Python[pandas]：通过另一个数据帧的索引选择某些行
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Python[pandas]: Select certain rows by index of another dataframe
提问by giupardeb
I have a dataframe and I would select only rows that contain index value into df1.index.
我有一个数据框，我只会选择包含索引值的行到 df1.index 中。
for Example:
例如：
In [96]: df
Out[96]:
A B C D
1 1 4 9 1
2 4 5 0 2
3 5 5 1 0
22 1 3 9 6
and these indexes
和这些索引
In[96]:df1.index
Out[96]:
Int64Index([ 1, 3, 4, 5, 6, 7, 22, 28, 29, 32,], dtype='int64', length=253)
I would like this output:
我想要这个输出：
In [96]: df
Out[96]:
A B C D
1 1 4 9 1
3 5 5 1 0
22 1 3 9 6
thanks
谢谢
回答by jezrael
Use isin:
使用isin：
df = df[df.index.isin(df1.index)]
Or get all intersectioned indices and select by loc:
或者获取所有交叉索引并选择loc：
df = df.loc[df.index & df1.index]
df = df.loc[np.intersect1d(df.index, df1.index)]
df = df.loc[df.index.intersection(df1.index)]
print (df)
A B C D
1 1 4 9 1
3 5 5 1 0
22 1 3 9 6
EDIT:
编辑：
I tried solution: df = df.loc[df1.index]. Do you think that this solution is correct?
我尝试了解决方案：df = df.loc[df1.index]。你认为这个解决方案正确吗？
Solution is incorrect:
解决方法不正确：
df = df.loc[df1.index]
print (df)
A B C D
1 1.0 4.0 9.0 1.0
3 5.0 5.0 1.0 0.0
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
6 NaN NaN NaN NaN
7 NaN NaN NaN NaN
22 1.0 3.0 9.0 6.0
28 NaN NaN NaN NaN
29 NaN NaN NaN NaN
32 NaN NaN NaN NaN
C:/Dropbox/work-joy/so/_t/t.py:23: FutureWarning:
Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative.
See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike
print (df)
回答by Spcogg the second
Passing the index to the row indexer/slicer of .loc now works, you just need to make sure to specify the columns as well, i.e.:
将索引传递给 .loc 的行索引器/切片器现在可以工作，您只需要确保也指定列，即：
df = df.loc[df1.index, :] # works
and NOT
并不是
df = df.loc[df1.index] # won't work
IMO This is more neater/consistent with the expected usage of .loc
IMO 这与 .loc 的预期用法更整洁/一致
