NumPy Cheat Sheet for Python

NumPy is a library for the Python programming language which is used for working with multi-dimensional arrays and matrices. This is very useful in large scientific computing. Because NumPy ndarrays is way faster compared to a regular python list. Arrays are very frequently used in data science too, where speed and resources are very important. That’s why NumPy is a very handy tool in data-science.

But remembering all the NumPy commands might be overwhelming for both beginners and professionals. So Datalators makes the complex simple.
It’s also a good idea to check the official NumPy documentation from time to time. Even if you can find what you need in the cheat sheet. Reading documentation is a skill every data professional needs. Also, the documentation goes into a lot more detail than we can fit in a single sheet anyway!

Creating Arrays:

np.array([1.3, 2, 3], dtype = float)	Creating a 1d array
np.array([1, 2, 3], [2, 3, 4], [3, 4, 5])	Creating a 3d array
np.arange(2, 8, 2)	Array of evenly spaced values
np.zeros((2, 3))	2×3 array consists of zeros
np.ones((3, 4))	3×4 array consists of ones
np.random.random((3, 3))	Array consists of random values
np.empty((2, 3))	Empty array
np.full((3,2), 5)	Constant array

Import Export:

np.save(‘saved_array’, a)	Save ‘a’ array as on disk
np.savez(‘array.npz’, a, b)	Save 2 arrays
np.savetxt(‘array.txt’, a, delimiter= ” “)	Saving as text file
np.genformatxt(‘array.csv’, a, delimiter= “,”)	Saving as CSV file
np.load(‘array.npz’)	Load from disk
np.loadtxt(‘array.txt’)	Load from text file

Inspecting Array:

a.shape	Dimensions of ‘a’ array
len(a)	Length of array
a.ndim	Array dimensions
a.size	Number of elements
a.dtype	Data type of elements
a == b	Element-wise comparison
a > 1	Element-wise comparison
np.array_equal(a, b)	Array-wise comparison

Data Types:

np.int64	Signed integer types
np.float32	Floating point
np.complex	Complex number
np.bool	Boolean type
np.object	Python object type
np.string_	String type
np.unicode	Unicode type
a.astype(int)	Convert to int type

Array Mathematics:

np.subtract(a, b)	Subtraction
np.add(a, b)	Addition
np.devide(a, b)	Division
np.multiply(a, b)	Multiplication
a+b, a-b, a*b, a/b	Operation – arithmetic sign
np.sin(a), np.cos(a), np.log(a)	Mathematical operation
a.dot(b)	Dot product
np.exp(a), np.sqrt(a)	Exponentiation and Square root

Statistics on NumPy:

a.sum()	Array-wise sum
a.min(), a.max(axis = 0)	Minimum and Maximum value
a.mean(), a.median()	Mean and Median
a.corrcoef()	Correlation coefficient
np.std(a)	Standard deviation
b.cumsum(axis = 1)	Cumulative sum of elements
a.sort(), a.sort(axis = 0)	Sort an array

Indexing and Slicing

b = a.view() / a.copy()	Create a copy of array
a[1, 2]	Subsetting
a[0:2]	Slicing
a[0:2, :-1]	Slicing
a[a<2]	Boolean Indexing

Array Manipulation:

b = np.transpose(a)	Permute array dimensions
a.reshape(3,-2)	Reshape but don’t change data
h.resize((2,4))	Return a new array with shape (2,4)
np.append(a,b)	Append items to an array
np.insert(a, 1, 5)	Insert items in an array
np.delete(a,[1])	Delete items from an array
np.hsplit(a,3)	Split the array horizontally at the 3rd index

I hope this cheat sheet will be useful to you. No matter you are new to python who is learning python for data science or a data professional. Happy Programming.

You can also download the printable PDF file from here.

The source code for NumPy is located at this GitHub repository.
You might also be interested in Pandas Cheat Sheet For Data Science In Python.