01Numpy.md

import numpy as np

array = np.array([1,2,3,4,5])
print(type(array))

array2 = array + 1
array2

array2 + array

array2 * array

array[0]

array[3]

array

array.shape

np.array([[1,2,3],[4,5,6]])

np.array([[[1,2,3],[4,5,6]]])

np.array([[[1,2,3],[4,5,6]]]).shape

np.array([1,2,3,5,6])

np.array([1,2,3,5,6.0])

test_array = np.array([1,2,3,5,'6'])
test_array

type(test_array)

test_array.dtype

test_array.shape

test_array.itemsize

np.size(test_array)

np.shape(test_array)

test_array.ndim

test_array

test_array = np.array([1,2,3,4,5])
test_array

test_array.fill(0)
test_array

test_list = [1,2,3,5,6]
test_array = np.array(test_list)
test_array

test_array[0]

test_array[1:3]

test_array[-2:]

test_array = np.array([[1,2,3],[4,5,6],[7,8,9]])
test_array

test_array.shape

test_array.size

test_array.ndim

test_array[1,1]

test_array[1,1] = 10
test_array

test_array[1]

test_array[:, 1]

test_array[0,0:2]

test_array

test_array2 = test_array
test_array2

test_array2[1,1] = 100
test_array2

test_array

test_array2 = test_array.copy()
test_array2

test_array2[1,1] = 10
test_array2

test_array

test_array = np.arange(0,100,10)
test_array

mask = np.array([0,0,0,1,1,1,0,0,1,1],dtype=bool)
mask

test_array[mask]

random_array = np.random.rand(10)
random_array

mask = random_array > 0.5
mask

random_array[mask]

import os
print(os.path.abspath('.'))

test_array = np.array([10,20,30,40,50])
test_array > 30

np.where(test_array > 30)

test_array[np.where(test_array > 30)]

test_array = np.array([1,2,3,4,5],dtype=np.float32)
test_array

test_array.dtype

test_array.nbytes

test_array = np.array([1,10,3.5,'str'],dtype=np.object)
test_array

test_array = np.array([1,2,3,4,5])
test_array

test_array.astype(np.float32)

test_array = np.array([[1,2,3],[4,5,6]])
test_array

np.sum(test_array)

np.sum(test_array, axis=0)

np.sum(test_array, axis=1)

test_array.ndim

test_array.prod(axis=0)

test_array.prod(axis=1)

test_array.min()

test_array.min(axis=0)

test_array.min(axis=1)

test_array.max()

test_array.argmin()

test_array.argmin(axis=0)

test_array.argmin(axis=1)

test_array.argmax()

test_array.mean()

test_array.std()

test_array.std(axis=1)

test_array.var()

test_array

test_array.clip(2,4)

test_array = np.array([1.2,3.56,6.41])
test_array

test_array.round()

test_array.round(decimals=1)

test_array = np.array([[1.5,1.3,7.5],[5.6,7.8,1.2]])
test_array

np.sort(test_array)

np.sort(test_array, axis=0)

test_array

np.argsort(test_array)

test_array = np.linspace(0,10,10)
test_array

values = np.array([2.5,6.5,9.5])
values

np.searchsorted(test_array,values)

test_array = np.array([[1,0,6],[1,7,0],[2,3,1],[2,4,0]])
test_array

index = np.lexsort([-1*test_array[:,0],test_array[:,2]])
index

test_array[index]

test_array = np.arange(10)
test_array

test_array.shape

test_array.shape = 2,5
test_array

test_array.reshape(1,10)

test_array = np.arange(10)
test_array.shape

test_array = test_array[np.newaxis,:]
print(test_array)
test_array.shape

test_array = np.arange(10)
test_array.shape

test_array = test_array[:,np.newaxis]
print(test_array)
test_array.shape

test_array = test_array[:,np.newaxis,np.newaxis]
test_array.shape

test_array

test_array = test_array.squeeze()
test_array.shape

test_array.shape = 2,5
test_array

test_array.transpose()

test_array.T

a = np.array([[123,456,678],[3214,456,134]])
a

b = np.array([[1235,3124,432],[43,13,134]])
b

c = np.concatenate((a,b))
c

np.concatenate((a,b), axis=0)

c = np.concatenate((a,b), axis=1)
c

c.shape

np.vstack((a,b))

np.hstack((a,b))

a

a.flatten()

np.array([1,2,3])

np.arange(10)

np.arange(2,20,2)

np.arange(2,20,2,dtype=np.float32)

np.linspace(0,10,10)

np.logspace(0,1,5)

x = np.linspace(-10,10,5)
x

np.zeros(3)

np.zeros((3,3))

np.ones((3,3))

np.ones((3,3)) * 8

np.ones((3,3), dtype = np.float32)

a = np.empty(6)
a.shape

a

a.fill(1)
a

test_array = np.array([1,2,3,4])
test_array

np.zeros_like(test_array)

np.ones_like(test_array)

np.identity(5)

x = np.array([5,5])
y = np.array([2,2])

np.multiply(x,y)

np.dot(x, y)

y.shape = 1,2
y

x.shape = 2,1
x

print(x)
print(y)

np.dot(x,y)

np.dot(y,x)

x = np.array([1,1,1,2])
y = np.array([1,1,1,4])
x == y

np.logical_and(x,y)

np.logical_or(x,y)

np.logical_not(x,y)

np.random.rand(3,2)

np.random.randint(10, size=(5,4))

np.random.rand()

np.random.random_sample()

np.random.randint(0,10,3)

mu, sigma = 0, 0.1
np.random.normal(mu, sigma, 10)

np.set_printoptions(precision=2)

mu, sigma = 0, 0.1
np.random.normal(mu, sigma, 10)

test_array = np.arange(10)
test_array

np.random.shuffle(test_array)

test_array

np.random.seed(100)
mu, sigma = 0, 0.1
np.random.normal(mu, sigma, 10)

data = []
with open('./data/test1.txt') as f:
    for line in f.readlines():
        fileds = line.split()
        cur_data = [float(x) for x in fileds]
        data.append(cur_data)
data = np.array(data)
data

data = np.loadtxt('./data/test1.txt')
data

data = np.loadtxt('./data/test2.txt', delimiter=',')
data

data = np.loadtxt('./data/test3.txt',delimiter=',',skiprows=1)
data

test_array = np.array([[1,2,3],[4,5,6]])
test_array

np.savetxt('./data/test4.txt', test_array)

np.savetxt('./data/test4.txt', test_array, fmt='%d')

np.savetxt('./data/test4.txt', test_array, fmt='%d', delimiter=',')

np.savetxt('./data/test4.txt', test_array, fmt='%.2f', delimiter=',')

test_array = np.array([[1,2,3],[4,5,6]])
np.save('./data/test_array.npy', test_array)

test_array2 = np.load('./data/test_array.npy')
test_array2

test_array2 = np.arange(10)
test_array2

np.savez('./data/test.npz', a=test_array, b=test_array2)

data = np.load('./data/test.npz')

data.keys()

data['a']