Introduction to Pandas

Pandas is the most widely used Python library for working with structured, tabular data - the kind of row-and-column data you'd normally find in a spreadsheet or a database table. If NumPy gave you fast numerical arrays, Pandas gives you a powerful, flexible way to work with real-world, labeled data.

Why Pandas Exists

While NumPy arrays are excellent for pure numerical computation, real-world data is rarely just numbers - it has column names, mixed data types, missing values, and needs to be filtered, grouped, and reshaped constantly. Pandas was built specifically to handle exactly these kinds of everyday, messy, real-world data tasks.

Installing and Importing Pandas

pip install pandas

import pandas as pd
print(pd.__version__)

A Quick First Look

import pandas as pd

data = {
    "Name": ["Abhay", "Priya", "Rohit"],
    "Score": [85, 92, 78]
}

df = pd.DataFrame(data)
print(df)
#      Name  Score
# 0   Abhay     85
# 1   Priya     92
# 2   Rohit     78

Coming Up Next

Next, you'll learn about the two core data structures in Pandas - the Series and the DataFrame - and how they organize data.