In [1]:

    

import numpy
print numpy.__version__

import matplotlib.pyplot as plt
%matplotlib inline

x = [1, 2, 3, 4, 5, 6]
np_arr = numpy.array(x)

# output
x


    

    




1.9.0






    
Out[1]:





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

In [2]:

    

np_arr


    

    
Out[2]:





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

In [3]:

    

np_arr.shape


    

    
Out[3]:





(6,)

In [4]:

    

x == np_arr


    

    
Out[4]:





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

In [5]:

    

x = [ [1,2], [3,4], [5,6]]
np_arr = numpy.array(x)
np_arr.shape


    

    
Out[5]:





(3, 2)

In [6]:

    

array_slice = np_arr[:,1]
array_slice


    

    
Out[6]:





array([2, 4, 6])

In [7]:

    

array_slice[2] = 7
np_arr * 3
np_arr.T.dot(np_arr)


    

    
Out[7]:





array([[35, 49],
       [49, 69]])

In [8]:

    

numpy.average(np_arr) + 1


    

    
Out[8]:





4.6666666666666661

In [9]:

    

numpy.average(np_arr[:,1])


    

    
Out[9]:





4.333333333333333

In [10]:

    

np_arr
numpy.cov(np_arr)


    

    
Out[10]:





array([[ 0.5,  0.5,  1. ],
       [ 0.5,  0.5,  1. ],
       [ 1. ,  1. ,  2. ]])

In [2]:

    

x = [ 0, 1, 2, 3, 4, 5, 6 ]
y = [ 0, 2, 4, 8, 16, 32, 64 ]
plt.plot(x, y)


    

    
Out[2]:





[<matplotlib.lines.Line2D at 0x105a5d850>]






    

In [12]:

    

plt.plot(x, y, 'g--', label='my line')
plt.legend()


    

    
Out[12]:





<matplotlib.legend.Legend at 0x108176a90>






    

In [13]:

    

plt.plot(x, y, 'b*')
plt.axis(xmin=-10, xmax=8, ymin=-10)


    

    
Out[13]:





(-10, 8, -10, 70.0)






    

In [14]:

    

x = numpy.linspace(0, 25, 100)
y = x ** 2


    

In [15]:

    

plt.plot(x,y, 'r*')


    

    
Out[15]:





[<matplotlib.lines.Line2D at 0x10b27e6d0>]






    

In [16]:

    

import sklearn.datasets as datasets
plt.jet()
X, Y = datasets.make_blobs(centers=4, cluster_std=0.5, random_state=0)
plt.scatter(X[:,0], X[:,1], c=Y)


    

    
Out[16]:





<matplotlib.collections.PathCollection at 0x10c233750>






    

In [ ]: