Numpy-based polynomial fitting to predict population values

code show as below:

#-*- encoding:utf-8 -*-
import sys
reload(sys)
sys.setdefaultencoding
from sklearn import linear_model
import numpy as np
import pandas as pd

    
#Read data and create a data table named cost_and_click
year_population=pd.DataFrame(pd.read_excel('year_population.xls'))

X=np.array(year_population['year'])
X=X.tolist()


#Set the number of clicks as the dependent variable Y
Y=np.array(year_population['population'])
Y=Y.tolist()

#---------------------------------------------------------------
z1 = np.polyfit(X, Y, 4) #polynomial fit
p1 = e.g. poly1d (z1)
print"-"*40
print z1 #polynomial coefficients
print"-"*40
print p1 # complete output
print"☆☆☆☆☆☆☆☆Start prediction☆☆☆☆☆☆☆☆☆☆"
print p1(2010)

Data are as follows:

year	population
1790	3.9
1800	5.3
1810	7.2
1820	9.6
1830	12.9
1840	17.1
1850	23.2
1860	31.4
1870	38.6
1880	50.2
1890	62.9
1900	76
1910	92
1920	106.5
1930	123.2
1940	131.7
1950	150.7
1960	179.3
1970	204
1980	226.5
1990	251.4
2000	281.4