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


## 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.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 np.info(np.eye) 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

 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

 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

## Indexing

 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
wines[high_quality,:][:3,:]

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)
wines[high_quality_and_alcohol,10:]

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

## 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

## Acknowledgement

The original post can be found at dataquest.io.