Bestseller हिन्दी में

Pandas Complete Professional Bootcamp

Complete Pandas mastery in one bootcamp—Become a data professional employers seek!

4.8

Expert

Certificate of Completion

Complete this course and earn a verified certificate to showcase your achievement.

Verified & ShareableShare on LinkedIn, resume, or portfolio

QR Code VerificationEmployers can instantly verify online

Unique Certificate IDTamper-proof with unique serial number

Industry RecognisedAccepted by 500+ companies across India

This is to certify that

Your Name Here

has successfully completed

Pandas Complete Professional Bootcamp

14 Modules

Course Curriculum

14 Modules · 21 Chapters · 71 Topics · 471 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

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

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 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

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

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

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

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

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

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

Advanced Indexing

4 Topics

MultiIndex Fundamentals

5 Sub-topics

Creating MultiIndex from Tuples

Creating MultiIndex from Arrays

Creating MultiIndex from Product

Setting MultiIndex

Resetting MultiIndex

MultiIndex Operations

6 Sub-topics

Accessing Data with MultiIndex

Slicing with MultiIndex

Cross-sections with xs()

Swapping Index Levels

Sorting MultiIndex

MultiIndex in Columns

Advanced Indexing Techniques

8 Sub-topics

Using IndexSlice for MultiIndex

Boolean Indexing with loc

Conditional Selection with Multiple Criteria

Using at and iat for Scalar Access

Fancy Indexing

Index Alignment in Operations

Reindexing DataFrames

Setting Values with Indexing

Index Operations

8 Sub-topics

Index Arithmetic

Index Set Operations

Index Alignment Deep Dive

Custom Index Classes

Index Metadata

Index Performance

Index Memory Considerations

Index Best Practices

Categorical and Specialized Data

3 Topics

Categorical Data

9 Sub-topics

Creating Categorical Data

Categorical Data Type Benefits

Ordered vs Unordered Categories

Renaming Categories

Adding Categories

Removing Categories

Setting Categories as Ordered

Memory Savings with Categorical

Grouping with Categorical Data

Sparse Data

6 Sub-topics

Understanding Sparse Data

Creating Sparse Arrays

Converting to Sparse

Memory Benefits of Sparse

Operations on Sparse Data

When to Use Sparse Data Types

Panel Data

8 Sub-topics

Understanding Panel Data Structure

Wide vs Long Format for Panels

Entity-Time Indexing

Panel Data Transformations

Within and Between Variations

Lagging Panel Data

Panel Data Aggregations

Reshaping Panel Data

Advanced Features

3 Topics

Custom Extensions

7 Sub-topics

Understanding Accessor Concept

Creating Custom Accessors

Registering Accessors

Extending Series Functionality

Extending DataFrame Functionality

Creating Reusable Pandas Extensions

Accessor Best Practices

Extension Arrays

3 Sub-topics

Understanding Extension Arrays

Creating Custom Data Types

Extension Array API

Method Chaining

8 Sub-topics

Understanding Method Chaining

Using pipe() for Readability

Chaining with Assignments

Breaking Long Chains

Debugging Method Chains

Performance of Chaining

When to Avoid Chaining

Building Reusable Chains

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

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

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

0.0

Course Rating

No reviews yet. Be the first to review this course!

Frequently Asked Questions

No FAQs for this course yet.

Preview this course

₹9,999 ₹14,999 33% OFF

Lifetime access to all materials

Certificate of completion

Available in multiple languages

Access on mobile & desktop

7-Day Money-Back Guarantee Not satisfied? Get a full refund within 7 days, no questions asked. Zero risk.

Pandas Complete Professional Bootcamp

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

Start Your Journey Today

Pandas Complete Professional Bootcamp

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

Related Courses

NumPy for Data Analysis

Seaborn Best Practices

Plotly Express Mastery

Start Your Journey Today