NumPy - Array Attributes
In this chapter, we will discuss the various array attributes of NumPy.
ndarray.shape
This array attribute returns a tuple consisting of array dimensions. It can also be used to resize the array.
Example 1
import numpy as np
a = np.array([[1,2,3],[4,5,6]])
print a.shape
The output is as follows −
(2, 3)
Example 2
# this resizes the ndarray
import numpy as np
a = np.array([[1,2,3],[4,5,6]])
a.shape = (3,2)
print a
The output is as follows −
[[1, 2]
[3, 4]
[5, 6]]
Example 3
NumPy also provides a reshape function to resize an array.
import numpy as np
a = np.array([[1,2,3],[4,5,6]])
b = a.reshape(3,2)
print b
The output is as follows −
[[1, 2]
[3, 4]
[5, 6]]
ndarray.ndim
This array attribute returns the number of array dimensions.
Example 1
# an array of evenly spaced numbers
import numpy as np
a = np.arange(24)
print a
The output is as follows −
[0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
Example 2
# this is one dimensional array
import numpy as np
a = np.arange(24)
a.ndim
# now reshape it
b = a.reshape(2,4,3)
print b
# b is having three dimensions
The output is as follows −
[[[ 0, 1, 2]
[ 3, 4, 5]
[ 6, 7, 8]
[ 9, 10, 11]]
[[12, 13, 14]
[15, 16, 17]
[18, 19, 20]
[21, 22, 23]]]
numpy.itemsize
This array attribute returns the length of each element of array in bytes.
Example 1
# dtype of array is int8 (1 byte)
import numpy as np
x = np.array([1,2,3,4,5], dtype = np.int8)
print x.itemsize
The output is as follows −
1
Example 2
# dtype of array is now float32 (4 bytes)
import numpy as np
x = np.array([1,2,3,4,5], dtype = np.float32)
print x.itemsize
The output is as follows −
4
numpy.flags
The ndarray object has the following attributes. Its current values are returned by this function.
| Sr.No. |
Attribute & Description |
| 1 |
C_CONTIGUOUS (C)
The data is in a single, C-style contiguous segment
|
| 2 |
F_CONTIGUOUS (F)
The data is in a single, Fortran-style contiguous segment
|
| 3 |
OWNDATA (O)
The array owns the memory it uses or borrows it from another object
|
| 4 |
WRITEABLE (W)
The data area can be written to. Setting this to False locks the data, making it read-only
|
| 5 |
ALIGNED (A)
The data and all elements are aligned appropriately for the hardware
|
| 6 |
UPDATEIFCOPY (U)
This array is a copy of some other array. When this array is deallocated, the base array will be updated with the contents of this array
|
Example
The following example shows the current values of flags.
import numpy as np
x = np.array([1,2,3,4,5])
print x.flags
The output is as follows −
C_CONTIGUOUS : True
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
