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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
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