Array From Existing Data using Numpy

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.

a
Input data in any form such as list, list of tuples, tuples, tuple of tuples or tuple of lists
dtype
By default, the data type of input data is applied to the resultant ndarray
order
C (row major) or F (column major). C is default
The following examples show how you can use the as array function.

Example 1

# convert list to ndarray
import numpy as np
x = [69, 79, 109]
a = np.asarray(x)
print(a)

Its output would be as follows −

[ 69  79 109]

Example 2

# dtype is set
import numpy as np
x = [67.9, 78.54, 34.57]
a = np.asarray(x, dtype = float)
print(a)

Now, the output would be as follows −

[67.9  78.54 34.57]

Example 3

# ndarray from tuple
import numpy as np
x = (89, 90, 91)
a = np.asarray(x)
print(a)

Its output would be −

[89 90 91]

Example 4

# ndarray from list of tuples
import numpy as np
x =  [(1, 1.71), (2, 1.32), (3, 1.398)]
a = np.asarray(x)
print(a)

Here, the output would be as follows −

[[1.    1.71 ]
[2.    1.32 ]
[3.    1.398]]

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.

buffer
Any object that exposes buffer interface
dtype
Data type of returned ndarray. Defaults to float
count
The number of items to read, default -1 means all data
offset
The starting position to read from. Default is 0

Example

The following examples demonstrate the use of frombuffer function.

import numpy as np
s ='Pragya'
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.

iterable
Any iterable object
dtype
Data type of resultant array
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

# create list object using range function
import numpy as np
list = [*range(19)]
print(list)

Its output is as follows −

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]

Example 2

# obtain iterator object from list
list = range(9)
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. 5. 6. 7. 8.]