Issue
import pandas as pd
mydata = [{'ID' : '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016'},
{'ID' : '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016'}]
mydata2 = [{'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '10/10/2016'},
{'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '11/10/2016'},
{'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '12/10/2016'},
{'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '13/10/2016'},
{'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '14/10/2016'},
{'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '15/10/2016'},
{'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '10/10/2016'},
{'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '11/10/2016'},
{'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '12/10/2016'},
{'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '13/10/2016'},
{'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '14/10/2016'},
{'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '15/10/2016'},
{'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '16/10/2016'},
{'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '17/10/2016'},
{'ID': '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016', 'Date': '18/10/2016'},]
df = pd.DataFrame(mydata)
df2 = pd.DataFrame(mydata2)
I can't find an answer on how to change 'df' into 'df2'. Maybe I'm not phrasing it right.
I want to take all dates between the dates in two columns 'Entry Date', 'Exit Date', and make a row for each, entering a corresponding date for each row in a new column, 'Date'.
Any help would be greatly appreciated.
Solution
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
Answered By - jezrael
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