熊猫: 把多列合并成一列，而不是最后一列

如果我有一个类似的数据框架

Apples   Bananas   Grapes   Kiwis
2        3         nan      1
1        3         7        nan
nan      nan       2        3

我想添加一个这样的专栏

Apples   Bananas   Grapes   Kiwis   Fruit Total
2        3         nan      1        6
1        3         7        nan      11
nan      nan       2        3        5

我想您可以使用 df['Apples'] + df['Bananas']等等，但是我的实际数据框架要比这个大得多。我希望像 df['Fruit Total']=df[-4:-1].sum这样的公式可以在一行代码中完成这个任务。但是没有用。有没有办法在不显式总结所有列的情况下做到这一点？

172934 次浏览
小开
最佳答案

You can first select by iloc and then sum:

df['Fruit Total']= df.iloc[:, -4:-1].sum(axis=1)
print (df)
Apples  Bananas  Grapes  Kiwis  Fruit Total
0     2.0      3.0     NaN    1.0          5.0
1     1.0      3.0     7.0    NaN         11.0
2     NaN      NaN     2.0    3.0          2.0

For sum all columns use:

df['Fruit Total']= df.sum(axis=1)
小开

Using df['Fruit Total']= df.iloc[:, -4:-1].sum(axis=1) over your original df won't add the last column ('Kiwis'), you should use df.iloc[:, -4:] instead to select all columns:

print(df)
Apples  Bananas  Grapes  Kiwis
0     2.0      3.0     NaN    1.0
1     1.0      3.0     7.0    NaN
2     NaN      NaN     2.0    3.0


df['Fruit Total']=df.iloc[:,-4:].sum(axis=1)


print(df)
Apples  Bananas  Grapes  Kiwis  Fruit Total
0     2.0      3.0     NaN    1.0          6.0
1     1.0      3.0     7.0    NaN         11.0
2     NaN      NaN     2.0    3.0          5.0
小开

It is possible to do it without knowing the number of columns and even without iloc:

print(df)
Apples  Bananas  Grapes  Kiwis
0     2.0      3.0     NaN    1.0
1     1.0      3.0     7.0    NaN
2     NaN      NaN     2.0    3.0


cols_to_sum = df.columns[ : df.shape[1]-1]


df['Fruit Total'] = df[cols_to_sum].sum(axis=1)


print(df)
Apples   Bananas Grapes  Kiwis   Fruit Total
0  2.0      3.0     NaN     1.0     5.0
1  1.0      3.0     7.0     NaN     11.0
2  NaN      NaN     2.0     3.0     5.0
小开

I want to build on Ramon's answer if you want to come up with the total without knowing the shape/size of the dataframe. I will use his answer below but fix one item that didn't include the last column for the total. I have removed the -1 from the shape:

cols_to_sum = df.columns[ : df.shape[1]-1]

To this:

cols_to_sum = df.columns[ : df.shape[1]]

print(df)
Apples  Bananas  Grapes  Kiwis
0     2.0      3.0     NaN    1.0
1     1.0      3.0     7.0    NaN
2     NaN      NaN     2.0    3.0


cols_to_sum = df.columns[ : df.shape[1]]


df['Fruit Total'] = df[cols_to_sum].sum(axis=1)


print(df)
Apples   Bananas Grapes  Kiwis   Fruit Total
0  2.0      3.0     NaN     1.0     6.0
1  1.0      3.0     7.0     NaN     11.0
2  NaN      NaN     2.0     3.0     5.0

Which then gives you the correct total without skipping the last column.

小开

This may be helpful for beginners, so for the sake of completeness, if you know the column names (e.g. they are in a list), you can use:

column_names = ['Apples', 'Bananas', 'Grapes', 'Kiwis']
df['Fruit Total']= df[column_names].sum(axis=1)

This gives you flexibility about which columns you use as you simply have to manipulate the list column_names and you can do things like pick only columns with the letter 'a' in their name. Another benefit of this is that it's easier for humans to understand what they are doing through column names. Combine this with list(df.columns) to get the column names in a list format. Thus, if you want to drop the last column, all you have to do is:

column_names = list(df.columns)
df['Fruit Total']= df[column_names[:-1]].sum(axis=1)

提交答案