Published: April 13, 2017

NumPy is the library that gives Python its ability to work with data at speed. Originally, launched in 1995 as ‘Numeric,’ NumPy is the foundation on which many important Python data science libraries are built, including Pandas, SciPy and scikit-learn. It’s common when first learning NumPy to have trouble remembering all the functions and methods that you need, and while at Dataquest we advocate getting used to consulting the NumPy documentation, sometimes it’s nice to have a handy reference, so we’ve put together this cheat sheet to help you out! If you’re interested in learning NumPy, you can consult our NumPy tutorial blog post, or you can signup for free and start learning NumPy through our interactive Python data science course. Download a Printable PDF of this Cheat Sheet

Key and Imports

In this cheat sheet, we use the following shorthand:

arr | A NumPy Array object You’ll also need to import numpy to get started:

import numpy as np

Importing/exporting

np.loadtxt('file.txt') | From a text file np.genfromtxt('file.csv',delimiter=',') | From a CSV file np.savetxt('file.txt',arr,delimiter=' ') | Writes to a text file np.savetxt('file.csv',arr,delimiter=',') | Writes to a CSV file

Creating Arrays

np.array([1,2,3]) | One dimensional array np.array([(1,2,3),(4,5,6)]) | Two dimensional array np.zeros(3) | 1D array of length 3 all values 0 np.ones((3,4)) | 3x4 array with all values 1 np.eye(5) | 5x5 array of 0 with 1 on diagonal (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 01 np.random.rand(6,7)*100 | 6x7 array of random floats between 0100 np.random.randint(5,size=(2,3)) | 2x3 array with random ints between 04

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

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

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

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

Download a printable version of this cheat sheet

If you’d like to download a printable version of this cheat sheet you can do so below.

Download a Printable PDF of this Cheat Sheet


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