CODEWITHSUNDEEP
pythonpandascumulative-sum
Regi Mathew
Regi Mathew

Reputation: 2893

reset cumulative sum based on another column

There are many queries and answers related to conditional cumulative sum (Cumsum Reset based on a condition in Pandas, Reset Cumulative sum base on condition Pandas, Cumsum Reset based on a condition in Pandas). But I am not able to solve the problem I face. Below is the part of data I have and the requiremet is to keep a count of change in 'type' and corresponding cumulative sum.

  type sale
    y   10   
    y   20   
    y    5   
    n   30   
    n   20   
    n    5   
    y   10   
    y   40   
    y   15

My requirement is to get a serial count of change in type and cumulative sale as below.

   type sale tp_cum cum_sale
    y   10    1      10
    y   20    1      30
    y    5    1      35
    n   30    2      30
    n   20    2      50 
    n    5    2      55
    y   10    3      10
    y   40    3      50
    y   15    3      65

I tried various modifications of the code below but is not exactly meeting the requirements. Please help.

sales['cum_sale'] = stock.groupby('type')['sale'].cumsum()

Dataframe:

df = pd.DataFrame([["y",10 ], 
["y",20  ],
["y",5  ],
["n",30   ],
["n",20   ],
["n",5 ],
["y",10  ], 
["y",40  ],
["y",15 ]],columns = ["type","sale"])

Upvotes: 3

Views: 1425

Answers (1)

Let's try
Let's try

Reputation: 1058

Here is an option, you first create the tp_cum column and then cumsum()

import pandas as pd
import numpy as np

df = pd.DataFrame([["y",10 ], 
["y",20  ],
["y",5  ],
["n",30   ],
["n",20   ],
["n",5 ],
["y",10  ], 
["y",40  ],
["y",15 ]],columns = ["type","sale"])

df["type2"] = np.cumsum((df["type"] != df["type"].shift(1)))
df["cum_sale"] = df[["sale","type2"]].groupby("type2").cumsum()
df

Output:

    type    sale    type2  cum_sale
0   y       10      1      10
1   y       20      1      30
2   y       5       1      35
3   n       30      2      30
4   n       20      2      50
5   n       5       2      55
6   y       10      3      10
7   y       40      3      50
8   y       15      3      65

Upvotes: 4

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