python - 用 pandas 下一列中存在的记录替换匹配的记录

Question

我有两个数据框A，B

“A”数据框由1列组成(列名称:str1)

“B”数据框由2列组成(列名称:m2，m3)

我正在比较“A”的 str1 与“B”的 m2

我想要的是当“str1”与“m2”匹配时我希望“str1”被“m3”替换。以下是我的数据，

    data frame A.str1="gmaps","facebook","gmail","linkedin"
    data frame B.m1  ="gmaps","oracle","gmail","intel"
    data frame B.m2  ="Google","xyz","Google","nvidia"

我想要输出为

    data frame A.str1="Google","facebook","Google","linkedin"

因此，只要 A.str1 与 B.m1 匹配，A.str1 就会被 B.m2 替换提前致谢

Answer 1

我认为您需要创建助手Series和replace或使用map ，但它会为不匹配的列创建 NaN，因此这是必要的 fillna或combine_first :

df1 = pd.DataFrame({'str1': ["gmaps","facebook","gmail","linkedin"]})

df2 = pd.DataFrame({'m1': ["gmaps","oracle","gmail","intel"],
                    'm2': ["Google","xyz","Google","nvidia"]})

s = df2.set_index('m1')['m2']
print (s)
m1
gmaps     Google
oracle       xyz
gmail     Google
intel     nvidia
Name: m2, dtype: object

df1['str1'] = df1['str1'].replace(s)
#alternatives
#df1['str1'] = df1['str1'].map(s).fillna(df1['str1'])
#df1['str1'] = df1['str1'].map(s).combine_first(df1['str1'])
print (df1)

       str1
0    Google
1  facebook
2    Google
3  linkedin