Data Analysis with Pandas

Pandas is the most popular Python library for data analysis. It provides high-performance, easy-to-use data structures and data analysis tools.

The primary data structure in Pandas is the 'DataFrame', which is like a table or a spreadsheet.

First, you'll need to import pandas. It's common practice to import it with the alias 'pd'.

import pandas as pd

You can create a DataFrame from a dictionary:

data = {
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35]
}
df = pd.DataFrame(data)

print(df)

This will output a nicely formatted table.

A common task is to read data from a file, like a CSV:

# df = pd.read_csv('your_data.csv')

You can then easily select columns, filter rows, and perform complex calculations on your data. For example, to get the 'Age' column:

print(df['Age'])

Pandas is a fundamental tool for anyone working with data in Python.