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NumPy Cheat Sheet

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Python NumPy Cheat Sheet

Python is an ever-growing programming language which entices the developers with its flourishing libraries. Numpy is one of the libraries in python, which supports the most important and most utilized functionalities of the Mathematical world making it absolutely indispensable. Numpy provides a great number of easy to understand formulae and functions to perform not only on basic mathematics but also with scientific and statistical problems.

In this blog, you shall learn numpy from the basic to advanced functions necessary for excelling in the mathematical library.

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Python NumPy Cheat Sheet

This cheat sheet has been designed assuming, one has basic python knowledge and will provide you with all the basics that you need to get started with NumPy in Python.

Watch this Python Numpy Tutorial Video for Beginners:

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Advanced NumPy Cheat Sheet

NumPy Cheat Sheet

Download a Printable PDF of this Advanced NumPy Cheat Sheet

What is NumPy?

It is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. NumPy has put python lists out of the job as NumPy arrays are more efficient, convenient and make it faster to read or write an item.

Import Convention

Since NumPy is a Python library, it has to be imported first before you start using NumPy. To import NumPy, type in the following command:

Absolute Import Import numpy as np

N-dimensional Array

Syntax np.array()
1D np.array([1,2,3,4])

Output: array([1, 2, 3, 4])
2D np.array([[1,2,3], [4,5,6]])

Output: array([[1, 2, 3],

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

Output: array([[[1, 2, 3],

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

Output: array([[[[1, 2, 3]]]])

Creating array

Just knowing what a NumPy array is not enough, we need to know how to create a Numpy array. You can create a NumPy array in the following ways:

a = np.array([1,3,4,8])

Output: [1 3 4 8]
b = np.array([1.4, 3.2, 5], dtype = float)

Output: [1.4 3.2 5. ]
c = np.array([1,3,5,7,9,11], dtype = int64)

Output: [ 1  3  5  7  9 11]

Initial Placeholders

When you have the data you need to import to python, you can use NumPy to convert that data into NumPy arrays but sometimes when you don’t initially have any data or when you are starting from scratch and need an empty array you can use later then you can use NumPy.zeros() function.
You can create empty arrays in following ways:

Create an array with zeros. np.zeros((3,8))

Output: array([[0., 0., 0., 0., 0., 0., 0., 0.],

       [0., 0., 0., 0., 0., 0., 0., 0.],

       [0., 0., 0., 0., 0., 0., 0., 0.]])
Create an array with ones. np.ones((3,8))

Output: array([[1., 1., 1., 1., 1., 1., 1., 1.],

       [1., 1., 1., 1., 1., 1., 1., 1.],

       [1., 1., 1., 1., 1., 1., 1., 1.]])
Create an array with even-spaced step values. np.arange(1, 30, 5)

Output: array([ 1,  6, 11, 16, 21, 26])
Create an evenly-spaced array with a specified number of values. np.linspace(0,4,8)

Output: array([0.  , 0.57142857, 1.14285714, 1.71428571, 2.28571429,

       2.85714286, 3.42857143, 4.  ])
Create an entire array with a constant value. np.full((4,4),9)

Output: array([[9, 9, 9, 9],

       [9, 9, 9, 9],

       [9, 9, 9, 9],

       [9, 9, 9, 9]])
Create an identity matrix. np.identity(3)

Output: array([[1., 0., 0.],

       [0., 1., 0.],

       [0., 0., 1.]])
Create an identity matrix with m*n dimension. np.eye(3,5)

Output: array([[1., 0., 0., 0., 0.],

       [0., 1., 0., 0., 0.],

       [0., 0., 1., 0., 0.]])
Create an identity matrix with the m*n dimension with step shift. np.eye(3, k=1)

Output: array([[0., 1., 0.],

       [0., 0., 1.],

       [0., 0., 0.]])
Create a diagonal matrix. np.diag([1,1,1])

Output: array([[1, 0, 0],

       [0, 1, 0],

       [0, 0, 1]])
Create an array with random integers. np.random.randint(2,8,3)

Output: array([2, 6, 3])
Create an array between [0, 1). np.random.rand(5,2)

Output: array([[0.52620975, 0.27200853],

       [0.04753095, 0.38419669],

       [0.29254718, 0.66309665],

       [0.09115936, 0.62305064],

       [0.7984203 , 0.39769068]])
Create an array with a mean 0 and a standard deviation of 1. np.random.randn(2,3)

Output: array([[-0.98863765,  0.33808866,  0.07083797],

       [-0.58465781, -1.53241981, -1.03018067]])
Create a random array in the [0.0, 1.0) interval np.random.random_sample(10)

Output: array([0.33360613, 0.54360933, 0.61117749, 0.52978991, 0.70083391,

       0.61659811, 0.87412798, 0.62451739, 0.03659576, 0.12249504])

np.random.random(10)

Output: array([0.79213708, 0.65407051, 0.70394522, 0.72384937, 0.80480269,

       0.51195845, 0.00852462, 0.571088  , 0.59704964, 0.74560852])

Saving and Loading

Next comes, how to save or load an array or a file in NumPy

Saving and Loading on Disk
a = np.array([[1,2,3], [4,5,6]])

np.save(‘arr’ ,a)
b = np.savez(‘arr’, a) #saving array
c =np.load(‘arr’) #loading array

print(c)
Saving and Loading  text/csv Files
np.loadtxt(‘New_file.txt’) #From a text file
np.savetxt(‘New_file.txt’,arr,delimiter=’ ‘) #Writes to a text file
np.genfromtxt(‘New_file.csv’,delimiter=’,’) #From a CSV file
np.savetxt(‘New_file.csv’,arr,delimiter=’,’) #Writes to a CSV file

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Properties of Array

NumPy offers a few numbers of what we call ‘properties of NumPy array’ which can be used to check the nature of the array, that is, what kind of elements it contains or what is the size,etc.

arr1 = np.array([[1,2,3], [4,5,6]])

 

Array dimensions arr1.ndim

Output: 2
Length of array len(arr1)

Output: 2
Shape of the array arr1.shape

Output: (2,3)
Name of datatype arr1.dtype.name

Output: ’int’
Type-cast array  arr1.astype(int)

Output: array([[1, 2, 3],

       [4, 5, 6]])
Data type of an array arr1.dtype

Output: dtype(‘int64’)

Indexing and Slicing, Subsetting

  • Boolean Indexing
  • Fancy Indexing
  • Subsetting
  • Slicing
Indexing #forward Index 

arr=         [‘a’, ‘b’, ‘c’, ‘d’, ‘e’]

index->     0  , 1 ,  2,  3,   4

#backward indexing

arr=         [‘a’,  ‘b’,  ‘c’,  ‘d’,  ‘e’]

index->    -5 , -4 ,  -3,  -2,  -1
Boolean Indexing arr1 = np.array([[1,2,3], [4,5,6]])

arr1[arr1 > 3] #Selects elements from an array based on boolean condition

Output: array([4, 5, 6])
Fancy Indexing arr = np.array([0, 10, 20, 30, 40, 50])

indices = np.array([0, 1, 5])

#indexing using fancy indexing

fancy_ind = arr[indices]

print(fancy_ind)  # Output: [ 0 10 50]

Output: [ 0 10 50]
Subset a = arr1[1] #selects the second element

Output: [4 5 6]
Slicing arr1[0:3] #selecting elements from 0-3

Output: array([[1, 2, 3],

       [4, 5, 6]])

Array Mathematics

Following are some mathematical operations you can perform using NumPy arrays:Note: While performing arithmetic operations in NumPy arrays, you should make sure that the items are of same shape.

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

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

Output: [1 2 3]

Output: [4 5 6]
Arithmetic Function 
Addition  np.add(arr1, arr2)

Output: array([5, 7, 9])
Subtraction  np.subtract(arr1, arr2)

Output: array([-3, -3, -3])
Multiplication np.multiply(arr1, arr2)

Output: array([ 4, 10, 18])
Division np.divide(arr1,arr2)

Output: array([0.25, 0.4 , 0.5 ])
Exponentiation np.power(arr1, arr2)

Output: array([  1,  32, 729])
Floor/Integer Division np.floor_divide(arr1, arr2)

Output: array([0, 0, 0])
Modulus np.mod(arr1, arr2)

Output: array([1, 2, 3])
Square Root np.sqrt(arr1)

Output: array([1.        , 1.41421356, 1.73205081])
Unary Negation  np.negative(arr1, arr2)

Output: array([-1, -2, -3])
Mathematical Functions
Trigonometric Functions np.sin(arr1)

Output: array([0.84147098, 0.90929743, 0.14112001])

np.cos(arr1)

Output: array([ 0.54030231, -0.41614684, -0.9899925 ])

np.tan(arr1)

Output: array([ 1.55740772, -2.18503986, -0.14254654])
Exponential and Logarithmic Functions np.exp(arr1)

Output: array([ 2.71828183,  7.3890561 , 20.08553692])
np.log(arr1)

Output: array([0.        , 0.69314718, 1.09861229])
Statistical Functions np.mean(arr1) #Finding mean 

Output: 2.0
np.std(arr1)#Finding standard deviation

Output: 0.816496580927726
np.median(arr1) #finding median

Output: 2.0
Comparison and Logical Operator
np.equal(arr1, arr2)

Output: [False False False]

np.not_equal(arr1, arr2)

Output: [ True  True  True]

np.greater(arr1, arr2)

Output: [False False False]
np.logical_and(arr1, arr2)

Output: [ True  True  True]

np.logical_or(arr1,arr2)

Output: [ True  True  True]

np.logical_not(arr1)

Output: [False False False]
Aggregation Functions
arr1 = np.array([1,2,3])

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

np.sum(arr1 +  arr2) #Summation

Output: 21
np.corrcoef(arr1)

Output: 1.0
np.max(np.concatenate(arr1, arr2)) #max of two

Output: 6

np.min(np.concatenate(arr1, arr2)) #min of two

Output: 1
np.cumsum(arr1) #cumulative sum

Output: [1 3 6]
np.cumprod(arr1) #cumulative multiplication

Output: array([1, 2, 6])

Data Types

signed 64-bit int type np.int64
Double standard float point np.float32
Complex numbers  np.complex
Boolean data type np.bool
Object type np.object
Fixed-length string np.string_
Unicode  np.unicode_

Functions

  • Updating array elements
  • Removing array elements
  • Copy array
  • Return condition specific element
  • Find unique element
  • Sorting
Updating array elements arr = [11,12,15,17,19] #update 12 with 13

arr[1]= 13

arr

Output: [11,13,15,17,19]
Removing array elements  np.delete(arr, 2) #removes element at index 2

Output: [11,13,17,19]
Copying arrays arr.view() #create view of the array with the same data

np.copy(arr1) #creates a copy of array

arr1.copy() #creates deep copy of array
Return a condition-specific element. ele = arr[arr > 3] # return elements greater than 3

Output: [11 12 15 17 19]
Find a unique element np.unique(arr) #returns unique elements

Output: array([11, 12, 15, 17, 19])
Sorting #ascending order

np.sort(arr)

Output: array([11, 12, 15, 17, 19])

#descending order

np.sort(arr)[ ::-1]

Output: array([19, 17, 15, 12, 11])

Array Manipulation

  • Reshaping array
  • Flattening array
  • Transposing array
  • Concatenating arrays vertically and horizontally
  • Splitting array
  • Flip array
Reshaping Array arr.reshape(3,4) #changes to 3*4 matrix

Output: array([[1, 4, 7, 9],

       [2, 4, 7, 9],

       [2, 3, 4, 5]])
Flattening Array arr.flatten()

Output: array([1, 4, 7, 9, 2, 4, 7, 9, 2, 3, 4, 5])

arr.ravel()

Output: array([1, 4, 7, 9, 2, 4, 7, 9, 2, 3, 4, 5])
Transposing Array arr.T

Output: array([[1, 2, 2],

       [4, 4, 3],

       [7, 7, 4],

       [9, 9, 5]])
Concatenating Arrays #vertical 

np.vstack((arr1, arr2))

Output: array([[1, 2, 3],

       [4, 5, 6]])

#horizontal

np.hstack((arr1, arr2))

Output: array([1, 2, 3, 4, 5, 6])
Splitting Array #splitting array into n sub-arrays

np.split(arr, 3)

Output: [array([[1, 4, 7, 9]]), array([[2, 4,       7, 9]]), array([[2, 3, 4, 5]])]
Flip Array np.flip(arr, axis=0) #vertical flip

np.flip(arr, axis=1) #horizontal flip

Linear Algebra

  • Matrix Operations
  • Matrix Properties
  • Solving Equations
  • Eigenvalues and Eigenvectors
Matrix Operations
c = np.add(a, b) # Matrix addition

d = np.subtract(a, b) # Matrix subtraction

e = np.dot(a, b) # Matrix multiplication

f = np.divide(a, b) # Matrix division
Matrix Properties
det_a = np.linalg.det(a) # Determinant

a_transpose = np.transpose(a) # Transpose

a_inv = np.linalg.inv(a)# Inverse
Solving Equations
# Solving linear equations

x = np.linalg.solve(a, b)
Eigenvalues and Eigenvectors
# Eigenvalues and eigenvectorseigenvalues, eigenvectors = np.linalg.eig(a)

Help in Numpy

Help in NumPy
np.info(np.ndarray.dtype)

help(np.ndarray.dtype)

Conclusion

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About the Author

Sahil Mattoo
Sahil Mattoo
Senior Consultant Analytics & Data Science

Sahil Mattoo, a Senior Software Engineer at Eli Lilly and Company, is an accomplished professional with 14 years of experience in languages such as Java, Python, and JavaScript. Sahil has a strong foundation in system architecture, database management, and API integration.