In Numpy, its library provides us various ways to create a NumPy Array from existing data. They are as follows:
It is used to create array by using existing data which is in form of lists, or tuples.It is somehow similar to numpy.array but it has fewer parameters as compared to
numpy.array. It is used to convert python sequence into the numpay array.
numpy.asarray(sequence, dtype , order)
- sequence:It is used to represent the input data which is python sequence which will be used to for converting into python array.
- dtype: It is used to describe the data type of each element in ndarray.
- order: It can either be set to C or F. Its default is C.
import numpy as np x = [1,2,3,4,5,6,7] y = ('a','b','c','d','e','f','g','h') a = np.asarray(x) b = np.asarray(y) print(a) print(b) print(type(a)) print(type(b))
[1 2 3 4 5 6 7] ['a' 'b' 'c' 'd' 'e' 'f' 'g' 'h'] <class 'numpy.ndarray'> <class 'numpy.ndarray'>
This function returns an ndarray by using the specified buffer. It is used to interpret a buffer as 1-D array.
numpy.frombuffer(buffer, dtype=float, count=-1, offset=0)
buffer: It represent an object that exposes a buffer interface.
type: It represent the data type of ndarray.
count: It is used to represent the length of ndarray.
offset: It is the starting position from where to read from. Its default value is 0.
import numpy as np x= b'ProgramsBuzz' a = np.frombuffer(x, dtype = "S1") print(a) print(type(a))
[b'P' b'r' b'o' b'g' b'r' b'a' b'm' b's' b'B' b'u' b'z' b'z'] <class 'numpy.ndarray'>
This function is used to create a ndarray by using an iterable object. It returns a on1-D ndarray.
numpy.fromiter(iterable, dtype, count = -1)
- iterable: It is used to an iterable object.
- type: It is used to describe data type of ndarray.
- count: It is used to represents the number of items to read from the buffer in the array.
import numpy as np x=(x**2 for x in range(6)) a=np.fromiter(x,dtype=float) print(a)
Output: [ 0. 1. 4. 9. 16. 25.]