Mastering NumPy: 100 Exercises with solutions for Python numerical computing
Welcome to w3resource's 100 NumPy exercises collection! This comprehensive set of exercises is designed to help you master the fundamentals of NumPy, a powerful numerical computing library in Python. Whether you're a beginner or an experienced user looking to improve your skills, these exercises cover a wide range of topics. They provide practical challenges to enhance your NumPy understanding.
Exercise 1:
Create a 1D array with values ranging from 0 to 9.
import numpy as np
arr = np.arange(10)
print(arr)
[0 1 2 3 4 5 6 7 8 9]
Exercise 2:
Convert a 1D array to a 2D array with 2 rows.
import numpy as np
arr = np.arange(10).reshape(2, -1)
print(arr)
[[0 1 2 3 4] [5 6 7 8 9]]
Exercise 3:
Multiply a 5x3 matrix by a 3x2 matrix.
import numpy as np
mat1 = np.random.random((5, 3))
mat2 = np.random.random((3, 2))
result = np.dot(mat1, mat2)
print(result)
[[1.05787846 0.32439392] [2.18241234 0.68182548] [1.23629853 0.39596044] [0.42973189 0.12403945] [1.08039539 0.36356378]]
Exercise 4:
Extract all odd numbers from an array of 1-10.
import numpy as np
arr = np.arange(1, 11)
odd_numbers = arr[arr % 2 != 0]
print(odd_numbers)
[1 3 5 7 9]
Exercise 5:
Replace all odd numbers in an array of 1-10 with -1.
import numpy as np
arr = np.arange(1, 10)
arr[arr % 2 != 0] = -1
print(arr)
[-1 2 -1 4 -1 6 -1 8 -1]
Exercise 6:
Convert a 1D array to a boolean array where all positive values become True.
import numpy as np
arr = np.array([-1, 2, 0, -4, 5])
boolean_arr = arr > 0
print(boolean_arr)
[False True False False True]
Exercise 7:
Replace all even numbers in a 1D array with their negative.
import numpy as np
arr = np.arange(1, 10)
arr[arr % 2 == 0] *= -1
print(arr)
[ 1 -2 3 -4 5 -6 7 -8 9]
Exercise 8:
Create a random 3x3 matrix and normalize it.
import numpy as np
matrix = np.random.random((3,3))
normalized_matrix = (matrix - np.mean(matrix)) / np.std(matrix)
print(normalized_matrix)
[[ 1.07755282 -0.27940552 -1.57739216] [ 1.53962723 1.25274094 -0.97454418] [-0.30801978 -0.26192698 -0.46863236]]
Exercise 9:
Calculate the sum of the diagonal elements of a 3x3 matrix.
import numpy as np
matrix = np.random.random((3, 3))
diagonal_sum = np.trace(matrix)
print(diagonal_sum)
1.0183501284750802
Exercise 10:
Find the indices of non-zero elements from [1,2,0,0,4,0].
import numpy as np
arr = np.array([1, 2, 0, 0, 4, 0])
non_zero_indices = np.nonzero(arr)
print(non_zero_indices)
(array([0, 1, 4], dtype=int64),)
Exercise 11:
Reverse a 1D array (first element becomes the last).
import numpy as np
arr = np.arange(10)
reversed_arr = np.flip(arr)
print(reversed_arr)
[9 8 7 6 5 4 3 2 1 0]
Exercise 12:
Create a 3x3 identity matrix.
import numpy as np
identity_matrix = np.eye(3)
print(identity_matrix)
[[1. 0. 0.] [0. 1. 0.] [0. 0. 1.]]
Exercise 13:
Reshape a 1D array to a 2D array with 5 rows and 2 columns.
import numpy as np
arr = np.arange(10)
reshaped_arr = arr.reshape(5, 2)
print(reshaped_arr)
[[0 1] [2 3] [4 5] [6 7] [8 9]]
Exercise 14:
Stack two arrays vertically.
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
stacked_arr = np.vstack((arr1, arr2))
print(stacked_arr)
[[1 2 3] [4 5 6]]
Exercise 15:
Get the common items between two arrays.
import numpy as np
arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.array([3, 4, 5, 6, 7])
common_items = np.intersect1d(arr1, arr2)
print(common_items)
[3 4 5]
Exercise 16:
Create a 5x5 matrix with row values ranging from 0 to 4.
import numpy as np
matrix = np.zeros((5, 5))
matrix += np.arange(5)
print(matrix)
[[0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.]]
Exercise 17:
Find the index of the maximum value in a 1D array.
import numpy as np
arr = np.array([3, 7, 1, 10, 4])
max_index = np.argmax(arr)
print(max_index)
3
Exercise 18:
Normalize the values in a 1D array between 0 and 1.
import numpy as np
arr = np.array([2, 5, 10, 3, 8])
normalized_arr = (arr - np.min(arr)) / (np.max(arr) - np.min(arr))
print(normalized_arr)
[0. 0.375 1. 0.125 0.75 ]
Exercise 19:
Calculate the dot product of two arrays.
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
dot_product = np.dot(arr1, arr2)
print(dot_product)
32
Exercise 20:
Count the number of elements in an array within a specific range.
import numpy as np
arr = np.array([2, 5, 8, 10, 12, 15])
count_within_range = np.sum((arr >= 5) & (arr <= 12))
print(count_within_range)
4
Exercise 21:
Find the mean of each row in a 2D array.
import numpy as np
matrix = np.random.random((3, 4))
row_means = np.mean(matrix, axis=1)
print(row_means)
[0.437043 0.73541944 0.45005375]
Exercise 22:
Create a random 4x4 matrix and extract the diagonal elements.
import numpy as np
matrix = np.random.random((4, 4))
diagonal_elements = np.diag(matrix)
print(diagonal_elements)
[0.3968107 0.3355669 0.91924761 0.907174 ]
Exercise 23:
Count the number of occurrences of a specific value in an array.
import numpy as np
arr = np.array([1, 2, 3, 4, 2, 3, 2, 1])
count_of_2 = np.count_nonzero(arr == 2)
print(count_of_2)
3
Exercise 24:
Replace all values in a 1D array with the mean of the array.
import numpy as np
arr = np.array([10, 20, 30, 40, 50])
arr[:] = np.mean(arr)
print(arr)
[30 30 30 30 30]
Exercise 25:
Find the indices of the maximum and minimum values in a 1D array.
import numpy as np
arr = np.array([5, 2, 8, 1, 7])
max_index = np.argmax(arr)
min_index = np.argmin(arr)
print("Index of max:", max_index)
print("Index of min:", min_index)
Index of max: 2 Index of min: 3
Exercise 26:
Create a 2D array with 1 on the border and 0 inside.
import numpy as np
arr = np.ones((5, 5))
arr[1:-1, 1:-1] = 0
print(arr)
[[1. 1. 1. 1. 1.] [1. 0. 0. 0. 1.] [1. 0. 0. 0. 1.] [1. 0. 0. 0. 1.] [1. 1. 1. 1. 1.]]
Exercise 27:
Find the unique values and their counts in a 1D array.
import numpy as np
arr = np.array([1, 2, 3, 2, 4, 1, 5, 4, 6])
unique_values, counts = np.unique(arr, return_counts=True)
print("Unique values:", unique_values)
print("Counts:", counts)
Unique values: [1 2 3 4 5 6] Counts: [2 2 1 2 1 1]
Exercise 28:
Create a 3x3 matrix with values ranging from 0 to 8.
import numpy as np
matrix = np.arange(9).reshape(3, 3)
print(matrix)
[[0 1 2] [3 4 5] [6 7 8]]
Exercise 29:
Calculate the exponential of all elements in a 1D array.
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
exponential_arr = np.exp(arr)
print(exponential_arr)
[ 2.71828183 7.3890561 20.08553692 54.59815003 148.4131591 ]
Exercise 30:
Swap two rows in a 2D array.
import numpy as np
matrix = np.random.random((3, 4))
matrix[[0, 1]] = matrix[[1, 0]]
print(matrix)
[[0.64447186 0.98641154 0.7336092 0.79829912] [0.00753743 0.65414365 0.74147161 0.14819561] [0.53158903 0.89859906 0.75709264 0.49165449]]
Exercise 31:
Create a random 3x3 matrix and replace all values greater than 0.5 with 1 and all others with 0.
import numpy as np
matrix = np.random.random((3, 3))
matrix[matrix > 0.5] = 1
matrix[matrix <= 0.5] = 0
print(matrix)
[[1. 0. 1.] [0. 0. 0.] [0. 0. 0.]]
Exercise 32:
Find the indices of the top N maximum values in a 1D array.
import numpy as np
arr = np.array([10, 5, 8, 20, 15])
top_indices = np.argsort(arr)[-2:] # Replace 2 with desired top N
print(top_indices)
[4 3]
Exercise 33:
Calculate the mean of each column in a 2D array.
import numpy as np
matrix = np.random.random((4, 3))
column_means = np.mean(matrix, axis=0)
print(column_means)
[0.54904302 0.4902671 0.42925161]
Exercise 34:
Normalize the values in each column of a 2D array.
import numpy as np
matrix = np.random.random((4, 3))
normalized_matrix = (matrix - np.mean(matrix, axis=0)) / np.std(matrix, axis=0)
print(normalized_matrix)
[[-0.46748521 -0.77417852 -0.84330049] [ 1.0967056 1.71309044 0.71160507] [ 0.7764295 -0.58168559 1.24351291] [-1.4056499 -0.35722632 -1.11181749]]
Exercise 35:
Concatenate two 1D arrays.
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
concatenated_arr = np.concatenate((arr1, arr2))
print(concatenated_arr)
[1 2 3 4 5 6]
Exercise 36:
Create a 2D array with random values and sort each row.
import numpy as np
matrix = np.random.random((3, 4))
sorted_matrix = np.sort(matrix, axis=1)
print(sorted_matrix)
[[0.10858953 0.71557663 0.7986983 0.90525131] [0.318373 0.50887498 0.51900254 0.7860126 ] [0.06242782 0.12665803 0.12884579 0.1440853 ]]
Exercise 37:
Compute the mean squared error between two arrays.
import numpy as np
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([2, 3, 4, 5])
mse = np.mean((arr1 - arr2) ** 2)
print(mse)
1.0
Exercise 38:
Replace all negative values in an array with 0.
import numpy as np
arr = np.array([-1, 2, -3, 4, -5])
arr[arr < 0] = 0
print(arr)
[0 2 0 4 0]
Exercise 39:
Find the 5th and 95th percentiles of an array.
import numpy as np
arr = np.random.random(10)
percentile_5th = np.percentile(arr, 5)
percentile_95th = np.percentile(arr, 95)
print("5th Percentile:", percentile_5th)
print("95th Percentile:", percentile_95th)
5th Percentile: 0.04785597751800983 95th Percentile: 0.8708086577975086
Exercise 40:
Create a random 2x2 matrix and compute its determinant.
import numpy as np
matrix = np.random.random((2, 2))
determinant = np.linalg.det(matrix)
print(determinant)
0.0019446951056262907
Exercise 41:
Count the number of elements in an array that are greater than the mean.
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
count_above_mean = np.sum(arr > np.mean(arr))
print(count_above_mean)
2
Exercise 42:
Calculate the square root of each element in a 1D array.
import numpy as np
arr = np.array([4, 9, 16, 25])
sqrt_arr = np.sqrt(arr)
print(sqrt_arr)
[2. 3. 4. 5.]
Exercise 43:
Create a 3x3 matrix and compute the matrix square root.
import numpy as np
matrix = np.random.random((3, 3))
matrix_sqrt = np.linalg.matrix_power(matrix, 2)
print(matrix_sqrt)
[[0.62607741 0.64326801 0.24778017] [1.35236384 1.43972006 0.56587005] [0.617024 0.71519995 0.29469311]]
Exercise 44:
Convert the data type of an array to float.
import numpy as np
arr = np.array([1, 2, 3, 4], dtype=int)
float_arr = arr.astype(float)
print(float_arr)
[1. 2. 3. 4.]
Exercise 45:
Calculate the element-wise absolute values of an array.
import numpy as np
arr = np.array([-1, -2, 3, -4])
abs_values = np.abs(arr)
print(abs_values)
[1 2 3 4]
Exercise 46:
Find the indices where elements of two arrays match.
import numpy as np
arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.array([3, 2, 8, 4, 5])
matching_indices = np.where(arr1 == arr2)
print(matching_indices)
(array([1, 3, 4], dtype=int64),)
Exercise 47:
Calculate the cumulative sum of elements in a 1D array.
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
cumulative_sum = np.cumsum(arr)
print(cumulative_sum)
[ 1 3 6 10 15]
Exercise 48:
Compute the inverse of a 2x2 matrix.
import numpy as np
matrix = np.random.random((2, 2))
inverse_matrix = np.linalg.inv(matrix)
print(inverse_matrix)
[[ 1.22685384 -4.14864181] [-0.2704788 6.62123806]]
Exercise 49:
Count the number of non-zero elements in a 2D array.
import numpy as np
matrix = np.array([[0, 1, 0], [2, 0, 3], [0, 4, 0]])
non_zero_count = np.count_nonzero(matrix)
print(non_zero_count)
4
Exercise 50:
Create a 2D array and replace all nan values with 0.
import numpy as np
matrix = np.array([[1, np.nan, 3], [4, 5, np.nan], [7, 8, 9]])
matrix[np.isnan(matrix)] = 0
print(matrix)
[[1. 0. 3.] [4. 5. 0.] [7. 8. 9.]]
Exercise 51:
Find the correlation coefficient between two arrays.
import numpy as np
arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.array([3, 4, 5, 6, 7])
correlation_coefficient = np.corrcoef(arr1, arr2)[0, 1]
print(correlation_coefficient)
0.9999999999999999
Exercise 52:
Create a 1D array and remove all duplicate values.
import numpy as np
arr = np.array([1, 2, 3, 2, 4, 5, 1])
unique_arr = np.unique(arr)
print(unique_arr)
[1 2 3 4 5]
Exercise 53:
Compute the element-wise product of two arrays.
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
elementwise_product = np.multiply(arr1, arr2)
print(elementwise_product)
[ 4 10 18]
Exercise 54:
Calculate the standard deviation of each column in a 2D array.
import numpy as np
matrix = np.random.random((4, 3))
column_stddev = np.std(matrix, axis=0)
print(column_stddev)
[0.22702366 0.27411548 0.25568707]
Exercise 55:
Create a 2D array and set all values above a certain threshold to that threshold.
import numpy as np
matrix = np.random.random((3, 4))
threshold = 0.7
matrix[matrix > threshold] = threshold
print(matrix)
[[0.7 0.54918234 0.7 0.01893358] [0.09114833 0.00268936 0.13766009 0.28160436] [0.40448374 0.7 0.7 0.30830747]]
Exercise 56:
Create a random 5x5 matrix and replace the maximum value by -1.
import numpy as np
matrix = np.random.random((5, 5))
max_value_index = np.unravel_index(np.argmax(matrix), matrix.shape)
matrix[max_value_index] = -1
print(matrix)
[[ 0.45916565 0.79856137 0.68998918 0.04439129 0.84814684] [ 0.4442666 0.2661443 0.83980951 0.71727557 0.30334519] [ 0.47332068 0.50437988 0.51222628 0.65334221 0.21664521] [ 0.7511065 0.77837283 0.29019334 0.82695944 0.41608473] [ 0.23775068 0.07539172 -1. 0.14166163 0.56071446]]
Exercise 57:
Convert a 1D array of Fahrenheit temperatures to Celsius.
import numpy as np
fahrenheit_temps = np.array([32, 68, 100, 212])
celsius_temps = (fahrenheit_temps - 32) * 5/9
print(celsius_temps)
[ 0. 20. 37.77777778 100. ]
Exercise 58:
Compute the outer product of two arrays.
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
outer_product = np.outer(arr1, arr2)
print(outer_product)
[[ 4 5 6] [ 8 10 12] [12 15 18]]
Exercise 59:
Create a 1D array with 10 equidistant values between 0 and 1.
import numpy as np
equidistant_arr = np.linspace(0, 1, 10)
print(equidistant_arr)
[0. 0.11111111 0.22222222 0.33333333 0.44444444 0.55555556 0.66666667 0.77777778 0.88888889 1. ]
Exercise 60:
Compute the cross product of two 3D arrays.
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
cross_product = np.cross(arr1, arr2)
print(cross_product)
[-3 6 -3]
Exercise 61:
Calculate the percentile along a specific axis of a 2D array.
import numpy as np
matrix = np.random.random((3, 4))
percentiles_axis1 = np.percentile(matrix, 75, axis=1)
print(percentiles_axis1)
[0.73954718 0.63159296 0.49097014]
Exercise 62:
Create a 1D array and add a border of 0s around it.
import numpy as np
arr = np.array([1, 2, 3, 4])
arr_with_border = np.pad(arr, (1, 1), mode='constant', constant_values=0)
print(arr_with_border)
[0 1 2 3 4 0]
Exercise 63:
Compute the histogram of a 1D array.
import numpy as np
arr = np.array([1, 1, 2, 2, 2, 3, 3, 3, 3])
hist, bins = np.histogram(arr, bins=[1, 2, 3, 4])
print("Histogram:", hist)
print("Bin edges:", bins)
Histogram: [2 3 4] Bin edges: [1 2 3 4]
Exercise 64:
Create a 2D array with random values and normalize each row.
import numpy as np
matrix = np.random.random((4, 3))
normalized_rows = matrix / np.linalg.norm(matrix, axis=1, keepdims=True)
print(normalized_rows)
[[0.61881278 0.58433001 0.52500398] [0.50152886 0.39533452 0.76953195] [0.18399814 0.89840062 0.39877439] [0.76636647 0.25223155 0.59081442]]
Exercise 65:
Create a random 2D array and sort it by the second column.
import numpy as np
matrix = np.random.random((3, 4))
sorted_matrix_by_column2 = matrix[matrix[:, 1].argsort()]
print(sorted_matrix_by_column2)
[[0.28214407 0.30234856 0.56159219 0.34651641] [0.72579465 0.64605243 0.14824549 0.6364958 ] [0.4852539 0.96161675 0.41756529 0.7079494 ]]
Exercise 66:
Calculate the determinant of a 3x3 matrix.
import numpy as np
matrix = np.random.random((3, 3))
determinant = np.linalg.det(matrix)
print(determinant)
0.09550080558000806
Exercise 67:
Calculate the element-wise exponentiation of a 1D array.
import numpy as np
arr = np.array([2, 3, 4])
exponentiated_arr = np.exp(arr)
print(exponentiated_arr)
[ 7.3890561 20.08553692 54.59815003]
Exercise 68:
Calculate the Frobenius norm of a 2D array.
import numpy as np
matrix = np.random.random((3, 4))
frobenius_norm = np.linalg.norm(matrix)
print(frobenius_norm)
1.8692600488600242
Exercise 69:
Create a 2D array with random values and replace the maximum value with the minimum.
import numpy as np
matrix = np.random.random((3, 4))
max_value_index = np.unravel_index(np.argmax(matrix), matrix.shape)
min_value = np.min(matrix)
matrix[max_value_index] = min_value
print(matrix)
[[0.29383093 0.01637657 0.7794734 0.01637657] [0.76911564 0.22745882 0.32986131 0.84500937] [0.94922056 0.54607467 0.80921816 0.28474546]]
Exercise 70:
Compute the matrix multiplication of two 2D arrays.
import numpy as np
matrix1 = np.random.random((3, 4))
matrix2 = np.random.random((4, 5))
matrix_multiplication = np.dot(matrix1, matrix2)
print(matrix_multiplication)
[[0.99119942 0.85513185 0.57664501 0.31851386 0.84601174] [1.38306551 1.16773145 0.83001893 0.68922723 1.12666608] [1.72827308 1.44430652 1.03782659 1.28492828 1.24906297]]
Exercise 71:
Create a 1D array and set the values between 10 and 20 to 0.
import numpy as np
arr = np.array([5, 15, 12, 18, 25])
arr[(arr >= 10) & (arr <= 20)] = 0
print(arr)
[ 5 0 0 0 25]
Exercise 72:
Compute the inverse hyperbolic sine of each element in a 1D array.
import numpy as np
arr = np.array([1, 2, 3, 4])
inverse_sineh_arr = np.arcsinh(arr)
print(inverse_sineh_arr)
[0.88137359 1.44363548 1.81844646 2.09471255]
Exercise 73:
Compute the Kronecker product of two arrays.
import numpy as np
arr1 = np.array([1, 2])
arr2 = np.array([3, 4])
kronecker_product = np.kron(arr1, arr2)
print(kronecker_product)
[3 4 6 8]
Exercise 74:
Calculate the mean absolute deviation of a 1D array.
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
mean_absolute_deviation = np.mean(np.abs(arr - np.mean(arr)))
print(mean_absolute_deviation)
1.2
Exercise 75:
Create a 3x3 matrix and set all values above the main diagonal to zero.
import numpy as np
matrix = np.random.random((3, 3))
matrix[np.triu_indices(3, 1)] = 0
print(matrix)
[[0.7344708 0. 0. ] [0.91154103 0.58004909 0. ] [0.53953411 0.73231191 0.8315308 ]]
Exercise 76:
Count the number of occurrences of each unique value in a 1D array.
import numpy as np
arr = np.array([2, 2, 1, 3, 3, 3, 4])
unique_values, counts = np.unique(arr, return_counts=True)
print("Unique values:", unique_values)
print("Counts:", counts)
Unique values: [1 2 3 4] Counts: [1 2 3 1]
Exercise 77:
Compute the cumulative product of elements along a given axis in a 2D array.
import numpy as np
matrix = np.random.random((3, 4))
cumulative_product_axis0 = np.cumprod(matrix, axis=0)
print(cumulative_product_axis0)
[[0.65005294 0.01951926 0.25636043 0.30961964] [0.28738887 0.00883133 0.24993819 0.06504888] [0.21895124 0.00590692 0.0116853 0.03512077]]
Exercise 78:
Round elements of a 1D array to the nearest integer.
import numpy as np
arr = np.array([1.2, 2.7, 3.5, 4.9])
rounded_arr = np.round(arr)
print(rounded_arr)
[1. 3. 4. 5.]
Exercise 79:
Create a 1D array and append a new element to the end.
import numpy as np
arr = np.array([1, 2, 3])
new_element = 4
arr = np.append(arr, new_element)
print(arr)
[1 2 3 4]
Exercise 80:
Calculate the element-wise absolute difference between two arrays.
import numpy as np
arr1 = np.array([3, 7, 1, 10, 4])
arr2 = np.array([2, 5, 8, 1, 7])
absolute_difference = np.abs(arr1 - arr2)
print(absolute_difference)
[1 2 7 9 3]
Exercise 81:
Create a 2D array with random values and replace the maximum value in each row with -1.
import numpy as np
matrix = np.random.random((3, 4))
max_values_indices = np.argmax(matrix, axis=1)
matrix[np.arange(matrix.shape[0]), max_values_indices] = -1
print(matrix)
[[ 0.14015869 0.13279371 -1. 0.4665563 ] [ 0.73795369 0.311774 -1. 0.05087541] [ 0.6516183 -1. 0.30233177 0.48674956]]
Exercise 82:
Normalize the columns of a 2D array to have a sum of 1.
import numpy as np
matrix = np.random.random((3, 4))
normalized_columns = matrix / np.sum(matrix, axis=0, keepdims=True)
print(normalized_columns)
[[0.00548299 0.49881449 0.16102029 0.23643045] [0.58935257 0.27548946 0.54365728 0.57390056] [0.40516443 0.22569604 0.29532244 0.18966899]]
Exercise 83:
Find the indices of the top N minimum values in a 1D array.
import numpy as np
arr = np.array([10, 5, 8, 1, 7])
top_indices = np.argsort(arr)[:2] # Replace 2 with desired top N
print(top_indices)
[3 1]
Exercise 84:
Convert the elements of a 1D array to strings.
import numpy as np
arr = np.array([1, 2, 3, 4])
string_arr = arr.astype(str)
print(string_arr)
['1' '2' '3' '4']
Exercise 85:
Compute the percentile rank of each element in a 1D array.
import numpy as np
from scipy.stats import percentileofscore
arr = np.array([1, 2, 3, 4, 5])
# Calculate percentile rank for each element in arr
percentile_rank = np.array([percentileofscore(arr, value) for value in arr])
print(percentile_rank)
[ 20. 40. 60. 80. 100.]
Exercise 86:
Create a 1D array and shuffle its elements randomly.
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
np.random.shuffle(arr)
print(arr)
[4 2 5 1 3]
Exercise 87:
Check if all elements in a 1D array are non-zero.
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
all_nonzero = np.all(arr != 0)
print(all_nonzero)
True
Exercise 88:
Find the indices of the maximum value in each row of a 2D array.
import numpy as np
matrix = np.random.random((3, 4))
max_indices_per_row = np.argmax(matrix, axis=1)
print(max_indices_per_row)
[1 3 2]
Exercise 89:
Create a 2D array and replace all nan values with the mean of the array.
import numpy as np
matrix = np.array([[1, np.nan, 3], [4, 5, np.nan], [7, 8, 9]])
nan_mean = np.nanmean(matrix)
matrix[np.isnan(matrix)] = nan_mean
print(matrix)
[[1. 5.28571429 3. ] [4. 5. 5.28571429] [7. 8. 9. ]]
Exercise 90:
Calculate the mean of each row in a 2D array ignoring nan values.
import numpy as np
matrix = np.array([[1, 2, np.nan], [4, np.nan, 6], [7, 8, 9]])
row_means_ignore_nan = np.nanmean(matrix, axis=1)
print(row_means_ignore_nan)
[1.5 5. 8. ]
Exercise 91:
Compute the sum of diagonal elements in a 2D array.
import numpy as np
matrix = np.random.random((3, 3))
diagonal_sum = np.trace(matrix)
print(diagonal_sum)
1.375162416021255
Exercise 92:
Convert radians to degrees for each element in a 1D array.
import numpy as np
arr_in_radians = np.array([np.pi/2, np.pi, 3*np.pi/2])
arr_in_degrees = np.degrees(arr_in_radians)
print(arr_in_degrees)
[ 90. 180. 270.]
Exercise 93:
Calculate the pairwise Euclidean distance between two arrays.
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
euclidean_distance = np.linalg.norm(arr1 - arr2)
print(euclidean_distance)
5.196152422706632
Exercise 94:
Create a 1D array and set the values between the 25th and 75th percentile to 0.
import numpy as np
arr = np.array([10, 20, 30, 40, 50])
percentile_25th = np.percentile(arr, 25)
percentile_75th = np.percentile(arr, 75)
arr[(arr >= percentile_25th) & (arr <= percentile_75th)] = 0
print(arr)
[10 0 0 0 50]
Exercise 95:
Calculate the element-wise square of the difference between two arrays.
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
squared_difference = (arr1 - arr2)**2
print(squared_difference)
[9 9 9]
Exercise 96:
Replace all even numbers in a 1D array with the next odd number.
import numpy as np
arr = np.array([2, 5, 8, 12, 15])
arr[arr % 2 == 0] += 1
print(arr)
[ 3 5 9 13 15]
Exercise 97:
Create a 2D array and normalize each column by its range..
import numpy as np
matrix = np.random.random((3, 4))
normalized_columns_range = (matrix - np.min(matrix, axis=0)) / (np.max(matrix, axis=0) - np.min(matrix, axis=0))
print(normalized_columns_range)
[[0. 1. 0. 1. ] [0.38748809 0. 1. 0. ] [1. 0.39378719 0.83348354 0.78448847]]
Exercise 98:
Compute the cumulative sum of elements along a given axis in a 2D array.
import numpy as np
matrix = np.random.random((3, 4))
cumulative_sum_axis1 = np.cumsum(matrix, axis=1)
print(cumulative_sum_axis1)
[[0.23623571 0.76181793 1.40183102 2.02358543] [0.89415619 1.38540147 1.46977913 1.99923603] [0.3626984 0.94484957 1.24671758 2.19056585]]
Exercise 99:
Check if any element in a 1D array is non-zero.
import numpy as np
arr = np.array([0, 0, 0, 1, 0])
any_nonzero = np.any(arr != 0)
print(any_nonzero)
True
Exercise 100:
Create a 2D array with random integers and replace all values greater than a certain threshold with that threshold.
import numpy as np
matrix = np.random.randint(0, 100, size=(3, 4))
threshold = 75
matrix[matrix > threshold] = threshold
print(matrix)
[[72 55 56 26] [ 8 16 54 75] [13 74 36 75]]
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