NumPy Cheat Sheet

 

install the NumPy

             >> pip install numpy

*if you use online notebooks like colab then numpy is built it in.

Import NumPy 

            >>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

 

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Best Reference on numpy : https://betterprogramming.pub/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d