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Pandas for Database Professionals

From SQL to Pandas - Elevate Your Data Career with Python Power!

4.5

Intermediate

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Pandas for Database Professionals

8 Modules

Course Curriculum

8 Modules · 10 Chapters · 29 Topics · 211 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

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

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

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

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

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

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

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

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

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Pandas for Database Professionals

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

Start Your Journey Today

Pandas for Database Professionals

Certificate of Completion

Course Curriculum

Student Reviews

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