CODEWITHSUNDEEP
pythonpandasdataframe
tompal18
tompal18

Reputation: 1216

dataframe set true after the time that meets specific condition daily

I need to set condition column to True after the time at which the price was 20 or higher daily like below.

enter image description here

I want to avoid using apply function because I got several millions data. I think apply requires too much time.

Upvotes: 1

Views: 46

Answers (2)

jezrael
jezrael

Reputation: 862661

Use GroupBy.cummax or GroupBy.cumsum per days and compare for greater or equal by Series.ge:

df['datetime'] = pd.to_datetime(df['datetime'])

df['condition'] = df.groupby([df['datetime'].dt.date])['price'].cummax().ge(20)

If need test also per compid:

df['condition'] = df.groupby(['compid', df['datetime'].dt.date])['price'].cummax().ge(20)

print (df)
   compid            datetime  price  condition
0       1 2020-11-06 00:00:00     10      False
1       1 2020-11-06 00:00:10     20       True
2       1 2020-11-06 00:00:20      5       True
3       1 2020-11-07 00:00:00     20       True
4       1 2020-11-07 00:00:10      5       True
5       1 2020-11-07 00:00:20     25       True

Upvotes: 1

Mayank Porwal
Mayank Porwal

Reputation: 34056

You can use np.where with df.cumsum:

In [1306]: import numpy as np

In [1307]: df['condition'] = np.where(df.groupby(df.datetime.dt.date).price.cumsum().ge(20), 'TRUE', 'FALSE')

In [1308]: df
Out[1308]: 
   compid            datetime  price condition
0       1 2020-11-06 00:00:00     10     FALSE
1       1 2020-11-06 00:00:10     20      TRUE
2       1 2020-11-06 00:00:20      5      TRUE
3       1 2020-11-07 00:00:00     20      TRUE
4       1 2020-11-07 00:00:10      5      TRUE
5       1 2020-11-07 00:00:20     25      TRUE

OR, if you need bool values in condition column, do this:

In [1309]: df['condition'] = np.where(df.groupby(df.datetime.dt.date).price.cumsum().ge(20), True, False)

In [1310]: df
Out[1310]: 
   compid            datetime  price  condition
0       1 2020-11-06 00:00:00     10      False
1       1 2020-11-06 00:00:10     20       True
2       1 2020-11-06 00:00:20      5       True
3       1 2020-11-07 00:00:00     20       True
4       1 2020-11-07 00:00:10      5       True
5       1 2020-11-07 00:00:20     25       True

Upvotes: 1

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