Pandas Tutorial Repository

Welcome to this Pandas tutorial repository! This repository covers fundamental to intermediate Pandas concepts and operations. It's designed to help you get started with data manipulation, analysis, and Machine Learning using Python3.

Topics Covered:

In this repo, I have covered the following topics for you!

1. Pandas Series Introduction

• Learn about the fundamental data structure in Pandas, Series.
• Understand its creation, indexing, and basic operations.
• Pandas Series Documentation

2. Pandas Series Read from File

• Explore how to import data from various file formats (CSV, Excel, etc.) into Pandas Series.
• Pandas IO Documentation

3. Apply Python Built-in Functions to Series

• Discover how to apply Python's built-in functions like len, sum, max, etc., to Series elements.
• Python Built-in Functions

4. apply() for Pandas

• Learn about the apply() function for performing custom operations on Series elements.
• Pandas Series apply()

5. Pandas DataFrame Creation from Scratch

• Create DataFrames manually using various methods and data structures.
• Pandas DataFrame Documentation

6. Read Files as DataFrame

• Import data from different file formats (CSV, Excel, etc.) into Pandas DataFrames.
• Pandas IO Documentation

7. Columns Manipulation Part 1

• Manipulate DataFrame columns: renaming, adding, deleting, and basic modifications.
• Pandas DataFrame Documentation

8. Columns Manipulation Part 2

• Advanced column operations: filtering, sorting, and creating new columns based on existing ones.
• Pandas DataFrame Documentation

9. Arithmetic Operations

• Perform arithmetic operations on DataFrames and Series, including addition, subtraction, multiplication, and division.
• Pandas Arithmetic Operations

10. Null Values Handling

• Learn techniques to handle missing data in DataFrames, such as filling, dropping, or imputing values.
• Pandas Missing Data

11. DataFrame Filtering Part 1

• Filter DataFrames based on conditions using boolean indexing.
• Pandas Boolean Indexing

12. DataFrame Filtering Part 2

• Advanced filtering techniques, including multiple conditions and complex queries.
• Pandas Boolean Indexing

13. Handling Unique and Duplicated Values

• Identify and handle unique and duplicated values in DataFrames.
• Pandas DataFrame duplicated

14. Retrieve Rows by Index Level

• Access specific rows in a DataFrame using index levels.
• Pandas Indexing

15. Replace Cell Values

• Modify individual cell values in a DataFrame.
• Pandas Indexing and Selection

16. Rename, Delete Index and Columns

• Rename, delete, and manipulate DataFrame indices and columns.
• Pandas Indexing

17. Lambda Apply

• Apply lambda functions to DataFrames for flexible operations.
• Pandas Series apply()

18. Pandas Groupby

• Group data and perform aggregations based on specified columns.
• Pandas Groupby

19. Groupby Multiple Columns

• Group data based on multiple columns and perform hierarchical aggregations.
• Pandas Groupby

20. Concatenation

• Combine multiple DataFrames into a single DataFrame using different methods.
• Pandas Concatenation

21. Working with Datetime

• Handle datetime data in Pandas, including parsing, formatting, and time-based operations.
• Pandas Time Series

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Today's Quote: "Good code is its own best documentation." - Steve McConnell, a prominent software engineering author. 💻📄✨