0

I have a data set that I want to plot with a bar chart. There is a categorical column products and a categorical column satisfaction. I wanted to plot the 3 flop products, i.e. products where satisfaction = 1. I tried .sort_values(by='products', ascending = False) but unfortunately it orders me the products alphabetically descending.

How can I sort by the category satisfaction = 1 and limit the plot (only the flop3)?

0 stands for satisfied 1 stands for not satisfied

Code:

product_satisfaction= pd.crosstab(index=df.loc[:,'products'],
            columns=df.loc[:,'satisfaction'], normalize='index')

product_satisfaction.plot(kind='bar');

Example extract of the dataframe

Data:

df = pd.DataFrame([['product1', 0], ['product3', 0], ['product1', 0],
              ['product6', 1], ['product1', 1], ['product1', 1],
              ['product1', 0], ['product4', 0], ['product1', 1],
              ['product1', 0], ['product2', 0], ['product3', 0],
              ['product1', 0], ['product6', 0], ['product3', 1],
              ['product1', 0], ['product4', 0], ['product4', 0],
              ['product1', 0], ['product2', 0], ['product3', 0],
              ['product1', 0], ['product1', 0], ['product3', 1],
              ['product2', 0], ['product1', 0], ['product3', 1],
               ['product2', 0], ['product4', 0], ['product3', 1],
               ['product1', 0], ['product6', 0], ['product6', 0],
               ['product3', 0], ['product3', 0], ['product1', 0],
               ['product4', 0], ['product3', 0], ['product5', 0],
               ['product6', 1], ['product2', 0], ['product1', 0],
               ['product1', 1], ['product5', 0], ['product6', 0],
               ['product6', 1], ['product1', 0], ['product5', 1],
               ['product4', 1], ['product4', 0], ['product6', 1],
               ['product5', 0], ['product2', 0], ['product1', 1],
               ['product6', 0], ['product1', 0], ['product6', 1],
               ['product3', 0], ['product2', 0], ['product6', 1],
               ['product2', 0], ['product1', 0], ['product5', 0],
               ['product6', 0], ['product6', 0], ['product1', 1],
               ['product1', 0], ['product2', 1], ['product1', 1],
               ['product5', 1], ['product3', 1], ['product5', 0],
               ['product5', 1], ['product2', 1], ['product1', 0],
               ['product3', 1], ['product1', 1], ['product5', 0],
               ['product3', 0], ['product2', 0], ['product2', 0],
               ['product2', 0], ['product1', 0], ['product1', 1],
               ['product2', 0], ['product1', 0], ['product2', 1],
               ['product4', 0], ['product3', 0], ['product1', 1],
               ['product2', 1], ['product5', 0], ['product5', 0],
               ['product5', 1], ['product1', 0], ['product2', 0],
               ['product6', 1], ['product5', 0], ['product2', 0],
               ['product2', 0], ['product6', 0], ['product6', 1],
               ['product4', 0], ['product3', 0], ['product5', 0],
               ['product6', 1], ['product2', 0], ['product1', 0],
               ['product1', 1], ['product5', 0], ['product6', 0],
               ['product6', 1], ['product1', 0], ['product5', 1],
               ['product4', 1], ['product4', 1], ['product6', 1],
               ['product5', 0], ['product2', 1], ['product1', 1],
               ['product6', 0], ['product1', 1], ['product6', 1],
               ['product3', 0], ['product2', 0], ['product6', 0],
               ['product2', 0], ['product1', 0], ['product5', 0],
               ['product6', 0], ['product6', 0], ['product1', 1],
               ['product1', 0], ['product2', 1], ['product1', 1],
               ['product5', 1], ['product3', 1], ['product5', 1],
               ['product5', 1], ['product2', 1], ['product1', 0],
               ['product3', 1], ['product1', 1], ['product5', 1],
               ['product4', 0], ['product4', 0], ['product2', 0]
              ],columns=['products', 'satisfaction'])

product_satisfaction= pd.crosstab(index=df.loc[:,'products'],
        columns=df.loc[:,'satisfaction'], normalize='index')

product_satisfaction
jch
  • 3,600
  • 1
  • 15
  • 17
Gottchaaaaa
  • 1
  • 1
  • Please provide some example data rather than just a description. – BraisLP Jul 01 '22 at 15:02
  • Images of data aren't data. Are we suppose to type that out ourselves? Read this: https://stackoverflow.com/questions/20109391/how-to-make-good-reproducible-pandas-examples – Paul H Jul 01 '22 at 16:56
  • Done, it is a link to the image of the dataframe, as I am not yet allowed to insert images in any other way. – Gottchaaaaa Jul 01 '22 at 16:58

0 Answers0