i get an empty df although i know some rows should be in it
any thoughts how to fix this?
the df after the 7th line run looks like this:
| long_date | country |
|---|---|
| 2020-11-07 | Portugal |
| 2020-01-01 | Portugal |
the holy_date looks like this: ['2020-01-01','2020-01-06']
from numpy.ma.extras import isin
import holidays
df = df[(df['country'] == 'Portugal')]
min_year = (pd.DatetimeIndex(df.long_date).year.min())
max_year = (pd.DatetimeIndex(df.long_date).year.max())+1
holy_date = [i.strftime('%Y-%m-%d') for i in [*holidays.CountryHoliday('Portugal',years = np.arange(min_year,max_year,1)).keys()]]
df.long_date= pd.to_datetime(df.long_date).dt.date
df = pd.concat([df,df.long_date.isin(holy_date).rename('bh')],axis =1)
df[df['bh']==True]
The problem come from the fact that you are trying to identify strings in a datetime column. What you should do is to eliminate the row
df.long_date= pd.to_datetime(df.long_date).dt.date
and use this instead: I added a few dates to your data
long_date country
0 2020-11-07 Portugal
1 2020-11-01 Portugal
2 2020-10-01 Portugal
3 2020-06-11 Portugal
and
from numpy.ma.extras import isin
import holidays
import pandas as pd
df = pd.read_csv('holyday.csv', sep=";")
print(df)
df = df[(df['country'] == 'Portugal')]
min_year = (pd.DatetimeIndex(df.long_date).year.min())
max_year = (pd.DatetimeIndex(df.long_date).year.max())+1
holy_date = [i.strftime('%Y-%m-%d') for i in [*holidays.CountryHoliday('Portugal',years = np.arange(min_year,max_year,1)).keys()]]
holy_date = list(holy_date)
#df.long_date= pd.to_datetime(df.long_date).dt.date
df = pd.concat([df,df['long_date'].isin(holy_date).rename('bh')],axis =1)
print(df)
df[df['bh']==True]
produces this:
long_date country
0 2020-11-07 Portugal
1 2020-11-01 Portugal
2 2020-10-01 Portugal
3 2020-06-11 Portugal
long_date country bh
0 2020-11-07 Portugal False
1 2020-11-01 Portugal True
2 2020-10-01 Portugal False
3 2020-06-11 Portugal True
long_date country bh
1 2020-11-01 Portugal True
3 2020-06-11 Portugal True