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Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas (Chapter 3) are built around the NumPy array. This section will present several examples of using NumPy array manipulation to access data and subarrays, and to split, reshape, and join the arrays. While the types of operations shown here may seem a bit dry and pedantic, they comprise the building blocks of many other examples used throughout the book. Get to know them well!

We'll cover a few categories of basic array manipulations here:

*Attributes of arrays*: Determining the size, shape, memory consumption, and data types of arrays*Indexing of arrays*: Getting and setting the value of individual array elements*Slicing of arrays*: Getting and setting smaller subarrays within a larger array*Reshaping of arrays*: Changing the shape of a given array*Joining and splitting of arrays*: Combining multiple arrays into one, and splitting one array into many

First let's discuss some useful array attributes.
We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array.
We'll use NumPy's random number generator, which we will *seed* with a set value in order to ensure that the same random arrays are generated each time this code is run:

Each array has attributes `ndim`

(the number of dimensions), `shape`

(the size of each dimension), and `size`

(the total size of the array):

x3 ndim: 3 x3 shape: (3, 4, 5) x3 size: 60

Another useful attribute is the `dtype`

, the data type of the array (which we discussed previously in Understanding Data Types in Python):

dtype: int64

Other attributes include `itemsize`

, which lists the size (in bytes) of each array element, and `nbytes`

, which lists the total size (in bytes) of the array:

itemsize: 8 bytes nbytes: 480 bytes

In general, we expect that `nbytes`

is equal to `itemsize`

times `size`

.

If you are familiar with Python's standard list indexing, indexing in NumPy will feel quite familiar. In a one-dimensional array, the $i^{th}$ value (counting from zero) can be accessed by specifying the desired index in square brackets, just as with Python lists:

array([5, 0, 3, 3, 7, 9])

5

7

To index from the end of the array, you can use negative indices:

9

7

In a multi-dimensional array, items can be accessed using a comma-separated tuple of indices:

array([[3, 5, 2, 4], [7, 6, 8, 8], [1, 6, 7, 7]])

3

1

7

Values can also be modified using any of the above index notation:

array([[12, 5, 2, 4], [ 7, 6, 8, 8], [ 1, 6, 7, 7]])

Keep in mind that, unlike Python lists, NumPy arrays have a fixed type. This means, for example, that if you attempt to insert a floating-point value to an integer array, the value will be silently truncated. Don't be caught unaware by this behavior!

array([3, 0, 3, 3, 7, 9])

Just as we can use square brackets to access individual array elements, we can also use them to access subarrays with the *slice* notation, marked by the colon (`:`

) character.
The NumPy slicing syntax follows that of the standard Python list; to access a slice of an array `x`

, use this:

If any of these are unspecified, they default to the values `start=0`

, `stop=`

* size of dimension*,

`step=1`

.
We'll take a look at accessing sub-arrays in one dimension and in multiple dimensions.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

array([0, 1, 2, 3, 4])

array([5, 6, 7, 8, 9])

array([4, 5, 6])

array([0, 2, 4, 6, 8])

array([1, 3, 5, 7, 9])

A potentially confusing case is when the `step`

value is negative.
In this case, the defaults for `start`

and `stop`

are swapped.
This becomes a convenient way to reverse an array:

array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])

array([5, 3, 1])

Multi-dimensional slices work in the same way, with multiple slices separated by commas. For example: