Dowload LifeExpectancy.csv from the course webpage, and read it into Python, skipping the necessary rows and reading the header. Make the country name be an index. Print the first few rows to ensure that you have it correct. [2 marks]In [2]:
Out[2]:Country IndicatorIndicator Code 1960 1961 1962 1963 1964 19 Code NameCountryName
Aruba ABW Life expectancy at birth, total (years) SP.DYN.LE00.IN 65.662 66.074 66.444 66.787 67.113 67.4Afghanistan AFG Life expectancy at birth, total (years) SP.DYN.LE00.IN 32.446 32.962 33.471 33.971 34.463 34.9Angola AGO Life expectancy at birth, total (years) SP.DYN.LE00.IN 37.524 37.811 38.113 38.430 38.760 39.1Albania ALB Life expectancy at birth, total (years) SP.DYN.LE00.IN 62.283 63.301 64.190 64.914 65.463 65.8Andorra AND Life expectancy at birth, total (years) SP.DYN.LE00.IN NaN NaN NaN NaN NaN N5 rows 63 columnsDrop rows that consist of NaN values. Be careful how you do this, the naive way of just using dropna() without looking at the data a bit might not do what you expect. You might need to know that to delete a column you can use life.drop([list of column names],axis=1 . [2 marks]In [3]:
Out[3]:Country IndicatorIndicator Code 1960 1961 1962 1963 Code NameCountryName
Aruba ABW Life expectancy at birth, total (years) SP.DYN.LE00.IN 65.662000 66.074000 66.444000 66.787000Afghanistan AFG Life expectancy at birth, total (years) SP.DYN.LE00.IN 32.446000 32.962000 33.471000 33.971000Angola AGO Life expectancy at birth, total (years) SP.DYN.LE00.IN 37.524000 37.811000 38.113000 38.430000Albania ALB Life expectancy at birth, total (years) SP.DYN.LE00.IN 62.283000 63.301000 64.190000 64.914000Arab World ARB Life expectancy at birth, total (years) SP.DYN.LE00.IN 46.546909 47.141621 47.731783 48.320432 Samoa WSM Life expectancy at birth, total (years) SP.DYN.LE00.IN 56.902000 57.188000 57.472000 57.756000Yemen, Rep. YEM Life expectancy at birth, total (years) SP.DYN.LE00.IN 29.919000 30.163000 30.500000 30.943000South Africa ZAF Life expectancy at birth, total (years) SP.DYN.LE00.IN 48.406000 48.777000 49.142000 49.509000Zambia ZMB Life expectancy at birth, total (years) SP.DYN.LE00.IN 46.687000 47.084000 47.446000 47.772000Zimbabwe ZWE Life expectancy at birth, total (years) SP.DYN.LE00.IN 53.019000 53.483000 53.946000 54.403000235 rows 61 columns
Now plot the curves of life expectancy against time on 1 plot for the following countries:Afghanistan, Nepal, New Zealand, Netherlands Include a legend, and make the labels on the x-axis readable. [2 marks] In [4]:
Plot Rwanda separately, and explain briefly why it has that shape (hint: use wikipedia) [2 marks]In [5]:
Can you detect any other countries where the life expectancy drops significantly?To compute this write some loops over each country and each year. If the next value is below 95% of the current value, print the name of the country. [4 marks]In [6]:
# convert to a numpy array, because iterating over a dataframe is non-pattern# and computationlally expensive due to the constant creation of Series objects df_array = df.reset_index().to_numpy() start_year = 1960 for row in df_array:prev = 0 for i, item in enumerate(row[4:]):if prev != 0: if get_drop(prev, item) < 0.95: year = start_year+iprint(.join([In , str(year), there is a significant (<95%) drop from , str(year-1), in , row[0],s life expectancy, going from , str(prev), to , s tr(item),
]))prev = itemIn 1972 there is a significant (<95%) drop from 1971 in Cambodias life ex pectancy, going from 39.699 to 36.676In 1973 there is a significant (<95%) drop from 1972 in Cambodias life ex pectancy, going from 36.676 to 32.667In 1974 there is a significant (<95%) drop from 1973 in Cambodias life ex pectancy, going from 32.667 to 28.04In 1975 there is a significant (<95%) drop from 1974 in Cambodias life ex pectancy, going from 28.04 to 23.595In 1976 there is a significant (<95%) drop from 1975 in Cambodias life ex pectancy, going from 23.595 to 20.317In 1977 there is a significant (<95%) drop from 1976 in Cambodias life ex pectancy, going from 20.317 to 18.907In 1987 there is a significant (<95%) drop from 1986 in Rwandas life expe ctancy, going from 50.233000000000004 to 47.409In 1988 there is a significant (<95%) drop from 1987 in Rwandas life expe ctancy, going from 47.409 to 43.361000000000004In 1989 there is a significant (<95%) drop from 1988 in Rwandas life expe ctancy, going from 43.361000000000004 to 38.439In 1990 there is a significant (<95%) drop from 1989 in Rwandas life expe ctancy, going from 38.439 to 33.413000000000004In 1991 there is a significant (<95%) drop from 1990 in Rwandas life expe ctancy, going from 33.413000000000004 to 29.248In 1992 there is a significant (<95%) drop from 1991 in Rwandas life expe ctancy, going from 29.248 to 26.691Compute the mean life expectancy for each of the countries over the whole 57 years. Plot a bar chart of this for the first 10 countries. [4 marks]
Find the 5 countries with the highest mean life expectancy and the 5 with the lowest. You might find life.sort_values() helpful, as well as pd.concat and life.transpose . Plot a box and whisker plot of these countries. [4 marks]
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