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Pandas Real-World Projects

Build real projects, become job-ready Data Analyst with Pandas mastery!

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Pandas Real-World Projects

8 Modules

Course Curriculum

8 Modules · 18 Chapters · 57 Topics · 381 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 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

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

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

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

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

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

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

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

Working with Dates and Times

4 Topics

DateTime Basics

9 Sub-topics

Creating DateTime Objects

Parsing String to DateTime

DateTime Components - year, month, day

DateTime Components - hour, minute, second

Day of Week and Day Name

Setting DateTime as Index

Datetime Arithmetic

Date Ranges with date_range()

Business Day Frequencies

Time Series Operations

8 Sub-topics

Resampling Time Series Data

Upsampling and Downsampling

Aggregating with Resample

Rolling Windows

Expanding Windows

Shifting Data

Period Objects and Operations

Time Deltas

Timezone Operations

10 Sub-topics

Working with Timezones

Converting Between Timezones

Timezone-Aware DateTimes

Localizing DateTimes

Converting Between Timezones

Handling Daylight Saving Time

UTC Standardization

Timezone Ambiguities

Business Rules with Timezones

Timezone Performance Considerations

Time Series Analysis

10 Sub-topics

Setting Datetime Index

Selecting Date Ranges

Partial String Indexing

Business Day Operations

Holiday Calendars

Time-based Grouping

Lag and Lead Features

Difference Operations

Percentage Change

Autocorrelation

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

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

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

Data Export and Serialization

2 Topics

Export Formats

5 Sub-topics

Exporting to CSV with Options

Exporting to Excel with Formatting

Exporting to JSON

Exporting to HTML

Exporting to LaTeX

Serialization

4 Sub-topics

Pickling DataFrames

Parquet Format for Efficient Storage

Feather Format for Fast I/O

HDF5 for Large Data

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

Styling and Display

2 Topics

Display Options

8 Sub-topics

Viewing All Options

Setting Display Options

Maximum Rows and Columns Display

Float Precision Display

Column Width Settings

Context Managers for Temporary Settings

Resetting Options

Commonly Used Options

DataFrame Styling

8 Sub-topics

Basic Styling with style

Highlighting Maximum and Minimum Values

Color Gradients

Background Gradients

Bar Charts in Cells

Conditional Formatting

Custom Styling Functions

Exporting Styled DataFrames

Data Export and Serialization

2 Topics

Export Formats

5 Sub-topics

Exporting to CSV with Options

Exporting to Excel with Formatting

Exporting to JSON

Exporting to HTML

Exporting to LaTeX

Serialization

4 Sub-topics

Pickling DataFrames

Parquet Format for Efficient Storage

Feather Format for Fast I/O

HDF5 for Large Data

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Pandas Real-World Projects

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

Start Your Journey Today

Pandas Real-World Projects

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

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