numpy provides a number of mathematical functions for performing various mathematical calculations on elements in an array. Here are some commonly used math functions:
Numerical calculation function:
np.add(): Perform an addition operation on elements in an array.
np.subtract(): Subtracts elements from an array.
np.multiply(): Multiplication of elements in an array.
np.divide(): Divides elements in an array.
np.power(): Perform a power operation on the elements in the array.
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Trigonometric function: npsin(): Calculates the sinusoidal value of the elements in the array.
np.cos(): Calculates the cosine value of an element in the array.
np.tan(): Calculates the tangent of an element in the array.
np.arcsin(): calculates the arcsine value of the elements in the array.
np.arccos(): Calculates the inverse cosine value of the elements in the array.
np.arctan(): Computes the arctangent values of elements in the array.
Exponential and logarithmic functions:
np.exp(): Calculates the exponential value of an element in the array.
np.log(): Calculates the natural logarithm of the elements in the array.
np.log10(): Computes the base logarithmic value of the elements in the array.
np.log2(): Calculates the base logarithmic value of the elements in the array.
Numerical calculation function:
np.abs(): Calculates the absolute value of the elements in the array.
np.sqrt(): Calculates the square root of an element in the array.
np.round(): Rounds the elements in the array.
np.floor(): Rounds down the elements in the array.
np.ceil(): Rounds up the elements in the array.
These functions can be applied to the numpy array to perform various numerical calculations on the elements in the array. You can select the appropriate function to implement the desired operation as needed.