Python Pandas：对给定列的 DataFrame 行求和
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/25748683/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas: sum DataFrame rows for given columns
提问by Colonel Beauvel
I have the following DataFrame:
我有以下数据帧：
In [1]:
import pandas as pd
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 column 'a', 'b'and 'd'.
我想增加一列'e'是列的总和'a'，'b'和'd'。
Going across forums, I thought something like this would work:
浏览论坛，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 dfas inputs.
我想知道列列表['a','b','d']和df作为输入的适当操作。
采纳答案by EdChum
You can just sumand set param axis=1to sum the rows, this will ignore none numeric columns:
您可以只sum设置 paramaxis=1对行求和，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
回答by Alex Riley
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 ewith the values:
这将创建e具有以下值的新列：
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。
回答by smartse
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 e.g. 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)
回答by Bibin Johny
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
回答by Cybernetic
You can simply pass your dataframeinto 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_frameinto the function, also specifying the nameof the new column, and a listof 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：
回答by makarand kulkarni
Following syntax helped me when I have columns in sequence
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)
