numpy.typename() function – Python

numpy.maximum_sctype() function – Python

Last Updated : 18 Jun, 2020

numpy.maximum_sctype() function return the scalar type of highest precision of the same kind as the input.

Syntax : numpy.maximum_sctype(t) Parameters : t : [dtype or dtype specifier] The input data type. This can be a dtype object or an object that is convertible to a dtype. Return : [dtype] The highest precision data type of the same kind as the input.

Code #1 :

Python3

# Python program explaining
# numpy.maximum_sctype() function
          
# importing numpy as geek 
import numpy as geek 

gfg = geek.maximum_sctype(int)

print (gfg)

Output :

class 'numpy.int64'

Code #2 :

Python3

# Python program explaining
# numpy.maximum_sctype() function
          
# importing numpy as geek 
import numpy as geek 

gfg = geek.maximum_sctype('f4')

print (gfg)

Output :

class 'numpy.float128'

numpy.typename() function – Python

sanjoy_62

Improve

Article Tags :

Practice Tags :

python

Similar Reads

numpy.sctype2char() function â€“ Python

numpy.sctype2char() function return the string representation of a scalar dtype. Syntax : numpy.sctype2char(sctype) Parameters : sctype : [scalar dtype or object] If a sctype is a scalar dtype, the corresponding string character is returned. If an object, sctype2char tries to infer its scalar type a

Numpy recarray.max() function | Python

In numpy, arrays may have a data-types containing fields, analogous to columns in a spreadsheet. An example is [(a, int), (b, float)], where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as arr['a'] and arr['b']. Record array

numpy.typename() function â€“ Python

numpy.typename() function return a description for the given data type code. Syntax : numpy.typename(char) Parameters : char : [str] Data type code. Return : [str] Description of the input data type code. Code #1 : Python3 # Python program explaining # numpy.typename() function # importing numpy as

numpy.maximum() in Python

numpy.maximum() function is used to find the element-wise maximum of array elements. It compares two arrays and returns a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned.

numpy.result_type() function â€“ Python

numpy.result_type() function returns the type that results from applying the NumPy type promotion rules to the arguments. NumPy type promotion by example : Suppose calculating 3*arr, where arr is an array of 32-bit floats, intuitively should result in a 32-bit float output. If the 3 is a 32-bit inte

type() function in Python

The type() function is mostly used for debugging purposes. Two different types of arguments can be passed to type() function, single and three arguments. If a single argument type(obj) is passed, it returns the type of the given object. If three argument types (object, bases, dict) are passed, it re

numpy.select() function - Python

The numpy.select() function is used to construct an array by selecting elements from a list of choices based on multiple conditions. It is particularly useful when dealing with conditional replacements or transformations in NumPy arrays. Example:Pythonimport numpy as np arr = np.array([10, 20, 30, 4

numpy.matlib.zeros() function | Python

numpy.matlib.zeros() function return matrix of given shape and type, filled with zeros. Syntax : numpy.matlib.zeros(shape, dtype = None, order = 'C') Parameters : shape : [sequence of ints] Shape of the empty matrix. dtype : [data-type, optional] The desired data-type for the matrix, default is floa

Numpy size() function | Python

numpy.size() function in Python is used to count the number of elements in a NumPy array. You can use it to get the total count of all elements, or to count elements along a specific axis, such as rows or columns in a multidimensional array. This makes it useful when quickly trying to understand the

numpy.common_type() function â€“ Python

numpy.common_type() function return a scalar type which is common to the input arrays. Syntax : numpy.common_type(arrays) Parameters : array1, array2, .... : [ndarrays] Input arrays. Return : [dtype] Return the data type which is common to the input arrays. Code #1 : Python3 # Python program explain