使用来自另一个数据框的数据更新 pandas 数据框 [英] Update a pandas dataframe with data from another dataframe

问题描述

我有两个类似的DataFrame.

I've got two similar DataFrames.

df1.head()
        1        2        3      4
3234    Lorum    Ipsum    Foo    Bar
8839    NaN      NaN      NaN    NaN
9911    Lorum    Ipsum    Bar    Foo
2256    NaN      NaN      NaN    NaN

df2.head()
        1        3        4
8839    Lorum    Ipsum    Foo
2256    Lorum    Ipsum    Bar

我想基于相同的索引和列合并/更新两者以更新NaN值.

I'd like to merge/update the two based on same index and column to update the NaN values.

理想的结果:

df3.head()
        1        2        3      4
3234    Lorum    Ipsum    Foo    Bar
8839    Lorum    NaN      Ipsum  Foo
9911    Lorum    Ipsum    Bar    Foo
2256    Lorum    NaN      Ipsum  Bar

df2并不像df1那样包含所有列，但是它确实包含匹配的列并且具有匹配的索引.

df2 doesn't contain all of the columns as df1 but the columns it does contain match and it has matching indexes.

我已经尝试过了:

df3 = df1.update(df2)

但是还没有成功.我一直在查看文档，认为pd.merge或pd.concat可能会有所帮助，但我有些困惑.

But haven't had any success. I've been looking at the docs and think pd.merge or pd.concat may help but I'm a bit confused.

谢谢

推荐答案

您可以使用 reindex :

You can use combine_first with reindex:

df3 = df2.combine_first(df1).reindex(df1.index)
print (df3)
          1      2      3    4
3234  Lorum  Ipsum    Foo  Bar
8839  Lorum    NaN  Ipsum  Foo
9911  Lorum  Ipsum    Bar  Foo
2256  Lorum    NaN  Ipsum  Bar

或使用您的解决方案，但 update 在原位工作，因此，如果将其分配给变量，则返回None:

Or use your solution, but update working inplace, so if assign to variable it return None:

df1.update(df2)
print (df1)
          1      2      3    4
3234  Lorum  Ipsum    Foo  Bar
8839  Lorum    NaN  Ipsum  Foo
9911  Lorum  Ipsum    Bar  Foo
2256  Lorum    NaN  Ipsum  Bar

print (df1.update(df2))
None

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