How to count subgroups of categorical data in a pandas Dataframe?

Question

I have the following pandas dataframe:

import pandas as pd
import numpy as np
df = pd.DataFrame({"shops": ["shop1", "shop2", "shop3", "shop4", "shop5", "shop6"], "franchise" : ["franchise_A", "franchise_A", "franchise_A", "franchise_A", "franchise_B", "franchise_B"],"items" : ["dog", "cat", "dog", "dog", "bird", "fish"]})
df = df[["shops", "franchise", "items"]]
print(df)

   shops    franchise items
0  shop1  franchise_A   dog
1  shop2  franchise_A   cat
2  shop3  franchise_A   dog
3  shop4  franchise_A   dog
4  shop5  franchise_B  bird
5  shop6  franchise_B  fish

So, each row is a unique sample shop1, shop2, etc. whereby each sample belongs to a subgroup franchise_A, franchise_B, franchise_C, etc. In the items column, there are only four categorical values possible: dog, cat, fish, bird. My motivation is to create a barplot of the number of dog, cat, fish, bird for each "franchise".

I would like the output to be

franchise        dogs    cats    birds    fish
franchise_A      3       1       0        0
franchise_B      0       0       1        1

I believe I first have to use groupby(), e.g.

df.groupby("franchise").count()
             shops  items
franchise                
franchise_A      4      4
franchise_B      2      2

But I'm not sure how I count the number of items for each franchise.

Answer 1

You can use value_counts with unstack, thanks Nickil Maveli:

from collections import Counter

print (df.groupby("franchise")['items'].value_counts().unstack(fill_value=0))
items        bird  cat  dog  fish
franchise                        
franchise_A     0    1    3     0
franchise_B     1    0    0     1

Another solutions with crosstab and pivot_table:

print (pd.crosstab(df["franchise"], df['items']))
items        bird  cat  dog  fish
franchise                        
franchise_A     0    1    3     0
franchise_B     1    0    0     1

---

print (df.pivot_table(index="franchise", columns='items', aggfunc='size', fill_value=0))
items        bird  cat  dog  fish
franchise                        
franchise_A     0    1    3     0
franchise_B     1    0    0     1

Answer 2

You could include the items column in the groupby, then use size.

>>> df.groupby(['franchise', 'items']).size().unstack(fill_value=0)

items        bird  cat  dog  fish
franchise                        
franchise_A     0    1    3     0
franchise_B     1    0    0     1

---

(Rough) Benchmark

%timeit df.groupby(['franchise', 'items']).size().unstack(fill_value=0)
100 loops, best of 3: 2.73 ms per loop

%timeit (df.groupby("franchise")['items'].apply(Counter).unstack(fill_value=0).astype(int))
100 loops, best of 3: 4.18 ms per loop

%timeit df.groupby('franchise')['items'].value_counts().unstack(fill_value=0)
100 loops, best of 3: 2.71 ms per loop