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# NumPy Array Attributes In this section, we will learn about some basic attributes of NumPy arrays. The dimension of a NumPy array is called its rank. The rank is the number of axes (dimensions). For example, the rank of a one-dimensional array is 1, the rank of a two-dimensional array is 2, and so on. In NumPy, each linear array is called an axis. For example, a two-dimensional array is equivalent to two one-dimensional arrays, where each element in the first one-dimensional array is itself a one-dimensional array. So, one-dimensional arrays are the axes in NumPy. The first axis is like the underlying array, and the second axis is the array within that underlying array. The number of axes—the rank—is the dimensionality of the array. Often, you can specify an axis. `axis=0` means performing an operation along the 0th axis (i.e., on each column), while `axis=1` means performing an operation along the 1st axis (i.e., on each row). The most important attributes of the NumPy ndarray object are: | Attribute | Description | | --- | --- | | `ndarray.ndim` | The rank (dimensionality) of the array, i.e., the number of axes or dimensions. | | `ndarray.shape` | The dimensions of the array, indicating the size along each axis. For a 2D array (matrix), it represents the number of rows and columns. | | `ndarray.size` | The total number of elements in the array, equal to the product of the sizes along each axis in `ndarray.shape`. | | `ndarray.dtype` | The data type of the elements in the array. | | `ndarray.itemsize` | The size of each element in the array in bytes. | | `ndarray.flags` | Contains information about the memory layout, such as whether it is C or Fortran contiguous, whether it is read-only, etc. | | `ndarray.real` | The real part of each element in the array (if the element type is complex). | | `ndarray.imag` | The imaginary part of each element in the array (if the element type is complex). | | `ndarray.data` | The buffer containing the actual elements of the array. Typically, elements are accessed via indexing, and this attribute is not used directly. | ### ndarray.ndim `ndarray.ndim` is used to get the number of dimensions (i.e., the number of axes) of the array. ## Example import numpy as np a = np.arange(24)print(a.ndim)# a now has only one dimension# Now adjust its size b = a.reshape(2,4,3)# b now has three dimensions print(b.ndim) The output is: 13 ### ndarray.shape `ndarray.shape` represents the dimensions of the array and returns a tuple. The length of this tuple is the number of dimensions, i.e., the ndim attribute (rank). For example, for a two-dimensional array, the dimensions represent "number of rows" and "number of columns". `ndarray.shape` can also be used to resize the array. ## Example import numpy as np a = np.array([[1,2,3],[4,5,6]])print(a.shape) The output is: (2, 3) Resize the array. ## Example import numpy as np a = np.array([[1,2,3],[4,5,6]])a.shape = (3,2)print(a) The output is: [ ] NumPy also provides the `reshape` function to resize arrays. ## Example import numpy as np a = np.array([[1,2,3],[4,5,6]])b = a.reshape(3,2)print(b) The output is: [ ] ### ndarray.itemsize `ndarray.itemsize` returns the size of each element in the array in bytes. For example, an array with a dtype of `float64` has an `itemsize` value of 8 (since `float64` occupies 64 bits, and each byte is 8 bits, so 64/8 = 8 bytes). Similarly, an array with a dtype of `complex32` has an `itemsize` of 4 (32/8). ## Example import numpy as np# Array dtype is int8 (one byte) x = np.array([1,2,3,4,5], dtype = np.int8)print(x.itemsize)# Array dtype is now float64 (eight bytes) y = np.array([1,2,3,4,5], dtype = np.float64)print(y.itemsize) The output is: 18 ### ndarray.flags `ndarray.flags` returns memory information about the ndarray object, containing the following attributes: | Attribute | Description | | --- | --- | | C_CONTIGUOUS (C) | Data is in a single, C-style contiguous segment. | | F_CONTIGUOUS (F) | Data is in a single, Fortran-style contiguous segment. | | OWNDATA (O) | The array owns the memory it uses or borrows it from another object. | | WRITEABLE (W) | The data area can be written to. Setting this to False makes the data read-only. | | ALIGNED (A) | Data and all elements are properly aligned for the hardware. | | UPDATEIFCOPY (U) | This array is a copy of another array. When this array is released, the contents of the original array will be updated. | ## Example import numpy as np x = np.array([1,2,3,4,5])print(x.flags) The output is: C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False