 7: Customizing and Extending Pandas Functionality

Lecture 25 User-Defined Functions (UDFs) for Data Transformations

Lecture 26 Lambda Functions and Applying Custom Logic

Lecture 27 Integrating Pandas with other Data Science Libraries (NumPy, Scikit-learn)

Section 8: Section 8: Performance Optimization and Best Practices

Lecture 28 Profiling DataFrames to Identify Bottlenecks

Lecture 29 Memory Optimization Techniques (dtypes, memory usage)

Lecture 30 Vectorized Operations vs. Loops for Efficiency

Lecture 31 Best Practices for Efficient & Clean Pandas Code

Data analysts, Data scientists, and Software developers who have some experience with Pandas and want to take their skills to the next level.,Professionals working with large or complex datasets who need to perform advanced data manipulation tasks efficiently.