📊 Introduction to Pandas

Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool built on top of the Python programming language.

📦 How to Install Pandas

pip install pandas

Or install it along with Jupyter and NumPy:

pip install jupyter pandas numpy

📂 Importing Pandas

Use this command in your Python script or notebook:

import pandas as pd

Why as pd? It allows you to use shorter aliases like pd.DataFrame().

📄 What is a DataFrame?

A DataFrame is a 2D labeled data structure (like a table with rows and columns). It’s the most used object in pandas.

✅ Creating a DataFrame

import pandas as pd
data = {
"Name": ["Alice", "Bob", "Charlie"],
"Age": [25, 30, 35],
"City": ["Delhi", "Mumbai", "Bangalore"]
}

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

💡 Output:

 Name Age City 0 Alice 25 Delhi 1 Bob 30 Mumbai 2 Charlie 35 Bangalore 

🧪 Common Pandas Operations

• df.head() – Shows top 5 rows
• df.tail() – Shows bottom 5 rows
• df.info() – Displays structure info
• df.describe() – Shows statistical summary
• df['ColumnName'] – Access a specific column

📥 Reading External Files

df = pd.read_csv("data.csv")

You can also read Excel, JSON, SQL, etc.

🎯 Conclusion

Pandas is your go-to toolkit for data wrangling in Python. It’s intuitive, powerful, and a must-have skill in data science, machine learning, and analytics.