pythonpandascumulative-frequency

Cumulative Sum using 2 columns


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

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)

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()

Solution

  • You are almost there, Ian!

    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
    

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