Basic of Python.

The library we are going to use are the following:

numpy



In [6]:

    
for i in range(10):
   y = i*40 +i**2 + 2
   print(y)
    
i = i+2



In [7]:

    
range(10)









    Out[7]:





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



In [8]:

    
for i in ["a","b","tt"]:
    print i









    



a
b
tt



In [20]:

    
x = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],[2, 1, 2, 3, 4, 5, 6, 7, 8, 9],[0,5, 2, 3, 4, 5, 6, 7, 8, 9]]
print(x)









    



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



In [23]:

    
np.mean(x)









    Out[23]:





4.7000000000000002



In [24]:

    
x= np.linspace(-1,1,100)



In [25]:

    
x









    Out[25]:





array([-1.        , -0.97979798, -0.95959596, -0.93939394, -0.91919192,
       -0.8989899 , -0.87878788, -0.85858586, -0.83838384, -0.81818182,
       -0.7979798 , -0.77777778, -0.75757576, -0.73737374, -0.71717172,
       -0.6969697 , -0.67676768, -0.65656566, -0.63636364, -0.61616162,
       -0.5959596 , -0.57575758, -0.55555556, -0.53535354, -0.51515152,
       -0.49494949, -0.47474747, -0.45454545, -0.43434343, -0.41414141,
       -0.39393939, -0.37373737, -0.35353535, -0.33333333, -0.31313131,
       -0.29292929, -0.27272727, -0.25252525, -0.23232323, -0.21212121,
       -0.19191919, -0.17171717, -0.15151515, -0.13131313, -0.11111111,
       -0.09090909, -0.07070707, -0.05050505, -0.03030303, -0.01010101,
        0.01010101,  0.03030303,  0.05050505,  0.07070707,  0.09090909,
        0.11111111,  0.13131313,  0.15151515,  0.17171717,  0.19191919,
        0.21212121,  0.23232323,  0.25252525,  0.27272727,  0.29292929,
        0.31313131,  0.33333333,  0.35353535,  0.37373737,  0.39393939,
        0.41414141,  0.43434343,  0.45454545,  0.47474747,  0.49494949,
        0.51515152,  0.53535354,  0.55555556,  0.57575758,  0.5959596 ,
        0.61616162,  0.63636364,  0.65656566,  0.67676768,  0.6969697 ,
        0.71717172,  0.73737374,  0.75757576,  0.77777778,  0.7979798 ,
        0.81818182,  0.83838384,  0.85858586,  0.87878788,  0.8989899 ,
        0.91919192,  0.93939394,  0.95959596,  0.97979798,  1.        ])



In [29]:

    
x[[34,67]]









    Out[29]:





array([-0.31313131,  0.35353535])



In [30]:

    
x[45:90]









    Out[30]:





array([-0.09090909, -0.07070707, -0.05050505, -0.03030303, -0.01010101,
        0.01010101,  0.03030303,  0.05050505,  0.07070707,  0.09090909,
        0.11111111,  0.13131313,  0.15151515,  0.17171717,  0.19191919,
        0.21212121,  0.23232323,  0.25252525,  0.27272727,  0.29292929,
        0.31313131,  0.33333333,  0.35353535,  0.37373737,  0.39393939,
        0.41414141,  0.43434343,  0.45454545,  0.47474747,  0.49494949,
        0.51515152,  0.53535354,  0.55555556,  0.57575758,  0.5959596 ,
        0.61616162,  0.63636364,  0.65656566,  0.67676768,  0.6969697 ,
        0.71717172,  0.73737374,  0.75757576,  0.77777778,  0.7979798 ])



In [31]:

    
x[::3]









    Out[31]:





array([-1.        , -0.93939394, -0.87878788, -0.81818182, -0.75757576,
       -0.6969697 , -0.63636364, -0.57575758, -0.51515152, -0.45454545,
       -0.39393939, -0.33333333, -0.27272727, -0.21212121, -0.15151515,
       -0.09090909, -0.03030303,  0.03030303,  0.09090909,  0.15151515,
        0.21212121,  0.27272727,  0.33333333,  0.39393939,  0.45454545,
        0.51515152,  0.57575758,  0.63636364,  0.6969697 ,  0.75757576,
        0.81818182,  0.87878788,  0.93939394,  1.        ])



In [32]:

    
x[10::3]









    Out[32]:





array([-0.7979798 , -0.73737374, -0.67676768, -0.61616162, -0.55555556,
       -0.49494949, -0.43434343, -0.37373737, -0.31313131, -0.25252525,
       -0.19191919, -0.13131313, -0.07070707, -0.01010101,  0.05050505,
        0.11111111,  0.17171717,  0.23232323,  0.29292929,  0.35353535,
        0.41414141,  0.47474747,  0.53535354,  0.5959596 ,  0.65656566,
        0.71717172,  0.77777778,  0.83838384,  0.8989899 ,  0.95959596])



In [33]:

    
x[::-1]









    Out[33]:





array([ 1.        ,  0.97979798,  0.95959596,  0.93939394,  0.91919192,
        0.8989899 ,  0.87878788,  0.85858586,  0.83838384,  0.81818182,
        0.7979798 ,  0.77777778,  0.75757576,  0.73737374,  0.71717172,
        0.6969697 ,  0.67676768,  0.65656566,  0.63636364,  0.61616162,
        0.5959596 ,  0.57575758,  0.55555556,  0.53535354,  0.51515152,
        0.49494949,  0.47474747,  0.45454545,  0.43434343,  0.41414141,
        0.39393939,  0.37373737,  0.35353535,  0.33333333,  0.31313131,
        0.29292929,  0.27272727,  0.25252525,  0.23232323,  0.21212121,
        0.19191919,  0.17171717,  0.15151515,  0.13131313,  0.11111111,
        0.09090909,  0.07070707,  0.05050505,  0.03030303,  0.01010101,
       -0.01010101, -0.03030303, -0.05050505, -0.07070707, -0.09090909,
       -0.11111111, -0.13131313, -0.15151515, -0.17171717, -0.19191919,
       -0.21212121, -0.23232323, -0.25252525, -0.27272727, -0.29292929,
       -0.31313131, -0.33333333, -0.35353535, -0.37373737, -0.39393939,
       -0.41414141, -0.43434343, -0.45454545, -0.47474747, -0.49494949,
       -0.51515152, -0.53535354, -0.55555556, -0.57575758, -0.5959596 ,
       -0.61616162, -0.63636364, -0.65656566, -0.67676768, -0.6969697 ,
       -0.71717172, -0.73737374, -0.75757576, -0.77777778, -0.7979798 ,
       -0.81818182, -0.83838384, -0.85858586, -0.87878788, -0.8989899 ,
       -0.91919192, -0.93939394, -0.95959596, -0.97979798, -1.        ])



In [34]:

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



In [35]:

    
x[::-1]









    Out[35]:





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



In [37]:

    
y = np.linspace(0,10,100)
x= np.linspace(-1,1,100)



In [38]:

    
x









    Out[38]:





array([-1.        , -0.97979798, -0.95959596, -0.93939394, -0.91919192,
       -0.8989899 , -0.87878788, -0.85858586, -0.83838384, -0.81818182,
       -0.7979798 , -0.77777778, -0.75757576, -0.73737374, -0.71717172,
       -0.6969697 , -0.67676768, -0.65656566, -0.63636364, -0.61616162,
       -0.5959596 , -0.57575758, -0.55555556, -0.53535354, -0.51515152,
       -0.49494949, -0.47474747, -0.45454545, -0.43434343, -0.41414141,
       -0.39393939, -0.37373737, -0.35353535, -0.33333333, -0.31313131,
       -0.29292929, -0.27272727, -0.25252525, -0.23232323, -0.21212121,
       -0.19191919, -0.17171717, -0.15151515, -0.13131313, -0.11111111,
       -0.09090909, -0.07070707, -0.05050505, -0.03030303, -0.01010101,
        0.01010101,  0.03030303,  0.05050505,  0.07070707,  0.09090909,
        0.11111111,  0.13131313,  0.15151515,  0.17171717,  0.19191919,
        0.21212121,  0.23232323,  0.25252525,  0.27272727,  0.29292929,
        0.31313131,  0.33333333,  0.35353535,  0.37373737,  0.39393939,
        0.41414141,  0.43434343,  0.45454545,  0.47474747,  0.49494949,
        0.51515152,  0.53535354,  0.55555556,  0.57575758,  0.5959596 ,
        0.61616162,  0.63636364,  0.65656566,  0.67676768,  0.6969697 ,
        0.71717172,  0.73737374,  0.75757576,  0.77777778,  0.7979798 ,
        0.81818182,  0.83838384,  0.85858586,  0.87878788,  0.8989899 ,
        0.91919192,  0.93939394,  0.95959596,  0.97979798,  1.        ])

careful with "=":

xx= x means they are the same object
xx = x .... whatever or x.copy() they are two different objects



In [47]:

    
xx = x.copy()



In [48]:

    
xx+=2



In [49]:

    
xx









    Out[49]:





array([ 3.        ,  3.02020202,  3.04040404,  3.06060606,  3.08080808,
        3.1010101 ,  3.12121212,  3.14141414,  3.16161616,  3.18181818,
        3.2020202 ,  3.22222222,  3.24242424,  3.26262626,  3.28282828,
        3.3030303 ,  3.32323232,  3.34343434,  3.36363636,  3.38383838,
        3.4040404 ,  3.42424242,  3.44444444,  3.46464646,  3.48484848,
        3.50505051,  3.52525253,  3.54545455,  3.56565657,  3.58585859,
        3.60606061,  3.62626263,  3.64646465,  3.66666667,  3.68686869,
        3.70707071,  3.72727273,  3.74747475,  3.76767677,  3.78787879,
        3.80808081,  3.82828283,  3.84848485,  3.86868687,  3.88888889,
        3.90909091,  3.92929293,  3.94949495,  3.96969697,  3.98989899,
        4.01010101,  4.03030303,  4.05050505,  4.07070707,  4.09090909,
        4.11111111,  4.13131313,  4.15151515,  4.17171717,  4.19191919,
        4.21212121,  4.23232323,  4.25252525,  4.27272727,  4.29292929,
        4.31313131,  4.33333333,  4.35353535,  4.37373737,  4.39393939,
        4.41414141,  4.43434343,  4.45454545,  4.47474747,  4.49494949,
        4.51515152,  4.53535354,  4.55555556,  4.57575758,  4.5959596 ,
        4.61616162,  4.63636364,  4.65656566,  4.67676768,  4.6969697 ,
        4.71717172,  4.73737374,  4.75757576,  4.77777778,  4.7979798 ,
        4.81818182,  4.83838384,  4.85858586,  4.87878788,  4.8989899 ,
        4.91919192,  4.93939394,  4.95959596,  4.97979798,  5.        ])



In [50]:

    
x









    Out[50]:





array([ 1.        ,  1.02020202,  1.04040404,  1.06060606,  1.08080808,
        1.1010101 ,  1.12121212,  1.14141414,  1.16161616,  1.18181818,
        1.2020202 ,  1.22222222,  1.24242424,  1.26262626,  1.28282828,
        1.3030303 ,  1.32323232,  1.34343434,  1.36363636,  1.38383838,
        1.4040404 ,  1.42424242,  1.44444444,  1.46464646,  1.48484848,
        1.50505051,  1.52525253,  1.54545455,  1.56565657,  1.58585859,
        1.60606061,  1.62626263,  1.64646465,  1.66666667,  1.68686869,
        1.70707071,  1.72727273,  1.74747475,  1.76767677,  1.78787879,
        1.80808081,  1.82828283,  1.84848485,  1.86868687,  1.88888889,
        1.90909091,  1.92929293,  1.94949495,  1.96969697,  1.98989899,
        2.01010101,  2.03030303,  2.05050505,  2.07070707,  2.09090909,
        2.11111111,  2.13131313,  2.15151515,  2.17171717,  2.19191919,
        2.21212121,  2.23232323,  2.25252525,  2.27272727,  2.29292929,
        2.31313131,  2.33333333,  2.35353535,  2.37373737,  2.39393939,
        2.41414141,  2.43434343,  2.45454545,  2.47474747,  2.49494949,
        2.51515152,  2.53535354,  2.55555556,  2.57575758,  2.5959596 ,
        2.61616162,  2.63636364,  2.65656566,  2.67676768,  2.6969697 ,
        2.71717172,  2.73737374,  2.75757576,  2.77777778,  2.7979798 ,
        2.81818182,  2.83838384,  2.85858586,  2.87878788,  2.8989899 ,
        2.91919192,  2.93939394,  2.95959596,  2.97979798,  3.        ])

Masking

This only works with numpy array.

numpy array vs. list



In [52]:

    
xlist = [3,4,5,6,7,8,9]
xarray = np.asarray([3,4,5,6,7,8,9]) # np.asarray(xlist)



In [53]:

    
xlist*2









    Out[53]:





[3, 4, 5, 6, 7, 8, 9, 3, 4, 5, 6, 7, 8, 9]



In [54]:

    
xarray*2









    Out[54]:





array([ 6,  8, 10, 12, 14, 16, 18])



In [56]:

    
strangelist = ["toto",3,{},[]]



In [60]:

    
np.asarray(strangelist)*2









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-60-ad3befd67480> in <module>()
----> 1 np.asarray(strangelist)*2

TypeError: unsupported operand type(s) for *: 'dict' and 'int'

how to apply masking?

Use Numpy ARRAY



In [64]:

    
x









    Out[64]:





array([ 1.        ,  1.02020202,  1.04040404,  1.06060606,  1.08080808,
        1.1010101 ,  1.12121212,  1.14141414,  1.16161616,  1.18181818,
        1.2020202 ,  1.22222222,  1.24242424,  1.26262626,  1.28282828,
        1.3030303 ,  1.32323232,  1.34343434,  1.36363636,  1.38383838,
        1.4040404 ,  1.42424242,  1.44444444,  1.46464646,  1.48484848,
        1.50505051,  1.52525253,  1.54545455,  1.56565657,  1.58585859,
        1.60606061,  1.62626263,  1.64646465,  1.66666667,  1.68686869,
        1.70707071,  1.72727273,  1.74747475,  1.76767677,  1.78787879,
        1.80808081,  1.82828283,  1.84848485,  1.86868687,  1.88888889,
        1.90909091,  1.92929293,  1.94949495,  1.96969697,  1.98989899,
        2.01010101,  2.03030303,  2.05050505,  2.07070707,  2.09090909,
        2.11111111,  2.13131313,  2.15151515,  2.17171717,  2.19191919,
        2.21212121,  2.23232323,  2.25252525,  2.27272727,  2.29292929,
        2.31313131,  2.33333333,  2.35353535,  2.37373737,  2.39393939,
        2.41414141,  2.43434343,  2.45454545,  2.47474747,  2.49494949,
        2.51515152,  2.53535354,  2.55555556,  2.57575758,  2.5959596 ,
        2.61616162,  2.63636364,  2.65656566,  2.67676768,  2.6969697 ,
        2.71717172,  2.73737374,  2.75757576,  2.77777778,  2.7979798 ,
        2.81818182,  2.83838384,  2.85858586,  2.87878788,  2.8989899 ,
        2.91919192,  2.93939394,  2.95959596,  2.97979798,  3.        ])



In [65]:

    
mask = x>2



In [66]:

    
mask









    Out[66]:





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



In [68]:

    
x[mask] # x[x>2]









    Out[68]:





array([ 2.01010101,  2.03030303,  2.05050505,  2.07070707,  2.09090909,
        2.11111111,  2.13131313,  2.15151515,  2.17171717,  2.19191919,
        2.21212121,  2.23232323,  2.25252525,  2.27272727,  2.29292929,
        2.31313131,  2.33333333,  2.35353535,  2.37373737,  2.39393939,
        2.41414141,  2.43434343,  2.45454545,  2.47474747,  2.49494949,
        2.51515152,  2.53535354,  2.55555556,  2.57575758,  2.5959596 ,
        2.61616162,  2.63636364,  2.65656566,  2.67676768,  2.6969697 ,
        2.71717172,  2.73737374,  2.75757576,  2.77777778,  2.7979798 ,
        2.81818182,  2.83838384,  2.85858586,  2.87878788,  2.8989899 ,
        2.91919192,  2.93939394,  2.95959596,  2.97979798,  3.        ])



In [76]:

    
x[ (x>2) & (x<2.5)  ] # x[ (x>2) * (x>1.5)  ] # both have to be true









    Out[76]:





array([ 2.01010101,  2.03030303,  2.05050505,  2.07070707,  2.09090909,
        2.11111111,  2.13131313,  2.15151515,  2.17171717,  2.19191919,
        2.21212121,  2.23232323,  2.25252525,  2.27272727,  2.29292929,
        2.31313131,  2.33333333,  2.35353535,  2.37373737,  2.39393939,
        2.41414141,  2.43434343,  2.45454545,  2.47474747,  2.49494949])



In [75]:

    
x[ (x>2) | (x>1.5)  ] # x[ (x>2) + (x>1.5)  ] # any have to be true









    Out[75]:





array([ 1.50505051,  1.52525253,  1.54545455,  1.56565657,  1.58585859,
        1.60606061,  1.62626263,  1.64646465,  1.66666667,  1.68686869,
        1.70707071,  1.72727273,  1.74747475,  1.76767677,  1.78787879,
        1.80808081,  1.82828283,  1.84848485,  1.86868687,  1.88888889,
        1.90909091,  1.92929293,  1.94949495,  1.96969697,  1.98989899,
        2.01010101,  2.03030303,  2.05050505,  2.07070707,  2.09090909,
        2.11111111,  2.13131313,  2.15151515,  2.17171717,  2.19191919,
        2.21212121,  2.23232323,  2.25252525,  2.27272727,  2.29292929,
        2.31313131,  2.33333333,  2.35353535,  2.37373737,  2.39393939,
        2.41414141,  2.43434343,  2.45454545,  2.47474747,  2.49494949,
        2.51515152,  2.53535354,  2.55555556,  2.57575758,  2.5959596 ,
        2.61616162,  2.63636364,  2.65656566,  2.67676768,  2.6969697 ,
        2.71717172,  2.73737374,  2.75757576,  2.77777778,  2.7979798 ,
        2.81818182,  2.83838384,  2.85858586,  2.87878788,  2.8989899 ,
        2.91919192,  2.93939394,  2.95959596,  2.97979798,  3.        ])

The case of the NaN Value



In [78]:

    
iamnan = np.NaN



In [79]:

    
iamnan









    Out[79]:





nan



In [80]:

    
iamnan==iamnan









    Out[80]:





False



In [82]:

    
np.inf==np.inf









    Out[82]:





True



In [83]:

    
xwithnan = np.asarray([3,4,5,6,7,2,3,np.NaN,75,75])



In [84]:

    
xwithnan









    Out[84]:





array([  3.,   4.,   5.,   6.,   7.,   2.,   3.,  nan,  75.,  75.])



In [85]:

    
xwithnan*2









    Out[85]:





array([   6.,    8.,   10.,   12.,   14.,    4.,    6.,   nan,  150.,  150.])



In [86]:

    
4+np.NaN









    Out[86]:





nan



In [87]:

    
4/np.NaN









    Out[87]:





nan



In [88]:

    
4**np.NaN









    Out[88]:





nan



In [89]:

    
np.mean(xwithnan)









    Out[89]:





nan



In [90]:

    
np.nanmean(xwithnan)









    Out[90]:





20.0



In [91]:

    
np.mean(xwithnan[xwithnan==xwithnan])









    Out[91]:





20.0



In [96]:

    
~(xwithnan==xwithnan)









    Out[96]:





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



In [93]:

    
xwithnan!=xwithnan









    Out[93]:





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



In [97]:

    
np.isnan(xwithnan)









    Out[97]:





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



In [98]:

    
xwithnan = [3,4,5,6,7,2,3,np.NaN,75,75]



In [100]:

    
xwithnan[xwithnan==xwithnan]









    Out[100]:





4



In [105]:

    
0 == False









    Out[105]:





True



In [ ]:

    
1 == True

Your first plot

For ploting we are going to use matplotlib. let's plot 2 random variable a vs. b



In [117]:

    
a = np.random.rand(30)
b = np.random.rand(30)



In [118]:

    
# plot within the notebook
%matplotlib inline
import matplotlib.pyplot as mpl



In [119]:

    
pl = mpl.hist(a)



In [126]:

    
mpl.scatter(a,b,s=150, facecolors="None", edgecolors="b",lw=3)









    Out[126]:





<matplotlib.collections.PathCollection at 0x1067e86d0>



In [ ]: