CODEWITHSUNDEEP
pythonpandasdatetimedataframefilter
Amartin
Amartin

Reputation: 337

Convert from a list of datetime to a date in Python to filter a dataframe

I have the next date:

0   2019-06-29
Name: end date, dtype: datetime64[ns]

This is a part of the dataframe named data:

 data
    PointAlias       start Date    end date
 0   AO122732        2018-06-29    2019-06-29

then I transform it with the next code:

end_date_transformed = []
for x in range(len(data['end date'])):
    transf = pd.to_datetime(data['end date'][x]).date()
    end_date_transformed.append(transf)
end_date_transformed
output: [datetime.date(2019, 6, 29)]

I want to know if it is possible to transform it from a list to just a string to apply datetime.datetime.strftime(end_date_transformed, %Y%m%d'). Is there a way of doing it? The final objective of this is to be able to filter a dataframe of dates to just get those dates that are <= end_date_transformed. Something like:

df
     date
0   2018-06-29
1   2018-10-29
2   2019-02-28
3   2019-06-29
4   2019-10-29
5   2020-02-29

df[df['date'] <= end_date_transformed] # This does not work, the output is:
ValueError: Arrays were different lengths: 44 vs 1

I understand that the code above will not work, but if I do a datetime.date(year, month, day) and use it like end_date_transformed it works so the problem is in the transformation of the end date. If someone can help, I will be grateful. Thank you so much for taking your time to read it and answer.

Upvotes: 0

Views: 2419

Answers (1)

rahlf23
rahlf23

Reputation: 9019

This should work for you for a single end date:

import pandas as pd
from datetime import datetime

data = pd.DataFrame([['A0122732','2018-06-29','2019-06-29']], columns=['PointAlias','start date','end date'])

df = pd.DataFrame([
['2018-06-29'],
['2018-10-29'],
['2019-02-28'],
['2019-06-29'],
['2019-10-29'],
['2020-02-29']],
columns=['date'])

df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')

end_date = datetime.strptime(data['end date'].values[0], '%Y-%m-%d')

df[df['date'] <= end_date]

Returns:

        date
0 2018-06-29
1 2018-10-29
2 2019-02-28
3 2019-06-29

Upvotes: 1

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