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Learn AI with Python · Lesson

EDA Workflow and Data Profiling

df.info(), df.describe(), missing value audit, data types check, cardinality analysis.

What Is Exploratory Data Analysis?

Exploratory Data Analysis (EDA) is the first thing you do with any dataset before modeling. Its goal is to understand structure, spot problems, and build intuition.

A disciplined EDA first-look checklist answers: How big is the data? What types are the columns? Where are the missing values? Are there duplicates? How are values distributed?

  • Catch data-quality issues early
  • Decide which features need cleaning
  • Form hypotheses to test later

Loading the Data

Everything starts with loading a DataFrame and taking a quick peek with head() and shape.

shape returns a (rows, columns) tuple so you instantly know the scale you are working with.

import pandas as pd

df = pd.read_csv("customers.csv")
print(df.shape)        # (10000, 8)
print(df.head())       # first 5 rows

All lessons in this course

  1. EDA Workflow and Data Profiling
  2. Univariate Analysis
  3. Bivariate and Multivariate Analysis
  4. Feature Distribution and Target Analysis
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