3.11 Basics of NumPy arrays
v Basics of NumPy arrays: -
Ø The basics of NumPy arrays consist
of manipulating arrays to access the data and sub arrays, to split, reshape and
join arrays.
Ø The basic array manipulations
are
1. Attributes of arrays.
2. Slicing and indexing of arrays.
3. Reshaping of arrays.
4. Joining and splitting of arrays.
1) Attributes of arrays: -
Ø The attributes of an array determine
the size, shape, memory consumption and data types of an array.
Ø Each array has attributes like ndim
(no of dimensions), shape (size of each dimension), and size
(total size of array).
Ø N-dimensional array (ndarray)
is most important object defined in NumPy.
Ø ndarray is a fixed size
multi-dimensional container of elements of same type and size.
Ø Ndarray attributes are:
a. ndarray.ndim
b. ndarray.shape
c. ndarray.size
d. ndarray.itemsize
a) ndarray.ndim: -
Ø This array attribute returns the
number of array dimensions.
Ø In NumPy, the number of
dimensions is referred to as Rank.
Ø We get the dimension of array
using ndim attribute.
Example: -
Output: -
b) ndarray.shape: -
This array attribute returns tuple describing the length of each dimension.
We use shape attribute to get the number of elements in the row.
Example: -
Output: -
c) ndarray.size: -
Ø The array size attribute
returns the total number of elements in the array.
Example: -
Output: -
d) ndarray.itemsize: -
Ø The attribute itemsize
returns the memory size (in bytes) of elements in the array.
Example: -
Output: -
2) Slicing and indexing of arrays:
-
Ø Slicing means accessing a range of
elements from an array.
Ø The syntax is similar to Python
lists.
·
Start:
- The starting
index. If omitted, defaults to 0th position.
·
End:
- The ending
index. (not including value at this index).
·
Step
size: - The
spacing between index values, default is 1.
Ø
Indexing means accessing individual elements of an array using
their position (index).
Ø
Indexing starts from 0 (like normal Python lists).
Ø
In multi-dimensional arrays, you use comma-separated
indices.
Example: -
Output: -
3) Reshaping of arrays: -
Ø The reshape( ) function
gives a new shape to an array without changing the data.
Ø This function takes a single
argument that specifies the new shape of the array.
Example: -
Output: -
4) Joining and splitting of arrays: -
Ø Array joining means combining
two or more arrays into a single array.
Ø The concatenate( ) function can
join two or more arrays into a single array.
Example: -
Output: -
Ø Array splitting means to split a
single array into multiple arrays.
Ø We use array_split( ) for
splitting an array.
Example: -
Output: -