如何计算熊猫数据框中的分类数据子组？

Question

我有以下熊猫数据框：

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

这样，每一行是一个独特的样品shop1，shop2等，由此每个样品属于一个子组franchise_A，franchise_B，franchise_C等。在items柱中，只有四个分类值可能是：dog，cat，fish，bird。我的动机是要创建的数量的barplot dog，cat，fish，bird每个“特许经营”。

我希望输出是

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

我相信我首先必须使用groupby()，例如

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

但是我不确定如何计算每个专营权的商品数量。

Answer 1

你可以用value_counts与unstack感谢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

与其他解决方案crosstab和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