In [4]:

    

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


    

In [5]:

    

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


    

    




Downloading:  https://raw.githubusercontent.com/ellisonbg/misc/master/vizarray.py
Using existing version:  github.ellisonbg.misc.vizarray

In [6]:

    

import numpy as np


    

In [7]:

    

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


    

    
Out[7]:





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

In [8]:

    

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


    

    
Out[8]:





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

In [9]:

    

a+b


    

    
Out[9]:





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

In [10]:

    

b/a


    

    
Out[10]:





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

In [11]:

    

a**2


    

    
Out[11]:





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

In [12]:

    

np.pi*b


    

    
Out[12]:





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

In [13]:

    

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


    

In [14]:

    

va.enable()


    

In [15]:

    

a


    

    
Out[15]:

In [16]:

    

a[0,0]


    

    
Out[16]:





0.27120754215927823

In [17]:

    

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


    

    
Out[17]:





True

In [18]:

    

a[:,0]


    

    
Out[18]:

In [19]:

    

a[-1,:]


    

    
Out[19]:

In [20]:

    

a[0:2,0:2]


    

    
Out[20]:

In [21]:

    

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


    

In [22]:

    

a


    

    
Out[22]:

In [23]:

    

va.disable()


    

In [24]:

    

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 [25]:

    

ages > 30


    

    
Out[25]:





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

In [26]:

    

(ages > 10) & (ages < 50)


    

    
Out[26]:





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

In [27]:

    

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


    

    
Out[27]:





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

In [28]:

    

ages[ages>30]


    

    
Out[28]:





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

In [29]:

    

va.enable()


    

In [30]:

    

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


    

In [31]:

    

a


    

    
Out[31]:

In [32]:

    

a.T


    

    
Out[32]:

In [33]:

    

a.reshape(2,6)


    

    
Out[33]:

In [34]:

    

a.reshape(6,2)


    

    
Out[34]:

In [35]:

    

a.ravel()


    

    
Out[35]:

In [36]:

    

va.disable()


    

In [37]:

    

va.set_block_size(5)
va.enable()


    

In [38]:

    

t = np.linspace(0.0, 4*np.pi, 100)
t


    

    
Out[38]:

In [39]:

    

np.sin(t)


    

    
Out[39]:

In [40]:

    

np.exp(np.sqrt(t))


    

    
Out[40]:

In [41]:

    

va.disable()
va.set_block_size(30)


    

In [42]:

    

plt.plot(t, np.exp(-0.1*t)*np.sin(t))


    

    
Out[42]:





[<matplotlib.lines.Line2D at 0x7f3dab437110>]






    

In [43]:

    

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 [44]:

    

ages.min(), ages.max()


    

    
Out[44]:





(8, 89)

In [45]:

    

ages.mean()


    

    
Out[45]:





43.299999999999997

In [46]:

    

ages.var(), ages.std()


    

    
Out[46]:





(711.21000000000004, 26.668520768876554)

In [47]:

    

np.bincount(ages)


    

    
Out[47]:





array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])

In [48]:

    

ages.cumsum()


    

    
Out[48]:





array([ 23,  79, 146, 235, 258, 314, 341, 353, 361, 433])

In [49]:

    

ages.cumprod()


    

    
Out[49]:





array([              23,             1288,            86296,
                7680344,        176647912,       9892283072,
           267091642944,    3205099715328,   25640797722624,
       1846137436028928])

In [50]:

    

a = np.random.randint(0,10,(3,4))
a


    

    
Out[50]:





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

In [51]:

    

a.sum(axis=0)


    

    
Out[51]:





array([23, 11, 12, 13])

In [52]:

    

a.sum(axis=1)


    

    
Out[52]:





array([31,  8, 20])

In [53]:

    

np.unique(genders)


    

    
Out[53]:





array(['f', 'm'], 
      dtype='|S1')

In [54]:

    

np.unique(genders,return_counts=True)


    

    
Out[54]:





(array(['f', 'm'], 
       dtype='|S1'), array([4, 6]))

In [55]:

    

np.where(ages>30, 0, 1)


    

    
Out[55]:





array([1, 0, 0, 0, 1, 0, 1, 1, 1, 0])

In [56]:

    

np.where(ages<30, 0, ages)


    

    
Out[56]:





array([ 0, 56, 67, 89,  0, 56,  0,  0,  0, 72])

In [57]:

    

a = np.random.rand(10)
a


    

    
Out[57]:





array([ 0.18830122,  0.83845397,  0.81110033,  0.47910764,  0.53906557,
        0.65843344,  0.86157557,  0.16586074,  0.92789509,  0.32601429])

In [58]:

    

np.save("array1", a)


    

In [59]:

    

ls


    

    




array1.npy  array2.txt  arrays.npz  Day05.ipynb  Numpy.ipynb  Untitled.ipynb

In [60]:

    

%pycat array1.npy


    

In [61]:

    

a_copy = np.load("array1.npy")


    

In [62]:

    

a_copy


    

    
Out[62]:





array([ 0.18830122,  0.83845397,  0.81110033,  0.47910764,  0.53906557,
        0.65843344,  0.86157557,  0.16586074,  0.92789509,  0.32601429])

In [63]:

    

b = np.random.randint(0,10,(5,3))
b


    

    
Out[63]:





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

In [64]:

    

np.savetxt("array2.txt", b)


    

In [65]:

    

ls


    

    




array1.npy  array2.txt  arrays.npz  Day05.ipynb  Numpy.ipynb  Untitled.ipynb

In [66]:

    

%pycat array2.txt


    

In [67]:

    

np.loadtxt("array2.txt")


    

    
Out[67]:





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

In [68]:

    

np.savez("arrays.npz", a=a, b=b)


    

In [69]:

    

a_and_b = np.load("arrays.npz")


    

In [70]:

    

a_and_b["a"]


    

    
Out[70]:





array([ 0.18830122,  0.83845397,  0.81110033,  0.47910764,  0.53906557,
        0.65843344,  0.86157557,  0.16586074,  0.92789509,  0.32601429])

In [71]:

    

a_and_b["b"]


    

    
Out[71]:





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

In [72]:

    

a = np.random.rand(5,5)
b = np.random.rand(5,5)


    

In [73]:

    

a*b


    

    
Out[73]:





array([[ 0.01136743,  0.15698163,  0.52870858,  0.8899581 ,  0.1370473 ],
       [ 0.37954382,  0.30941556,  0.17197787,  0.44698928,  0.13509894],
       [ 0.21000253,  0.22872409,  0.38693399,  0.5640231 ,  0.00657149],
       [ 0.10725132,  0.40439514,  0.20743501,  0.09689429,  0.0076696 ],
       [ 0.74808798,  0.04407103,  0.32688194,  0.48602074,  0.12518149]])

In [74]:

    

np.dot(a,b)


    

    
Out[74]:





array([[ 1.04211097,  2.01031903,  2.16461426,  1.96228326,  0.57390927],
       [ 1.30087571,  1.74397962,  1.89791141,  1.77485553,  0.68247609],
       [ 0.727867  ,  1.3503134 ,  1.39182029,  1.49457338,  0.29619683],
       [ 1.2977354 ,  1.6613309 ,  1.77800595,  1.77124449,  0.69617672],
       [ 0.62476232,  1.90453303,  1.93090159,  1.7455144 ,  0.45203689]])

In [76]:

    

m1 = np.matrix(a)
m2 = np.matrix(b)


    

In [77]:

    

m1*m2


    

    
Out[77]:





matrix([[ 1.04211097,  2.01031903,  2.16461426,  1.96228326,  0.57390927],
        [ 1.30087571,  1.74397962,  1.89791141,  1.77485553,  0.68247609],
        [ 0.727867  ,  1.3503134 ,  1.39182029,  1.49457338,  0.29619683],
        [ 1.2977354 ,  1.6613309 ,  1.77800595,  1.77124449,  0.69617672],
        [ 0.62476232,  1.90453303,  1.93090159,  1.7455144 ,  0.45203689]])

In [78]:

    

np.linalg.det(a)


    

    
Out[78]:





0.027541253108440668

In [80]:

    

np.linalg.inv(a)


    

    
Out[80]:





array([[ 0.11836647, -3.8677372 , -0.54499402,  3.51266091,  1.2214507 ],
       [-2.26695293,  2.8804356 ,  1.75295624, -1.48915052,  0.07985372],
       [ 2.6210383 , -4.15157723,  0.63421879,  2.77555757, -1.52341498],
       [-1.39613586,  5.51572396,  0.58297095, -4.83465679,  0.53226709],
       [ 1.6531463 , -0.79215028, -1.82217837,  1.35068052, -0.58282749]])

In [81]:

    

np.linalg.eigvals(a)


    

    
Out[81]:





array([ 2.64444128+0.j        ,  0.21777936+0.23387031j,
        0.21777936-0.23387031j, -0.40151018+0.j        , -0.25399719+0.j        ])

In [82]:

    

c = np.random.rand(2000,2000)


    

In [83]:

    

%timeit -n1 -r1 evs = np.linalg.eigvals(c)


    

    




1 loops, best of 1: 29.5 s per loop

In [85]:

    

plt.hist(np.random.random(250))
plt.title("Uniform Random Distribution $[0,1]$")
plt.xlabel("value")
plt.ylabel('count')


    

    
Out[85]:





<matplotlib.text.Text at 0x7f3daab12b50>






    

In [86]:

    

plt.hist(np.random.randn(250))
plt.title("Standard Random Distribution $[0,1]$")
plt.xlabel("value")
plt.ylabel('count')


    

    
Out[86]:





<matplotlib.text.Text at 0x7f3da8b10e50>






    

In [87]:

    

a = np.arange(0,10)
np.random.shuffle(a)
a


    

    
Out[87]:





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

In [88]:

    

a = np.arange(0,10)
print(np.random.permutation(a))
print(a)


    

    




[1 6 0 9 5 3 7 8 4 2]
[0 1 2 3 4 5 6 7 8 9]

In [ ]:

    

np.random.choice(["m","f"], 20, p=[])