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Pandas for Data Analysis

Transform data into insights with Pandas—Your gateway to Data Analytics careers!

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Pandas for Data Analysis
6 Modules

Course Curriculum

6 Modules · 11 Chapters · 34 Topics · 231 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
Data Structures and Input/Output
3 Chapters · 9 Topics · 55 Sub-topics
Series Fundamentals
2 Topics
Creating and Understanding Series
5 Sub-topics
Creating Series from Lists
Creating Series from Dictionaries
Creating Series from NumPy Arrays
Creating Series with Custom Index
Series Attributes - index, values, dtype, shape, size
Series Operations and Manipulation
7 Sub-topics
Accessing Elements by Position
Accessing Elements by Label
Slicing Series
Boolean Indexing in Series
Series Arithmetic Operations
Series Mathematical Methods
Checking and Handling Missing Values in Series
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
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
03
Data Exploration
2 Chapters · 6 Topics · 38 Sub-topics
Data Exploration and Inspection
3 Topics
Statistical Summaries
5 Sub-topics
Understanding describe() for Statistical Summary
Using info() for DataFrame Overview
Checking Unique Values
Value Counts for Frequency Distribution
Correlation Analysis
Data Quality Assessment
5 Sub-topics
Finding Null Values
Data Type Inspection
Checking Duplicates
Memory Usage Analysis
Exploring Categorical Data
Data Profiling
9 Sub-topics
Comprehensive Summary Statistics
Distribution Analysis
Skewness and Kurtosis
Percentile Calculations
Value Counts and Frequencies
Unique Values Analysis
Data Cardinality
Creating Data Profiles
Automated Profiling Reports
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
04
Data Cleaning and Transformation
3 Chapters · 11 Topics · 82 Sub-topics
Data Cleaning - Missing Data
3 Topics
Detecting Missing Data
3 Sub-topics
Detecting Missing Values with isnull() and notnull()
Counting Missing Values
Visualizing Missing Data Patterns
Handling Missing Data
8 Sub-topics
Dropping Rows with Missing Values
Dropping Columns with Missing Values
Filling Missing Values with Scalar
Forward Fill Method
Backward Fill Method
Filling with Mean, Median, Mode
Filling with Interpolation
Replacing Specific Values
Missing Data Strategies
7 Sub-topics
Missing Data Imputation Strategies
Forward and Backward Fill Limitations
Interpolation Methods Comparison
Multivariate Imputation Concepts
Missing Indicator Variables
Dropping vs Imputing Decision Framework
Validating Imputation Results
Data Cleaning - Duplicates and Quality
3 Topics
Handling Duplicates
5 Sub-topics
Detecting Duplicate Rows
Removing Duplicate Rows
Keeping First or Last Occurrence
Finding Duplicates in Specific Columns
Handling Duplicates with Custom Logic
Data Validation
8 Sub-topics
Checking Data Ranges
Validating Data Types
Checking for Impossible Values
Identifying Outliers
Ensuring Data Consistency
Cross-Field Validation
Creating Validation Rules
Generating Data Quality Reports
Data Quality Frameworks
8 Sub-topics
Defining Data Quality Rules
Implementing Validation Checks
Creating Quality Scorecards
Automated Quality Monitoring
Data Lineage Tracking
Quality Issue Documentation
Remediation Strategies
Quality Reporting
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
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
06
Visualization Integration
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

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