How can I add rows for all dates between two columns?

Question

import pandas as pd

mydata = [{\'ID\' : \'10\', \'Entry Date\': \'10/10/2016\', \'Exit Date\': \'15/10/2016\'},
          {\'ID\' : \'20\', \'Entry Date\': \'10/10/         


        

Answer 1

You can use melt for reshaping, set_index and remove column variable:

#convert columns to datetime
df['Entry Date'] = pd.to_datetime(df['Entry Date'])
df['Exit Date'] = pd.to_datetime(df['Exit Date'])

df2 = pd.melt(df, id_vars='ID', value_name='Date')
df2.Date = pd.to_datetime(df2.Date)
df2.set_index('Date', inplace=True)
df2.drop('variable', axis=1, inplace=True)
print (df2)
            ID
Date          
2016-10-10  10
2016-10-10  20
2016-10-15  10
2016-10-18  20

Then groupby with resample and ffill missing values:

df3 = df2.groupby('ID').resample('D').ffill().reset_index(level=0, drop=True).reset_index()
print (df3)
         Date  ID
0  2016-10-10  10
1  2016-10-11  10
2  2016-10-12  10
3  2016-10-13  10
4  2016-10-14  10
5  2016-10-15  10
6  2016-10-10  20
7  2016-10-11  20
8  2016-10-12  20
9  2016-10-13  20
10 2016-10-14  20
11 2016-10-15  20
12 2016-10-16  20
13 2016-10-17  20
14 2016-10-18  20

Last merge original DataFrame:

print (pd.merge(df, df3))
   Entry Date  Exit Date  ID       Date
0  2016-10-10 2016-10-15  10 2016-10-10
1  2016-10-10 2016-10-15  10 2016-10-11
2  2016-10-10 2016-10-15  10 2016-10-12
3  2016-10-10 2016-10-15  10 2016-10-13
4  2016-10-10 2016-10-15  10 2016-10-14
5  2016-10-10 2016-10-15  10 2016-10-15
6  2016-10-10 2016-10-18  20 2016-10-10
7  2016-10-10 2016-10-18  20 2016-10-11
8  2016-10-10 2016-10-18  20 2016-10-12
9  2016-10-10 2016-10-18  20 2016-10-13
10 2016-10-10 2016-10-18  20 2016-10-14
11 2016-10-10 2016-10-18  20 2016-10-15
12 2016-10-10 2016-10-18  20 2016-10-16
13 2016-10-10 2016-10-18  20 2016-10-17
14 2016-10-10 2016-10-18  20 2016-10-18