i have like this data frame

• make loop to data frame
• in each row per (id) group except first row in group, compute difference between date[i] and date[i-1] if result <30 or >56, put in 'status' column "no"
• else the 'status' in this row be "yes" -the output data frame must be:

CodePudding user response：

try this:

from datetime import datetime
df['date'] = pd.to_datetime(df['date']) #in case of you didn't set it as datetime.

df['status'] = pd.Series(np.nan)
for i in range(1,len(df)):
    if (df['date'][i]-df['date'][i-1]).days < 30 or (df['date'][i]-df['date'][i-1]).days > 56:
        df['status'][i] = 'no'
    else:
        df['status'][i] = 'yes'
print(df)

the output is:

  id       date status
0  a 2020-01-20    NaN
1  a 2020-03-22     no
2  a 2020-03-15     no
3  b 2019-05-02     no
4  b 2019-09-05     no

CodePudding user response：

You can use shift to calculate difference between the rows and replace with your values based on the condition

df['status'] = (df['date'] - df['date'].shift()).dt.days
df['status'][df['status'].notnull()] = ((df['status'] < 30) | (df['status'] > 65)).replace({True: 'No', False: 'Yes'})
df = df.fillna('') # if you want to replace the first value with an empty string

Output

  id       date status
0  a 2020-01-20       
1  a 2020-03-22    Yes
2  a 2020-04-15     No
3  b 2019-02-05     No
4  b 2019-05-09     No