如何找到一列的总和？

Question

dict={"asset":["S3","S2","E4","E1","A6","A8"],
"Rank":[1,2,3,4,5,6],"number_of_attributes":[2,1,2,2,1,1],
"number_of_cards":[1,2,2,1,2," "],"cards_plus1":[2,3,3,2,3," "]}

dframe=pd.DataFrame(dict,index=[1,2,3,4,5,6],
columns=["asset","Rank","number_of_attributes","number_of_cards","cards_plus1"])

我想对"cards_plus1"列进行求和。 我该怎么做？ 列cumsum的输出应为： 0 2. 5. 8. 10 十三,

Answer 1

试试这个：

首先，用NAN/P>替换空白值

import pandas as pd
import numpy as np

dict={"asset":["S3","S2","E4","E1","A6","A8"],"Rank":[1,2,3,4,5,6],"number_of_attributes":[2,1,2,2,1,1],
          "number_of_cards":[1,2,2,1,2," "],"cards_plus1":[2,3,3,2,3," "]}

dframe=pd.DataFrame(dict,index=[1,2,3,4,5,6],
columns=["asset","Rank","number_of_attributes","number_of_cards","cards_plus1"])

## replace blank values by nan
print(dframe.replace(r'^\s*$', np.nan, regex=True, inplace=True))

print (dframe)

>>> asset  Rank  number_of_attributes  number_of_cards  cards_plus1
1    S3     1                     2              1.0          2.0
2    S2     2                     1              2.0          3.0
3    E4     3                     2              2.0          3.0
4    E1     4                     2              1.0          2.0
5    A6     5                     1              2.0          3.0
6    A8     6                     1              NaN          NaN

现在cards\u plus1列的数据类型是object-更改为numeric

### convert data type of the cards_plus1 to numeric 
dframe['cards_plus1'] = pd.to_numeric(dframe['cards_plus1'])

现在计算累计总和

### now we can calculate cumsum
dframe['cards_plus1_cumsum'] = dframe['cards_plus1'].cumsum()

print(dframe)

>>>
asset  Rank  number_of_attributes  number_of_cards  cards_plus1  \
1    S3     1                     2              1.0          2.0   
2    S2     2                     1              2.0          3.0   
3    E4     3                     2              2.0          3.0   
4    E1     4                     2              1.0          2.0   
5    A6     5                     1              2.0          3.0   
6    A8     6                     1              NaN          NaN   

   cards_plus1_cumsum  
1                 2.0  
2                 5.0  
3                 8.0  
4                10.0  
5                13.0  
6                 NaN 

<>而不是用NaN替换空白值，你可以用零替换它们，取决于你想要什么。希望这有帮助

Answer 2

i want to start with zero instead of 2.. i want this outup : cards_plus1_cumsum 0 2 5 8 10 13

我们可以在求和之前加一个零：

dframe["cumsum"] = np.pad(dframe["cards_plus1"][:-1].cumsum(), (1, 0), 'constant')