Blog

In this chapter, we will discuss about NumPy Array From Existing Data is how to create an array from existing data.

NumPy 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	aInput data in any form such as list, list of tuples, tuples, tuple of tuples or tuple of lists
2	dtypeBy default, the data type of input data is applied to the resultant ndarray
3	orderC (row-major) or F (column-major). C is default

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

Example

# 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

# 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

# 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

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

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

The constructor takes the following parameters.

Sr.No.	Parameter & Description
1	bufferAny object that exposes buffer interface
2	dtypeData type of returned ndarray. Defaults to float
3	countThe number of items to read, default -1 means all data
4	offsetThe starting position to read from. Default is 0

Example

The following examples demonstrate the use of from buffer function.

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 a 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	iterableAny iterable object
2	dtypeData type of resultant array
3	countThe number of items to be read from the 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 a ndarray object.

Example 1

# 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

# 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.]