使用2列的累计总和 [英] Cumulative Sum using 2 columns

问题描述

我正在尝试创建一个使用2列进行累加总和的列，请参见示例:@Faith Akici

I am trying to create a column that does a cumulative sum using 2 columns , please see example of what I am trying to do :@Faith Akici

  index lodgement_year  words       sum  cum_sum
    0   2000            the          14     14
    1   2000            australia    10     10
    2   2000            word         12     12
    3   2000            brand         8      8
    4   2000            fresh         5      5
    5   2001            the           8      22
    6   2001            australia     3      13
    7   2001            banana        1       1
    8   2001            brand         7      15
    9   2001            fresh         1       6

我使用了下面的代码，但是我的计算机不断崩溃，我不确定代码还是计算机.任何帮助将不胜感激:

I have used the code below , however my computer keep crashing , I am unsure if is the code or the computer. Any help will be greatly appreciated:

   df_2['cumsum']= df_2.groupby('lodgement_year')['words'].transform(pd.Series.cumsum)

更新；我还使用了下面的代码，它起作用了，并说退出代码0.但是有一些警告.

Update ; I have also used the code below , it worked and said exit code 0 . However with some warnings.

df_2['cum_sum'] =df_2.groupby(['words'])['count'].cumsum()

推荐答案

伊恩，您快到了！

cumsum()方法计算Pandas列的累积和.您正在寻找应用于分组的words的那个.因此:

cumsum() method calculates the cumulative sum of a Pandas column. You are looking for that applied to the grouped words. Therefore:

In [303]: df_2['cumsum'] = df_2.groupby(['words'])['sum'].cumsum()

In [304]: df_2
Out[304]: 
   index  lodgement_year      words  sum  cum_sum  cumsum
0      0            2000        the   14       14      14
1      1            2000  australia   10       10      10
2      2            2000       word   12       12      12
3      3            2000      brand    8        8       8
4      4            2000      fresh    5        5       5
5      5            2001        the    8       22      22
6      6            2001  australia    3       13      13
7      7            2001     banana    1        1       1
8      8            2001      brand    7       15      15
9      9            2001      fresh    1        6       6

如果在您更大的数据集上失败了，请发表评论，我们将致力于此版本的更准确的版本.

Please comment if this fails on your bigger data set, and we'll work on a possibly more accurate version of this.

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