解决90%的常见问题的8个python NumPy函数

# Create an array using np.array()
 arr = np.array([1, 2, 3, 4, 5])
 print(arr)
 Ouput: [1 2 3 4 5]

# Create a 2-dimensional array of zeros
 arr = np.zeros((3, 4))
 [[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]

# Generate a random integer between 0 and 9
 rand_int = np.random.randint(10)
 print(rand_int)

# Generate an array of 5 values from 0 to 10 (inclusive)
 arr = np.linspace(0, 10, 5)
 # Print the array
 print(arr)
 [ 0.   2.5  5.   7.5 10. ]

# Generate an array from 0 to 10 (exclusive) with step size 1
 arr = np.arange(0, 10, 2)
 # Print the array
 print(arr)
 [1 3 5 7 9]

# Create a 1-dimensional array
 arr = np.array([1, 2, 3, 4, 5, 6])
 # Reshape the array to a 2x3 matrix
 reshaped_arr = np.reshape(arr, (2, 3))
 [[1 2 3]
  [4 5 6]]

# Create a 2-dimensional array
 arr = np.array([[1, 2, 3],
                 [4, 5, 6]])
 # Transpose the array
 transposed_arr = np.transpose(arr)
 [[1 4]
  [2 5]
  [3 6]]

# Create two 1-dimensional arrays
 arr1 = np.array([1, 2, 3])
 arr2 = np.array([4, 5, 6])
 # Concatenate the arrays along axis 0 (default)
 concatenated_arr = np.concatenate((arr1, arr2))
 [1 2 3 4 5 6]

# Create two 1-dimensional arrays
 arr1 = np.array([1, 2, 3])
 arr2 = np.array([4, 5, 6])
 # Vertically stack the arrays
 stacked_arr = np.vstack((arr1, arr2))
 [[1 2 3]
  [4 5 6]]

# Create a 1-dimensional array
 arr = np.array([1, 2, 3, 4, 5])
 # Compute the standard deviation of the array
 std = np.std(arr)
 1.4142135623730951

data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
 # Calculate the 50th percentile (median) of the data
 median = np.percentile(data, 50)
 # Calculate the 25th and 75th percentiles (quartiles) of the data
 q1 = np.percentile(data, 25)
 q3 = np.percentile(data, 75)
 Median: 5.5
 Q1: 3.25
 Q3: 7.75

# Generate a 1-dimensional array of random numbers
 random_array = np.random.rand(5)
 [0.35463311 0.67659889 0.5865293  0.77127035 0.13949178]

# Generate a random number from a normal distribution
 random_number = np.random.normal()
 -0.6532785285205665

# Create two arrays
 a = np.array([1, 2, 3])
 b = np.array([4, 5, 6])
 # Compute the dot product of the arrays
 dot_product = np.dot(a, b)
 32

# Create a 2D array
 arr = np.array([[3, 1, 5], [2, 4, 6]])
 # Sort the array along the second axis (columns)
 sorted_arr = np.sort(arr, axis=1)
 [[1 3 5]
  [2 4 6]]

# Create an array
 arr = np.array([3, 1, 5, 2, 4])
 # Get the indices that would sort the array
 sorted_indices = np.argsort(arr)
 [1 3 0 4 2]

arr = np.array([2, 1, 3, 2, 1, 4, 5, 4])
 # Get the unique elements of the array
 unique_values = np.unique(arr)
 [1 2 3 4 5]

arr = np.array([1, 2, 3, np.nan, 5])
 # Create a masked array by masking the invalid values
 masked_arr = ma.masked_invalid(arr)
 [1 2 3 5]

condition = np.array([True, False, True, False])
 # Create two arrays
 array_true = np.array([1, 2, 3, 4])
 array_false = np.array([5, 6, 7, 8])
 result = np.where(condition, array_true, array_false)
 [1 6 3 8]