NumPy Mathematical Functions:

Trigonometric functions in Python for Data Science :

Numpy.sin(x[, out]) = ufunc ‘sin’): It is a function which helps us to find the trigonometric sine value of x (where x is an array element).

NumPy Mathematical Functions in Python for Data Science - PST Analytics

numpy.cos(x[, out]) = ufunc ‘cos’): ): It is a function which helps us to find the trigonometric cosine value of x (where x is an array element).

NumPy Mathematical Functions in Python for Data Science - PST Analytics

NumPy Mathematical Functions in Python for Data Science - PST Analytics

Hyperbolic functions:

Numpy.sinh(x[, out]) = ufunc ‘sin’): ): It is a function which helps us to find the hyperbolic sine value of x (where x is an array element).

NumPy Mathematical Functions in Python for Data Science - PST Analytics

Numpy.cosh(x[, out]) = ufunc ‘cos’): So, it is a function which helps us to find the hyperbolic cosine value of x.

Functions for rounding:

numpy.around(arr, decimals = 0, out = None): It is a function which helps us to evenly round array elements to specific number of decimals.

numpy.round_(arr,decimals = 0, out = None): It is a function that rounds off an array to decimal places we have.

NumPy Mathematical Functions in Python for Data Science - PST Analytics

Exponents and logarithmic functions:

numpy.exp (): So, it is a function which helps users to find the exponential of the elements in the array.

numpy.log(x[, out] =ufunc ‘log1p’): It is a function which helps users to calculate the natural log of x (x being the input array elements).

Natural log is the inverse of exp() so, log(exp(x)) = x.

Arithmetic functions:

Numpy.reciprocal(x, /, out=None, *, where =True): It is for calculation of reciprocal of all the elements present in the input array.

numpy.divide (arr1, arr2, out=None, where=True, casting= ‘same_kind’, order=’K’, dtype=None): In this the array elements from the first array are divide by array elements of second array. Both the arrays should have same shape and second array should not have 0 as an element.

NumPy Mathematical Functions in Python for Data Science - PST Analytics

Complex number functions:

numpy.isreal(array): This function will test element wise whether the element is real or not and return a Boolean array.

NumPy Mathematical Functions in Python for Data Science - PST Analytics

numpy.conj(x[, out] = ufunc ‘conjugate’): It is a function which will help the user find the conjugate of any complex number. Conjugate is obtaine by altering the sign of the imaginary part.

Special functions:

numpy.cbrt(arr, out =None, ufunc’cbrt’): It will help in finding the cube root o x (x being array element).

numpy.clip(): It is a function is for limiting the values in an array.

When an interval is provide, the values outside the interval are clip to the interval edges.

So, to learn more about trignometric functions in python for data science, you can check this and this as well.

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