Advanced Data Wrangling With Pandas
Advanced Data Wrangling With Pandas
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.79 GB | Duration: 2h 54m
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.
![Advanced Data Wrangling With Pandas]()
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.
