Python 在熊猫中合并和减去 DataFrame 列？
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Merging and subtracting DataFrame columns in pandas?
提问by user1566200
I have a pandas DataFrame, something like:
我有一个 Pandas DataFrame，类似于：
col1 col2 col3 col5
NaN 1 2 8
2 NaN 4 8
4 NaN 4 8
I want to do two things:
我想做两件事：
1) Merge columns 1 and 2:
1) 合并第 1 列和第 2 列：
newcol1 col3 col5
1 2 8
2 4 8
4 4 8
I have tried using .concat, but that just concatenates the rows. Doesn't seem like I can use standard +operators with NaN values.
我试过使用 .concat，但这只是连接行。似乎我不能使用+带有 NaN 值的标准运算符。
2) Subtract column 5 from new column 1 and column 3, so I end up with:
2) 从新的第 1 列和第 3 列中减去第 5 列，所以我最终得到：
newcol1 col3
-7 -6
-6 -4
-4 -4
Tried doing it this way:
尝试这样做：
dataframe[['newcol1', 'col2']] - dataframe['col5']
and
和
dataframe[['newcol1', 'col2']].subtract(dataframe['col5'])
but neither works.
但两者都不起作用。
回答by EdChum
In [58]:
df['newcol'] = df[['col1','col2']].sum(axis=1) - df['col5']
df['col3'] = df['col3'] - df['col5']
df
Out[58]:
col1 col2 col3 col5 newcol
0 NaN 1 -6 8 -7
1 2 NaN -4 8 -6
2 4 NaN -4 8 -4
You can then drop col1 and col2:
然后你可以删除 col1 和 col2：
In [59]:
df = df.drop(['col1','col2'],axis=1)
df
Out[59]:
col3 col5 newcol
0 -6 8 -7
1 -4 8 -6
2 -4 8 -4
回答by Alex Riley
To get the new column, you could use fillna(or combine_first):
要获取新列，您可以使用fillna（或combine_first）：
df['newcol1'] = df.col1.fillna(df.col2)
Then for the subtraction, use suband specify axis=0since we want to consider the row indices when matching up labels (not the column indices as is the default):
然后对于减法，使用sub并指定，axis=0因为我们要在匹配标签时考虑行索引（而不是默认的列索引）：
>>> df[['newcol1', 'col3']].sub(df['col5'], axis=0)
newcol1 col3
0 -7 -6
1 -6 -4
2 -4 -4
回答by Zero
Here's one approach.
这是一种方法。
You could create newcol1by sum(axis=1)
你可以newcol1通过创建sum(axis=1)
In [256]: df['newcol1'] = df[['col1', 'col2']].sum(axis=1)
In [257]: df
Out[257]:
col1 col2 col3 col5 newcol1
0 NaN 1 2 8 1
1 2 NaN 4 8 2
2 4 NaN 4 8 4
Then use df.sub()on axis=0
然后用df.sub()在axis=0
In [258]: df[['newcol1', 'col3']].sub(df['col5'], axis=0)
Out[258]:
newcol1 col3
0 -7 -6
1 -6 -4
2 -4 -4
