Monday, 10 March 2025

Python Program to print any table of any number entered by user

Copied! 

#Program to print any table of any number entered by user
#Name and Roll Number
import math

n = int(input('Enter any number whose table you want to print: '))
print('Table of', n, 'is given by\n')

for i in range(1, 11):
    print(n, 'x', i, '=', n * i)
at

7 comments:

  1. Anonymous17 March 2025 at 22:15

    #37.Tushar k. Patil
    #Program to read students result file and plot normalization curve

    import pandas as pd
    import matplotib.pyplot as plt
    from scipy.stats import norm
    import statistics as st
    import numpy as np

    #Read the student's results file
    df=pd.read_csv("")

    #Replace'AB' with NaN for better handling of absent students
    df.replace('AB',np.nan,inplace=True)

    #Extract columns for each subject
    rn=df['Roll No']
    sub_em4=df['EM-4'].dropna().astype(float)
    sub_FM=df['FM'].dropna().astype(float)
    sub_kom=df['KOM'].dropna.astype(float)
    sub_cadcam=df['CAD/CAM'].dropna.astype(float)
    sub_ie=df['IE'].dropna.astype(float)

    #Function to calculate and plot normalization curve for each subject
    def plot_normalization_curve(subject,title,color):
    mean=st.mean(subject)
    sd=st.stdev(subject)
    x_values=np.linspace(mini(subject),max(subject),100)
    y_values=norm.pdf(x_values,mean,sd)
    plt.plot(x_values,y_values,label=f'{title}Normal Distribution',color=color)
    plt.hist(subject,bins=18,density=True,alpha=0.5,color=color,edgecolor='black')
    plt.title(f'{title}Marks')
    plt.xlabel('Marks')
    plt.ylabel('Density')
    plt.legend()

    #Set general plot dimensions
    plt.rcParams["figure.figure"]=(20,15)


    #Create subplots for each subject
    plt.subplot(2,3,1)
    plot_normalization_curve(sub_em4,'EM-4','blue')

    plt.subplot(2,3,2)
    plot_normalization_curve(sub_fm,'FM','green')

    plt.subplot(2,3,3)
    plot_normalization_curve(sub_kom,'KOM','red')

    plt.subplot(2,3,4)
    plot_normalization_curve(sub_cadcam,'CAD/CAM','purple')

    plt.subplot(2,3,5)
    plot_normalization_curve(sub_ie,'IE','orange')

    #Set the global title
    plt.suptitle("Normality check of students scores using histograms")

    #Show the plot
    plt.tight_layout(rect=[0,0.003,1,0.95])
    plt.show()




    ReplyDelete
  2. Anonymous17 March 2025 at 22:27

    import pandas as pd
    import matplotlib.pyplot as plt
    from scipy.stats import norm
    import statistics as st
    import numpy as np

    # Read the student's result file
    df = pd.read_csv("C:\\Users\\mechsim-07\\Downloads\\grades.csv")

    # Replace 'AB' with NaN for better handling of absent students
    df.replace('AB', np.nan, inplace=True)

    # Extract columns for each subject
    rn = df['Roll No']
    sub_em4 = df['EM-4'].dropna().astype(float)
    sub_fm = df['FM'].dropna().astype(float)
    sub_kom = df['KOM'].dropna().astype(float)
    sub_cadcam = df['CAD/CAM'].dropna().astype(float)
    sub_ie = df['IE'].dropna().astype(float)

    # Function to calculate and plot normalization curve for each subject
    def plot_normalization_curve(subject, title, color):
    mean = st.mean(subject)
    sd = st.stdev(subject)
    x_values = np.linspace(min(subject), max(subject), 100)
    y_values = norm.pdf(x_values, mean, sd)
    plt.plot(x_values, y_values, label=f'{title} Normal Distribution', color=color)
    plt.hist(subject, bins=18, density=True, alpha=0.5, color=color, edgecolor='black')
    plt.title(f'{title} Marks')
    plt.xlabel('Marks')
    plt.ylabel('Density') # Corrected this
    plt.legend()

    # Set general plot dimensions
    plt.rcParams["figure.figsize"] = (20, 15)

    # Create subplots for each subject
    plt.subplot(2, 3, 1)
    plot_normalization_curve(sub_em4, 'EM-4', 'blue')

    plt.subplot(2, 3, 2)
    plot_normalization_curve(sub_fm, 'FM', 'green')

    plt.subplot(2, 3, 3)
    plot_normalization_curve(sub_kom, 'KOM', 'red')

    plt.subplot(2, 3, 4) # Corrected this line
    plot_normalization_curve(sub_cadcam, 'CAD/CAM', 'purple')

    plt.subplot(2, 3, 5)
    plot_normalization_curve(sub_ie, 'IE', 'orange')

    # Set the global title
    plt.suptitle("Normality check of students' scores using histograms")

    # Show the plot
    plt.tight_layout(rect=[0, 0.03, 1, 0.95])
    plt.show()

    ReplyDelete
  3. Anonymous19 March 2025 at 22:49

    // Include the necessary libraries
    #include

    // Define the pins for the PIR sensor and motor
    #define PIR_PIN 2
    #define MOTOR_PIN 3

    // Create a servo object for the motor
    Servo motor;

    // Initialize the PIR sensor pin as input and motor pin as output
    void setup() {
    pinMode(PIR_PIN, INPUT);
    pinMode(MOTOR_PIN, OUTPUT);

    // Attach the motor to the motor pin
    motor.attach(MOTOR_PIN);
    }

    // Main loop
    void loop() {
    // Read the value from the PIR sensor
    int pirValue = digitalRead(PIR_PIN);

    // If the PIR sensor detects motion (human hand), turn off the motor
    if (pirValue == HIGH) {
    motor.write(0); // Set the motor to 0 degrees (off)
    }
    }

    // Reference: https://www.arduino.cc/en/Tutorial/BuiltInExamples/StateChangeDetection

    ReplyDelete
  4. Anonymous24 March 2025 at 22:03

    exp6.py
    Index(['Car', 'Model', 'Volume', 'Weight', 'CO2'], dtype='object')
    Enter the engine weight in kg: 2300
    Enter the engine volume in cubic centimeters: 1300
    C:\Users\mechsim-18\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\utils\validation.py:2739: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
    warnings.warn(

    The predicted value of CO2 emission based on the engine weight and engine volume is = [107.2087328]
    If the weight increases by 1kg, the CO2 emission increases by 0.0075509472703006895 grams
    If the engine size (volume) increases by 1cm³, the CO2 emission increases by 0.007805257527747124 grams

    ReplyDelete
  5. Anonymous24 March 2025 at 22:18

    #experiment 6
    #example of machine learning to predict the co2 emission of a car based on the size of the engine,but
    #with multiple regression we can throw in more variables, like the weight of the car, to make the prediction more accurate.

    import pandas

    from sklearn import linear_model

    df=pandas.read_csv("C:\\Users\\mechsim-18\\Downloads\\data.csv")

    X = df[['Weight', 'Volume']]
    y = df['CO2']


    regr=linear_model.LinearRegression()
    regr.fit(X,y)

    #predict the co2 emission of a car where the weight is 2300kg, and the volume is 13oocm:
    w = float(input('Enter the engine weight in kg: '))
    v = float(input('Enter the engine volume in cubic centimeters: '))

    predictedCO2 = regr.predict([[w,v]])
    print('\nThe predicted value of CO2 emission based on the engine weight and engine volume is =\n', predictedCO2)

    print('nIf the weight increases by 1kg, the CO2 emission increases by',regr.coef_[0], 'grams')
    print('If the engine size (volume) Increases by 1cm3, the CO2 emission increases by', regr.coef_[1], 'grams')

    ReplyDelete
  6. Sahil kirtane5 April 2025 at 01:53

    #Experiment 8b)-Program to plot SFD and BMD of Simply Supported Beam under the point load

    import numpy as np

    import matplotlib.pyplot as plt

    P= float(input('load = '))

    ul = input('load unit = ')

    L= float(input('Length of the beam = '))

    u2= input('length unit = ')

    a = float(input('Distance of Point load from left end = '))

    b=L-a

    R1Pb/L #reaction force at the left support

    R2P-R1 #reaction force at the right support

    R1= round(R1.3)

    R2= round(R2.3)

    print(f"'
    As per the static equilibrium, net moment sum at either end is zero,

    hence Reaction R1 = P*b/L = {R1} (ul),

    Also Net sum of vertical forces is zero,

    hence R1+R2 = P, R2 = P-R1 = {R2} {ul}.

    "")

    1 = np.linspace(0, L, 1000)

    X = []

    SF = []

    M = []

    maxBM= float()

    for x in 1:

    if x <= a:

    m = R1*x

    sf = R1
    elif x > a:

    m = R1*x - P*(x-a)

    I

    sf = -R2

    M.append(m)

    X.append(x)

    SF.append(sf)

    print(f'""

    Shear Force at x (x<{a}), Vx = R1 ={R1) (ul)

    at x (x>{a}), SF = R1 - P = (R1) - (P) = -(R1-P) (ul)

    Bending Moment at x (x<{a}), Mx = R1*x = (R1)*x
    at x (x>=(a)), Mx = RI*x-P*(x-{a})

    = {R1}x-{P}(x-{a}) = -(R2}x+ {P*a}

    "")

    max_SF = 0

    for k in SF:

    if max_SF =a:
    Mx = R1*x-P*(x-a)

    if maxBM == Mx:

    print(f'maximum BM at x = (round(x,3)] (u2}')

    I

    plt.plot(X, SF)

    plt.plot([0, L], [0, 0])

    plt.plot([0, 0], [0, R1], [L, L], [0, -R2])

    plt.title("SFD")

    plt.xlabel("Length in m")

    plt.ylabel("Shear Force")

    plt.show()

    plt.plot(X. M)
    plt.plot([0, L], [0, 0])

    plt.title("BMD")

    plt.xlabel("Length in m")

    plt.ylabel("Bending Moment")

    plt.show()

    ReplyDelete
  7. sahil kirtane 7 April 2025 at 18:24

    #Experiment No 9 Python Program to process above csv data file and print Amplitude v/s Time plot on
    the output screen
    import pandas
    import numpy as np
    import matplotlib.pyplot as plt
    import statistics as st
    #read the csv data file and assign df as the object to access the same
    df = pandas.read_csv("expt9-data.csv")
    #as per the column heads assign them to two different lists
    t = df['Time']
    y = df['v0']
    #using stats module find the mean of amplitude
    m0 = st.mean(y)
    #print the value of time and amplitude
    print(t)
    print(y)
    #plot the amplitude v/s time plot
    plt.plot(t,y,color='brown')
    plt.axhline(y=m0,color='black',ls='-.')
    plt.xlabel('Time in Seconds')
    plt.ylabel('Amplitude')
    plt.title('Amplitude v/s Time Plot')
    plt.show()

    ReplyDelete

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