In [1]:

    

import numpy as np
from __future__ import print_function


    

In [2]:

    

x = np.arange(10)
print (x)


    

    




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

In [3]:

    

print(x * 2 + 1)


    

    




[ 1  3  5  7  9 11 13 15 17 19]

In [4]:

    

x = np.random.rand(10000000)


    

In [5]:

    

%%timeit

y = 2*x + 1


    

    




10 loops, best of 3: 34.9 ms per loop

In [6]:

    

import pandas as pd


    

In [7]:

    

df = pd.DataFrame({'id' : ['A', 'B', 'A', 'b'], 'val' : [1, 2, 3, 4]})
print(df)


    

    




  id  val
0  A    1
1  B    2
2  A    3
3  b    4

In [8]:

    

grouped = df.groupby('id').sum()
print(grouped)


    

    




    val
id     
A     4
B     2
b     4

In [9]:

    

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


    

In [10]:

    

x = np.linspace(0 ,10 ,1000)
plt.plot(x, np.sin(x))


    

    
Out[10]:





[<matplotlib.lines.Line2D at 0x1231a5278>]






    

In [11]:

    

import numpy as np
import matplotlib.pyplot as plt
from scipy import special, optimize

x = np.linspace(0, 10, 1000)
opt = optimize.minimize(special.j1, x0=3)
plt.plot(x, special.j1(x))
plt.plot(opt.x, special.j1(opt.x), marker='o', color='red')


    

    
Out[11]:





[<matplotlib.lines.Line2D at 0x1232492e8>]






    

In [14]:

    

x = 10*np.random.rand(100)
y = np.sin(x) + 0.1*np.random.rand(100)
plt.plot(x, y, '.k')


    

    
Out[14]:





[<matplotlib.lines.Line2D at 0x12474ec18>]






    

In [16]:

    

from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor()

model.fit(x[:, np.newaxis], y)
xfit = np.linspace(-1, 11, 1000)
yfit = model.predict(xfit[:, np.newaxis])

plt.plot(x, y, '.k')
plt.plot(xfit, yfit)


    

    
Out[16]:





[<matplotlib.lines.Line2D at 0x12575eb70>]






    

In [ ]: