Exploratory Data Analysis (EDA) template in Data Analysis Python - Step-by-Step Execution

Concept Flow - Exploratory Data Analysis (EDA) template

Load Data

↓

View Data Sample

↓

Check Data Info

↓

Summary Statistics

↓

Check Missing Values

↓

Visualize Data

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Draw Insights

This flow shows the main steps of EDA: loading data, viewing samples, checking info, summarizing stats, finding missing data, visualizing, and drawing insights.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.read_csv('data.csv')
print(df.head())
df.info()
print(df.describe())

This code loads a CSV file into a DataFrame, shows the first rows, data info, and summary statistics.

Execution Table

Step	Action	Code/Method	Output Example
1	Load Data	pd.read_csv('data.csv')	DataFrame with rows and columns loaded
2	View Data Sample	df.head()	First 5 rows of the DataFrame displayed
3	Check Data Info	df.info()	Shows column names, data types, non-null counts
4	Summary Statistics	df.describe()	Count, mean, std, min, max for numeric columns
5	Check Missing Values	df.isnull().sum()	Number of missing values per column
6	Visualize Data	df.hist() or sns.pairplot(df)	Histograms or pair plots to see distributions and relations
7	Draw Insights	Based on above outputs	Notes on data quality, trends, outliers
Exit	EDA Complete	All steps done	Ready for modeling or deeper analysis

💡 All EDA steps completed to understand data structure and quality

Variable Tracker

Variable	Start	After Load	After Head	After Info	After Describe	After Missing Check	After Visualization	Final
df	None	DataFrame loaded	First 5 rows shown	Info printed	Summary stats printed	Missing counts calculated	Plots generated	Insights noted

Key Moments - 3 Insights

Why do we look at df.head() before other steps?

df.head() shows a small sample of data so we can quickly see what columns and values look like before deeper checks (see execution_table step 2).

What does df.info() tell us that df.describe() does not?

df.info() shows data types and missing values count, while df.describe() only summarizes numeric columns (see execution_table steps 3 and 4).

Why check for missing values early in EDA?

Missing values affect analysis and modeling, so identifying them early (step 5) helps decide how to handle them.

Visual Quiz - 3 Questions

Look at the execution table, what output do we get from df.info() at step 3?

A Summary statistics like mean and std

B First 5 rows of data

C Data types and non-null counts per column

D Number of missing values per column

💡 Hint

Check the 'Output Example' column for step 3 in the execution_table

At which step do we calculate the number of missing values per column?

A Step 2

B Step 5

C Step 4

D Step 6

💡 Hint

Look for 'Check Missing Values' in the Action column of execution_table

If df.head() showed unexpected columns, which step would you repeat or check again?

A Step 1: Load Data

B Step 3: Check Data Info

C Step 6: Visualize Data

D Step 7: Draw Insights

💡 Hint

Unexpected columns usually mean data loaded incorrectly, so check step 1

Concept Snapshot

Exploratory Data Analysis (EDA) template:
1. Load data with pd.read_csv()
2. View sample rows with df.head()
3. Check data types and completeness with df.info()
4. Get summary stats with df.describe()
5. Find missing values with df.isnull().sum()
6. Visualize distributions and relations
7. Draw insights for next steps

Full Transcript

Exploratory Data Analysis (EDA) is a step-by-step process to understand data. First, we load the data into a DataFrame. Then, we look at a small sample using df.head() to see what the data looks like. Next, we check data types and missing values with df.info(). We get summary statistics for numeric columns using df.describe(). We count missing values with df.isnull().sum(). We visualize data distributions and relationships using plots. Finally, we draw insights about data quality and patterns to guide further analysis or modeling.