✅ Part 1: Matplotlib (Step-by-Step)

📌 Step 1: Install and Import

pip install matplotlib

import matplotlib.pyplot as plt

📌 Step 2: Line Chart

x = [1, 2, 3, 4]
y = [10, 20, 30, 25]

plt.plot(x, y)
plt.title("Line Chart Example")
plt.xlabel("X Values")
plt.ylabel("Y Values")
plt.show()

📈 Used For: Show progress over time (e.g. sales growth)

📌 Step 3: Bar Chart

subjects = ['Math', 'Science', 'English']
marks = [85, 90, 78]

plt.bar(subjects, marks)
plt.title("Student Marks")
plt.xlabel("Subjects")
plt.ylabel("Marks")
plt.show()

📊 Used For: Compare items like subject-wise marks or sales.

📌 Step 4: Pie Chart

fruits = ['Apple', 'Banana', 'Orange']
quantities = [40, 35, 25]

plt.pie(quantities, labels=fruits, autopct='%1.1f%%')
plt.title("Fruit Distribution")
plt.show()

🥧 Used For: Show percentage distribution (e.g. market share)

📌 Step 5: Histogram

ages = [18, 22, 22, 25, 26, 28, 28, 30, 35, 35]

plt.hist(ages, bins=5)
plt.title("Age Group Distribution")
plt.xlabel("Age")
plt.ylabel("Frequency")
plt.show()

📚 Used For: See how data is spread out (e.g. age of people)

📌 Step 6: Scatter Plot

hours = [1, 2, 3, 4, 5]
marks = [40, 50, 65, 75, 85]

plt.scatter(hours, marks)
plt.title("Study Time vs Marks")
plt.xlabel("Hours Studied")
plt.ylabel("Marks")
plt.show()

📌 Used For: Relationship between two things (e.g. effort vs result)

✅ Part 2: Data Cleaning with Pandas (Step-by-Step)

📌 Step 1: Import Pandas

import pandas as pd

📌 Step 2: Check Missing Values

df = pd.read_csv("students.csv")
print(df.isnull())          # Shows True/False
print(df.isnull().sum())    # Shows total missing per column

📌 Step 3: Drop Missing Rows

df_clean = df.dropna()

🗑️ Removes rows with any missing value.

📌 Step 4: Fill Missing Values

df.fillna(0, inplace=True)  # Fill missing with 0
df['Marks'].fillna(df['Marks'].mean(), inplace=True)  # Fill with average

📌 Step 5: Remove Duplicate Rows

df = df.drop_duplicates()

🧹 Removes repeated rows in data.

📌 Step 6: Change Data Type

df['Age'] = df['Age'].astype(int)

🧠 Convert from float or string to int.

📌 Step 7: Rename Columns

df.rename(columns={'Full Name': 'Name'}, inplace=True)

📌 Step 8: Clean Strings

df['Name'] = df['Name'].str.strip().str.title()

✍️ Clean unwanted spaces and format properly.

✅ Part 3: Excel Integration with Pandas (Step-by-Step)

📌 Step 1: Install Required Library

pip install openpyxl

(openpyxl is needed for Excel support)

📌 Step 2: Read Excel File

df = pd.read_excel("students.xlsx")

📥 Load Excel file into Pandas.

📌 Step 3: Read Specific Sheet

df = pd.read_excel("students.xlsx", sheet_name='Marks')

📄 Only read one sheet by name.

📌 Step 4: Write to Excel

df.to_excel("output.xlsx", index=False)

📤 Save DataFrame to Excel file.

📌 Step 5: Save Multiple Sheets

with pd.ExcelWriter("multi_sheet.xlsx") as writer:
    df1.to_excel(writer, sheet_name='Sheet1')
    df2.to_excel(writer, sheet_name='Sheet2')

📚 Save multiple reports in one Excel file.

✅ Real-Life Use Cases

✅ Interview Questions & Answers (Matplotlib + Pandas Data Cleaning + Excel Integration)

+ Hands-on Practice Tasks for Students

🎯 Section 1: Interview Questions & Answers

🔹 1. What is Matplotlib?

Answer:
Matplotlib is a Python library used to create visualizations like line charts, bar graphs, pie charts, histograms, and scatter plots.

🔹 2. How do you create a bar chart in Matplotlib?

Answer:
You use the bar() function:

import matplotlib.pyplot as plt
plt.bar(['A', 'B'], [10, 20])
plt.show()

🔹 3. What is the use of plt.show()?

Answer:
plt.show() displays the graph or plot in a new window.

🔹 4. What is the difference between plot() and scatter()?

Answer:

• plot() is used for line charts (connected data).
• scatter() is for individual data points (used to find patterns).

🔹 5. What is Pandas?

Answer:
Pandas is a Python library used to store and analyze data in table-like formats using DataFrames.

🔹 6. How do you handle missing data in Pandas?

Answer:
You can:

• Use dropna() to remove missing rows.
• Use fillna() to fill missing values.

🔹 7. How do you find missing values in a DataFrame?

Answer:
Use:

df.isnull()
df.isnull().sum()

🔹 8. How to remove duplicate values in a dataset?

Answer:
Use df.drop_duplicates().

🔹 9. How do you read and write Excel files in Pandas?

Answer:

• Read: pd.read_excel("file.xlsx")
• Write: df.to_excel("output.xlsx", index=False)

🔹 10. What is the use of ExcelWriter in Pandas?

Answer:
It allows saving multiple DataFrames into one Excel file with multiple sheets.

🧪 Section 2: Hands-on Examples (Student Practice)

✅ 1. Create a Line Chart for Weekly Sales

import matplotlib.pyplot as plt

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
sales = [100, 120, 90, 150, 130]

plt.plot(days, sales)
plt.title("Weekly Sales")
plt.xlabel("Days")
plt.ylabel("Sales")
plt.show()

✅ 2. Clean Student Data (CSV)

CSV Example:

Name,Age,Marks
John,21,85
Sara,,78
,20,90
Anna,22,
John,21,85

Python Code:

import pandas as pd

df = pd.read_csv("students.csv")

# Step 1: Show missing values
print(df.isnull().sum())

# Step 2: Fill missing values
df['Name'].fillna('Unknown', inplace=True)
df['Age'].fillna(df['Age'].mean(), inplace=True)
df['Marks'].fillna(df['Marks'].mean(), inplace=True)

# Step 3: Remove duplicates
df = df.drop_duplicates()

print(df)

✅ 3. Read Excel and Show Subject-wise Marks

import pandas as pd

df = pd.read_excel("marks.xlsx", sheet_name="Sheet1")

print("Average marks per subject:")
print(df.mean())

✅ 4. Save Cleaned Data to Excel

df.to_excel("cleaned_students.xlsx", index=False)

✅ 5. Practice Task: Fruit Pie Chart

Create a pie chart for this data:

Code:

import matplotlib.pyplot as plt

fruits = ['Apple', 'Banana', 'Mango', 'Orange']
quantities = [40, 30, 20, 10]

plt.pie(quantities, labels=fruits, autopct='%1.1f%%')
plt.title("Fruit Sale Distribution")
plt.show()

📘 Summary for Students