问题描述
我有以下3个数据框
df1
id first_name surname state
1
88
190
2509
....
df2
id given_name surname state street_num
17 John Doe NY 5
88 Tom Murphy CA 423
190 Dave Casey KY 250
....
df3
id first_name family_name state car
1 John Woods NY ford
74 Tom Kite FL vw
2509 Mike Johnson KY toyota
df1中的某些ID在df2中，而其他在df3中。 df2和df3中也有ID，而df1中没有。
Some id's from df1 are in df2 and others are in df3. There are also id's in df2 and df3 that are not in df1.
编辑：：df1中也有一些ID不在df2或df3中。
there are also some id's in df1 that re not in either df2 or df3.
我想用包含id的数据框中的值填充df1中的列。但是，我不想所有的列（所以我认为合并是不合适的）。我尝试使用isin函数，但是那样我无法单独更新记录并出现错误。这是我使用isin的尝试：
I want to fill the columns in df1 with the values in the dataframe containing the id. However, I do not want all columns (so i think merge is not suitable). I have tried to use the isin function but that way I could not update records individually and got an error. This was my attempt using isin:
df1.loc[df1.index.isin(df2.index), 'first_name'] = df2.given_name
是否有一种简便的方法，而无需遍历数据帧来检查索引是否匹配？
Is there an easy way to do this without iterating through the dataframes checking if index matches?
推荐答案
我认为您首先需要重命名您的列才能对齐，然后过滤以 df1.index 和 df1.columns ：
I think you first need to rename your columns to align the DataFrames in concat and then reindex to filter by df1.index and df1.columns:
df21 = df2.rename(columns={'given_name':'first_name'})
df31 = df3.rename(columns={'family_name':'surname'})
df = pd.concat([df21, df31]).reindex(index=df1.index, columns=df1.columns)
print (df)
first_name surname state
d
1 John Woods NY
88 Tom Murphy CA
190 Dave Casey KY
2509 Mike Johnson KY
编辑：如果需要仅索引：
If need intersection of indices only:
df4 = pd.concat([df21, df31])
df = df4.reindex(index=df1.index.intersection(df4.index), columns=df1.columns)
这篇关于根据索引使Pandas Dataframe列等于另一个Dataframe中的值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！