# numpy Array slicing and indexing

## Bracket Indexing and Selection

The simplest way to pick one or some elements of an array looks very similar to python lists:

` ````
```#Get a value at an index
arr[8]

` ````
```#Get values in a range
arr[1:5]

` ````
```#Get values in a range
arr[2:]

## Broadcasting

Numpy arrays differ from a normal Python list because of their ability to broadcast:

` ````
```#Setting a value with index range (Broadcasting)
arr = np.arange(10,21)
arr[0:5]=200
#Show
arr

` ````
```# Reset array, we'll see why I had to reset in a moment
arr = np.arange(10,21)
#Show
arr

` ````
```#Important notes on Slices
slice_of_arr = arr[0:6]
#Show slice
slice_of_arr

` ````
```#Change Slice
slice_of_arr[:]=99
#Show Slice again
slice_of_arr

Now note the changes also occur in our original array!

` ````
```arr

Data is not copied, it’s a view of the original array! This avoids memory problems!

` ````
```#To get a copy, need to be explicit
arr_copy = arr.copy()
arr_copy

## Indexing a 2D array (matrices)

The general format is **arr_2d[row][col]** or **arr_2d[row,col]**. I recommend usually using the comma notation for clarity.

` ````
```arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))
#Show
arr_2d

` ````
```#Indexing row [row,column]
arr_2d[1]

` ````
```# Format is arr_2d[row][col] or arr_2d[row,col]
# Getting individual element value
arr_2d[1][0]

` ````
```# Getting individual element value
#arr_2d[1,0]
arr_2d

` ````
```# 2D array slicing
#Shape (2,2) from top right corner
arr_2d[:2,1:]

` ````
```#Shape bottom row
arr_2d[2]

` ````
```#Shape bottom row
arr_2d[2,1]

## Fancy Indexing

Fancy indexing allows you to select entire rows or columns out of order,to show this, let’s quickly build out a numpy array:

` ````
```#Set up matrix
arr2d = np.zeros((10,10))
arr2d

` ````
```#Length of array
arr_length = arr2d.shape[1]
arr_length

` ````
```#Set up array
for i in range(arr_length):
arr2d[i] = i
arr2d

Fancy indexing allows the following

` ````
```arr2d[[2,4,6,8]]

` ````
```#Allows in any order
arr2d[[6,4,2,7]]

## More Indexing Help

Indexing a 2d matrix can be a bit confusing at first, especially when you start to add in step size. Try google image searching NumPy indexing to fins useful images, like this one:

## Selection

Let’s briefly go over how to use brackets for selection based off of comparison operators.

` ````
```arr = np.arange(1,11)
arr

` ````
```arr[arr > 4]

` ````
```bool_arr = arr>4

` ````
```bool_arr

` ````
```arr[bool_arr]

` ````
```arr[arr>2]

` ````
```x = 5
arr[arr>x]