基于列间部分字符串匹配的连接数据帧

df1 和 df2 pd.Series.isin . 要对齐电影字符串的格式,首先需要连接电影和年份 :

s = df1['movie'] + ' (' + df1['year'].astype(str) + ')'

res = df2[df2['FILM'].isin(s)]

print(res)

               FILM  VOTES
4  Max Steel (2016)    560

1. 获取部分匹配的行索引: FILM.startswith(title) 或 FILM.contains(title)

   df1[ df1.movie.apply( lambda title: df2.FILM.str.startswith(title) ).any(1) ]

   df1[ df1['movie'].apply(lambda title: df2['FILM'].str.contains(title)).any(1) ]

     movie      year      ratings
106  Max Steel  2016      3.5

2. 或者, merge() 如果将复合字符串列df2['FILM']转换为它的两个组件列 movie_title (year) .

.

# see code at bottom to recreate your dataframes
df2[['movie','year']] = df2.FILM.str.extract('([^\(]*) \(([0-9]*)\)')
# reorder columns and drop 'FILM' now we have its subfields 'movie','year'
df2 = df2[['movie','year','Votes']]
df2['year'] = df2['year'].astype(int)

df2.merge(df1)
       movie  year  Votes  ratings
0  Max Steel  2016    560      3.5

import pandas as pd
from pandas.compat import StringIO

dat1 = """movie           year   ratings
108  Mechanic: Resurrection   2016     4.0
206  Warcraft                 2016     4.0
106  Max Steel                2016     3.5
107  Me Before You            2016     4.5"""

dat2 = """FILM                   Votes
0   Avengers: Age of Ultron (2015)   4170
1   Cinderella (2015)                 950
2   Ant-Man (2015)                   3000
3   Do You Believe? (2015)            350
4   Max Steel (2016)                  560"""

df1 = pd.read_csv(StringIO(dat1), sep='\s{2,}', engine='python', index_col=0)
df2 = pd.read_csv(StringIO(dat2), sep='\s{2,}', engine='python')

smci 的选项1就快到了,下面这些对我很有用:

df1['Votes'] = ''
df1['Votes']=df1['movie'].apply(lambda title: df2[df2['FILM'].str.startswith(title)]['Votes'].any(0))

在df1中创建投票列

lambda查找df2,选择df2中电影以电影标题开头的所有行

选择df2的结果子集的投票列