Numpy (Python).md

Installation

pip install numpy

import numpy as np

Array creation

Note: axis=0 y-axis; axis=1 x-axis; axis=2 z-axis Create a numpy array np.array([]): numpy.array

arr1 = np.array([1,2,3])
print(arr)
#--> [1,2,3]

arr2 = np.array([[1,2,3],[4,5,6])
print(arr)
#--> [[1,2,3],
#-->   4,5,6]]

Get array dimension arr.ndim:

print(arr2.ndim)
#--> 2

Get array shapearr.shape:

print(arr2.shape)
#--> (2,3) (row, col)

Get data type arr.dtype

Get size (bytes) of each item in array arr.itemsize

Get total size of an array arr.nbytes

Create an array with full of a number np.full(shape,num):

arr3 = np.full((2,2),99)
#--> [[99,99],[99,99]]

Create an array with random decimal numbers np.random.rand(shape)

Create an array with random integer numbers np.random.randint(lowest,highest=None,size=None)

Create an identity matrix np.identity(size) Repeat elements of an array numpy.repeat(arr,repeats,axis=None)

x = np.array([[1,2],[3,4]])
np.repeat(x, [1, 2], axis=0)
#-->array([[1, 2],
#          [3, 4],
#          [3, 4]])

Manipulate array

arr = np.array([[1,2,3],[4,5,6])
arr2 = np.array([[[1,2],[3,4]],
                 [[5,6],[7,8]]])

Basic select:

print(arr[1,2])
#--> 6
arr[0,:]
#--> [1,2,3]
arr[:,0]
#--> [1,4]

Using :

#arr[startidx:endidx:step]
arr[0:2:1,1]
#--> [4,6]
arr[0:-1:1,1]
#--> [4,6]
arr2[:,0,:]
#--> [[1,2],[5,6]]

Select a list:

arr = np.array([1,2,3,4,5,6])
arr[[1,4]] #--> [2,5]

Using > <:

arr = np.array([1,2,3,4,5,6])
z > 3 #--> [False, False, False, True, True, True]
z[z > 3] #--> [4,5,6]

Change value of items

arr[0,2] = 9 #one item
arr[:,1] = 8 #entire col
arr[:,1] = [10, 11] #entire col with specifix number
arr2[:,0,:] = [[1,1],[2,2]] #need to be the same dimension to the selected

Reshape an array np.reshape(a,newshape) or arr.reshape(newshape):

arr = np.array([[1,2,3,4],[5,6,7,8]])
arr.reshape((4,2))
#--> [[1,2],
#--> [[3,4],
#--> [[5,6],
#-->  [7.8]]

#The new shape should be compatible with the number of items 
#in the old shape
arr.reshape((2,3)) #--> err

Vertically vectors stacking np.vstack([v1,v2]):

v1 = np.array([1,2,3])
v2 = np.array([4,5,6])
np.vstack([v1,v2])
#-->[[1,2,3],[4,5,6]]

Horizontally vectors stacking np.hstack([v1,v2]): Test whether any array element along a given axis evaluates to True. np.any(arr, axis=None)

Returns a ndarray object containing evenly spaced values within the given range np.arrange(start, stop, step, dtype)

np.arrange(start=1,stop=8, step=2)
#--> [1,3,5,7]

Returns a ndarray object containing evenly spaced numbers over a specified interval np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0):

np.linspace(2.0, 3.0, num=5)
#--> array([2.0, 2.25, 2.5, 2.75, 3.0])
np.linspace(2.0, 3.0, num=5, endpoint=False)
#--> array([2.0,  2.2,  2.4,  2.6,  2.8])
np.linspace(2.0, 3.0, num=5, retstep=True)
#--> (array([2.0,  2.25,  2.5 ,  2.75,  3.0]), 0.25)

Return elements chosen from x or y depending on condition. where True, yield x, False yield y. numpy.where(condition,x,y)

arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
np.where(a < 5, a, 10*a)
#--> array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

Return coordinate matrices from coordinate vectors. (for making linear grid) np.meshgrid(x,y) :

xvalues = np.array([0, 1, 2, 3, 4]);
yvalues = np.array([0, 1, 2, 3, 4]);
xx, yy = np.meshgrid(xvalues, yvalues)
# xx
[[0 1 2 3 4]
 [0 1 2 3 4]
 [0 1 2 3 4]
 [0 1 2 3 4]
 [0 1 2 3 4]]
 # yy
[[0 0 0 0 0]
 [1 1 1 1 1]
 [2 2 2 2 2]
 [3 3 3 3 3]
 [4 4 4 4 4]]

Math with Numpy

Numpy Math Expression + - / * **, sin(arr) cos(arr) tan(arr):

arr = np.array([1,2,3,4])
arr + 2
#--> [3,4,5,6]

Find sum np.sum(arr,axis=None):

np.sum(arr1,axis=0)
#--> [2,4]

Find product np.prod(arr,axis=None)

Linear Algebra

Linear Algebra docs

Multiply 2 matrices np.matmul(arr1,arr2):

arr1 = np.array([[1,2,3],
                 [4,5,6]])
arr2 = np.array([[1,2],
                 [3,4],
                 [5,6])
np.matmul(arr1,arr2)

Create an identity matrix np.identity(size) Calculate determinant (định thức) np.linalg.det(arr)

Statistics

Numpy Statistics Return the minimum of an array or minimum along an axis np.amin(arr,axis=None):

arr1 = np.array([[0, 1],
                 [2, 3]])
np.amin(arr1) #--> 0
np.amin(arr1, axis=0) #--> [0,1]

Same to np.amax(arr,axis=None) Find mean np.mean(arr,axis=None) Find variance np.var(arr,axis=None) Find average np.average(arr,axis=None)