
import numpy as np
a=np.array([1,2,3,4,5])
b=np.array([[1,2,3], [4,5,6]])
b
b[:,1:]

import random
r=rand(5,3)

v=np.array([1,2,3,4,5])
v[v>2]
np.array(range(1,6,2))

m=np.array([[1,4,7,10],[2,5,8,11],[3,6,9,12]])
m
v[v>2]

m=np.array(range(1,13))
m=m.reshape([3,4])

m[0:2,1:]

m=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]], )

m=np.array(range(1,13))
m=m.reshape([4,3]).T
m
m[:2,1:]

v[5]=6
v=[v,6]
v

import random
random.random()

m=np.array([
[0.9347052, 0.1255551, 0.01339033]
, [0.2121425, 0.2672207, 0.38238796]
, [0.6516738, 0.3861141, 0.86969085]
])

print(m.dot(m))

m.diagonal()

import pandas as pd

df = pd.DataFrame([[1,4],[2,5],[3,6]])
df.columns=['c1', 'c2']
df.sum()


m
pd.DataFrame(m)



m=np.array(range(1,13))
m.reshape([4,3]).T


m=np.array(range(1,13))
m.reshape([3,4], order='F')



np.zeros((5,6))+1

np.arange(10,0)

np.linspace(0,10,5)

set_printoptions(threshold=10)
pd.DataFrame(np.ones(10000).reshape(2000,5))

m

m*m
m.dot(m)

import random

np.random.random((4,5))


np.random.normal(0,1,10)


np.random.uniform(low=0, high=10,size=5)

np.random.normal(loc=0,scale=1, size=5)

np.random.poisson(lam=5, size=10)

m.min(axis=0)

m=arange(1,13).reshape(3,4, order='F')

m.sum(axis=0)

m.cumsum(axis=0)



a=[
    [1,2,3]
    , [4,6,5]
    , [5,4,5]
]


import numpy as np
aa=np.array(a)
aa
np.cov(aa)

3ff03695d43f85daca58c415759f96af8cbda617
<mask token>

<mask token>
b
b[:, 1:]
<mask token>
v[v > 2]
np.array(range(1, 6, 2))
<mask token>
m
v[v > 2]
<mask token>
m[0:2, 1:]
<mask token>
m
m[:2, 1:]
<mask token>
v
<mask token>
random.random()
<mask token>
print(m.dot(m))
m.diagonal()
<mask token>
df.sum()
m
pd.DataFrame(m)
<mask token>
m.reshape([4, 3]).T
<mask token>
m.reshape([3, 4], order='F')
np.zeros((5, 6)) + 1
np.arange(10, 0)
np.linspace(0, 10, 5)
set_printoptions(threshold=10)
pd.DataFrame(np.ones(10000).reshape(2000, 5))
m
m * m
m.dot(m)
<mask token>
np.random.random((4, 5))
np.random.normal(0, 1, 10)
np.random.uniform(low=0, high=10, size=5)
np.random.normal(loc=0, scale=1, size=5)
np.random.poisson(lam=5, size=10)
m.min(axis=0)
<mask token>
m.sum(axis=0)
m.cumsum(axis=0)
<mask token>
aa
np.cov(aa)

<mask token>
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3], [4, 5, 6]])
b
b[:, 1:]
<mask token>
r = rand(5, 3)
v = np.array([1, 2, 3, 4, 5])
v[v > 2]
np.array(range(1, 6, 2))
m = np.array([[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]])
m
v[v > 2]
m = np.array(range(1, 13))
m = m.reshape([3, 4])
m[0:2, 1:]
m = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
m = np.array(range(1, 13))
m = m.reshape([4, 3]).T
m
m[:2, 1:]
v[5] = 6
v = [v, 6]
v
<mask token>
random.random()
m = np.array([[0.9347052, 0.1255551, 0.01339033], [0.2121425, 0.2672207, 
    0.38238796], [0.6516738, 0.3861141, 0.86969085]])
print(m.dot(m))
m.diagonal()
<mask token>
df = pd.DataFrame([[1, 4], [2, 5], [3, 6]])
df.columns = ['c1', 'c2']
df.sum()
m
pd.DataFrame(m)
m = np.array(range(1, 13))
m.reshape([4, 3]).T
m = np.array(range(1, 13))
m.reshape([3, 4], order='F')
np.zeros((5, 6)) + 1
np.arange(10, 0)
np.linspace(0, 10, 5)
set_printoptions(threshold=10)
pd.DataFrame(np.ones(10000).reshape(2000, 5))
m
m * m
m.dot(m)
<mask token>
np.random.random((4, 5))
np.random.normal(0, 1, 10)
np.random.uniform(low=0, high=10, size=5)
np.random.normal(loc=0, scale=1, size=5)
np.random.poisson(lam=5, size=10)
m.min(axis=0)
m = arange(1, 13).reshape(3, 4, order='F')
m.sum(axis=0)
m.cumsum(axis=0)
a = [[1, 2, 3], [4, 6, 5], [5, 4, 5]]
<mask token>
aa = np.array(a)
aa
np.cov(aa)

import numpy as np
a = np.array([1, 2, 3, 4, 5])
b = np.array([[1, 2, 3], [4, 5, 6]])
b
b[:, 1:]
import random
r = rand(5, 3)
v = np.array([1, 2, 3, 4, 5])
v[v > 2]
np.array(range(1, 6, 2))
m = np.array([[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]])
m
v[v > 2]
m = np.array(range(1, 13))
m = m.reshape([3, 4])
m[0:2, 1:]
m = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
m = np.array(range(1, 13))
m = m.reshape([4, 3]).T
m
m[:2, 1:]
v[5] = 6
v = [v, 6]
v
import random
random.random()
m = np.array([[0.9347052, 0.1255551, 0.01339033], [0.2121425, 0.2672207, 
    0.38238796], [0.6516738, 0.3861141, 0.86969085]])
print(m.dot(m))
m.diagonal()
import pandas as pd
df = pd.DataFrame([[1, 4], [2, 5], [3, 6]])
df.columns = ['c1', 'c2']
df.sum()
m
pd.DataFrame(m)
m = np.array(range(1, 13))
m.reshape([4, 3]).T
m = np.array(range(1, 13))
m.reshape([3, 4], order='F')
np.zeros((5, 6)) + 1
np.arange(10, 0)
np.linspace(0, 10, 5)
set_printoptions(threshold=10)
pd.DataFrame(np.ones(10000).reshape(2000, 5))
m
m * m
m.dot(m)
import random
np.random.random((4, 5))
np.random.normal(0, 1, 10)
np.random.uniform(low=0, high=10, size=5)
np.random.normal(loc=0, scale=1, size=5)
np.random.poisson(lam=5, size=10)
m.min(axis=0)
m = arange(1, 13).reshape(3, 4, order='F')
m.sum(axis=0)
m.cumsum(axis=0)
a = [[1, 2, 3], [4, 6, 5], [5, 4, 5]]
import numpy as np
aa = np.array(a)
aa
np.cov(aa)


import numpy as np
a=np.array([1,2,3,4,5])
b=np.array([[1,2,3], [4,5,6]])
b
b[:,1:]

import random
r=rand(5,3)

v=np.array([1,2,3,4,5])
v[v>2]
np.array(range(1,6,2))

m=np.array([[1,4,7,10],[2,5,8,11],[3,6,9,12]])
m
v[v>2]

m=np.array(range(1,13))
m=m.reshape([3,4])

m[0:2,1:]

m=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]], )

m=np.array(range(1,13))
m=m.reshape([4,3]).T
m
m[:2,1:]

v[5]=6
v=[v,6]
v

import random
random.random()

m=np.array([
[0.9347052, 0.1255551, 0.01339033]
, [0.2121425, 0.2672207, 0.38238796]
, [0.6516738, 0.3861141, 0.86969085]
])

print(m.dot(m))

m.diagonal()

import pandas as pd

df = pd.DataFrame([[1,4],[2,5],[3,6]])
df.columns=['c1', 'c2']
df.sum()


m
pd.DataFrame(m)



m=np.array(range(1,13))
m.reshape([4,3]).T


m=np.array(range(1,13))
m.reshape([3,4], order='F')



np.zeros((5,6))+1

np.arange(10,0)

np.linspace(0,10,5)

set_printoptions(threshold=10)
pd.DataFrame(np.ones(10000).reshape(2000,5))

m

m*m
m.dot(m)

import random

np.random.random((4,5))


np.random.normal(0,1,10)


np.random.uniform(low=0, high=10,size=5)

np.random.normal(loc=0,scale=1, size=5)

np.random.poisson(lam=5, size=10)

m.min(axis=0)

m=arange(1,13).reshape(3,4, order='F')

m.sum(axis=0)

m.cumsum(axis=0)



a=[
    [1,2,3]
    , [4,6,5]
    , [5,4,5]
]


import numpy as np
aa=np.array(a)
aa
np.cov(aa)
