Convert a common date format in an ISO week date format

Question

i have this dataframe with this kind of date format

           Date  Week Number   Influenza[it]  Febbre[it]  Rinorrea[it]  
0    2008-01-01             1            220         585           103   
1    2008-01-08             2            403         915           147   
2    2008-01-15             3            366         895           136   
3    2008-01-22             4            305         825           136   
4    2008-01-29             5            311         837           121 
... ...

I’d like to convert the date format in the ISO week date format like this dataframe (because i need to intersect the two dataframes with the same dates, based on the years and weeks). The format is like “year-weeknumberoftheyear”.

0     2007-42
1     2007-43
2     2007-44
3     2007-45
4     2007-46
... ...

So i was able just to find the ISO weeks of the first dataframe in this way:

wiki = pd.read_csv('file.csv', parse_dates=['Date'])
for i,d in wiki.iterrows():
    print d.Date.isocalendar()[1]

Output:

1
2
3
4
...

But i don’t know how to make a date format like the second dataframe (in the way “year-weeknumberoftheyear”)

Asked By: Alex Punkallo

||

Source

Answer 1

You could use a vectorized approach instead after the read operation:

df['Date'] = pd.to_datetime(df['Date']).dt.strftime('%Y-%V')
df['Date']
0    2008-01
1    2008-02
2    2008-03
3    2008-04
4    2008-05
Name: Date, dtype: object

Here, %V is the directive corresponding to ISO 8601 week number.

demo:

from io import StringIO
data = StringIO(
'''
Date     Week Number   Influenza[it]  Febbre[it]  Rinorrea[it]  
2008-01-01             1            220         585           103   
2008-01-08             2            403         915           147   
2008-01-15             3            366         895           136   
2008-01-22             4            305         825           136   
2008-01-29             5            311         837           121
''')
df = pd.read_csv(data, sep='s{2,}', parse_dates=['Date'], engine='python')
df

df['Date'].dtypes
dtype('<M8[ns]')

df['Date'].dt.strftime('%Y-%V')
0    2008-01
1    2008-02
2    2008-03
3    2008-04
4    2008-05
Name: Date, dtype: object

edit: (though inefficient, only for reproducibility purposes)

L = ['{}-{}'.format(d.Date.isocalendar()[0], str(d.Date.isocalendar()[1]).zfill(2)) for i,d in wiki.iterrows()]

Construct series:

>>> pd.Series(L)
0    2008-01
1    2008-02
2    2008-03
3    2008-04
4    2008-05
dtype: object

Answered By: Nickil Maveli

Answer 2

time.strftime(‘%Y-%W’) may work for you. It is used for format the time.

import pandas as pd
pd.to_datatime(time.time()).strftime(‘%Y-%W’)
‘1970-00’ will be seen in the ouput

Answered By: Tian Wolf

Answer 3

The currently accepted answer is incorrect because 2022-01-01 would be converted to 2022-01
and the correct ISO week for this date is 2021-52 because by ISO definition it’s still included in the last week of the previous year. You can see this here:

The best way I found to have a real ISO week so far is this:

df = pd.DataFrame({'date':['2021-12-31', '2022-01-01', '2022-01-04']})
df['date'] = pd.to_datetime(df['date'])

# df['wrong year-week'] = df['date'].dt.strftime('%Y-%V') ## wrong ISO!

df['monday'] = df['date'] - pd.to_timedelta(arg=df['date'].dt.weekday, unit='D')
df['year-week'] = df['monday'].dt.strftime('%Y-%V')

## or in one-line:
df['year-week'] = (df['date'] - pd.to_timedelta(arg=df['date'].dt.weekday, unit='D')).dt.strftime('%Y-%V')

date	year-week
2021-12-31	2021-52
2022-01-01	2021-52
2022-01-04	2022-01

Answered By: stallingOne

Convert a common date format in an ISO week date format

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