让我们按步骤进行：

df2 = df.copy()
df2["fruits"] = df["fruits"].str.split(", ")
df2["places"] = df["places"].str.split(", ")
df2

df3 = df2.explode("fruits").explode("places")
df3.head()

pd.pivot_table(df3, index="fruits", columns="places", aggfunc=len, fill_value=0)
# Or, using crosstab: 
# pd.crosstab(df3["fruits"], df3["places"])

它是留给读者的一个练习，把所有这些步骤放在一起：）

您可以在分割/分解的栏上使用pandas.crosstab：

df2 = (df.apply(lambda c: c.str.split(', ')) # split all columns
         .explode('fruit').explode('places') # explode to new rows
       )
pd.crosstab(df2['fruit'], df2['places'])     # compute crosstab

输出：

places  Boston  Hong Kong  London  Manchester  New York  Tokyo
fruit                                                         
apple        2          1       1           1         2      1
banana       2          1       1           1         2      1
orange       2          1       1           1         2      0
others       0          0       1           0         0      0

一种方法是使用itertools.product创建笛卡尔积，然后使用pd.Series.explode和pd.crosstab

from itertools import product
f = lambda x: list(product(x['places'].split(','), x['fruit'].split(',')))
df['fruit_places'] = df.apply(f, axis=1)
ddf = pd.DataFrame.from_records(df['fruit_places'].explode().values, columns=['places', 'fruit'])

 pd.crosstab(ddf['fruit'], ddf['places'])

def function1(dd:pd.DataFrame):
    return pd.crosstab(dd.fruits,dd.places)
df.applymap(lambda x:x.split(',')).explode(column=['fruits', 'places']).pipe(function1)

places    Boston   London   Manchester  Hong Kong  London  New York  Tokyo
fruits                                                                    
 apple         0        1            0          0       0         0      0
 banana        2        0            1          0       0         0      0
apple          0        0            0          1       0         1      0
banana         0        0            0          0       0         0      1
orange         0        0            0          0       0         1      0
others         0        0            0          0       1         0      0