Duplicate of dataframe but increasing date

Question

I have a dataframe like this,

    a  b          
0  c1  y 
1  c2  n 
2  c3  n 
3  c4  y 
4  c5  y 

I want to make duplicate of n times for the same dataframe. to do that i used, pd.concat([df]*3).reset_index(drop=True)

But now i have a data frame like below,

    a  b          c
0  c1  y 2017-10-10
1  c2  n 2017-10-10
2  c3  n 2017-10-10
3  c4  y 2017-10-10
4  c5  y 2017-10-10

In this,I wanna do the same operation but column c should be added by one day. i.e.,

    a  b          c
0  c1  y 2017-10-10
1  c2  n 2017-10-10
2  c3  n 2017-10-10
3  c4  y 2017-10-10
4  c5  y 2017-10-10
0  c1  y 2017-10-11
1  c2  n 2017-10-11
2  c3  n 2017-10-11
3  c4  y 2017-10-11
4  c5  y 2017-10-11
0  c1  y 2017-10-12
1  c2  n 2017-10-12
2  c3  n 2017-10-12
3  c4  y 2017-10-12
4  c5  y 2017-10-12
0  c1  y 2017-10-13
1  c2  n 2017-10-13
2  c3  n 2017-10-13
3  c4  y 2017-10-13
4  c5  y 2017-10-13

I tried like this,

df1=df.copy()
df2=df.copy()
df3=df.copy()
df1['c']=(df['c']+datetime.timedelta(days=1)).copy()
df2['c']=(df['c']+datetime.timedelta(days=2)).copy()
df3['c']=(df['c']+datetime.timedelta(days=3)).copy()
print pd.concat([df,df1,df2,df3])

My code works good, but i'm searching for pythonic efficient way to solve this.

Answer 1

One way is to use pd.DataFrame.assign within a list comprehension:

initial_date = pd.Timestamp('2017-10-10')

# original dataframe already loaded in df
res = pd.concat([df.assign(c=initial_date + pd.Timedelta(days=i)) for i in range(4)])

print(res)

    a  b          c
0  c1  y 2017-10-10
1  c2  n 2017-10-10
2  c3  n 2017-10-10
3  c4  y 2017-10-10
4  c5  y 2017-10-10
0  c1  y 2017-10-11
1  c2  n 2017-10-11
2  c3  n 2017-10-11
3  c4  y 2017-10-11
4  c5  y 2017-10-11
0  c1  y 2017-10-12
1  c2  n 2017-10-12
2  c3  n 2017-10-12
3  c4  y 2017-10-12
4  c5  y 2017-10-12
0  c1  y 2017-10-13
1  c2  n 2017-10-13
2  c3  n 2017-10-13
3  c4  y 2017-10-13
4  c5  y 2017-10-13