In [40]:

    

%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns


    

In [41]:

    

import antipackage
import github.ellisonbg.misc.vizarray as va


    

In [42]:

    

import numpy as np


    

In [43]:

    

data = [0, 2, 4, 6]
a = np.array(data)


    

In [44]:

    

type(a)


    

    
Out[44]:





numpy.ndarray

In [45]:

    

a


    

    
Out[45]:





array([0, 2, 4, 6])

In [46]:

    

va.vizarray(a)


    

    
Out[46]:

In [47]:

    

a.shape


    

    
Out[47]:





(4,)

In [48]:

    

a.ndim


    

    
Out[48]:





1

In [49]:

    

a.size


    

    
Out[49]:





4

In [50]:

    

a.nbytes


    

    
Out[50]:





32

In [51]:

    

a.dtype


    

    
Out[51]:





dtype('int64')

In [57]:

    

data = [[0.0,2.0,4.0,6.0],[1.0,3.0,5.0,7.0]]
b = np.array(data)


    

In [58]:

    

b


    

    
Out[58]:





array([[ 0.,  2.,  4.,  6.],
       [ 1.,  3.,  5.,  7.]])

In [59]:

    

va.vizarray(b)


    

    
Out[59]:

In [60]:

    

b.shape, b.ndim, b.size, b.nbytes


    

    
Out[60]:





((2, 4), 2, 8, 64)

In [61]:

    

c = np.arange(0.0, 10.0, 1.0)
c


    

    
Out[61]:





array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])

In [62]:

    

e = np.linspace(0.0, 5.0, 11)
e


    

    
Out[62]:





array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ])

In [63]:

    

np.empty


    

    
Out[63]:





<function numpy.core.multiarray.empty>

In [64]:

    

np.zeros((3, 3))


    

    
Out[64]:





array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])

In [65]:

    

np.ones((3, 3))


    

    
Out[65]:





array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.],
       [ 1.,  1.,  1.]])

In [66]:

    

a = np.array([0, 1, 2, 3])


    

In [67]:

    

a, a.dtype


    

    
Out[67]:





(array([0, 1, 2, 3]), dtype('int64'))

In [68]:

    

b = np.zeros((2, 2), dtype = np.complex64)


    

In [69]:

    

c = np.arange(0, 10, 2, dtype = np.float)
c


    

    
Out[69]:





array([ 0.,  2.,  4.,  6.,  8.])

In [70]:

    

d = c.astype(dtype = np.int)


    

In [71]:

    

np.float*?


    

In [72]:

    

a = np.empty((3,3))
a.fill(0.1)
a


    

    
Out[72]:





array([[ 0.1,  0.1,  0.1],
       [ 0.1,  0.1,  0.1],
       [ 0.1,  0.1,  0.1]])

In [73]:

    

b = np.ones((3,3))
b


    

    
Out[73]:





array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.],
       [ 1.,  1.,  1.]])

In [75]:

    

a + b


    

    
Out[75]:





array([[ 1.1,  1.1,  1.1],
       [ 1.1,  1.1,  1.1],
       [ 1.1,  1.1,  1.1]])

In [76]:

    

b / a


    

    
Out[76]:





array([[ 10.,  10.,  10.],
       [ 10.,  10.,  10.],
       [ 10.,  10.,  10.]])

In [78]:

    

a ** 2


    

    
Out[78]:





array([[ 0.01,  0.01,  0.01],
       [ 0.01,  0.01,  0.01],
       [ 0.01,  0.01,  0.01]])

In [79]:

    

np.pi * b


    

    
Out[79]:





array([[ 3.14159265,  3.14159265,  3.14159265],
       [ 3.14159265,  3.14159265,  3.14159265],
       [ 3.14159265,  3.14159265,  3.14159265]])

In [80]:

    

a = np.random.rand(10, 10)


    

In [81]:

    

va.enable


    

    
Out[81]:





<function github.ellisonbg.misc.vizarray.enable>

In [82]:

    

a


    

    
Out[82]:





array([[ 0.06042759,  0.82013966,  0.20923969,  0.82298253,  0.39777137,
         0.16697057,  0.04840525,  0.23235343,  0.05670063,  0.89884396],
       [ 0.12870561,  0.9150747 ,  0.51818695,  0.31086148,  0.53228308,
         0.63529253,  0.9676085 ,  0.03045213,  0.65915349,  0.40700536],
       [ 0.8439568 ,  0.94820609,  0.3254754 ,  0.65060662,  0.57249243,
         0.81023562,  0.90328015,  0.55998516,  0.53926775,  0.42595367],
       [ 0.17024377,  0.64445812,  0.05133403,  0.70428481,  0.73077548,
         0.92033752,  0.39276949,  0.23240558,  0.25952686,  0.21507777],
       [ 0.0811034 ,  0.20740161,  0.33901776,  0.95946552,  0.72580022,
         0.27755957,  0.79775125,  0.78949877,  0.40720512,  0.60632164],
       [ 0.2554695 ,  0.16763672,  0.2923767 ,  0.5206425 ,  0.23372713,
         0.89713344,  0.76508931,  0.07417076,  0.72614281,  0.54217027],
       [ 0.34992143,  0.94686597,  0.61246075,  0.09645634,  0.06365613,
         0.63373767,  0.49652203,  0.31006348,  0.57712143,  0.22748162],
       [ 0.04810715,  0.44407407,  0.77675673,  0.81068316,  0.83242107,
         0.54330581,  0.1655478 ,  0.89636253,  0.15001998,  0.77406012],
       [ 0.04717436,  0.38467088,  0.00454725,  0.45303743,  0.6681668 ,
         0.51275731,  0.46619851,  0.26924213,  0.61938163,  0.37868359],
       [ 0.5604671 ,  0.91342881,  0.61635751,  0.21843716,  0.00197656,
         0.70564673,  0.6618094 ,  0.55786198,  0.58353724,  0.36190531]])

In [83]:

    

a[0,0]


    

    
Out[83]:





0.060427589208566168

In [84]:

    

a[-1, -1] == a[9, 9]


    

    
Out[84]:





True

In [86]:

    

a[:,0]


    

    
Out[86]:





array([ 0.06042759,  0.12870561,  0.8439568 ,  0.17024377,  0.0811034 ,
        0.2554695 ,  0.34992143,  0.04810715,  0.04717436,  0.5604671 ])

In [87]:

    

a[-1, :]


    

    
Out[87]:





array([ 0.5604671 ,  0.91342881,  0.61635751,  0.21843716,  0.00197656,
        0.70564673,  0.6618094 ,  0.55786198,  0.58353724,  0.36190531])

In [89]:

    

a[0:2, 0:2]


    

    
Out[89]:





array([[ 0.06042759,  0.82013966],
       [ 0.12870561,  0.9150747 ]])

In [90]:

    

a[0:5, 0:5] = 1.0


    

In [91]:

    

a


    

    
Out[91]:





array([[ 1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         0.16697057,  0.04840525,  0.23235343,  0.05670063,  0.89884396],
       [ 1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         0.63529253,  0.9676085 ,  0.03045213,  0.65915349,  0.40700536],
       [ 1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         0.81023562,  0.90328015,  0.55998516,  0.53926775,  0.42595367],
       [ 1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         0.92033752,  0.39276949,  0.23240558,  0.25952686,  0.21507777],
       [ 1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         0.27755957,  0.79775125,  0.78949877,  0.40720512,  0.60632164],
       [ 0.2554695 ,  0.16763672,  0.2923767 ,  0.5206425 ,  0.23372713,
         0.89713344,  0.76508931,  0.07417076,  0.72614281,  0.54217027],
       [ 0.34992143,  0.94686597,  0.61246075,  0.09645634,  0.06365613,
         0.63373767,  0.49652203,  0.31006348,  0.57712143,  0.22748162],
       [ 0.04810715,  0.44407407,  0.77675673,  0.81068316,  0.83242107,
         0.54330581,  0.1655478 ,  0.89636253,  0.15001998,  0.77406012],
       [ 0.04717436,  0.38467088,  0.00454725,  0.45303743,  0.6681668 ,
         0.51275731,  0.46619851,  0.26924213,  0.61938163,  0.37868359],
       [ 0.5604671 ,  0.91342881,  0.61635751,  0.21843716,  0.00197656,
         0.70564673,  0.6618094 ,  0.55786198,  0.58353724,  0.36190531]])

In [93]:

    

va.disable()


    

In [97]:

    

ages = np.array([23, 56,67, 89, 23, 56, 27, 12, 8, 72])
genders = np.array(['m', 'm', 'f', 'f', 'm', 'f', 'm' ,'m', 'm', 'f'])


    

In [98]:

    

ages > 30


    

    
Out[98]:





array([False,  True,  True,  True, False,  True, False, False, False,  True], dtype=bool)

In [99]:

    

genders == 'm'


    

    
Out[99]:





array([ True,  True, False, False,  True, False,  True,  True,  True, False], dtype=bool)

In [100]:

    

(ages > 10) & (ages < 50)


    

    
Out[100]:





array([ True, False, False, False,  True, False,  True,  True, False, False], dtype=bool)

In [101]:

    

mask = (genders == 'f')
ages[mask]


    

    
Out[101]:





array([67, 89, 56, 72])

In [102]:

    

ages[ages>30]


    

    
Out[102]:





array([56, 67, 89, 56, 72])

In [104]:

    

va.enable()


    

In [105]:

    

a = np.random.rand(3,4)


    

In [106]:

    

a


    

    
Out[106]:

In [107]:

    

a.T


    

    
Out[107]:

In [ ]:

    

a.reshape(6,2)


    

In [ ]:

    

a.ravel()


    

In [ ]:

    

va.disable


    

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