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 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

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

下载pdf

原文
NumPy Cheat Sheet — Python for Data Science

文章来源: pengshiyu.blog.csdn.net，作者：彭世瑜，版权归原作者所有，如需转载，请联系作者。

原文链接：pengshiyu.blog.csdn.net/article/details/80364753