Learning Pandas 🐼

Welcome to the Learning Pandas repository! This project documents my journey in mastering the Pandas library for data manipulation and analysis in Python. It contains a collection of organized examples and demos ranging from basic Series and DataFrames to more advanced operations like joining and concatenation.

📂 Repository Structure

The code is organized into folders based on the specific Pandas concept being demonstrated:

1. Pandas DataFrame (01_Pandas DataFrame)

Basic examples of creating and manipulating DataFrames.

• Creating DataFrames from dictionaries.
• Custom indexing (index=['RowA', ...]).
• Accessing data using .loc and .iloc.
• Iterating over columns.

2. DataFrame Attributes & Methods (02_Pandas Dataframe Attribute And Methods)

Exploring built-in attributes and methods to inspect DataFrames.

• Attributes: .dtypes, .ndim (dimensions), .size (number of elements), .shape (tuple of rows/cols), .index, .T (Transpose).
• Methods: .head() (first n rows), .tail() (last n rows).

3. Joining DataFrames (03_Pandas Join DataFrame)

• Demonstrations of joining two DataFrames using the .join() method.

4. Concatenating DataFrames (04_Pandas Concat Data Frame)

• Examples of combining DataFrames vertically using pd.concat().

5. Pandas Series (05_Pandas Series)

Introduction to the 1-dimensional Series data structure.

• Creating Series from lists.
• Using custom row labels/indexes.
• Accessing values by label or position.

6. Series Attributes & Methods (06_Pandas Series Attribute Methods)

Deep dive into Series inspection and manipulation.

• Attributes: .dtype, .ndim, .size, .name, .index, .hasnans (checking for NaNs).
• Methods: .head(), .tail(), .info() (summary).

7. Combining Series (07_Pandas Combine Series)

• Using the .combine() method with custom functions to merge data from two Series.

8. Categorical Data (08_Pandas Categorical Data)

• Working with memory-efficient category data types in both Series and DataFrames.

🚀 Getting Started

Prerequisites

Make sure you have Python installed along with the Pandas library.

pip install pandas

How to Run

You can run any of the demo scripts individually to see the output. For example:

python "01_Pandas DataFrame/Pandas_DataFrame_Demo-1.py"

🛠 Tools Used

• Python: Programming language.
• Pandas: Data manipulation library.
• NumPy: Used for numerical operations and np.NaN.
• PyCharm: IDE (indicated by the .idea configuration files).

---

Created by Dharm Patel