I have a DataFrame containing three datetime columns:
tp.loc[:, ['Arrival1', 'Arrival2', 'Departure']].head()
Arrival1 Arrival2 Departure
0 2018-11-26 05:45:00 2018-11-26 12:00:00 2018-1-26 08:00:00
1 2018-11-26 22:00:00 2018-11-27 00:00:00 2018-11-26 23:00:00
2 2018-11-26 05:45:00 2018-11-26 08:15:00 2018-11-26 06:45:00
3 2018-11-26 07:30:00 2018-11-26 10:15:00 2018-11-26 08:30:00
4 2018-12-02 07:30:00 2018-12-02 21:30:00 2018-12-02 08:00:00
I want to get only the rows of tp whose Arrival 1, Arrival 2 or Departure (any of the three) are within the following column ranges (any of the rows):
db.loc[db['country'] == 'AT']
country banStartDate banEndDate
102 AT 2018-12-01 14:00:00 2018-12-01 22:59:00
161 AT 2018-12-01 23:00:00 2018-12-02 21:00:00
51 AT 2018-12-07 23:00:00 2018-12-08 22:59:00
In this example, I want only row #4 to be retrieved from tp since Arrival2 is within the date range of db.
Is there an easy way to do so?
After reading in your dataframes with pd.read_csv()
, you can use pd.concat()
with a boolean mask and list comprehension, followed by drop_duplicates()
:
from io import StringIO
import pandas as pd
df1 = StringIO('''
Arrival1 Arrival2 Departure
0 2018-11-26 05:45:00 2018-11-26 12:00:00 2018-1-26 08:00:00
1 2018-11-26 22:00:00 2018-11-27 00:00:00 2018-11-26 23:00:00
2 2018-11-26 05:45:00 2018-11-26 08:15:00 2018-11-26 06:45:00
3 2018-11-26 07:30:00 2018-11-26 10:15:00 2018-11-26 08:30:00
4 2018-12-02 07:30:00 2018-12-02 21:30:00 2018-12-02 08:00:00
''')
df2 = StringIO('''
country banStartDate banEndDate
102 AT 2018-12-01 14:00:00 2018-12-01 22:59:00
161 AT 2018-12-01 23:00:00 2018-12-02 21:00:00
51 AT 2018-12-07 23:00:00 2018-12-08 22:59:00
''')
tp = pd.read_csv(df1, sep=r'\s{2,}', engine='python', parse_dates=[0,1,2])
db = pd.read_csv(df2, sep=r'\s{2,}', engine='python', parse_dates=[1,2]).reset_index()
pd.concat([tp.loc[((tp>db.loc[i,'banStartDate']) & (tp<db.loc[i,'banEndDate'])).any(axis=1)] for i in range(db.shape[0])]).drop_duplicates()
Returns:
Arrival1 Arrival2 Departure
4 2018-12-02 07:30:00 2018-12-02 21:30:00 2018-12-02 08:00:00
You can use the pandas.DataFrame.any with axis = 'row'(or 1) to find where the dates are between start and end. You will need 3 of these or a for loop for however many 'country' column of db there are.
Also, I believe(I could be wrong) you will need to convert those strings into python datetime variables. The code would look similar to this;
tp[(datetime.strptime(Start_Date, '%Y-%d-%m %H:%M:%S')> tp >datetime.strptime(End_Date, '%Y-%d-%m %H:%M:%S')).any(axis=1)]
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