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.

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


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.empty((1, 2)) | create an empty 1x2 array. The value at each position is uninitialized (random value depending on the memory location). np.array([1,2,3]) | One dimensional array. Keyword argument dtype converts elements into specified type. 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 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

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 View documentation for np.eye
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


np.vstack((arr1, arr2)) Vertically stack multiple arrays. Think of it like the second arrays’s items being added as new rows to the first array.
np.hstack((arr1, arr2)) horizontally stack multiple arrays.
np.concatenate((arr1,arr2),axis=0) Adds arr2 as rows to the end of arr1. It’s a general-purpose vstack.
np.concatenate((arr1,arr2),axis=1) Adds arr2 as columns to end of arr1. It’s a general-purpose hstack.
np.split(arr,3) Splits arr into 3 sub-arrays
np.hsplit(arr,5) Splits arr horizontally on the 5th index


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

Conditional Selecting

NumPy makes it possible to test to see if rows match certain values using mathematical comparison operations like <, >, >=, <=, and ==. For example, if we want to see which wines have a quality rating higher than 5, we can do this:

wines[:,11] > 5

array([False, False, False, ..., True, False, True], dtype=bool)

We get a Boolean array that tells us which of the wines have a quality rating greater than 5. We can do something similar with the other operators. For instance, we can see if any wines have a quality rating equal to 10:

wines[:,11] == 10

array([False, False, False, ..., False, False, False], dtype=bool)

One of the powerful things we can do with a Boolean array and a NumPy array is select only certain rows or columns in the NumPy array. For example, the below code will only select rows in wines where the quality is over 7:

high_quality = wines[:,11] > 7

array([[ 7.90000000e+00, 3.50000000e-01, 4.60000000e-01, 3.60000000e+00, 7.80000000e-02, 1.50000000e+01, 3.70000000e+01, 9.97300000e-01, 3.35000000e+00, 8.60000000e-01, 1.28000000e+01, 8.00000000e+00], [ 1.03000000e+01, 3.20000000e-01, 4.50000000e-01, 6.40000000e+00, 7.30000000e-02, 5.00000000e+00, 1.30000000e+01, 9.97600000e-01, 3.23000000e+00, 8.20000000e-01, 1.26000000e+01, 8.00000000e+00], [ 5.60000000e+00, 8.50000000e-01, 5.00000000e-02, 1.40000000e+00, 4.50000000e-02, 1.20000000e+01, 8.80000000e+01, 9.92400000e-01, 3.56000000e+00, 8.20000000e-01, 1.29000000e+01, 8.00000000e+00]])

We select only the rows where high_quality contains a True value, and all of the columns. This subsetting makes it simple to filter arrays for certain criteria. For example, we can look for wines with a lot of alcohol and high quality. In order to specify multiple conditions, we have to place each condition in parentheses, and separate conditions with an ampersand (&):

high_quality_and_alcohol = (wines[:,10] > 10) & (wines[:,11] > 7)

array([[ 12.8, 8. ], [ 12.6, 8. ], [ 12.9, 8. ], [ 13.4, 8. ], [ 11.7, 8. ], [ 11. , 8. ], [ 11. , 8. ], [ 14. , 8. ], [ 12.7, 8. ], [ 12.5, 8. ], [ 11.8, 8. ], [ 13.1, 8. ], [ 11.7, 8. ], [ 14. , 8. ], [ 11.3, 8. ], [ 11.4, 8. ]])

We can combine subsetting and assignment to overwrite certain values in an array:

high_quality_and_alcohol = (wines[:,10] > 10) & (wines[:,11] > 7)
wines[high_quality_and_alcohol,10:] = 20

Reshaping NumPy Arrays

numpy.transpose(arr) Transpose the array.
numpy.ravel(arr) Turn an array into a one-dimensional representation.
numpy.reshape(arr) Reshape an array to a certain shape we specify.

Scalar Math

If you do any of the basic mathematical operations (/, *, -, +, ^) with an array and a value, it will apply the operation to each of the elements in the array.

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

Note that the above operation won’t change the wines array – it will return a new 1-dimensional array where 10 has been added to each element in the quality column of wines.

If we instead did +=, we’d modify the array in place.

Vector Math

All of the common operations (/, *, -, +, ^) will work between arrays.

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


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


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