[英]Pandas: sum DataFrame rows for given columns
I have the following DataFrame:我有以下 DataFrame:
In [1]:
df = pd.DataFrame({'a': [1, 2, 3],
'b': [2, 3, 4],
'c': ['dd', 'ee', 'ff'],
'd': [5, 9, 1]})
df
Out [1]:
a b c d
0 1 2 dd 5
1 2 3 ee 9
2 3 4 ff 1
I would like to add a column 'e'
which is the sum of columns 'a'
, 'b'
and 'd'
.我想添加一个
'e'
列,它是'a'
、 'b'
和'd'
列的总和。
Going across forums, I thought something like this would work:跨过论坛,我认为这样的事情会起作用:
df['e'] = df[['a', 'b', 'd']].map(sum)
But it didn't.但它没有。
I would like to know the appropriate operation with the list of columns ['a', 'b', 'd']
and df
as inputs.我想知道使用列列表
['a', 'b', 'd']
和df
作为输入的适当操作。
You can just sum
and set param axis=1
to sum the rows, this will ignore none numeric columns:您可以只求
sum
并设置参数axis=1
来求和行,这将忽略非数字列:
In [91]:
df = pd.DataFrame({'a': [1,2,3], 'b': [2,3,4], 'c':['dd','ee','ff'], 'd':[5,9,1]})
df['e'] = df.sum(axis=1)
df
Out[91]:
a b c d e
0 1 2 dd 5 8
1 2 3 ee 9 14
2 3 4 ff 1 8
If you want to just sum specific columns then you can create a list of the columns and remove the ones you are not interested in:如果您只想对特定列求和,则可以创建列列表并删除您不感兴趣的列:
In [98]:
col_list= list(df)
col_list.remove('d')
col_list
Out[98]:
['a', 'b', 'c']
In [99]:
df['e'] = df[col_list].sum(axis=1)
df
Out[99]:
a b c d e
0 1 2 dd 5 3
1 2 3 ee 9 5
2 3 4 ff 1 7
If you have just a few columns to sum, you can write:如果你只有几列要总结,你可以写:
df['e'] = df['a'] + df['b'] + df['d']
This creates new column e
with the values:这将创建具有以下值的新列
e
:
a b c d e
0 1 2 dd 5 8
1 2 3 ee 9 14
2 3 4 ff 1 8
For longer lists of columns, EdChum's answer is preferred.对于更长的列列表,首选 EdChum 的答案。
Create a list of column names you want to add up.创建要添加的列名称列表。
df['total']=df.loc[:,list_name].sum(axis=1)
If you want the sum for certain rows, specify the rows using ':'如果您想要某些行的总和,请使用“:”指定行
This is a simpler way using iloc to select which columns to sum:这是使用 iloc 选择要求和的列的更简单方法:
df['f']=df.iloc[:,0:2].sum(axis=1)
df['g']=df.iloc[:,[0,1]].sum(axis=1)
df['h']=df.iloc[:,[0,3]].sum(axis=1)
Produces:产生:
a b c d e f g h
0 1 2 dd 5 8 3 3 6
1 2 3 ee 9 14 5 5 11
2 3 4 ff 1 8 7 7 4
I can't find a way to combine a range and specific columns that works eg something like:我找不到组合范围和特定列的方法,例如:
df['i']=df.iloc[:,[[0:2],3]].sum(axis=1)
df['i']=df.iloc[:,[0:2,3]].sum(axis=1)
You can simply pass your dataframe into the following function :您可以简单地将您的数据框传递到以下函数中:
def sum_frame_by_column(frame, new_col_name, list_of_cols_to_sum):
frame[new_col_name] = frame[list_of_cols_to_sum].astype(float).sum(axis=1)
return(frame)
Example :示例:
I have a dataframe (awards_frame) as follows:我有一个数据框(awards_frame)如下:
...and I want to create a new column that shows the sum of awards for each row : ...我想创建一个新列,显示每行的奖励总和:
Usage :用法:
I simply pass my awards_frame into the function, also specifying the name of the new column, and a list of column names that are to be summed:我只是将我的Awards_frame传递给函数,同时指定新列的名称,以及要求和的列名称列表:
sum_frame_by_column(awards_frame, 'award_sum', ['award_1','award_2','award_3'])
Result :结果:
当我按顺序排列列时,以下语法对我有帮助
awards_frame.values[:,1:4].sum(axis =1)
You can use the function aggragate
or agg
:您可以使用
aggragate
或agg
:
df[['a','b','d']].agg('sum', axis=1)
The advantage of agg
is that you can use multiple aggregation functions: agg
的优点是可以使用多个聚合函数:
df[['a','b','d']].agg(['sum', 'prod', 'min', 'max'], axis=1)
Output: Output:
sum prod min max
0 8 10 1 5
1 14 54 2 9
2 8 12 1 4
这里最短和最简单的方法是使用
df.eval('e = a + b + d')
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