Class 10th AI (417) CBSE Practical Sheet – Python Programs with Solutions

Practical Notes

This page contains all 15 questions in one document. Open any question to view its complete solution. Use the Copy Code button to copy a program into your Python editor.

Required libraries: NumPy, Pandas, Matplotlib and OpenCV.

Installation: Run this command in Terminal or Command Prompt: pip install numpy pandas matplotlib opencv-python

Important: OpenCV programs that use cv2.imshow() need a desktop environment. Keep the sample image flower.jpg in the Python script's working directory for Questions 7, 11, 12 and 13. Keep MelaSales.csv in the working directory for Question 6.

Arithmetic operators on a 2D NumPy array

Write a Python program to perform all arithmetic operators on two 2D arrays.

Question

Write a Python program to perform all arithmetic operators on two 2D arrays.

Python Solution

import numpy as np

# Creating two 2D arrays
A = np.array([[10, 20, 30],
              [40, 50, 60]])

B = np.array([[2, 4, 5],
              [5, 10, 3]])

print("First 2D Array:")
print(A)

print("\nSecond 2D Array:")
print(B)

print("\nAddition:")
print(A + B)

print("\nSubtraction:")
print(A - B)

print("\nMultiplication:")
print(A * B)

print("\nDivision:")
print(A / B)

print("\nModulus:")
print(A % B)

print("\nFloor Division:")
print(A // B)

print("\nExponentiation:")
print(A ** B)

Sample Output / Expected Result

Addition: [[12 24 35] [45 60 63]] Subtraction: [[ 8 16 25] [35 40 57]] Multiplication: [[ 20 80 150] [200 500 180]] Division: [[ 5. 5. 6.] [ 8. 5. 20.]] Modulus: [[0 0 0] [0 0 0]] Floor Division: [[ 5 5 6] [ 8 5 20]] Exponentiation: [[ 100 160000 24300000] [ 102400000 9765625000000000 216000]]

Explanation

`np.array()` creates NumPy arrays.
The operators `+`, `-`, `*`, `/`, `%`, `//` and `**` perform element-wise addition, subtraction, multiplication, division, modulus, floor division and exponentiation.
Both arrays have the same shape, so corresponding elements are operated on.

Note: NumPy's `*` performs element-wise multiplication. It is not matrix multiplication.

Create a DataFrame for interview candidates

Create a DataFrame with Name, Score, Attempts and Qualify columns. Use C001, C002, and so on as row indexes.

Question

Create a DataFrame with Name, Score, Attempts and Qualify columns. Use C001, C002, and so on as row indexes.

Python Solution

import pandas as pd

# Creating a dictionary to store candidate details
data = {
    'Name': ['Hardik', 'Rahul', 'Priya', 'Amit', 'Neha'],
    'Score': [85, 72, 90, 65, 78],
    'Attempts': [1, 2, 1, 3, 2],
    'Qualify': ['Yes', 'Yes', 'Yes', 'No', 'Yes']
}

# Creating custom row indexes
index = ['C001', 'C002', 'C003', 'C004', 'C005']

# Creating a DataFrame
df = pd.DataFrame(data, index=index)

print("Interview Candidates DataFrame:")
print(df)

Sample Output / Expected Result

Interview Candidates DataFrame: Name Score Attempts Qualify C001 Hardik 85 1 Yes C002 Rahul 72 2 Yes C003 Priya 90 1 Yes C004 Amit 65 3 No C005 Neha 78 2 Yes

Explanation

A dictionary stores the candidate details as columns.
The `index` list supplies custom row labels.
`pd.DataFrame(data, index=index)` converts the dictionary into a DataFrame with the specified indexes.

Filter players with more than 1000 runs

Create a DataFrame named player with Team, Matches, Runs and Average. Use player names as row indexes and display players whose runs exceed 1000.

Question

Create a DataFrame named player with Team, Matches, Runs and Average. Use player names as row indexes and display players whose runs exceed 1000.

Python Solution

import pandas as pd

# Creating a dictionary to store player details
data = {
    'Team': ['India', 'India', 'Australia', 'England', 'India'],
    'Matches': [100, 80, 90, 70, 60],
    'Runs': [4500, 3200, 2800, 950, 850],
    'Average': [45.0, 40.0, 31.1, 13.5, 14.2]
}

# Assigning player names as row indexes
names = ['Virat', 'Rohit', 'Smith', 'Root', 'Rahul']

# Creating a DataFrame named player
player = pd.DataFrame(data, index=names)

print("Original Player DataFrame:")
print(player)

# Displaying players whose runs are more than 1000
print("\nPlayers with Runs More Than 1000:")
print(player[player['Runs'] > 1000])

Sample Output / Expected Result

Players with Runs More Than 1000: Team Matches Runs Average Virat India 100 4500 45.0 Rohit India 80 3200 40.0 Smith Australia 90 2800 31.1

Explanation

`player['Runs'] > 1000` creates a Boolean condition.
Using it inside `player[...]` filters and displays only rows where the Runs value is greater than 1000.

Bar chart of mobile game ratings

Represent the ratings of PUBG, FreeFire, MineCraft, GTA-V, Call of Duty and FIFA 22 using a bar chart.

Question

Represent the ratings of PUBG, FreeFire, MineCraft, GTA-V, Call of Duty and FIFA 22 using a bar chart.

Python Solution

import matplotlib.pyplot as plt

games = ['PUBG', 'FreeFire', 'MineCraft',
         'GTA-V', 'Call of Duty', 'FIFA 22']

ratings = [4.5, 4.8, 4.7, 4.6, 4.1, 4.3]

plt.bar(games, ratings)

plt.title("Ratings of Mobile Games")
plt.xlabel("Mobile Games")
plt.ylabel("Ratings")

plt.show()

Sample Output / Expected Result

Matplotlib opens a bar chart with six bars. The game names appear on the X-axis and their ratings on the Y-axis.

Explanation

`plt.bar()` creates vertical bars.
`plt.title()`, `plt.xlabel()` and `plt.ylabel()` add the chart title and axis labels.
`plt.show()` displays the chart.

Line chart of average height and weight

Plot weight on the X-axis and height on the Y-axis. Use a green dashed line, star markers of size 10 and line width 2.

Question

Plot weight on the X-axis and height on the Y-axis. Use a green dashed line, star markers of size 10 and line width 2.

Python Solution

import matplotlib.pyplot as plt

height = [121.9, 124.5, 129.5, 134.6, 139.7,
          147.3, 152.4, 157.5, 162.6]

weight = [19.7, 21.3, 23.5, 25.9, 28.5,
          32.1, 35.7, 39.6, 43.2]

plt.plot(weight, height, color='green',
         marker='*', markersize=10,
         linestyle='dashed', linewidth=2)

plt.title("Average weight with respect to average height")
plt.xlabel("Weight in kg")
plt.ylabel("Height in cm")

plt.show()

Sample Output / Expected Result

The chart plots the nine weight-height pairs. It uses a green dashed line with star markers.

Explanation

`plt.plot(weight, height, ...)` assigns weight to the X-axis and height to the Y-axis.
`color`, `marker`, `markersize`, `linestyle` and `linewidth` control the line's appearance.

Menu-driven Mean, Mode and Median for MelaSales.csv

Smile NGO recorded day-wise sales for three weeks in MelaSales.csv. Read the CSV and calculate the mean, mode and median for each week.

Question

Smile NGO recorded day-wise sales for three weeks in MelaSales.csv. Read the CSV and calculate the mean, mode and median for each week.

Python Solution

import pandas as pd

# Reading data from CSV file
df = pd.read_csv("MelaSales.csv")

while True:
    print("\n----- SMILE NGO SALES ANALYSIS -----")
    print("1. Calculate Mean")
    print("2. Calculate Mode")
    print("3. Calculate Median")
    print("4. Exit")

    choice = int(input("Enter your choice (1-4): "))

    if choice == 1:
        print("\nMean of Sales:")
        print(df.mean(numeric_only=True))

    elif choice == 2:
        print("\nMode of Sales:")
        for col in df.select_dtypes(include='number').columns:
            modes = df[col].mode()
            if modes.empty:
                print(col, ": No Mode")
            elif df[col].value_counts().max() == 1:
                print(col, ": No repeated value (no mode)")
            else:
                print(col, ":", modes.tolist())

    elif choice == 3:
        print("\nMedian of Sales:")
        print(df.median(numeric_only=True))

    elif choice == 4:
        print("Thank you!")
        break

    else:
        print("Invalid Choice! Please enter 1 to 4.")

Sample Output / Expected Result

Example menu: ----- SMILE NGO SALES ANALYSIS ----- 1. Calculate Mean 2. Calculate Mode 3. Calculate Median 4. Exit Enter your choice (1-4): 1 Mean of Sales: Week 1 5442.857143 Week 2 4385.714286 Week 3 4300.000000

Explanation

`pd.read_csv()` loads the CSV file into a DataFrame.
`mean()` calculates the arithmetic average, `mode()` returns the most frequent value(s), and `median()` returns the middle value after ordering the data.
The loop repeats until option 4 is selected.

Note: Keep MelaSales.csv in the same folder as the Python file. The mean and median values shown are based on the sales table supplied earlier. A column in which every value occurs only once has no repeated mode.

OpenCV image loading, colour conversion, inspection, cropping and saving

Load an image, display it, convert its colour representation and grayscale version, inspect its shape and pixel range, crop it and save the results.

Question

Load an image, display it, convert its colour representation and grayscale version, inspect its shape and pixel range, crop it and save the results.

Python Solution

import cv2

# Loading an image
img = cv2.imread("flower.jpg")

if img is None:
    print("Image not found!")
else:
    # Displaying the original image
    cv2.imshow("Original Image", img)

    # Converting BGR to RGB
    color_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # Converting image into grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cv2.imshow("Grayscale Image", gray)

    # Printing the shape
    print("Shape of Image:", img.shape)

    # Displaying maximum and minimum pixel values
    print("Maximum Pixel Value:", img.max())
    print("Minimum Pixel Value:", img.min())

    # Cropping the image (rows 50-199, columns 50-199)
    crop = img[50:200, 50:200]
    cv2.imshow("Cropped Image", crop)

    # Saving the images
    cv2.imwrite("gray_image.jpg", gray)
    cv2.imwrite("cropped_image.jpg", crop)

    print("Images saved successfully!")

    cv2.waitKey(0)
    cv2.destroyAllWindows()

Sample Output / Expected Result

Example console output (depends on the input image): Shape of Image: (300, 400, 3) Maximum Pixel Value: 255 Minimum Pixel Value: 0 Images saved successfully!

Explanation

`cv2.imread()` loads the image; `cv2.imshow()` displays it.
`cv2.cvtColor()` converts BGR to RGB or grayscale.
`img.shape` gives height, width and channels.
`img.max()` and `img.min()` return the largest and smallest pixel values.
NumPy slicing crops the image, and `cv2.imwrite()` saves the output.

Note: `color_img` is converted to RGB for use in RGB-based workflows; OpenCV's `imshow()` expects BGR, so the original `img` is displayed in the OpenCV window. Keep flower.jpg beside the script or provide its full path.

Histogram chart using Matplotlib

Draw a histogram to show the frequency distribution of students' marks.

Question

Draw a histogram to show the frequency distribution of students' marks.

Python Solution

import matplotlib.pyplot as plt

marks = [45, 56, 67, 78, 89, 90, 55, 65,
         75, 85, 95, 40, 50, 60, 70, 80]

plt.hist(marks, bins=5, color='skyblue',
         edgecolor='black')

plt.title("Histogram of Students Marks")
plt.xlabel("Marks")
plt.ylabel("Frequency")

plt.show()

Sample Output / Expected Result

A histogram displays the distribution of marks grouped into five intervals (bins).

Explanation

`plt.hist()` creates the histogram.
`bins=5` divides the range into five intervals.
`color` sets the bar fill and `edgecolor` sets the bar outline.

Pie chart using Matplotlib

Draw a pie chart to represent the values for Python, Java, C++, HTML and SQL.

Question

Draw a pie chart to represent the values for Python, Java, C++, HTML and SQL.

Python Solution

import matplotlib.pyplot as plt

subjects = ['Python', 'Java', 'C++', 'HTML', 'SQL']
marks = [30, 25, 20, 15, 10]

plt.pie(marks, labels=subjects, autopct='%1.1f%%')

plt.title("Students Subject Preference")

plt.show()

Sample Output / Expected Result

The pie chart displays five sectors. Each sector is labelled with a subject and its percentage of the total.

Explanation

`plt.pie()` creates the pie chart.
`labels` supplies category names, and `autopct='%1.1f%%'` prints each sector's percentage to one decimal place.

Scatter chart using Matplotlib

Draw a scatter chart showing the relationship between study hours and marks obtained.

Question

Draw a scatter chart showing the relationship between study hours and marks obtained.

Python Solution

import matplotlib.pyplot as plt

hours = [1, 2, 3, 4, 5, 6, 7, 8]
marks = [35, 40, 50, 55, 65, 70, 80, 90]

plt.scatter(hours, marks, color='blue',
            marker='o', s=50)

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

plt.show()

Sample Output / Expected Result

The scatter chart displays one point for each study-hours and marks pair.

Explanation

`plt.scatter()` plots individual points.
`color` sets their colour, `marker='o'` selects circular markers, and `s=50` sets marker size.

Read and display original and grayscale images using OpenCV

Read an image using cv2 and display both the original image and its grayscale version.

Question

Read an image using cv2 and display both the original image and its grayscale version.

Python Solution

import cv2

# Reading the original image
img = cv2.imread("flower.jpg")

if img is None:
    print("Image not found!")
else:
    # Displaying the original image
    cv2.imshow("Original Image", img)

    # Converting the image into grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Displaying the grayscale image
    cv2.imshow("Gray Scale Image", gray)

    cv2.waitKey(0)
    cv2.destroyAllWindows()

Sample Output / Expected Result

Two OpenCV windows appear: one showing the original colour image and one showing the grayscale image.

Explanation

`cv2.imread()` reads the image.
`cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)` converts it to grayscale.
`cv2.waitKey(0)` waits for a key press, and `cv2.destroyAllWindows()` closes the windows.

Crop and resize an image using OpenCV

Crop a selected part of an image, resize the original image and save both results.

Question

Crop a selected part of an image, resize the original image and save both results.

Python Solution

import cv2

# Reading the original image
img = cv2.imread("flower.jpg")

if img is None:
    print("Image not found!")
else:
    cv2.imshow("Original Image", img)

    # Cropping the image
    crop = img[50:200, 50:200]
    cv2.imshow("Cropped Image", crop)

    # Resizing the image (width=300, height=300)
    resize = cv2.resize(img, (300, 300))
    cv2.imshow("Resized Image", resize)

    # Saving cropped and resized images
    cv2.imwrite("cropped.jpg", crop)
    cv2.imwrite("resized.jpg", resize)

    cv2.waitKey(0)
    cv2.destroyAllWindows()

Sample Output / Expected Result

Three windows display the original, cropped and resized images. The cropped and resized results are saved as cropped.jpg and resized.jpg.

Explanation

`img[50:200, 50:200]` extracts rows 50–199 and columns 50–199.
`cv2.resize(img, (300, 300))` changes the image to 300 pixels wide and 300 pixels high.
`cv2.imwrite()` saves each result.

Note: The crop coordinates must fall within the image dimensions. The resize tuple is (width, height), whereas NumPy image slicing uses (rows, columns).

OpenCV image inspection and processing

Load and title an image, convert it to grayscale, print its shape and maximum/minimum pixel values, crop it and save the processed images.

Question

Load and title an image, convert it to grayscale, print its shape and maximum/minimum pixel values, crop it and save the processed images.

Python Solution

import cv2

# Loading an image
img = cv2.imread("flower.jpg")

if img is None:
    print("Image not found!")
else:
    # Displaying the original image with a title
    cv2.imshow("Original Flower Image", img)

    # Converting image into grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cv2.imshow("Grayscale Image", gray)

    # Printing the shape of the image
    print("Shape of Image:", img.shape)

    # Displaying maximum and minimum pixel values
    print("Maximum Pixel Value:", img.max())
    print("Minimum Pixel Value:", img.min())

    # Cropping the image
    crop = img[50:200, 50:200]
    cv2.imshow("Cropped Image", crop)

    # Saving the images
    cv2.imwrite("gray_image.jpg", gray)
    cv2.imwrite("cropped_image.jpg", crop)

    print("Images saved successfully!")

    cv2.waitKey(0)
    cv2.destroyAllWindows()

Sample Output / Expected Result

Console output depends on the dimensions and pixel values of flower.jpg. The program also displays the original, grayscale and cropped images, and saves the grayscale and cropped versions.

Explanation

This combines the OpenCV operations: `imread()` loads, `imshow()` displays, `cvtColor()` converts to grayscale, `shape` reports dimensions, `max()` and `min()` inspect pixel intensity, slicing crops, and `imwrite()` saves images.

Note: Keep flower.jpg in the script's working directory. For an 8-bit image, pixel intensities normally range from 0 to 255.

Menu-driven NumPy program

Create a menu-driven program to perform common array operations using NumPy.

Question

Create a menu-driven program to perform common array operations using NumPy.

Python Solution

import numpy as np

# Initializing an empty array
arr = np.array([])

while True:
    print("\n========== NUMPY MENU ==========")
    print("1. Create an Array")
    print("2. Display Array")
    print("3. Find Sum of Array")
    print("4. Find Mean of Array")
    print("5. Find Median of Array")
    print("6. Find Maximum and Minimum")
    print("7. Sort Array")
    print("8. Reverse Array")
    print("9. Find Standard Deviation")
    print("10. Find Variance")
    print("11. Reshape Array")
    print("12. Perform Arithmetic Operations")
    print("13. Search an Element")
    print("14. Count Elements")
    print("15. Exit")

    choice = int(input("\nEnter your choice: "))

    if choice == 1:
        n = int(input("Enter number of elements: "))
        values = []

        for i in range(n):
            value = int(input("Enter element: "))
            values.append(value)

        arr = np.array(values)
        print("Array Created Successfully!")

    elif choice == 15:
        print("Thank You!")
        break

    elif arr.size == 0:
        print("Please create an array first!")

    elif choice == 2:
        print("Array:", arr)

    elif choice == 3:
        print("Sum of Array:", np.sum(arr))

    elif choice == 4:
        print("Mean of Array:", np.mean(arr))

    elif choice == 5:
        print("Median of Array:", np.median(arr))

    elif choice == 6:
        print("Maximum Element:", np.max(arr))
        print("Minimum Element:", np.min(arr))

    elif choice == 7:
        print("Sorted Array:", np.sort(arr))

    elif choice == 8:
        print("Reversed Array:", arr[::-1])

    elif choice == 9:
        print("Standard Deviation:", np.std(arr))

    elif choice == 10:
        print("Variance:", np.var(arr))

    elif choice == 11:
        r = int(input("Enter number of rows: "))
        c = int(input("Enter number of columns: "))

        if r > 0 and c > 0 and r * c == arr.size:
            matrix = arr.reshape(r, c)
            print("Reshaped Array:")
            print(matrix)
        else:
            print("Invalid dimensions!")

    elif choice == 12:
        num = int(input("Enter a number: "))
        print("1. Addition")
        print("2. Subtraction")
        print("3. Multiplication")
        print("4. Division")

        op = int(input("Select operation: "))

        if op == 1:
            print("Result:", arr + num)
        elif op == 2:
            print("Result:", arr - num)
        elif op == 3:
            print("Result:", arr * num)
        elif op == 4:
            if num != 0:
                print("Result:", arr / num)
            else:
                print("Division by zero is not allowed!")
        else:
            print("Invalid operation!")

    elif choice == 13:
        num = int(input("Enter element to search: "))

        if num in arr:
            print("Element Found!")
            print("Position:", np.where(arr == num)[0])
        else:
            print("Element Not Found!")

    elif choice == 14:
        print("Total Number of Elements:", np.size(arr))

    else:
        print("Invalid Choice! Please try again.")

Sample Output / Expected Result

Example: Enter your choice: 1 Enter number of elements: 5 Enter element: 10 Enter element: 20 Enter element: 30 Enter element: 40 Enter element: 50 Array Created Successfully! Enter your choice: 3 Sum of Array: 150 Enter your choice: 4 Mean of Array: 30.0

Explanation

The program first creates a NumPy array.
The menu then allows the user to display it, calculate sum/mean/median, find maximum and minimum, sort or reverse it, calculate standard deviation and variance, reshape it, perform scalar arithmetic, search for an element and count elements.

Note: Install NumPy with `pip install numpy`. Reshaping is possible only when rows × columns equals the number of array elements.

Menu-driven Pandas program

Create a menu-driven program to perform common DataFrame operations using Pandas.

Question

Create a menu-driven program to perform common DataFrame operations using Pandas.

Python Solution

import pandas as pd

# Creating an empty DataFrame
df = pd.DataFrame()

while True:
    print("\n========== PANDAS MENU ==========")
    print("1. Create a DataFrame")
    print("2. Display DataFrame")
    print("3. Display First 5 Records")
    print("4. Display Last 5 Records")
    print("5. Display DataFrame Information")
    print("6. Display Statistical Summary")
    print("7. Find Maximum and Minimum")
    print("8. Calculate Mean")
    print("9. Calculate Median")
    print("10. Sort DataFrame")
    print("11. Add a New Column")
    print("12. Delete a Column")
    print("13. Search / Filter Records")
    print("14. Display Column Names")
    print("15. Count Rows and Columns")
    print("16. Exit")

    choice = int(input("\nEnter your choice: "))

    if choice == 1:
        n = int(input("Enter number of students: "))

        names = []
        marks = []
        ages = []

        for i in range(n):
            print("\nEnter details of student", i + 1)
            name = input("Enter name: ")
            mark = int(input("Enter marks: "))
            age = int(input("Enter age: "))

            names.append(name)
            marks.append(mark)
            ages.append(age)

        data = {
            'Name': names,
            'Marks': marks,
            'Age': ages
        }

        df = pd.DataFrame(data)
        print("\nDataFrame Created Successfully!")

    elif choice == 16:
        print("Thank You!")
        break

    elif df.empty:
        print("Please create a DataFrame first!")

    elif choice == 2:
        print("\nComplete DataFrame:")
        print(df)

    elif choice == 3:
        print("\nFirst 5 Records:")
        print(df.head())

    elif choice == 4:
        print("\nLast 5 Records:")
        print(df.tail())

    elif choice == 5:
        print("\nDataFrame Information:")
        df.info()

    elif choice == 6:
        print("\nStatistical Summary:")
        print(df.describe())

    elif choice == 7:
        print("\nMaximum Values:")
        print(df.max(numeric_only=True))
        print("\nMinimum Values:")
        print(df.min(numeric_only=True))

    elif choice == 8:
        print("\nMean of Numeric Columns:")
        print(df.mean(numeric_only=True))

    elif choice == 9:
        print("\nMedian of Numeric Columns:")
        print(df.median(numeric_only=True))

    elif choice == 10:
        col = input("Enter column name for sorting: ")

        if col in df.columns:
            print("\nSorted DataFrame:")
            print(df.sort_values(by=col))
        else:
            print("Column not found!")

    elif choice == 11:
        col = input("Enter new column name: ")
        values = []

        for i in range(len(df)):
            value = input("Enter value: ")
            values.append(value)

        df[col] = values
        print("New Column Added Successfully!")
        print(df)

    elif choice == 12:
        col = input("Enter column name to delete: ")

        if col in df.columns:
            df.drop(columns=[col], inplace=True)
            print("Column Deleted Successfully!")
            print(df)
        else:
            print("Column not found!")

    elif choice == 13:
        col = input("Enter column name: ")

        if col in df.columns:
            value = input("Enter value to search: ")

            if pd.api.types.is_numeric_dtype(df[col]):
                value = pd.to_numeric(value, errors='coerce')

            result = df[df[col] == value]

            if not result.empty:
                print("\nMatching Records:")
                print(result)
            else:
                print("No matching record found!")
        else:
            print("Column not found!")

    elif choice == 14:
        print("\nColumn Names:")
        print(df.columns.tolist())

    elif choice == 15:
        print("\nNumber of Rows:", df.shape[0])
        print("Number of Columns:", df.shape[1])

    else:
        print("Invalid Choice! Please try again.")

Sample Output / Expected Result

Example DataFrame: Name Marks Age 0 Rahul 85 17 1 Priya 92 18 2 Amit 75 17 3 Neha 88 18 4 Karan 65 17 Mean of Numeric Columns: Marks 81.0 Age 17.4

Explanation

The menu lets the user create and display a DataFrame, inspect the first or last five rows, view its information and descriptive statistics, calculate maximum/minimum/mean/median, sort rows, add or delete a column, filter records, list column names and count rows and columns.

Note: Install Pandas with `pip install pandas`. The program expects student marks and ages to be integers.

No matching question found.

Practical Notes

Arithmetic operators on a 2D NumPy array

Question

Python Solution

Sample Output / Expected Result

Explanation

Create a DataFrame for interview candidates

Question

Python Solution

Sample Output / Expected Result

Explanation

Filter players with more than 1000 runs

Question

Python Solution

Sample Output / Expected Result

Explanation

Bar chart of mobile game ratings

Question

Python Solution

Sample Output / Expected Result

Explanation

Line chart of average height and weight

Question

Python Solution

Sample Output / Expected Result

Explanation

Menu-driven Mean, Mode and Median for MelaSales.csv

Question

Python Solution

Sample Output / Expected Result

Explanation

OpenCV image loading, colour conversion, inspection, cropping and saving

Question

Python Solution

Sample Output / Expected Result

Explanation

Histogram chart using Matplotlib

Question

Python Solution

Sample Output / Expected Result

Explanation

Pie chart using Matplotlib

Question

Python Solution

Sample Output / Expected Result

Explanation

Scatter chart using Matplotlib

Question

Python Solution

Sample Output / Expected Result

Explanation

Read and display original and grayscale images using OpenCV

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Python Solution

Sample Output / Expected Result

Explanation

Crop and resize an image using OpenCV

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Python Solution

Sample Output / Expected Result

Explanation

OpenCV image inspection and processing

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Python Solution

Sample Output / Expected Result

Explanation

Menu-driven NumPy program

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Python Solution

Sample Output / Expected Result

Explanation

Menu-driven Pandas program

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Python Solution

Sample Output / Expected Result

Explanation

Codemaniac Team

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