使用熊猫合并相似数据

Question

如何将类似“推荐”之类的数据合并为一个值？

df['Why you choose us'].str.lower().value_counts()

location                           35
recommendation                     23
recommedation                       8
confort                             7
availability                        4
reconmmendation                     3
facilities                          3

Answer 1

print（df）

            reason  count
0         location     35
1   recommendation     23
2    recommedation      8
3          confort      7
4     availability      4
5  reconmmendation      3
6       facilities      3

.groupby()，部分字符串。.transform()，同时找到sum

df['groupcount']=df.groupby(df.reason.str[0:4])['count'].transform('sum')



          reason  count  groupcount
0         location     35          35
1   recommendation     23          34
2    recommedation      8          34
3          confort      7           7
4     availability      4           4
5  reconmmendation      3          34
6       facilities      3           3

如果需要并排查看字符串和部分字符串。试试

df=df.assign(groupname=df.reason.str[0:4])
df['groupcount']=df.groupby(df.reason.str[0:4])['count'].transform('sum')
print(df)


      reason  count groupname  groupcount
0         location     35      loca          35
1   recommendation     23      reco          34
2    recommedation      8      reco          34
3          confort      7      conf           7
4     availability      4      avai           4
5  reconmmendation      3      reco          34
6       facilities      3      faci           3

如果您像csv中一样连续有多个项目，然后

#Read csv
df=pd.read_csv(r'path')
#Create another column which is a list of values 'Why you choose us' in each row
df['Why you choose us']=(df['Why you choose us'].str.lower().fillna('no comment given')).str.split(',')
#Explode group to ensure each unique reason is int its own row but with all the otehr attrutes intact
df=df.explode('Why you choose us')
#remove any white spaces before values in the column group and value_counts
df['Why you choose us'].str.strip().value_counts()
print(df['Why you choose us'].str.strip().value_counts())

location            48
no comment given    34
recommendation      25
confort              8
facilities           8
recommedation        8
price                7
availability         6
reputation           5
reconmmendation      3
internet             3
ac                   3
breakfast            3
tranquility          2
cleanliness          2
aveilable            1
costumer service     1
pool                 1
comfort              1
search engine        1
Name: group, dtype: int64