FYBCS HTML & CSS

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11 Comments

  1. adminOctober 27, 2021 at 8:54 PM

    Thank You for Sharing FYBCS HTML & CSS

    ReplyDelete
  2. AnonymousMarch 20, 2023 at 3:51 PM

    Thank you so much

    ReplyDelete
  3. AnonymousMarch 3, 2025 at 8:05 AM

    import numpy as np
    a = np.array([[0, 1],[2, 3]])
    fa = a.flatten()
    mv = np.max(fa)
    miv = np.min(fa)
    print("Original flattened array:")
    print(a)
    print("Maximum value of the above flattened array:")
    print(mv)
    print("Minimum value of the above flattened array:")
    print(miv)

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  4. AnonymousMarch 3, 2025 at 8:06 AM

    import numpy as np
    from numpy.linalg import norm
    p1 = [1, 2, 3]
    p2 = [4, 5, 6]
    dist = norm(np.array(p1) - np.array(p2))
    print("Euclidean distance between the two data points:", dist)

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  5. AnonymousMarch 3, 2025 at 8:06 AM

    import pandas as pd
    import numpy as np
    df = pd.DataFrame({'data': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})
    m = df['data'].mean()
    r = df['data'].max() - df['data'].min()
    iqr = df['data'].quantile(0.75) - df['data'].quantile(0.25)
    print("Mean:", m)
    print("Range:", r)
    print("IQR:", iqr)

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  6. AnonymousMarch 3, 2025 at 8:19 AM

    import itertools as i
    def m_d(point1, point2):
    return abs(point1[0] - point2[0]) + abs(point1[1]-point2[1])

    def sum_of_m_d(points):

    sum_of_d= 0
    for point1,point2 in i.combinations(points, 2):

    sum_of_d += m_d(point1, point2)
    return sum_of_d

    points = [(1, 2), (3, 4), (5, 6)]
    print(sum_of_m_d(points))

    ReplyDelete
  7. AnonymousMarch 3, 2025 at 8:19 AM

    import numpy as np
    import matplotlib.pyplot as plt
    nums = [0.5, 0.7, 1.0, 1.2, 1.3, 2.1]
    bins = [0, 1, 2, 3]
    hist, bins = np.histogram(nums, bins=bins)
    print("nums:", nums)
    print("bins:", bins)
    print("Result:", (hist, bins))
    plt.bar(bins[:-1], hist, width=0.8)
    plt.xlabel('Bins')
    plt.ylabel('Frequency')
    plt.title('Histogram of nums against the bins')
    plt.show()

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  8. AnonymousMarch 3, 2025 at 8:20 AM

    import pandas as pd
    students = pd.DataFrame({'Name': ['om1', 'om2', 'om3', 'om4', 'om5'],'Graduation Percentage': [85, 90, 75, 95, 80],'Age': [22, 21, 23, 20, 24]})
    print("Average age of students:", students['Age'].mean())
    print("Average of graduation percentage:", students['Graduation Percentage'].mean())
    print(students.describe())

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  9. AnonymousMarch 3, 2025 at 8:31 AM

    import pandas as pd
    i= pd.read_csv("D:\WT & FDS I\Revised Data Science_Workbook_ Assignment Solution\Data Science Assignment Solution\Iris.csv")
    sample = i.sample(100)
    print(sample.describe())

    ReplyDelete
  10. AnonymousMarch 3, 2025 at 8:32 AM

    import pandas as pd
    iris = pd.read_csv('D:\WT & FDS I\Revised Data Science_Workbook_ Assignment Solution\Data Science Assignment Solution\Iris.csv')
    count = iris['Species'].value_counts()
    print(count)

    ReplyDelete
  11. AnonymousMarch 3, 2025 at 8:33 AM

    import pandas as pd
    iris = pd.read_csv('D:\WT & FDS I\Revised Data Science_Workbook_ Assignment Solution\Data Science Assignment Solution\Iris.csv')
    print(iris.mean())
    print(iris.median())

    ReplyDelete