Introduction to Pandas: DataFrames, Indexing and GroupBy

Introduction

If you work with data in Python, you will use Pandas.

It is the standard library for loading, cleaning, transforming and exploring tabular data. Every data science project begins with Pandas.

What is a DataFrame?

A DataFrame is a two-dimensional table — rows and columns — like a spreadsheet or a SQL table. Each column is a Series, which is a one-dimensional labeled array.

Think of it like this:

• A Series is a single column
• A DataFrame is a collection of Series sharing the same index

Creating a DataFrame

import pandas as pd

# From a dictionary
data = {
    'name': ['Alice', 'Bob', 'Carol', 'David'],
    'age': [28, 34, 29, 45],
    'salary': [55000, 72000, 63000, 91000],
    'department': ['Engineering', 'Marketing', 'Engineering', 'Finance']
}

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

     name  age  salary   department
0   Alice   28   55000  Engineering
1     Bob   34   72000    Marketing
2   Carol   29   63000  Engineering
3   David   45   91000      Finance

Selecting Data

Selecting Columns

# Single column → Series
df['name']

# Multiple columns → DataFrame
df[['name', 'salary']]

Selecting Rows with loc and iloc

Use loc for label-based selection and iloc for integer position selection.

# loc: by label
df.loc[0]            # first row
df.loc[0:2]          # rows 0 to 2 (inclusive)
df.loc[df['age'] > 30]  # boolean mask

# iloc: by position
df.iloc[0]           # first row
df.iloc[0:2]         # rows 0 and 1
df.iloc[:, 0:2]      # all rows, first 2 columns

GroupBy: Aggregating Data

GroupBy splits your data into groups, applies a function, and combines the results.

# Average salary by department
df.groupby('department')['salary'].mean()

department
Engineering    59000.0
Finance        91000.0
Marketing      72000.0
Name: salary, dtype: float64

Summary

In this tutorial you learned:

• The difference between a Series and a DataFrame
• How to create DataFrames from dictionaries and CSV files
• How to select data using loc (label-based) and iloc (integer-based)
• How to aggregate data with GroupBy