NumPy - Array From Existing Data


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In this chapter, we will discuss how to create an array from existing data.

numpy.asarray

This function is similar to numpy.array except for the fact that it has fewer parameters. This routine is useful for converting Python sequence into ndarray.

numpy.asarray(a, dtype = None, order = None)

The constructor takes the following parameters.

Sr.No. Parameter & Description
1

a

Input data in any form such as list, list of tuples, tuples, tuple of tuples or tuple of lists

2

dtype

By default, the data type of input data is applied to the resultant ndarray

3

order

C (row major) or F (column major). C is default

The following examples show how you can use the asarray function.

Example 1

Live Demo
# convert list to ndarray 
import numpy as np 

x = [1,2,3] 
a = np.asarray(x) 
print a

Its output would be as follows −

[1  2  3] 

Example 2

Live Demo
# dtype is set 
import numpy as np 

x = [1,2,3]
a = np.asarray(x, dtype = float) 
print a

Now, the output would be as follows −

[ 1.  2.  3.] 

Example 3

Live Demo
# ndarray from tuple 
import numpy as np 

x = (1,2,3) 
a = np.asarray(x) 
print a

Its output would be −

[1  2  3]

Example 4

Live Demo
# ndarray from list of tuples 
import numpy as np 

x = [(1,2,3),(4,5)] 
a = np.asarray(x) 
print a

Here, the output would be as follows −

[(1, 2, 3) (4, 5)]

numpy.frombuffer

This function interprets a buffer as one-dimensional array. Any object that exposes the buffer interface is used as parameter to return an ndarray.

numpy.frombuffer(buffer, dtype = float, count = -1, offset = 0)

The constructor takes the following parameters.

Sr.No. Parameter & Description
1

buffer

Any object that exposes buffer interface

2

dtype

Data type of returned ndarray. Defaults to float

3

count

The number of items to read, default -1 means all data

4

offset

The starting position to read from. Default is 0

Example

The following examples demonstrate the use of frombuffer function.

Live Demo
import numpy as np 
s = 'Hello World' 
a = np.frombuffer(s, dtype = 'S1') 
print a

Here is its output −

['H'  'e'  'l'  'l'  'o'  ' '  'W'  'o'  'r'  'l'  'd']

numpy.fromiter

This function builds an ndarray object from any iterable object. A new one-dimensional array is returned by this function.

numpy.fromiter(iterable, dtype, count = -1)

Here, the constructor takes the following parameters.

Sr.No. Parameter & Description
1

iterable

Any iterable object

2

dtype

Data type of resultant array

3

count

The number of items to be read from iterator. Default is -1 which means all data to be read

The following examples show how to use the built-in range() function to return a list object. An iterator of this list is used to form an ndarray object.

Example 1

Live Demo
# create list object using range function 
import numpy as np 
list = range(5) 
print list

Its output is as follows −

[0,  1,  2,  3,  4]

Example 2

Live Demo
# obtain iterator object from list 
import numpy as np 
list = range(5) 
it = iter(list)  

# use iterator to create ndarray 
x = np.fromiter(it, dtype = float) 
print x

Now, the output would be as follows −

[0.   1.   2.   3.   4.]


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