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Code - Python
Language: python 3.5
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
df = pd.read_excel('data.xlsx')
df.set_index(["Year"], inplace=True)
X = [df.index[0], df.index[-1]]
popu = df["Population"]
gdp = df["GDP"]
xTicks = np.arange(min(X), max(X) + 1, 10)
if xTicks[-1] % 10 != 0:
xtemp = xTicks.tolist()
xtemp += [max(X)]
xTicks = np.array(xtemp)
plt.xticks(xTicks)
average_gdp = gdp * 1000000 / popu
# print(average_gdp)
average_gdp.plot()
Y = [average_gdp.tolist()[-1], average_gdp.tolist()[-1]]
yTicks = np.arange(min(average_gdp), max(average_gdp) + 1, 2500)
if yTicks[-1] % 2500 != 0:
ytemp = yTicks.tolist()
ytemp += [max(average_gdp)]
yTicks = np.array(ytemp)
plt.yticks(yTicks)
plt.plot(X, Y, 'r--')
plt.plot([df.index[-1], df.index[-1]], [average_gdp.tolist()[0], average_gdp.tolist()[-1]], 'r--')
plt.ylabel('GDP per capita / Pounds')
plt.title("GDP per capita Change in recent years(UK)")
plt.savefig('uk.png')
plt.show()
Data.xlsx detail
You can just copy it to an excel.
And the name of this excel file must be “data.xlsx”, then you can use the code above, otherwise you need to change the filename in the code.
Year Population GDP
1971 55928000 721255
1972 56096700 752283
1973 56222900 801247
1974 56235600 781509
1975 56225700 769950
1976 56216100 792356
1977 56189900 811714
1978 56178000 845821
1979 56240100 877467
1980 56329700 859674
1981 56357500 853046
1982 56290700 870197
1983 56315700 906936
1984 56409300 927580
1985 56554000 966495
1986 56683800 996691
1987 56804000 1049581
1988 56916400 1109907
1989 57076500 1138425
1990 57237500 1146756
1991 57438700 1134296
1992 57584500 1138538
1993 57713900 1167308
1994 57862100 1212600
1995 58024800 1242548
1996 58164400 1274093
1997 58314200 1325543
1998 58474900 1367136
1999 58684400 1411112
2000 58886100 1462818
2001 59113000 1500034
2002 59365700 1536903
2003 59636700 1588019
2004 59950400 1625567
2005 60413300 1675896
2006 60827100 1717055
2007 61319100 1757521
2008 61823800 1749216
2009 62260500 1675963
2010 62759500 1704364
2011 63285100 1729121
2012 63705000 1754736
2013 64105700 1790750
2014 64596800 1845444
2015 65110000 1888737
2016 65648100 1925299
2017 66040200 1959707