Exploratory data analysis workflow in Pandas

Introduction

Exploratory data analysis helps us understand data by looking at it in many ways. It shows patterns, problems, and important details before we do more work.

When you get a new dataset and want to know what it contains.

Before building a model to check data quality and find missing values.

To find interesting trends or unusual points in your data.

When you want to summarize data quickly with statistics and charts.

To decide how to clean or change data for better results.

Syntax

Pandas

import pandas as pd

df = pd.read_csv('file.csv')

# Step 1: Look at data
print(df.head())

# Step 2: Check data info
print(df.info())

# Step 3: Summary statistics
print(df.describe())

# Step 4: Check missing values
print(df.isnull().sum())

# Step 5: Visualize data (example)
df['column'].hist()

Use head() to see first rows and get a quick look.

info() shows data types and missing values.

Examples

Shows the first 10 rows of the data to get a bigger preview.

Pandas

df.head(10)

Gives summary statistics for all columns, including non-numeric ones.

Pandas

df.describe(include='all')

Counts how many missing values are in each column.

Pandas

df.isnull().sum()

Draws a histogram to see the distribution of the 'age' column.

Pandas

df['age'].hist()

Sample Program

This code creates a small table with some missing values. It then shows the first rows, data info, summary stats, missing values count, and draws a histogram for the age column.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

# Create a small sample dataset
data = {
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'age': [25, 30, 35, None, 40],
    'salary': [50000, 60000, 70000, 80000, None]
}

df = pd.DataFrame(data)

# Step 1: Look at first rows
print('First rows:')
print(df.head())

# Step 2: Data info
print('\nData info:')
df.info()

# Step 3: Summary statistics
print('\nSummary statistics:')
print(df.describe())

# Step 4: Missing values
print('\nMissing values per column:')
print(df.isnull().sum())

# Step 5: Visualize age distribution
print('\nShowing histogram for age column...')
df['age'].hist()
plt.title('Age Distribution')
plt.xlabel('Age')
plt.ylabel('Count')
plt.show()

Important Notes

Always check for missing values early to decide how to handle them.

Use visualizations to see data shape and spot outliers easily.

Summary statistics help understand data spread and central values.

Summary

Exploratory data analysis helps you know your data well before using it.

Look at data samples, info, statistics, missing values, and charts step-by-step.

This process guides better decisions for cleaning and modeling data.

Practice

1. Which step is usually done first in an exploratory data analysis workflow?

easy

A. Viewing the first few rows of the dataset

B. Creating complex machine learning models

C. Removing all missing values immediately

D. Generating final reports without checking data

Solution

1. Step 1: Understand the initial data

   Exploratory data analysis starts by looking at the first few rows to get a feel for the data structure and values.

2. Step 2: Avoid premature actions

   Jumping to modeling or removing missing data without understanding can cause mistakes.

3. Final Answer:

   Viewing the first few rows of the dataset -> Option A

4. Quick Check:

   First step = View data sample [OK]

Hint: Always start by looking at data samples [OK]

Common Mistakes:

• Starting with modeling before understanding data
• Removing missing values without checking their impact
• Ignoring data overview and jumping to conclusions

2. Which pandas command correctly shows summary statistics of numeric columns in a DataFrame df?

easy

A. df.head()

B. df.info()

C. df.describe()

D. df.sample()

Solution

1. Step 1: Identify summary statistics function

   df.describe() returns count, mean, std, min, max, and quartiles for numeric columns.

2. Step 2: Differentiate other commands

   df.head() shows first rows, df.info() shows data types, and df.sample() shows random rows.

3. Final Answer:

   df.describe() -> Option C

4. Quick Check:

   Summary stats = df.describe() [OK]

Hint: Use df.describe() for quick numeric summaries [OK]

Common Mistakes:

• Using df.head() to get statistics
• Confusing df.info() with statistical summary
• Expecting df.sample() to summarize data

3. Given the DataFrame df below, what is the output of df.isnull().sum()?

import pandas as pd
data = {'A': [1, 2, None], 'B': [None, 2, 3], 'C': [1, 2, 3]}
df = pd.DataFrame(data)

medium

A. {'A': 1, 'B': 1, 'C': 0}

B. {'A': 0, 'B': 0, 'C': 0}

C. {'A': 1, 'B': 2, 'C': 1}

D. {'A': 2, 'B': 1, 'C': 0}

Solution

1. Step 1: Check missing values per column

   Column 'A' has one None, 'B' has one None, 'C' has none.

2. Step 2: Use isnull().sum() to count missing

   This method counts missing values per column, so output is {'A': 1, 'B': 1, 'C': 0}.

3. Final Answer:

   {'A': 1, 'B': 1, 'C': 0} -> Option A

4. Quick Check:

   Missing counts = {'A':1,'B':1,'C':0} [OK]

Hint: Count missing with df.isnull().sum() [OK]

Common Mistakes:

• Counting total missing instead of per column
• Confusing None with zero or empty string
• Misreading which columns have missing values

4. You run df.info() but get an error: AttributeError: 'DataFrame' object has no attribute 'infoo'. What is the likely cause?

medium

A. The DataFrame is empty

B. You typed the method name incorrectly

C. The pandas library is not imported

D. The DataFrame has no columns

Solution

1. Step 1: Check the error message

   The error says 'infoo' attribute not found, which suggests a typo in method name.

2. Step 2: Confirm correct method name

   The correct method is df.info() with one 'o'.

3. Final Answer:

   You typed the method name incorrectly -> Option B

4. Quick Check:

   Typo in method name = error [OK]

Hint: Check spelling of method names carefully [OK]

Common Mistakes:

• Assuming empty DataFrame causes this error
• Forgetting to import pandas
• Thinking DataFrame must have columns to use info()

5. You have a DataFrame df with columns ['Age', 'Salary', 'Department']. You want to find the average salary per department but only for employees older than 30. Which pandas workflow is correct?

hard

A. df.groupby('Department')['Salary'].mean()

B. df['Salary'].mean().groupby('Department')[df['Age'] > 30]

C. df.groupby('Department').mean()['Salary'][df['Age'] > 30]

D. df[df['Age'] > 30].groupby('Department')['Salary'].mean()

Solution

1. Step 1: Filter rows where Age > 30

   Use df[df['Age'] > 30] to keep only employees older than 30.

2. Step 2: Group filtered data by Department and calculate mean Salary

   Apply .groupby('Department')['Salary'].mean() on filtered data to get average salary per department.

3. Final Answer:

   df[df['Age'] > 30].groupby('Department')['Salary'].mean() -> Option D

4. Quick Check:

   Filter then group and mean = df[df['Age'] > 30].groupby('Department')['Salary'].mean() [OK]

Hint: Filter first, then group and aggregate [OK]

Common Mistakes:

• Grouping before filtering data
• Trying to filter after aggregation
• Using mean() without grouping