Basics of NumPy

NumPy is a Python Library.

Numpy is used for working with arrays.

NumPy is short for Numerical Python.

It also has functions for working in domain of linear algebra, fourier transform, and matrices.

Why Use NumPy?

In Python we have lists that serve the purpose of arrays, but they are slow to process.

NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.

Arrays are very frequently used in data science, where speed and resources are very important.

Why is NumPy Faster Than Lists?

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently.

This behavior is called locality of reference in computer science.

This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures.

import numpy as np

arr = np.array([1,2,3,4,5])

print(arr)

print(type(arr))

Arrays has different types

0-D Array, 1-D Array, 2-D Arrays, 3-D Arrays 

arr = np.array([1,2,3,4], ndmin = 5)

print(arr)

print("number of dimensions :", arr.ndim)

Array Slicing

NumPy Data Types:

strings, integer, float, boolean, complex

Data Types in NumPy

NumPy has some extra data types, and refer to data types with one character, like i for integers, u for unsigned integers etc.

Below is a list of all data types in NumPy and the characters used to represent them.

i - integer

b - boolean

u - unsigned integer

f - float

c - complex float

m - timedelta

M - datetime

O - object

S - string

U - unicode string

V - fixed chunk of memory for other type (void)

To check data type:

arr = np.array([1,2,3,4])

print(arr.dtype)

Change data type from float to integer by using 'i' as parameter value:

arr = np.array([1.1, 2.1, 3.1])

newarr = arr.astype('i')

print(newarr)

print(newarr.dtype)

arr = np.array([1,2,3,4,5])

x = arr.copy()

arr[0] = 42

print(arr)

print(x)

arr = np.array([1,2,3,4,5])

x = arr.copy()

y = arr.view()

print(x.base)

print(y.base)

Get the Shape of an Array

NumPy arrays have an attribute called shape that returns a tuple with each index having the number of corresponding elements.

arr = np.array([[1,2,3,4], [5,6,7,8]])

print(arr.shape)

Reshaping arrays:

Reshaping means changing the shape of an array.

The shape of an array is the number of elements in each dimension.

By reshaping we can add or remove dimensions or change number of elements in each dimension.

Reshape From 1-D to 2-D

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])

newarr = arr.reshape(4, 3)

print(newarr)

Iterating Arrays:

Iterating means going through elements one by one.

As we deal with multi-dimensional arrays in numpy, we can do this using basic for loop of python.

If we iterate on a 1-D array it will go through each element one by one.

import numpy as np

arr = np.array([1, 2, 3])

for x in arr:

 print(x)

import numpy as np

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])

for x in arr:

 for y in x:

  for z in y:

   print(z)

arr = np.array([1,2,3])

for x in np.nditer(arr, flags = ["buffered"], op_dtypes = ['S']):  

 print(x)

import numpy as np

arr = np.array([[1,2,3,4],[5,6,7,8]])

for x in np.nditer(arr[:,::2]):

 print(x)

import numpy as np

arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])

for idx, x in np.ndenumerate(arr):

 print(idx, x)

Joining NumPy Arrays:

Joining means putting contents of two or more arrays in a single array.

In SQL we join tables based on a key, whereas in NumPy we join arrays by axes.

We pass a sequence of arrays that we want to join to the concatenate() function, along with the axis. If axis is not explicitly passed, it is taken as 0.

import numpy as np

arr1 = np.array([1, 2, 3])

arr2 = np.array([4, 5, 6])

arr = np.concatenate((arr1, arr2))

print(arr)

Splitting NumPy Arrays:

Splitting is reverse operation of Joining.

Joining merges multiple arrays into one and Splitting breaks one array into multiple.

We use array_split() for splitting arrays, we pass it the array we want to split and the number of splits.

arr = np.array([1,2,3,4,5,6])

newarr = np.array_split(arr, 3)

print(newarr)

arr = np.array([1,2,3,4,5,6])

newarr = np.array_split(arr, 3)

print(newarr[0])

print(newarr[1])

print(newarr[2])

Searching Arrays:

You can search an array for a certain value, and return the indexes that get a match.

To search an array, use the where() method.

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 4, 4])

x = np.where(arr == 4)

print(x)

Sorting Arrays:

Sorting means putting elements in an ordered sequence.

Ordered sequence is any sequence that has an order corresponding to elements, like numeric or alphabetical, ascending or descending.

The NumPy ndarray object has a function called sort(), that will sort a specified array.

Filtering Arrays:

Getting some elements out of an existing array and creating a new array out of them is called filtering.

In NumPy, you filter an array using a boolean index list.

arr = np.array([41,42,43,44])

x = [True, False, True, False]

newarr = arr[x]

print(newarr)