This thread didn't solved my problem.
This is my data:
Date Server
2019-02-13 A
2019-02-13 B
2019-02-13 B
2019-02-17 A
2019-02-17 B
2019-02-17 C
2019-02-19 C
2019-02-19 D
I need to get a list of the servers for a respective date range. I tried this code:
df['Date'] = pd.to_datetime(df['Date'], format='%Y%m%d').apply(lambda x: x.strftime(format='%Y-%m-%d'))
df = df.set_index(df['Date'])
### This formatting changes the cell content from a format like 20190217 to the
one represented above. Maybe there is already an error right here.###
start_date = pd.to_datetime('20190212', format='%Y%m%d').strftime(format='%Y-%m-%d')
end_date = pd.to_datetime('20190217', format='%Y%m%d').strftime(format='%Y-%m-%d')
The print statements however deliver the correct result, if I write the dates explicitly. However in my program I need to pipe in the dates by start_date and end_date.
print(df[df.Date.between('2019-02-12','2019-02-17')].Server.unique())
print(df.loc['2019-02-12':'2019-02-17'].Server.unique())
print(df.loc[start_date : end_date].Server.unique())
Output:
['A' 'B' 'C'] - correct
['A' 'B' 'C'] - correct
['A' 'B' 'C' 'D'] - incorrect
Which changes to my code do I need to apply?
you need not to make strftime
and change format to format='%Y-%m-%d'
import pandas as pd
df = pd.DataFrame({'Date': ['2019-02-13', '2019-02-13', '2019-02-13', '2019-02-17', '2019-02-17', '2019-02-17', '2019-02-19', '2019-02-19'],
'Server':['A','B','B','A','B','C','C','D']})
df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d')
df = df.set_index(df['Date'])
start_date = pd.to_datetime('20190212', format='%Y%m%d').strftime(format='%Y-%m-%d')
end_date = pd.to_datetime('20190217', format='%Y%m%d').strftime(format='%Y-%m-%d')
print(df[df.Date.between('2019-02-12','2019-02-17')].Server.unique())
print(df.loc['2019-02-12':'2019-02-17'].Server.unique())
print(df.loc[start_date : end_date].Server.unique())
output is
['A' 'B' 'C']
['A' 'B' 'C']
['A' 'B' 'C']
This should do the trick.
import pandas as pd
start_date = '2019-02-12'
end_date = '2019-02-17'
df['Date'] = pd.to_datetime(df['Date'])
print(df.loc[(df['Date'] > start_date) & (df['Date'] <= end_date)].Server.unique())
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