Python NumPy

In this class, you will learn various NumPy concepts like how to install NumPy, arrays, functions, matrix multiplication, etc. This NumPy in Python tutorial will help you learn all Python NumPy basics.

Numpy (‘Numerical Python’) is the core open source library for scientific computing in Python. It is a Linear Algebra Library for Python, it is so important for Finance with Python. It is a very useful library to perform mathematical and statistical operations in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. In this part, we will review the essential functions that you need to know for the tutorial on ‘TensorFlow.’

Why use NumPy?

NumPy is memory efficiency, meaning it can handle the vast amount of data more accessible than any other library. Besides, NumPy is very convenient to work with, especially for matrix multiplication and reshaping. On top of that, NumPy is fast. In fact, TensorFlow and Scikit learn to use NumPy array to compute the matrix multiplication in the back end.

Python NumPy Array:

A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension.

Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. We can initialize NumPy arrays from nested Python lists and access it elements.

How to Install NumPy?

NumPy is installed by default with Anaconda.

It is highly recommended you install Python using the Anaconda distribution to make sure all underlying dependencies (such as Linear Algebra libraries like numpy) sync up with the use of a conda install. If you have Anaconda, install NumPy by going to your terminal or command prompt and typing:

conda install numpy

Import NumPy and Check Version

The command to import numpy is

import numpy as np

Above code renames the numpy namespace to np.

# To check your installed version of Numpy use the command

print (np.__version__)

1.20.1

NumPy Basics

NumPy Basics

Operator	Description
np.array([1,2,3])	1d array
np.array([(1,2,3),(4,5,6)])	2d array
np.arange(start,stop,step)	range array

Placeholders

Operator	Description
np.linspace(0,2,9)	Add evenly spaced values btw interval to array of length
np.zeros((1,2))	Create and array filled with zeros
np.ones((1,2))	Creates an array filled with ones
np.random.random((5,5))	Creates random array
np.empty((2,2))	Creates an empty array

Array

Operator	Description
array.shape	Dimensions (Rows,Columns)
len(array)	Length of Array
array.ndim	Number of Array Dimensions
array.dtype	Data Type
array.astype(type)	Converts to Data Type
type(array)	Type of Array

Copying/Sorting

Operator	Description
np.copy(array)	Creates copy of array
other = array.copy()	Creates deep copy of array
array.sort()	Sorts an array
array.sort(axis=0)	Sorts axis of array

Array Manipulation

Adding or Removing Elements

Operator	Description
np.append(a,b)	Append items to array
np.insert(array, 1, 2, axis)	Insert items into array at axis 0 or 1
np.resize((2,4))	Resize array to shape(2,4)
np.delete(array,1,axis)	Deletes items from array

Combining Arrays

Operator	Description
np.concatenate((a,b),axis=0)	Split an array into multiple sub-arrays.
np.vstack((a,b))	Split an array in sub-arrays of (nearly) identical size
np.hstack((a,b))	Split the array horizontally at 3rd index

More

Operator	Description
other = ndarray.flatten()	Flattens a 2d array to 1d
array = np.transpose(other)	Transpose array
array.T	Transpose array
inverse = np.linalg.inv(matrix)	Inverse of a given matrix

Slicing and Subsetting

Operator	Description
array[i]	1d array at index i
array[i,j]	2d array at index[i][j]
array[i<4]	Boolean Indexing, see Tricks
array[0:3]	Select items of index 0, 1 and 2
array[0:2,1]	Select items of rows 0 and 1 at column 1
array[:1]	Select items of row 0 (equals array[0:1, :])
array[1:2, :]	Select items of row 1
[comment]: <> (	array[1,…]
array[ : :-1]	Reverses array

Mathematics

Operations

Operator	Description
np.add(x,y)	Addition
np.substract(x,y)	Subtraction
np.divide(x,y)	Division
np.multiply(x,y)	Multiplication
np.sqrt(x)	Square Root
np.sin(x)	Element-wise sine
np.cos(x)	Element-wise cosine
np.log(x)	Element-wise natural log
np.dot(x,y)	Dot product
np.roots([1,0,-4])	Roots of a given polynomial coefficients

Comparison

Operator	Description
==	Equal
!=	Not equal
<	Smaller than
>	Greater than
<=	Smaller than or equal
>=	Greater than or equal
np.array_equal(x,y)	Array-wise comparison

Basic Statistics

Operator	Description
np.mean(array)	Mean
np.median(array)	Median
array.corrcoef()	Correlation Coefficient
np.std(array)	Standard Deviation

More

Operator	Description
array.sum()	Array-wise sum
array.min()	Array-wise minimum value
array.max(axis=0)	Maximum value of specified axis
array.cumsum(axis=0)	Cumulative sum of specified axis