Python pandas - group, count, plot multi-series

Question

I have the below dataset from a survey that give each participant a list of foods and asks them to rank how likely they are to eat them this week. I want to plot the count for each likelhood of each food type on a graph.

Person    Food     Label
John      Pizza    Likely
John      Chinese  Unlikely
John      French   Very Unlikely
Debbie    Pizza    Unlikely
Debbie    Chinese  Very Likely
Debbie    French   Very Unlikely

For example:

Pizza     Likely         1
Pizza     Unlikely       1
Chinese   Unlikely       1
Chinese   Very Unlikely  1
French    Very Unlikely  2

So far I have read my file into a dataframe and done some basic cleaning.

import pandas as pd

raw_data = pd.read_excel('my_file_path')

#cleaning code
clean_data = raw_data(clean)

results = clean_data.groupby(['Food', 'Label']).count()

jezrael · Accepted Answer · 2018-10-19 12:56:52Z

4

I believe you need to add column Person after groupby, reshape by unstack and plot by DataFrame.plot.bar:

results = clean_data.groupby(['Food', 'Label'])['Person'].count().unstack(fill_value=0)

Another solution with crosstab:

results = pd.crosstab(clean_data['Food'], clean_data['Label'])

print (results)
Label    Likely  Unlikely  Very Likely  Very Unlikely
Food                                                 
Chinese       0         1            1              0
French        0         0            0              2
Pizza         1         1            0              0

results.plot.bar()

edited Oct 19, 2018 at 12:56

answered Oct 19, 2018 at 12:39

jezrael

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1 Comment

el_oso Over a year ago

when I unstack, I can no longer access rows and columns by their label. i.e. results['Food'] or results['Label']

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