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NumPy cheat sheet

NumPy cheat sheet. Explore our ultimate quick reference for NumPy.

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NumPy is the foundational library for scientific computing in Python, providing robust support for large, multi-dimensional arrays and matrices. With its comprehensive mathematical functions, tools for integrating C/C++ and Fortran code, and capabilities for random number generation and Fourier transform, NumPy is indispensable for data analysis, machine learning, and scientific research. This cheat sheet offers a quick overview of NumPy's array creation, manipulation, and fundamental mathematical operations, tailored for beginners eager to dive into the world of numerical computing.

Getting Started

Introduction

To utilize the extensive features of NumPy, start by importing the library using the following convention:

import numpy as np

This import statement is standard in Python scripting and notebooks, allowing access to all of NumPy's functions, classes, and modules under the alias np.

Data Types

Type	Description
`np.int64`	Signed 64-bit integer types
`np.float32`	Standard double-precision floating point
`np.complex`	Complex numbers represented by 128 floats
`np.bool`	Boolean type storing TRUE and FALSE values
`np.object`	Python object type
`np.string_`	Fixed-length string type
`np.unicode_`	Fixed-length unicode type

Initial Placeholders

Function	Description
`np.zeros((3,4))`	Create an array of zeros with shape (3,4)
`np.ones((2,3,4),dtype=np.int16)`	Create an array of ones with shape (2,3,4) and type int16
`np.arange(10,25,5)`	Create an array of evenly spaced values within a given interval
`np.linspace(0,2,9)`	Create an array of evenly spaced values (number of samples)
`np.full((3,5),7)`	Create a constant array with all values 7
`np.eye(2)`	Create a 2X2 identity matrix
`np.random.random((2,2))`	Create an array with random values
`np.empty((3,2))`	Create an empty array

Commands and Functions

Saving & Loading On Disk

Function	Description
`np.save('my_array', a)`	Save an array to a binary file in NumPy `.npy` format
`np.savez('array.npz', a, b)`	Save several arrays into a single file in uncompressed `.npz` format
`np.load('my_array.npy')`	Load arrays from a `.npy` file

Saving & Loading Text Files

Function	Description
`np.loadtxt("myfile.txt")`	Load data from a text file
`np.genfromtxt("my_file.csv", delimiter='')`	Load data from a text file, with missing values handled as specified
`np.savetxt("myarray.txt", a, delimiter=" ")`	Save an array to a text file

Creating Arrays

-	-
`np.array([1,2,3])`	1D array
`np.array([(1,2,3),(4,5,6)])`	2D array
`np.zeros(3)`	1D array of zeros
`np.ones((3,4))`	2D array of ones
`np.eye(5)`	5x5 Identity matrix
`np.linspace(0,100,6)`	Array of 6 evenly divided values from 0 to 100
`np.arange(0,10,3)`	Array of values from 0 to less than 10 with step 3 (eg [0,3,6,9])
`np.full((2,3),8)`	2x3 array with all values 8
`np.random.rand(4,5)`	4x5 array of random floats between 0–1
`np.random.rand(6,7)*100`	6x7 array of random floats between 0–100
`np.random.randint(5,size=(2,3))`	2x3 array with random ints between 0–4

Array Mathematics

Arithmetic Operations

Function	Description
`np.exp(b)`	Exponentiation
`np.sqrt(b)`	Square root
`np.sin(a)`	Sine of each element in the array
`np.cos(b)`	Element-wise cosine
`np.log(a)`	Element-wise natural logarithm
`e.dot(f)`	Dot product

Comparison

Function	Description
`a == b`	Element-wise comparison
`a < 2`	Element-wise comparison
`np.array_equal(a,b)`	Array-wise comparison

Aggregate Functions

Function	Description
`b.cumsum(axis=1)`	Cumulative sum of the elements

Array Manipulation

Transposing Array

Function	Description
`i = np.transpose(b)`	Permute array dimensions
`i.T`	Permute array dimensions again

Changing Array Shape

Function	Description
`b.ravel()`	Flatten the array
`g.reshape(3,-2)`	Reshape but don’t change data

Inspecting Properties

-	-
`arr.size`	Returns number of elements in arr
`arr.shape`	Returns dimensions of arr (rows,columns)
`arr.dtype`	Returns type of elements in arr
`arr.astype(dtype)`	Convert arr elements to type dtype
`arr.tolist()`	Convert arr to a Python list
`np.info(np.eye)`	View documentation for np.eye

Copying/sorting/reshaping

-	-
`np.copy(arr)`	Copies arr to new memory
`arr.view(dtype)`	Creates view of arr elements with type dtype
`arr.sort()`	Sorts arr
`arr.sort(axis=0)`	Sorts specific axis of arr
`two_d_arr.flatten()`	Flattens 2D array two_d_arr to 1D
`arr.T`	Transposes arr (rows become columns and vice versa)
`arr.reshape(3,4)`	Reshapes arr to 3 rows, 4 columns without changing data
`arr.resize((5,6))`	Changes arr shape to 5x6 and fills new values with 0

Adding/removing Elements

-	-
`np.append(arr,values)`	Appends values to end of arr
`np.insert(arr,2,values)`	Inserts values into arr before index 2
`np.delete(arr,3,axis=0)`	Deletes row on index 3 of arr
`np.delete(arr,4,axis=1)`	Deletes column on index 4 of arr

Combining/splitting

-	-
`np.concatenate((arr1,arr2),axis=0)`	Adds arr2 as rows to the end of arr1
`np.concatenate((arr1,arr2),axis=1)`	Adds arr2 as columns to end of arr1
`np.split(arr,3)`	Splits arr into 3 sub-arrays
`np.hsplit(arr,5)`	Splits arr horizontally on the 5th index

Indexing/slicing/subsetting

-	-
`arr[5]`	Returns the element at index 5
`arr[2,5]`	Returns the 2D array element on index [2][5]
`arr[1]=4`	Assigns array element on index 1 the value 4
`arr[1,3]=10`	Assigns array element on index [1][3] the value 10
`arr[0:3]`	Returns the elements at indices 0,1,2 (On a 2D array: returns rows 0,1,2)
`arr[0:3,4]`	Returns the elements on rows 0,1,2 at column 4
`arr[:2]`	Returns the elements at indices 0,1 (On a 2D array: returns rows 0,1)
`arr[:,1]`	Returns the elements at index 1 on all rows
`arr<5`	Returns an array with boolean values
`(arr1<3) & (arr2>5)`	Returns an array with boolean values
`~arr`	Inverts a boolean array
`arr[arr<5]`	Returns array elements smaller than 5

Vector Math

-	-
`np.add(arr1,arr2)`	Elementwise add arr2 to arr1
`np.subtract(arr1,arr2)`	Elementwise subtract arr2 from arr1
`np.multiply(arr1,arr2)`	Elementwise multiply arr1 by arr2
`np.divide(arr1,arr2)`	Elementwise divide arr1 by arr2
`np.power(arr1,arr2)`	Elementwise raise arr1 raised to the power of arr2
`np.array_equal(arr1,arr2)`	Returns True if the arrays have the same elements and shape
`np.sqrt(arr)`	Square root of each element in the array
`np.sin(arr)`	Sine of each element in the array
`np.log(arr)`	Natural log of each element in the array
`np.abs(arr)`	Absolute value of each element in the array
`np.ceil(arr)`	Rounds up to the nearest int
`np.floor(arr)`	Rounds down to the nearest int
`np.round(arr)`	Rounds to the nearest int

Scalar Math

-	-
`np.add(arr,1)`	Add 1 to each array element
`np.subtract(arr,2)`	Subtract 2 from each array element
`np.multiply(arr,3)`	Multiply each array element by 3
`np.divide(arr,4)`	Divide each array element by 4 (returns np.nan for division by zero)
`np.power(arr,5)`	Raise each array element to the 5th power

Statistics

-	-
`np.mean(arr,axis=0)`	Returns mean along specific axis
`arr.sum()`	Returns sum of arr
`arr.min()`	Returns minimum value of arr
`arr.max(axis=0)`	Returns maximum value of specific axis
`np.var(arr)`	Returns the variance of array
`np.std(arr,axis=1)`	Returns the standard deviation of specific axis
`arr.corrcoef()`	Returns correlation coefficient of array