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Python practice problems help beginners to become experts in a python programming language with a small amount of practice effort.
Write a NumPy program to access the first two columns of an array.
[[6, 5, 7], [10, 9, 1], [7, 8, 2]]
[[ 6 5] [10 9] [ 7 8]]
a[:, :2].import numpy as npa = np.array([[6, 5, 7], [10, 9, 1], [7, 8, 2]])print(a[:, :2])[[ 6 5] [10 9] [ 7 8]]
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Write a NumPy program to save an array to a CSV file.
a,b,c 1,2,3 4,5,6
np.savetxt with delimiter=",".import numpy as npa = np.array([[1, 2, 3], [4, 5, 6]])np.savetxt("data.csv", a, delimiter=",", fmt="%d", header="a,b,c", comments="")print(open("data.csv").read())a,b,c 1,2,3 4,5,6
fmt="%d" writes integers; comments="" stops NumPy putting “# ” before the header.
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Write a NumPy program to calculate the product of an array.
[3, 5, 1]
15
np.prod.import numpy as npprint(np.prod([3, 5, 1]))15
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Write a NumPy program to check whether an array is empty.
[4, 8, 0], []
[4 8 0] Array is not empty [] Array is empty
a.size == 0.import numpy as npfor a in [np.array([4, 8, 0]), np.array([])]: print(a, "Array is empty" if a.size == 0 else "Array is not empty")[4 8 0] Array is not empty [] Array is empty
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Write a NumPy program to count how often each unique value appears.
[8, 6, 7, 0, 7, 8, 6, 6]
[[0 6 7 8] [1 3 2 2]]
np.unique(..., return_counts=True).import numpy as npvalues, counts = np.unique([8, 6, 7, 0, 7, 8, 6, 6], return_counts=True)print(np.vstack((values, counts)))[[0 6 7 8] [1 3 2 2]]
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Write a NumPy program to get the magnitude (length) of a vector.
[3, 5, 1]
5.92
np.linalg.norm.import numpy as npprint(round(float(np.linalg.norm([3, 5, 1])), 2))5.92
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Write a NumPy program to remove rows that contain NaN.
[[4, nan, 8], [2, 4, 6]]
[[2. 4. 6.]]
import numpy as npa = np.array([[4, np.nan, 8], [2, 4, 6]])print(a[~np.isnan(a).any(axis=1)])[[2. 4. 6.]]
isnan(a).any(axis=1) is True for rows with a NaN; ~ inverts it.
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Write a NumPy program to replace negative values with 1.
[[4, 8, -5], [2, -9, 6]]
[[4 8 1] [2 1 6]]
import numpy as npa = np.array([[4, 8, -5], [2, -9, 6]])a[a < 0] = 1print(a)[[4 8 1] [2 1 6]]
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Write a NumPy program to add an extra column to an array.
[[4, 8, 0], [2, 0, 6]], column [5, 7]
[[4 8 0 5] [2 0 6 7]]
np.column_stack or np.hstack with a reshaped column.import numpy as npa = np.array([[4, 8, 0], [2, 0, 6]])print(np.column_stack((a, [5, 7])))[[4 8 0 5] [2 0 6 7]]
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Write a NumPy program to convert an array of floats to integers.
[[6.7, 5.2], [9.4, 2.7]]
[[6 5] [9 2]] [[7 5] [9 3]]
astype(int) truncates; round first if needed.import numpy as npa = np.array([[6.7, 5.2], [9.4, 2.7]])print(a.astype(int))print(np.rint(a).astype(int))[[6 5] [9 2]] [[7 5] [9 3]]
Truncation drops the decimals.
rint rounds to the nearest integer first.
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