NumPy 数组操作：全球数据科学家掌握数学计算的关键

            import numpy as np

# Creating a 1D array from a list
arr1d = np.array([1, 2, 3, 4, 5])
print(arr1d)

# Creating a 2D array (matrix) from a list of lists
arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(arr2d)
            

              
                Copy
              
            
          

            # Creating an array of zeros
zeros_array = np.zeros((3, 4))  # 3 rows, 4 columns
print(zeros_array)

# Creating an array of ones
ones_array = np.ones((2, 2))
print(ones_array)

# Creating an array with a range of values
range_array = np.arange(0, 10, 2) # Start, stop, step
print(range_array)

# Creating an array with evenly spaced values
linspace_array = np.linspace(0, 1, 5) # Start, stop, num samples
print(linspace_array)
            

              
                Copy
              
            
          

            import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

# Addition
add_result = arr1 + arr2
print(f'Addition: {add_result}')

# Subtraction
sub_result = arr2 - arr1
print(f'Subtraction: {sub_result}')

# Multiplication
mul_result = arr1 * arr2
print(f'Multiplication: {mul_result}')

# Division
div_result = arr2 / arr1
print(f'Division: {div_result}')
            

              
                Copy
              
            
          

            
# Exponentiation
arr = np.array([1, 2, 3])
exponentiation_result = arr ** 2
print(f'Exponentiation: {exponentiation_result}')

# Modulus
arr1 = np.array([7, 8, 9])
arr2 = np.array([2, 3, 4])
modulus_result = arr1 % arr2
print(f'Modulus: {modulus_result}')
            

              
                Copy
              
            
          

            import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Accessing a single element (row, column)
element = arr[1, 2] # Element in the second row, third column (value 6)
print(f'Element at [1, 2]: {element}')

# Accessing an entire row
row = arr[1, :]
print(f'Row 1: {row}')

# Accessing an entire column
column = arr[:, 2]
print(f'Column 2: {column}')
            

              
                Copy
              
            
          

            # Slicing to get a portion of the array
slice1 = arr[0:2, 1:3]  # Rows 0 and 1, columns 1 and 2
print(f'Slice: {slice1}')
            

              
                Copy
              
            
          

            import numpy as np

arr = np.array([1, 2, 3])
scalar = 2
result = arr + scalar  # Broadcasting the scalar to each element
print(f'Broadcasting scalar: {result}')
            

              
                Copy
              
            
          

            
arr1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
arr2 = np.array([10, 20, 30]) # Shape (3,)

result = arr1 + arr2 # Broadcasting
print(f'Broadcasting with different shapes: \n{result}')
            

              
                Copy
              
            
          

            import numpy as np

arr = np.array([0, np.pi/2, np.pi])  # Radians

sin_values = np.sin(arr)
print(f'Sine values: {sin_values}')

cos_values = np.cos(arr)
print(f'Cosine values: {cos_values}')
            

              
                Copy
              
            
          

            
arr = np.array([1, 2, 3])
exp_values = np.exp(arr) # e^x
print(f'Exponential values: {exp_values}')

log_values = np.log(arr)  # Natural logarithm (base e)
print(f'Natural Logarithm values: {log_values}')

log10_values = np.log10(arr)  # Base 10 logarithm
print(f'Base 10 Logarithm values: {log10_values}')
            

              
                Copy
              
            
          

            import numpy as np

arr = np.array([1, 2, 3, 4, 5])

mean_value = np.mean(arr)
print(f'Mean: {mean_value}')

median_value = np.median(arr)
print(f'Median: {median_value}')

std_dev = np.std(arr)
print(f'Standard Deviation: {std_dev}')

min_value = np.min(arr)
print(f'Minimum: {min_value}')

max_value = np.max(arr)
print(f'Maximum: {max_value}')
            

              
                Copy
              
            
          

            import numpy as np

arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])

# Matrix multiplication
matrix_product = np.dot(arr1, arr2)
print(f'Matrix Product: \n{matrix_product}')

# Transpose
transpose_arr = arr1.T
print(f'Transpose: \n{transpose_arr}')
            

              
                Copy
              
            
          

            import numpy as np

# Example: Solving the equation Ax = b
A = np.array([[2, 1], [1, 3]])
b = np.array([5, 8])

x = np.linalg.solve(A, b) # Solution for x
print(f'Solution for x: {x}')
            

              
                Copy
              
            
          

            import numpy as np

arr = np.array([[1, 2], [2, 3]])

eigenvalues, eigenvectors = np.linalg.eig(arr)

print(f'Eigenvalues: {eigenvalues}')
print(f'Eigenvectors: \n{eigenvectors}')
            

              
                Copy