Reputation: 51
I have pandas dataframe with a non-continuous date index (missing are weekends and holidays). I want to add column which would contain number of days until next day off.
Here is code generating example dataframe with desired values in till_day_off column:
import pandas as pd
df = pd.DataFrame(index=pd.date_range(start="2022-06-06", periods=15))
df["day_of_week"] = df.index.dayofweek # adding column with number of day in a week
df = df[(df.day_of_week < 5)] # remove weekends
df = df.drop(index="2022-06-15") # remove Wednesday in second week
df["till_day_off"] = [5,4,3,2,1,2,1,2,1,1] # desired values, end of column is treated as day off
Resulting dataframe:
day_of_week | till_day_off | |
---|---|---|
2022-06-06 | 0 | 5 |
2022-06-07 | 1 | 4 |
2022-06-08 | 2 | 3 |
2022-06-09 | 3 | 2 |
2022-06-10 | 4 | 1 |
2022-06-13 | 0 | 2 |
2022-06-14 | 1 | 1 |
2022-06-16 | 3 | 2 |
2022-06-17 | 4 | 1 |
2022-06-20 | 0 | 1 |
Real dataframe has over 7_000 rows so obviously I am trying to avoid iteration over rows. Any idea how to tackle the issue?
Upvotes: 3
Views: 439
Reputation: 59519
Create a DataFrame of the missing dates, then use a merge_asof
to match with the closest one in the future and calculate the time until that day off.
Here I assume days off are just missing dates, but this extends to the case where you have an explicit list of dates you want to use.
import pandas as pd
# DataFrame of missing dates, e.g. days off.
df1 = pd.DataFrame({'day_off': pd.date_range(df.index.min(), df.index.max()+pd.offsets.DateOffset(days=1), freq='D')})
df1 = df1[~df1['day_off'].isin(df.index)]
df = pd.merge_asof(df, df1, left_index=True, right_on='day_off', direction='forward')
df['till_day_off'] = (df['day_off'] - df.index).dt.days
print(df)
day_of_week day_off till_day_off
2022-06-06 0 2022-06-11 5
2022-06-07 1 2022-06-11 4
2022-06-08 2 2022-06-11 3
2022-06-09 3 2022-06-11 2
2022-06-10 4 2022-06-11 1
2022-06-13 0 2022-06-15 2
2022-06-14 1 2022-06-15 1
2022-06-16 3 2022-06-18 2
2022-06-17 4 2022-06-18 1
2022-06-20 0 2022-06-21 1
Upvotes: 1
Reputation: 260410
Assuming a sorted input (if not, sort it by days), you can use a mask to identify consecutive days and use it to group them and compute a cumcount:
mask = (-df.index.to_series().diff(-1)).eq('1d').iloc[::-1]
# reversing the Series to count until (not since) the value
df['till_day_off'] = mask.groupby((~mask).cumsum()).cumcount().add(1)
output:
day_of_week till_day_off
2022-06-06 0 5
2022-06-07 1 4
2022-06-08 2 3
2022-06-09 3 2
2022-06-10 4 1
2022-06-13 0 2
2022-06-14 1 1
2022-06-16 3 2
2022-06-17 4 1
2022-06-20 0 1
intermediates:
mask
2022-06-20 False
2022-06-17 False
2022-06-16 True
2022-06-14 False
2022-06-13 True
2022-06-10 False
2022-06-09 True
2022-06-08 True
2022-06-07 True
2022-06-06 True
dtype: bool
(~mask).cumsum()
2022-06-20 1
2022-06-17 2
2022-06-16 2
2022-06-14 3
2022-06-13 3
2022-06-10 4
2022-06-09 4
2022-06-08 4
2022-06-07 4
2022-06-06 4
dtype: int64
Upvotes: 3