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Pandas Data Cleaning Mastery

Clean data, clear insights - Master the art of data preprocessing with Pandas!

4.6

Intermediate

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Pandas Data Cleaning Mastery

8 Modules

Course Curriculum

8 Modules · 10 Chapters · 34 Topics · 232 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

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

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 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

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

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

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

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Pandas Data Cleaning Mastery

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

Start Your Journey Today

Pandas Data Cleaning Mastery

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

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