BCA & BBA(CA) Data Visualization | Exam Ready Questions + Solutions 💡

BCA & BBA(CA) Data Visualization | Exam Ready Questions + Solutions 💡

Q1. To perform exploratory data analysis (EDA) on with datasets like email data set..

# Import pandas → used to read and analyze data

import pandas as pd  

# Import matplotlib → used for basic graphs

import matplotlib.pyplot as plt  

# Import seaborn → used for advanced visualization

import seaborn as sns  

# Import files → used to upload file in Google Colab

from google.colab import files  

# 📂 Upload CSV file

uploaded = files.upload()  

# why we use this → to open "Choose File" button and upload dataset

# Get file name

file_name = list(uploaded.keys())[0]  

# why we use this → to access uploaded file name automatically

# Read CSV file

df = pd.read_csv(file_name)  

# why we use this → to load CSV data into dataframe

# Show first 5 rows

print(df.head())  

# why we use this → to quickly see sample data

# Show dataset information

print(df.info())  

# why we use this → to check data types & null values

# Show statistical summary

print(df.describe())  

# why we use this → to get mean, min, max, etc.

# Check missing values

print(df.isnull().sum())  

# why we use this → to find missing data in each column

# Histogram

df.hist()  

plt.show()  

# why we use this → to see data distribution

# Correlation heatmap

sns.heatmap(df.corr(numeric_only=True), annot=True)  

plt.show()  

# why we use this → to find relationship between columns

print("✅ EDA Completed")

CSV - DOWNLOAD 

email_id,sender,subject,message,spam,label
1,abc@gmail.com,Win Money Now,"Congratulations! You have won $1000. Click here",Yes,1
2,friend@gmail.com,Meeting Today,"Hey, let's meet at 5pm today",No,0
3,offer@shopping.com,Big Sale,"Flat 70% discount on all items. Hurry up!",Yes,1
4,boss@company.com,Project Update,"Please send the project report by evening",No,0
5,lottery@win.com,Lottery Winner,"You are selected for lottery prize. Claim now",Yes,1
6,colleague@office.com,Lunch Plan,"Shall we go for lunch together?",No,0
7,bank@secure.com,Account Alert,"Your account is blocked. Verify immediately",Yes,1
8,team@company.com,Team Meeting,"Weekly team meeting at 10 AM",No,0
9,deals@amazon.com,Exclusive Offer,"Buy 1 Get 1 Free on electronics",Yes,1
10,hr@company.com,Interview Schedule,"Your interview is scheduled tomorrow",No,0

Q2.  Write a program to perform data analysis and representation on a map using various map data sets with mouse rollover effect, user interaction etc

# Install folium

!pip install folium

# Import libraries

import pandas as pd

import folium

from google.colab import files

# Upload CSV

uploaded = files.upload()

# Get file name

file_name = list(uploaded.keys())[0]

# Read CSV

df = pd.read_csv(file_name)

# Create map

m = folium.Map(location=[20.5937, 78.9629], zoom_start=5)

# Add markers

for i in range(len(df)):

    folium.Marker(

        location=[df.loc[i, 'lat'], df.loc[i, 'lon']],

        popup=f"{df.loc[i, 'city']} - {df.loc[i, 'value']}",

        tooltip=df.loc[i, 'city']

    ).add_to(m)

# Save map

m.save("map.html")

# ✅ IMPORTANT: Download file

files.download("map.html")

print("✅ Map downloaded successfully!")

CSV - DOWNLOAD

city,lat,lon,value
Pune,18.5204,73.8567,100
Mumbai,19.0760,72.8777,200
Delhi,28.7041,77.1025,150
Bangalore,12.9716,77.5946,180
Hyderabad,17.3850,78.4867,130
Chennai,13.0827,80.2707,170

Q3. Build cartographic visualization for multiple datasets involving various countries of the world.

# Install folium

!pip install folium

# Import libraries

import pandas as pd          # used to read CSV data

import folium                # used to create maps

from google.colab import files  # used to upload/download files

# Upload CSV file

uploaded = files.upload()

# why we use this → to upload dataset

# Get file name

file_name = list(uploaded.keys())[0]

# Read CSV

df = pd.read_csv(file_name)

# why we use this → to load dataset into dataframe

# Show data

print(df.head())

# Create world map

m = folium.Map(location=[20, 0], zoom_start=2)

# why we use this → to show world-level map

# Add markers for each country

for i in range(len(df)):

    folium.CircleMarker(

        location=[df.loc[i, 'lat'], df.loc[i, 'lon']],

       

        radius=df.loc[i, 'value'] / 50,  

        # why we use this → size represents data value

       

        popup=f"{df.loc[i, 'country']} - {df.loc[i, 'value']}",

        # why we use this → show details on click

       

        tooltip=df.loc[i, 'country'],  

        # why we use this → show country name on hover

       

        color='blue',

        fill=True

    ).add_to(m)

# Save map

m.save("world_map.html")

# why we use this → save map as HTML

# Download map

files.download("world_map.html")

# why we use this → download map file

print("✅ World Map Created Successfully!")

CSV - DOWNLOAD

country,lat,lon,value
India,20.5937,78.9629,1400
USA,37.0902,-95.7129,331
UK,55.3781,-3.4360,67
Germany,51.1657,10.4515,83
Brazil,-14.2350,-51.9253,213
Japan,36.2048,138.2529,125
Australia,-25.2744,133.7751,26
Canada,56.1304,-106.3468,38
France,46.2276,2.2137,65
China,35.8617,104.1954,1440

Q4. Create basic visualizations using Python.

Instructions:

1. Import dataset.

2. Create the following plots using Matplotlib:

• Line chart

• Bar chart

• Pie chart

• Histogram

• 
  

# Import libraries

import pandas as pd          # used to read CSV file

import matplotlib.pyplot as plt   # used for creating graphs

from google.colab import files   # used to upload file

# Upload CSV file

uploaded = files.upload()

# why we use this → to upload dataset

# Get file name

file_name = list(uploaded.keys())[0]

# Read CSV file

df = pd.read_csv(file_name)

# why we use this → to load dataset into dataframe

# Show data

print(df.head())

# ===============================

# 📈 Line Chart

# ===============================

plt.plot(df['month'], df['sales'])

# why we use this → to show trend over time

plt.title("Sales Trend")

plt.xlabel("Month")

plt.ylabel("Sales")

plt.show()

# ===============================

# 📊 Bar Chart

# ===============================

plt.bar(df['month'], df['profit'])

# why we use this → to compare values

plt.title("Monthly Profit")

plt.xlabel("Month")

plt.ylabel("Profit")

plt.show()

# ===============================

# 🥧 Pie Chart

# ===============================

plt.pie(df['sales'], labels=df['month'], autopct='%1.1f%%')

# why we use this → to show percentage distribution

plt.title("Sales Distribution")

plt.show()

# ===============================

# 📉 Histogram

# ===============================

plt.hist(df['sales'])

# why we use this → to show data distribution

plt.title("Sales Histogram")

plt.xlabel("Sales")

plt.ylabel("Frequency")

plt.show()

print("✅ All Visualizations Created Successfully!")

CSV- DOWNLOAD

month,sales,profit
Jan,100,20
Feb,150,30
Mar,200,50
Apr,180,40
May,220,60
Jun,250,70

Q5 Perform Exploratory Data Analysis on the Wine Quality Dataset.

# Import libraries

import pandas as pd              # used to read dataset

import matplotlib.pyplot as plt  # used for graphs

import seaborn as sns            # used for advanced graphs

from google.colab import files   # used to upload file

# Upload CSV file

uploaded = files.upload()

# why we use this → to upload dataset

# Get file name

file_name = list(uploaded.keys())[0]

# Load dataset

df = pd.read_csv(file_name)

# why we use this → to read CSV into dataframe

# Show first rows

print(df.head())

# why we use this → to understand data

# Dataset info

print(df.info())

# why we use this → to check data types & null values

# Statistical summary

print(df.describe())

# why we use this → to get mean, min, max etc.

# ===============================

# 🔍 Analyze key attributes

# ===============================

# Acidity distribution

plt.hist(df['fixed_acidity'])

plt.title("Fixed Acidity Distribution")

plt.xlabel("Acidity")

plt.ylabel("Frequency")

plt.show()

# why we use this → to see acidity distribution

# Sugar vs Alcohol (scatter plot)

plt.scatter(df['residual_sugar'], df['alcohol'])

plt.title("Sugar vs Alcohol")

plt.xlabel("Sugar")

plt.ylabel("Alcohol")

plt.show()

# why we use this → to find relationship

# Alcohol vs Quality

plt.bar(df['quality'], df['alcohol'])

plt.title("Alcohol vs Quality")

plt.xlabel("Quality")

plt.ylabel("Alcohol")

plt.show()

# why we use this → compare alcohol with quality

# ===============================

# 🔗 Correlation Analysis

# ===============================

corr = df.corr(numeric_only=True)

# why we use this → to find relationship between variables

print(corr)

# Heatmap

sns.heatmap(corr, annot=True, cmap='coolwarm')

plt.title("Correlation Heatmap")

plt.show()

# why we use this → visual representation of correlation

print("✅ EDA Completed Successfully!")

CSV - DOWNLOAD 

fixed_acidity,volatile_acidity,citric_acid,residual_sugar,alcohol,quality
7.4,0.70,0.00,1.9,9.4,5
7.8,0.88,0.00,2.6,9.8,5
7.8,0.76,0.04,2.3,9.8,5
11.2,0.28,0.56,1.9,9.8,6
7.4,0.70,0.00,1.9,9.4,5
7.9,0.60,0.06,1.6,9.4,5
7.3,0.65,0.00,1.2,10.0,6
7.8,0.58,0.02,2.0,9.5,5
7.5,0.50,0.36,6.1,10.5,6
6.7,0.58,0.08,1.8,9.2,5

Q6 . Load a dataset in Python and display:

• First 5 rows

• Last 5 rows

• Dataset structure

Use Pandas.

# Import pandas

import pandas as pd  

# why we use this → to work with dataset (CSV file)

from google.colab import files  

# why we use this → to upload file in Colab

# Upload CSV file

uploaded = files.upload()  

# why we use this → to select and upload dataset

# Get file name

file_name = list(uploaded.keys())[0]

# Load dataset

df = pd.read_csv(file_name)  

# why we use this → to read CSV file into dataframe

# ===============================

# 📊 Display First 5 Rows

# ===============================

print("First 5 Rows:")

print(df.head())  

# why we use this → to see starting data

# ===============================

# 📊 Display Last 5 Rows

# ===============================

print("\nLast 5 Rows:")

print(df.tail())  

# why we use this → to see ending data

# ===============================

# 📊 Dataset Structure

# ===============================

print("\nDataset Structure:")

print(df.info())  

# why we use this → to check columns, data types, null values

CSV - DOWNLOAD

id,name,age,marks
1,Amit,18,85
2,Riya,19,90
3,Rahul,18,78
4,Pooja,20,88
5,Neha,19,92
6,Arjun,18,75
7,Sneha,20,89
8,Karan,19,80

Q7. Convert a dataset into a Pandas DataFrame and perform:

• Sorting

• Filtering

• Display summary statistics.

# Import pandas

import pandas as pd  

# why we use this → to work with dataset

from google.colab import files  

# why we use this → to upload CSV file

# Upload CSV file

uploaded = files.upload()  

# why we use this → to select dataset

# Get file name

file_name = list(uploaded.keys())[0]

# Convert dataset into DataFrame

df = pd.read_csv(file_name)  

# why we use this → to load CSV into dataframe

# ===============================

# 🔽 Sorting

# ===============================

sorted_df = df.sort_values(by='salary')  

# why we use this → to arrange data by salary (ascending)

print("Sorted Data (by salary):")

print(sorted_df)

# ===============================

# 🔍 Filtering

# ===============================

filtered_df = df[df['salary'] > 35000]  

# why we use this → to select rows with salary > 35000

print("\nFiltered Data (salary > 35000):")

print(filtered_df)

# ===============================

# 📊 Summary Statistics

# ===============================

summary = df.describe()  

# why we use this → to get mean, min, max, etc.

print("\nSummary Statistics:")

print(summary)

CSV - DOWNLAD

id,name,age,salary,department
1,Amit,25,30000,IT
2,Riya,30,40000,HR
3,Rahul,28,35000,Finance
4,Pooja,32,45000,IT
5,Neha,27,38000,HR
6,Arjun,29,42000,Finance
7,Sneha,31,46000,IT
8,Karan,26,32000,HR

Q8. Create the following plots using Python:

• Histogram

• Pie Chart

# Import libraries

import pandas as pd              # used to read dataset

import matplotlib.pyplot as plt  # used for graphs

from google.colab import files   # used to upload file

# Upload CSV file

uploaded = files.upload()

# why we use this → to upload dataset

# Get file name

file_name = list(uploaded.keys())[0]

# Load dataset

df = pd.read_csv(file_name)

# why we use this → to convert CSV into dataframe

# Show data

print(df.head())

# ===============================

# 📉 Histogram

# ===============================

plt.hist(df['marks'])

# why we use this → to show distribution of marks

plt.title("Marks Distribution")

plt.xlabel("Marks")

plt.ylabel("Frequency")

plt.show()

# ===============================

# 🥧 Pie Chart

# ===============================

plt.pie(df['marks'], labels=df['name'], autopct='%1.1f%%')

# why we use this → to show percentage share

plt.title("Marks Percentage")

plt.show()

print("✅ Plots Created Successfully!")

CSV - DOWNLOAD

name,marks
Amit,85
Riya,90
Rahul,78
Pooja,88
Neha,92
Arjun,75
Sneha,89
Karan,80

Q9  Perform EDA on the  Wine Quality Dataset.

Steps:

• Load dataset

• Display summary

• Plot graphs

# Import libraries

import pandas as pd              # used to load dataset

import matplotlib.pyplot as plt  # used for graphs

from google.colab import files   # used to upload file

# Upload CSV file

uploaded = files.upload()

# why we use this → to upload dataset

# Get file name

file_name = list(uploaded.keys())[0]

# Load dataset

df = pd.read_csv(file_name)

# why we use this → to convert CSV into dataframe

# ===============================

# 📊 Display Summary

# ===============================

print("First 5 Rows:")

print(df.head())

# why we use this → to view starting data

print("\nDataset Info:")

print(df.info())

# why we use this → to check structure

print("\nSummary Statistics:")

print(df.describe())

# why we use this → to get mean, min, max

# ===============================

# 📈 Plot Graphs

# ===============================

# Histogram (Alcohol)

plt.hist(df['alcohol'])

plt.title("Alcohol Distribution")

plt.xlabel("Alcohol")

plt.ylabel("Frequency")

plt.show()

# why we use this → to see distribution

# Line Plot (Quality vs Alcohol)

plt.plot(df['quality'], df['alcohol'])

plt.title("Quality vs Alcohol")

plt.xlabel("Quality")

plt.ylabel("Alcohol")

plt.show()

# why we use this → to see trend

# Bar Chart (Quality)

plt.bar(df['quality'], df['alcohol'])

plt.title("Alcohol by Quality")

plt.xlabel("Quality")

plt.ylabel("Alcohol")

plt.show()

# why we use this → to compare values

print("✅ EDA Completed Successfully!")

CSV - DOWNLOAD 

fixed_acidity,volatile_acidity,citric_acid,residual_sugar,alcohol,quality
7.4,0.70,0.00,1.9,9.4,5
7.8,0.88,0.00,2.6,9.8,5
7.8,0.76,0.04,2.3,9.8,5
11.2,0.28,0.56,1.9,9.8,6
7.4,0.70,0.00,1.9,9.4,5
7.9,0.60,0.06,1.6,9.4,5
7.3,0.65,0.00,1.2,10.0,6
7.8,0.58,0.02,2.0,9.5,5
7.5,0.50,0.36,6.1,10.5,6
6.7,0.58,0.08,1.8,9.2,5

Q10. Create the following graphs using Python:

• Line graph 

• Scatter plot    

# Import libraries

import pandas as pd              # used to read dataset

import matplotlib.pyplot as plt  # used for graphs

from google.colab import files   # used to upload file

# Upload CSV file

uploaded = files.upload()

# why we use this → to upload dataset

# Get file name

file_name = list(uploaded.keys())[0]

# Load dataset

df = pd.read_csv(file_name)

# why we use this → to convert CSV into dataframe

# Show data

print(df.head())

# ===============================

# 📈 Line Graph

# ===============================

plt.plot(df['month'], df['sales'])

# why we use this → to show trend over time

plt.title("Sales Trend")

plt.xlabel("Month")

plt.ylabel("Sales")

plt.show()

# ===============================

# 🔵 Scatter Plot

# ===============================

plt.scatter(df['advertising'], df['sales'])

# why we use this → to show relationship between variables

plt.title("Advertising vs Sales")

plt.xlabel("Advertising")

plt.ylabel("Sales")

plt.show()

print("✅ Graphs Created Successfully!")

CSV - DOWNLOAD

month,sales,advertising
Jan,100,20
Feb,150,25
Mar,200,30
Apr,180,28
May,220,35
Jun,250,40

Q11. Create a dataset and perform operations using  NumPy.

Operations:

• Mean

• Median

• Standard deviation

# Import libraries

import numpy as np              # used for numerical operations

import pandas as pd             # used to read CSV

from google.colab import files  # used to upload file

# Upload CSV file

uploaded = files.upload()

# why we use this → to upload dataset

# Get file name

file_name = list(uploaded.keys())[0]

# Load dataset using pandas

df = pd.read_csv(file_name)

# why we use this → to read CSV file

# Convert column into NumPy array

data = df['values'].values

# why we use this → to convert data into NumPy format

# ===============================

# 📊 Mean

# ===============================

mean_value = np.mean(data)

# why we use this → to find average

print("Mean:", mean_value)

# ===============================

# 📊 Median

# ===============================

median_value = np.median(data)

# why we use this → to find middle value

print("Median:", median_value)

# ===============================

# 📊 Standard Deviation

# ===============================

std_value = np.std(data)

# why we use this → to measure spread of data

print("Standard Deviation:", std_value)

CSV - DOWNLOAD

values
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