Data Visualization (Bar Charts, Scatter Plots, Pie Charts) and Working with CSV Files in Python

Definition

Data Visualization

Data visualization is the process of representing data graphically so that it is easier to understand, analyze, and identify patterns or trends. Python commonly uses the Matplotlib library for creating charts.

CSV File

A CSV (Comma-Separated Values) file is a plain text file used to store tabular data. Each line represents a row, and commas separate the values in each column.

Example:

Name,Age,City
Ali,20,Kabul
Sara,22,Herat
Ahmad,19,Mazar

1. Bar Chart

Definition

A Bar Chart is used to compare values among different categories using rectangular bars.

Key Points

• Best for comparing categories.
• Bar height represents the value.
• Can be vertical or horizontal.
• Created using plt.bar().

Example Code

import matplotlib.pyplot as plt

subjects = ["Math", "Physics", "English", "Programming"]
marks = [80, 70, 90, 95]

plt.bar(subjects, marks)

plt.title("Student Marks")
plt.xlabel("Subjects")
plt.ylabel("Marks")

plt.show()

Explanation

• plt.bar() creates the bar chart.
• title() adds a chart title.
• xlabel() labels the x-axis.
• ylabel() labels the y-axis.
• show() displays the chart.

Output

A vertical bar chart comparing marks in different subjects.

Common Mistakes

• Different lengths of labels and values.
• Forgetting plt.show().
• Incorrect data types.

Short Exam Notes

• Used to compare categories.
• Function: plt.bar(x, y)
• Library: matplotlib.pyplot

2. Scatter Plot

Definition

A Scatter Plot displays the relationship between two numerical variables using points.

Key Points

• Shows correlation between variables.
• Each point represents one observation.
• Created using plt.scatter().

Example Code

import matplotlib.pyplot as plt

hours = [1, 2, 3, 4, 5]
marks = [40, 55, 60, 75, 90]

plt.scatter(hours, marks)

plt.title("Study Hours vs Marks")
plt.xlabel("Study Hours")
plt.ylabel("Marks")

plt.show()

Explanation

Each point represents:

(Study Hours, Marks)

Example:

(3,60)

means:

• Studied 3 hours
• Scored 60 marks

Output

A graph containing several points showing the relationship between study hours and marks.

Common Mistakes

• Unequal x and y lists.
• Using text instead of numbers.
• Forgetting plt.show().

Short Exam Notes

• Used to show relationships.
• Function: plt.scatter(x, y)

3. Pie Chart

Definition

A Pie Chart shows how each category contributes to the whole using slices of a circle.

Key Points

• Represents percentages.
• Total equals 100%.
• Created using plt.pie().

Example Code

import matplotlib.pyplot as plt

languages = ["Python", "Java", "C++", "JavaScript"]
students = [40, 25, 20, 15]

plt.pie(students, labels=languages, autopct="%1.1f%%")

plt.title("Programming Language Popularity")

plt.show()

Explanation

• labels displays category names.
• autopct="%1.1f%%" displays percentages.

Output

A circular chart divided into slices representing each programming language.

Common Mistakes

• Values not representing meaningful proportions.
• Missing labels.
• Forgetting autopct.

Short Exam Notes

• Shows percentage distribution.
• Function: plt.pie()

Working with CSV Files

Definition

A CSV (Comma-Separated Values) file stores data in rows and columns using commas.

Example:

ID,Name,Age
1,Ali,20
2,Sara,22
3,Ahmad,19

Python provides the built-in csv module for reading and writing CSV files.

import csv

Reading Data from a CSV File

Example CSV (students.csv)

Name,Age,Department
Ali,20,CS
Sara,22,IT
Ahmad,19,SE

Example Code

import csv

with open("students.csv", "r") as file:
    reader = csv.reader(file)

    for row in reader:
        print(row)

Explanation

• open() opens the file.
• "r" means read mode.
• csv.reader() reads each row.
• Each row is returned as a list.

Output

['Name', 'Age', 'Department']
['Ali', '20', 'CS']
['Sara', '22', 'IT']
['Ahmad', '19', 'SE']

Writing Data into a CSV File

Example Code

import csv

data = [
    ["Name", "Age", "Department"],
    ["Ali", 20, "CS"],
    ["Sara", 22, "IT"],
    ["Ahmad", 19, "SE"]
]

with open("students.csv", "w", newline="") as file:
    writer = csv.writer(file)

    writer.writerows(data)

print("Data written successfully.")

Explanation

• "w" opens the file for writing.
• csv.writer() creates a writer object.
• writerows() writes multiple rows.
• newline="" prevents extra blank lines (especially on Windows).

Output

A file named students.csv is created containing:

Name,Age,Department
Ali,20,CS
Sara,22,IT
Ahmad,19,SE

Advantages of CSV Files

• Simple and lightweight.
• Human-readable.
• Supported by Excel, Google Sheets, databases, and many programming languages.
• Easy to import and export data.
• Fast for storing tabular data.

Common Mistakes

• Forgetting import csv.
• Opening a file in the wrong mode ("r" vs "w").
• Forgetting newline="" when writing.
• Using the wrong file path.
• Assuming numbers read from CSV are integers (they are read as strings by default).

Important Matplotlib Functions

Important CSV Functions

Short Exam Notes (Quick Revision)

• Bar Chart: Compares categories using bars (plt.bar()).
• Scatter Plot: Shows relationships between two numerical variables (plt.scatter()).
• Pie Chart: Displays percentage distribution (plt.pie()).
• CSV: Comma-Separated Values; stores tabular data in plain text.
• Import CSV module using import csv.
• Read CSV with csv.reader().
• Write CSV with csv.writer().
• Use newline="" when writing CSV files.
• Always use plt.show() to display Matplotlib charts.