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Introduction to NumPy NumPy (or Numpy) is a Linear Algebra Library for Python. NumPy is a Python package. It stands for 'Numerical Python'. It is a library consisting of multidimensional array objects and a collection of routines for processing of array.
Numpy is also incredibly fast, as it has bindings to C libraries.
About iPython Notebooks iPython Notebooks are interactive coding environments embedded in a webpage. You will be using iPython notebooks in this class. You only need to write code between the ### START CODE HERE ### and ### END CODE HERE ### comments.
After writing your code, you can run the cell by either pressing "SHIFT"+"ENTER" or by clicking on "Run Cell" (denoted by a play symbol) in the left bar of the cell.
In this notebook you will learn - * Numpy arrays
* Numpy Selecting and Indexing
* Numpy Operations
Using NumPy To import NumPy in Colaboratory simply type the following:
import numpy as np
NumPy Indexing and Selection In this section we will discuss how to select elements or groups of elements from an array.
import numpy as np
arr = np.arange(0,11)
arr
Bracket Indexing and Selection The simplest way to pick one or some elements of an array looks very similar to python lists:
arr[8] #Get a value at an index
arr[1:5] #Get values in a range
arr[0:5] #Get values in a range
Broadcasting Numpy arrays differ from a normal Python list because of their ability to broadcast:
arr[0:5]=100 #Setting a value with index range (Broadcasting)
arr
Reset array
arr = np.arange(0,11)
arr
slice_of_arr = arr[0:6]
slice_of_arr
slice_of_arr[:]=99
slice_of_arr
Note: The changes also occur in the original array.
arr
Data is not copied, it's a view of the original array. This avoids memory problems.
To get a copy of the array we can simply use the **copy** function.
arr_copy = arr.copy()
arr_copy
Indexing a 2D array (matrices) The general format is arr_2d[row][col] or arr_2d[row,col] .
arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))
arr_2d
arr_2d[1] #Indexing row
# 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]
# 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,:]
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]]
Selection Let's briefly go over how to use brackets for selection based off of comparison operators.
arr = np.arange(1,11)
arr
arr > 4
bool_arr = arr>4
bool_arr
arr[bool_arr]
arr[arr>2]
x = 2
arr[arr>x]