Exploring Datasets

Exploratory Data Analysis (EDA) is the process of examining a dataset to understand its structure, spot patterns, and catch problems before building any model. This page walks through a typical EDA workflow using everything covered so far.

Step 1: Load and Inspect

import pandas as pd

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

print(df.shape)
print(df.head())
print(df.info())

Step 2: Check for Data Quality Issues

print(df.isnull().sum())
print(df.duplicated().sum())
print(df.dtypes)

Step 3: Summarise the Numbers

print(df.describe())
print(df["Score"].value_counts())
print(df.groupby("City")["Score"].mean())

Step 4: Ask Questions of the Data

# Which city has the highest average score?
print(df.groupby("City")["Score"].mean().sort_values(ascending=False))

# How many students scored above 80?
print((df["Score"] > 80).sum())

Good exploration is rarely a single pass - you inspect, clean, summarise, and then ask new questions based on what you find, often looping back through these steps several times.

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