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Pandas for SQL Professionals

SQL + Pandas = Data Engineering powerhouse earning ₹6-12 LPA!

4.5
Intermediate

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Pandas for SQL Professionals
8 Modules

Course Curriculum

8 Modules · 10 Chapters · 36 Topics · 233 Sub-topics

01
Foundation
2 Chapters · 6 Topics · 27 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
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
02
SQL vs Pandas Concepts
2 Chapters · 8 Topics · 50 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
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
03
Database Integration
1 Chapters · 3 Topics · 25 Sub-topics
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
Query Translation
1 Chapters · 4 Topics · 17 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
05
Joins and Merges
1 Chapters · 3 Topics · 26 Sub-topics
Merging and Joining
3 Topics
Merge Fundamentals
8 Sub-topics
Understanding Different Join Types
Inner Join with merge()
Left Join with merge()
Right Join with merge()
Outer Join with merge()
Merging on Single Column
Merging on Multiple Columns
Specifying Left and Right Keys
Advanced Merging
13 Sub-topics
Handling Duplicate Column Names
Indicator Parameter for Merge Tracking
Validating Merges
Merging with Index
Using join() Method
Merging on Index
Merging with Suffixes
Fuzzy Matching Concepts
Cross Join Operations
Merge with Sorting
Handling Duplicate Keys
Merge Performance Optimization
Troubleshooting Merge Issues
Concatenation
5 Sub-topics
Concatenating DataFrames Vertically
Concatenating DataFrames Horizontally
Handling Index in Concatenation
Ignoring Index While Concatenating
Adding Keys to Concatenated Data
06
Grouping and Aggregation
1 Chapters · 4 Topics · 33 Sub-topics
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
Window Functions
1 Chapters · 3 Topics · 19 Sub-topics
Statistical Operations
3 Topics
Window Functions
7 Sub-topics
Rolling Mean and Sum
Rolling Min and Max
Rolling Standard Deviation
Expanding Windows
Exponentially Weighted Moving Average
Window Aggregations
Custom Window Functions
Statistical Measures
4 Sub-topics
Correlation and Covariance
Quantile Calculations
Cumulative Operations
Statistical Analysis Methods
Outlier Detection
8 Sub-topics
Detecting Outliers with Z-Score
Detecting Outliers with IQR
Detecting Outliers with Percentiles
Visualizing Outliers Concept
Handling Outliers - Removal
Handling Outliers - Capping
Handling Outliers - Transformation
Domain-Specific Outlier Rules
08
Performance Comparison
1 Chapters · 5 Topics · 36 Sub-topics
Performance Optimization
5 Topics
Memory Optimization
5 Sub-topics
Understanding Pandas Memory Usage
Reducing Memory with Appropriate dtypes
Using Category for String Columns
Chunking Large Files
Memory Profiling
Execution Optimization
8 Sub-topics
Avoiding Iterrows and Itertuples
Vectorized Operations
Using eval() for Efficient Computation
Using query() for Fast Filtering
Index Optimization
Copy vs View Understanding
Avoiding Chained Indexing
Using inplace Parameter Wisely
Large Dataset Strategies
7 Sub-topics
Sampling Large DataFrames
Column Selection for Memory
Using Appropriate Data Types
Processing in Chunks
Incremental Reading
Filtering Early
Using Databases Instead of CSV
Iteration Strategies
8 Sub-topics
When Iteration is Necessary
Using itertuples() Efficiently
Using iterrows() Wisely
Vectorized Alternatives to Iteration
NumPy Operations for Speed
Avoiding Anti-Patterns
Benchmarking Iteration Methods
Parallel Processing Considerations
Performance Profiling
8 Sub-topics
Using time and timeit
Memory Profiling Tools
Identifying Bottlenecks
Pandas Profiling Libraries
Debugging Common Errors
Handling Warnings
Performance Testing
Optimization Strategies

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