Is there an equivalent of rolling_apply
in pandas that applies function to the cumulative values of a series rather than the rolling values? I realize cumsum
, cumprod
, cummax
, and cummin
exist, but I'd like to apply a custom function.
Pandas DataFrame cumprod() Method The cumprod() method returns a DataFrame with the cumulative product for each row.
The cumsum() method returns a DataFrame with the cumulative sum for each row. The cumsum() method goes through the values in the DataFrame, from the top, row by row, adding the values with the value from the previous row, ending up with a DataFrame where the last row contains the sum of all values for each column.
apply() is used to apply a function along an axis of the DataFrame or on values of Series. applymap() is used to apply a function to a DataFrame elementwise. map() is used to substitute each value in a Series with another value.
You can use pd.expanding_apply
. Below is a simple example which only really does a cumulative sum, but you could write whatever function you wanted for it.
import pandas as pd
df = pd.DataFrame({'data':[10*i for i in range(0,10)]})
def sum_(x):
return sum(x)
df['example'] = pd.expanding_apply(df['data'], sum_)
print(df)
# data example
#0 0 0
#1 10 10
#2 20 30
#3 30 60
#4 40 100
#5 50 150
#6 60 210
#7 70 280
#8 80 360
#9 90 450
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