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Pandas for Excel Users

From Excel sheets to Pandas power—automate smarter, earn bigger!

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Pandas for Excel Users
8 Modules

Course Curriculum

8 Modules · 11 Chapters · 37 Topics · 242 Sub-topics

01
Foundation
1 Chapters · 2 Topics · 9 Sub-topics
Getting Started with Pandas
2 Topics
Introduction to Pandas
5 Sub-topics
What is Pandas and Why Use It
Installing Pandas and Verifying Installation
Understanding Pandas Data Structures Overview
Importing Pandas and Basic Conventions
Your First Pandas Program
Pandas Development Environment
4 Sub-topics
Setting Up Jupyter Notebook for Pandas
Using VS Code with Pandas
Understanding Pandas Documentation
Exploring Pandas Community Resources
02
From Excel to Pandas
1 Chapters · 4 Topics · 18 Sub-topics
DataFrame Fundamentals
4 Topics
Creating DataFrames
5 Sub-topics
Creating DataFrame from Dictionary of Lists
Creating DataFrame from List of Dictionaries
Creating DataFrame from NumPy Arrays
Creating DataFrame from Series
DataFrame Attributes - shape, size, columns, index, dtypes
DataFrame Inspection
3 Sub-topics
Viewing DataFrame - head(), tail(), sample()
DataFrame Info and Memory Usage
Understanding DataFrame Structure
DataFrame Access and Selection
6 Sub-topics
Accessing Columns
Accessing Rows with loc
Accessing Rows with iloc
Selecting Multiple Columns
Selecting Rows and Columns Together
Boolean Indexing in DataFrame
DataFrame Modification
4 Sub-topics
Adding New Columns
Deleting Columns
Renaming Columns and Index
Modifying DataFrame Values
03
Reading and Writing Excel
2 Chapters · 6 Topics · 50 Sub-topics
Data Input and Output
3 Topics
Reading Data from Files
11 Sub-topics
Reading CSV Files
Reading Excel Files
Reading JSON Files
Reading from Clipboard
Reading HTML Tables
Reading SQL Database Tables
Handling File Paths
Setting Custom Delimiters
Handling Missing Values While Reading
Specifying Data Types While Reading
Reading Large Files in Chunks
Writing Data to Files
4 Sub-topics
Writing to CSV Files
Writing to Excel Files
Writing to JSON Files
Export Options and Parameters
Advanced File Operations
10 Sub-topics
Reading Multiple Files
Handling Different Encodings
Parsing Fixed-Width Files
Reading Files with Custom Separators
Handling Messy Headers
Skipping Rows and Footers
Reading Only Specific Columns
Using Converters for Custom Parsing
Handling Bad Lines
Compression Support - gzip, zip, bz2
Database Integration
3 Topics
SQL Database Operations
9 Sub-topics
Connecting to SQL Databases
Reading SQL Query Results
Reading Entire Tables
Writing DataFrames to SQL
Appending to Existing Tables
Replacing Tables
Using SQL with SQLite
Parameterized Queries
Chunked SQL Reading
Working with JSON
8 Sub-topics
Reading Simple JSON Files
Reading Nested JSON
Normalizing JSON with json_normalize()
Handling JSON Arrays
Writing DataFrames to JSON
JSON Orientation Options
Handling Complex JSON Structures
Parsing JSON from APIs
Excel Advanced Features
8 Sub-topics
Reading Multiple Sheets
Writing to Multiple Sheets
Handling Excel Formulas
Preserving Excel Formatting
Using ExcelWriter Context Manager
Appending to Existing Excel Files
Reading Cell Ranges
Working with Excel Passwords
04
Data Operations Comparison
2 Chapters · 9 Topics · 60 Sub-topics
Filtering and Selection
4 Topics
Boolean Filtering
5 Sub-topics
Boolean Filtering with Single Condition
Boolean Filtering with Multiple Conditions
Using AND, OR, NOT Operators
Using isin() for Multiple Values
Using between() for Range Filtering
String-Based Filtering
3 Sub-topics
String Filtering with contains()
Filtering with str.startswith() and str.endswith()
Filtering with Regular Expressions
Query Method
3 Sub-topics
Using query() Method
Query with Multiple Conditions
Dynamic Filter Construction
Advanced Filtering
6 Sub-topics
Filtering Null and Non-Null Values
Combining Complex Conditions
Multi-condition Boolean Filtering
Filtering Numeric Ranges
Filtering with Lambda Functions
Filter Performance Optimization
Data Transformation Basics
5 Topics
Sorting and Ranking
8 Sub-topics
Sorting by Single Column
Sorting by Multiple Columns
Sorting in Ascending and Descending Order
Sorting Index
Sorting with Missing Values
Ranking Data
Ranking Methods - average, min, max, first, dense
Ranking with Ascending and Descending Order
String Operations
11 Sub-topics
Accessing String Methods with str
Converting Case - lower(), upper(), title()
Stripping Whitespace
Replacing Substrings
Splitting Strings
Concatenating Strings
Checking String Contains
Extracting Substrings
String Length
Finding and Replacing with Regex
Extracting with Regex Patterns
Advanced String Operations
8 Sub-topics
Complex String Parsing
Extracting with Named Groups
Multiple Pattern Matching
String Tokenization
Text Normalization
Handling Special Characters
Unicode Operations
String Vectorization Performance
Type Conversion
8 Sub-topics
Checking Data Types
Converting to Numeric with to_numeric()
Converting to Datetime
Converting to String
Converting to Category
Handling Errors in Conversion
Downcasting for Memory Optimization
Converting Multiple Columns at Once
Data Type Optimization
8 Sub-topics
Understanding Memory Layout
Integer Type Optimization
Float Type Optimization
String vs Category Decision
Boolean Type Usage
Date Type Optimization
Memory Profiling Tools
Creating Memory-Efficient Pipelines
05
Pivot Tables in Pandas
1 Chapters · 5 Topics · 25 Sub-topics
Reshaping and Pivoting
5 Topics
Pivot Tables
6 Sub-topics
Creating Basic Pivot Tables
Pivot with Multiple Aggregations
Pivot with Multiple Indices
Pivot with Multiple Columns
Filling Missing Values in Pivot
Pivot Table Margins
Cross Tabulations
3 Sub-topics
Cross Tabulation with crosstab()
Normalized Cross Tabulations
Cross Tab with Multiple Variables
Stack and Unstack
5 Sub-topics
Understanding Wide vs Long Format
Using stack() to Convert Wide to Long
Using unstack() to Convert Long to Wide
Multi-Level Index Stacking
Handling Missing Values in Stack/Unstack
Melt and Pivot
3 Sub-topics
Pivot and Melt for Reshaping
Using melt() for Unpivoting
Using pivot() for Pivoting
Advanced Reshaping
8 Sub-topics
Complex Pivot Operations
Multiple Index Pivoting
Pivoting with Aggregations
Melting with Multiple Value Columns
Cross Tabulation Advanced
Reshaping with MultiIndex
Conditional Reshaping
Memory-Efficient Reshaping
06
Formulas vs Pandas Operations
2 Chapters · 7 Topics · 49 Sub-topics
Applying Functions
3 Topics
Apply Basics
8 Sub-topics
Element-wise Operations
Using apply() on Series
Using apply() on DataFrame Columns
Using apply() on DataFrame Rows
Lambda Functions with apply()
Applying Functions with Arguments
Using applymap() for Element-wise Application
Vectorized Operations vs apply()
Map and Transform
4 Sub-topics
Using map() for Series Transformation
Using replace() with Dictionaries
Using transform() in Groupby
Using pipe() for Method Chaining
Conditional Operations
4 Sub-topics
Conditional Application with where()
Conditional Application with mask()
Using np.where() with Pandas
Creating Calculated Columns
Grouping and Aggregation
4 Topics
GroupBy Basics
8 Sub-topics
Understanding groupby() Concept
Grouping by Single Column
Grouping by Multiple Columns
Aggregation with sum()
Aggregation with mean()
Aggregation with count()
Aggregation with min() and max()
Multiple Aggregations with agg()
Advanced GroupBy
9 Sub-topics
Custom Aggregation Functions
Named Aggregations
Grouping with Transformations
Filtering Groups
Iterating Over Groups
Getting Group Keys
Size and Count Differences
Nunique for Unique Counts
Groupby with Multiple Statistics
GroupBy Performance
8 Sub-topics
GroupBy with Multiple Keys
GroupBy with Custom Functions
GroupBy with Filters
GroupBy with Apply
Nested GroupBy Operations
GroupBy Performance Tuning
Memory-Efficient GroupBy
GroupBy Result Formatting
Aggregation Patterns
8 Sub-topics
Custom Aggregation Functions
Multiple Aggregations Per Column
Different Aggregations Per Column
Aggregation with Filtering
Conditional Aggregation
Nested Aggregations
Aggregation with Transform
Weighted Aggregations
07
Charts and Visualization
1 Chapters · 2 Topics · 14 Sub-topics
Integration with Ecosystem
2 Topics
Library Integration
8 Sub-topics
Pandas with NumPy
Pandas with Matplotlib Basics
Pandas with Seaborn Basics
Pandas with Scikit-learn Basics
Converting Between Data Structures
Passing Data Between Libraries
Memory Sharing Considerations
Workflow Integration
Scaling Beyond Pandas
6 Sub-topics
Understanding Pandas Limitations
Introduction to Dask
Introduction to Polars
When to Use Alternative Libraries
Cloud-based Data Processing
Distributed Computing Concepts
08
Automation Benefits
1 Chapters · 2 Topics · 17 Sub-topics
Best Practices and Workflows
2 Topics
Code Quality
9 Sub-topics
Writing Clean and Readable Code
Code Organization for Pandas Projects
Function Design for Reusability
Error Handling Patterns
Logging in Data Pipelines
Testing Pandas Code
Documentation Best Practices
Version Control for Data Projects
Code Review Guidelines
Real-World Workflows
8 Sub-topics
ETL Pipeline Design
Data Cleaning Workflows
Feature Engineering Patterns
Reporting Automation
Incremental Data Processing
Batch Processing Patterns
Error Recovery Strategies
Monitoring and Alerting

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