# NumPy - Array From Existing Data

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

```# 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 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.

```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

```# 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.]
```