https://www.geeksforgeeks.org/numpy-mathematical-function/

Geeks for Geeks NumPy Mathematical Functions

First,
import numpy as np

- np.tan() calculates tangent of element
- np.arcsin() calculates inverse sine of element
- np.arccos() calculates inverse cosine of element
- np.arctan() calculates inverse tangent of element
- np.degrees() converts element from radians to degrees
- np.radians() converts element from degrees to radians

NumPy Trigonometric Functions

First, import numpy as np

- np.rint() rounds element to nearest integer towards zero
- np.floor() returns element rounded down to nearest integer (1.7 returns 1)
- np.ceil() returns element rounded up to nearest integer (1.1 returns 2)
- np.trunc() returns element without decimals (0.5 returns 0., 1.335 returns 1.)


NumPy Rounding Functions

First, import numpy as np

- np.exp() returns e to the power of the element 
- np.expm1() returns e to the power of the element minus 1
- np.exp2() calculates 2 to the power of the element
- np.log10() returns logarithm base 10 of the element
- np.log2() returns the logarithm base 2 of the element
- np.log1p() return the natural logarithm of one plus the element 

NumPy Exponential and Logarithmic Functions

First, import numpy as np

- np.add() adds elements from two arrays
- np.multiply() multiples elements from two arrays
- np.power() elements from first array are to powers from second array
- np.subtract() subtracts elements from two arrays
- np.true_divide() uses true division to divide elements from two arrays
- np.floor_divide() divides elements from two arrays and returns the floor value of the quotient (1 / 3 returns 0)
- np.mod() returns the remainder of the division between two arrays


NumPy Arithmetic Functions

First, import numpy as np

- np.sqrt() returns non-negative square root of element
- np.square() returns square of the element
- np.absolute() calculates absolute value of the element
- np.maximum() returns max of all array elements
- np.minimum() returns minimum of all array elements


Other Math Functions in NumPy 

Within the NumPy library, there are several types of mathematical functions built-in. These functions are typically used across elements of a NumPy array.

Types of Mathematical Functions:
- Trigonometric
- Rounding
- Exponents and Logarithms
- Arithmetic
- Other Special Functions

University of Michigan - Ann Arbor

NumPy is a Python library for scientific computing that contains support for some fundamental data structures used by scikit-learn, such as multi-dimensional arrays. Example import: `import numpy as np`

NumPy

NumPy Documentation

Typically, data that's input to scikit-learn will be in the form of a NumPy array.

NumPy vs. Scikit-learn

What does the following code do?

Recall that "np.dot(a,b)" performs a matrix multiplication on a and b, whereas "a*b" performs an element-wise multiplication. Consider the two following random arrays "a" and "b": what is the shape of c?

Consider the following code: what will be c?

Consider the two following random arrays "a" and "b" below: what will be the shape of "c"?

Because the Numpy array is densely packed in memory due to its homogeneous type, Numpy gets around 30 times faster than Python.

Make big difference when handling big data.

Numpy Speed


In NumPy, there are two objects that are crucial to understanding how this library works. Those are:

1. N-dimensional array object (ndarray)

2. Universal function object (ufunc)

However, keep in mind that there are other objects that are built upon these that can be incorporated into whatever you're doing.

Fundamental Objects in NumPy

In python, there  is already data types that it has by default such as boolean, float, integer, etc. However, NumPy offers additional data types:

- i - integer
- b - boolean
- u - unsigned integer
- f - float
- c - complex float
- m - time delta
- M - date time
- O - object
- S - string
- U - unicode String
- V - fixed chunk of memory for other type (void)

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