Exercises



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import pandas as pd
from matplotlib import pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
diamonds = pd.read_csv('diamonds.csv',index_col=0)

Task 1: Basics

have a look at the diamonds dataset: how many rows do we have?, what are the different columns?
create a DataFrame consisting only of the x, y and z columns
access row 5 to 15 in diamonds
create a DataFrame consisting only of row 5 to 15 and name the rows "A" to "K" (hint: each DataFrame has an .index attribute which can be modified)
access row "C" in the DataFrame you just created
use the mixed access operator (.ix) to get the price of the 500th diamond
group the diamnods by color and compute the mean of the price
find all the diamonds with more than 2 carat and plot their price distribution in a histogram
compute and plot the standard deviation of the x dimension for the different cuts



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Task 2: Import the "Kernmerkmale Bevölkerung (Geschlecht, Deutsche/Ausländer, 5 Altersgruppen)" data set

https://www.destatis.de/DE/PresseService/Presse/Pressekonferenzen/2013/Zensus2011/zensus_pk.html

download the excel file and use the read_excel method in pandas (hint: useful arguments are sheetname, header and index_col



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Task 4: Create a new DataFrame containing only the "Bundesländer" (hint: use the corresponding SATZART key)



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Task 5: Plot the age composition of the different Bundesländer



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Task 6: ...and now relative to the population in each Bundesland (hint the .div method of a DataFrame can be used to divide two dataframes)



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Task 7: Plot pie charts of the relative age distribution in Berlin and Sachsen



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Task 8: Import a health care data set from Zeit online

The file is in the git repository: multiresistente_keime.xlsx. Import it using the read_excel function.



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Task 9: Merge the two data sets (hint: "Kreisschlüssel" is the same as "AGS")



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Task 10: Plot number of hospitalized patients per inhabitant against relative number of old people (65+)



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Task 11: Compute the correlation



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