Pandas Tutorial

Pandas is a Python library for working with data in tables, with rows and columns, just like an Excel sheet.

With Pandas you can load a file of thousands of rows, clean it, filter it, and get answers such as "which product sold the most last month?" in a few lines of code.

Why Learn Pandas?

• It is the most used tool for data analysis in Python
• It reads and writes CSV, Excel, JSON and SQL data
• It makes cleaning messy data (missing values, duplicates, wrong types) much easier
• Data analysts, data scientists and AI engineers use it every day

A First Example

import pandas as pd

sales = pd.DataFrame({
    "product": ["Tea", "Sugar", "Rice"],
    "price": [1200, 180, 350],
    "sold": [40, 95, 60]
})
sales["revenue"] = sales["price"] * sales["sold"]
print(sales)

  product  price  sold  revenue
0     Tea   1200    40    48000
1   Sugar    180    95    17100
2    Rice    350    60    21000

In one line we added a new column that multiplies price by quantity for every row.

What You Will Learn

• Series and DataFrames, the two main Pandas objects
• Reading CSV and other files, and getting a first look at your data
• Selecting rows and columns with loc and iloc, and filtering
• Cleaning data: missing values, duplicates and wrong types
• Sorting, adding columns, grouping and summarising
• Joining tables together

You should know basic Python first, and it helps to know NumPy. New to Python? Start with the Python tutorial.

What to Learn After Pandas

Continue with SciPy for statistics and scientific tools, or try R, another language made for data analysis.