> ## Documentation Index
> Fetch the complete documentation index at: https://python4ai.codewithsiva.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Pandas

> Data manipulation and analysis using DataFrames and Series

## Introduction to Pandas

**Pandas** is the most popular Python library for data manipulation and analysis. It provides easy-to-use data structures and data analysis tools for handling structured (tabular) data, similar to working with an Excel spreadsheet or SQL table.

### Why use Pandas?

* Easy handling of missing data.
* Flexible tools for reshaping, pivoting, merging, and joining datasets.
* Fast data loading from files (CSV, Excel, JSON, SQL databases).

***

## Core Data Structures

Pandas has two primary data structures:

1. **Series**: A 1D labeled array (like a single column in Excel).
2. **DataFrame**: A 2D labeled data structure (like an entire Excel sheet/table).

### Creating a DataFrame

```python theme={null}
import pandas as pd

# Creating a DataFrame from a dictionary
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "London", "Paris"]
}

df = pd.DataFrame(data)
print(df)
```

**Output:**

```
      Name  Age      City
0    Alice   25  New York
1      Bob   30    London
2  Charlie   35     Paris
```

***

## Basic Operations

### Reading a CSV file

```python theme={null}
# Load dataset
df = pd.read_csv('data.csv')

# View the first 5 rows
print(df.head())
```

### Filtering Data

```python theme={null}
# Get all people older than 28
older_than_28 = df[df['Age'] > 28]
print(older_than_28)
```

***

## Next Steps

After manipulation, visualizing your data is key. Let's explore how to create charts using **Matplotlib**.