I have a large dataframe and I need a new column sig
with values 0 or 1.
The conditions:
Add value = 1 in 3rd row of each day starting at 08:30, if data
in row 3 > data
row 2 > data
row 1, else 0
Limitations: In the original dataframe the intervals of the seconds in the timestamps are not equal, so you can't go by time intervals. The amount of rows per day varies.
Sample dataframe ( I don't know how to randomize seconds, so the intervals here are equal, also the amount of rows are equal):
import pandas as pd
import numpy as np
pd.set_option('display.max_rows', 500)
np.random.seed(100)
dates = pd.date_range("2022.01.01", "2022.01.31", freq="s")
dates=dates[:-1]
df = pd.DataFrame({'date':dates,
'data':np.random.randint(low=0, high=100, size=len(dates)).tolist()})
df['_date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
df = df.loc[(df._date.dt.hour == 8) & (df._date.dt.minute == 30) & ((df._date.dt.second >= 0) & (df._date.dt.second <= 10))].head(30)
df.drop(['_date'], axis=1, inplace=True)
data
date
2022-01-01 08:30:00 14
2022-01-01 08:30:01 27
2022-01-01 08:30:02 33
2022-01-01 08:30:03 77
2022-01-01 08:30:04 66
2022-01-01 08:30:05 60
2022-01-01 08:30:06 72
2022-01-01 08:30:07 21
2022-01-01 08:30:08 70
2022-01-01 08:30:09 60
2022-01-01 08:30:10 76
2022-01-02 08:30:00 13
2022-01-02 08:30:01 73
2022-01-02 08:30:02 71
2022-01-02 08:30:03 78
2022-01-02 08:30:04 50
2022-01-02 08:30:05 80
2022-01-02 08:30:06 48
2022-01-02 08:30:07 24
2022-01-02 08:30:08 29
2022-01-02 08:30:09 43
2022-01-02 08:30:10 75
2022-01-03 08:30:00 11
2022-01-03 08:30:01 52
How to accomplish this?
Desired outcome:
data sig
date
2022-01-01 08:30:00 14 0
2022-01-01 08:30:01 27 0
2022-01-01 08:30:02 33 1
2022-01-01 08:30:03 77 0
2022-01-01 08:30:04 66 0
2022-01-01 08:30:05 60 0
2022-01-01 08:30:06 72 0
2022-01-01 08:30:07 21 0
2022-01-01 08:30:08 70 0
2022-01-01 08:30:09 60 0
2022-01-01 08:30:10 76 0
2022-01-02 08:30:00 13 0
2022-01-02 08:30:01 73 0
2022-01-02 08:30:02 71 0
2022-01-02 08:30:03 78 0
2022-01-02 08:30:04 50 0
2022-01-02 08:30:05 80 0
2022-01-02 08:30:06 48 0
2022-01-02 08:30:07 24 0
2022-01-02 08:30:08 29 0
2022-01-02 08:30:09 43 0
2022-01-02 08:30:10 75 0
2022-01-03 08:30:00 11 0
2022-01-03 08:30:01 32 0
2022-01-03 08:30:02 52 1
2022-01-03 08:30:03 44 0
2022-01-03 08:30:03 75 0
CodePudding user response:
I took your code to create the input data, but it looks bit different to the printed version of yours:
data
date
2022-01-01 08:30:00 14
2022-01-01 08:30:01 27
2022-01-01 08:30:02 33
2022-01-01 08:30:03 77
2022-01-01 08:30:04 66
2022-01-01 08:30:05 60
2022-01-01 08:30:06 72
2022-01-01 08:30:07 21
2022-01-01 08:30:08 70
2022-01-01 08:30:09 60
2022-01-01 08:30:10 76
2022-01-02 08:30:00 13
2022-01-02 08:30:01 73
2022-01-02 08:30:02 71
2022-01-02 08:30:03 78
2022-01-02 08:30:04 50
2022-01-02 08:30:05 80
2022-01-02 08:30:06 48
2022-01-02 08:30:07 24
2022-01-02 08:30:08 29
2022-01-02 08:30:09 43
2022-01-02 08:30:10 75
2022-01-03 08:30:00 11
2022-01-03 08:30:01 52
2022-01-03 08:30:02 40
2022-01-03 08:30:03 30
2022-01-03 08:30:04 44
2022-01-03 08:30:05 71
2022-01-03 08:30:06 64
2022-01-03 08:30:07 60
Your rules could be described as well as a rolling window of 3 rows, check if the window is already sorted (value3 bigger than 2 bigger than 1).
Knowing that we could use this condition on the whole data (without paying attention to date
) and create a Series with values of 1 if condition is True and 0 for False (named cond
)
Then search for the 3rd value of each day and map the value of that index in cond
to the new column.
def window_sorted(grp):
return (np.diff(grp) > 0).all()
cond = df['data'].rolling(window=3, min_periods=1).apply(window_sorted)
df['sig'] = 0
grp = df.groupby(pd.Grouper(level=0, freq='D'), as_index=False)['data'].nth(2).index
df.loc[grp, 'sig'] = cond[grp]
print(df)
Output:
data sig
date
2022-01-01 08:30:00 14 0
2022-01-01 08:30:01 27 0
2022-01-01 08:30:02 33 1
2022-01-01 08:30:03 77 0
2022-01-01 08:30:04 66 0
2022-01-01 08:30:05 60 0
2022-01-01 08:30:06 72 0
2022-01-01 08:30:07 21 0
2022-01-01 08:30:08 70 0
2022-01-01 08:30:09 60 0
2022-01-01 08:30:10 76 0
2022-01-02 08:30:00 13 0
2022-01-02 08:30:01 73 0
2022-01-02 08:30:02 71 0
2022-01-02 08:30:03 78 0
2022-01-02 08:30:04 50 0
2022-01-02 08:30:05 80 0
2022-01-02 08:30:06 48 0
2022-01-02 08:30:07 24 0
2022-01-02 08:30:08 29 0
2022-01-02 08:30:09 43 0
2022-01-02 08:30:10 75 0
2022-01-03 08:30:00 11 0
2022-01-03 08:30:01 52 0
2022-01-03 08:30:02 40 0
2022-01-03 08:30:03 30 0
2022-01-03 08:30:04 44 0
2022-01-03 08:30:05 71 0
2022-01-03 08:30:06 64 0
2022-01-03 08:30:07 60 0