I have following DataFrame which start by an index of 1.10 and increment by 0.05:
A B C D E
1.10 3 2 2 1 0
1.15 1 2 0 1 0
1.20 0 0 0 1 -1
1.25 1 1 3 -5 2
1.30 -3 4 2 6 0
...
I want to extend this DataFrame with zeros that the result looks like this:
A B C D E
0 0 0 0 0 0
0.05 0 0 0 0 0
0.10 0 0 0 0 0
...
1.10 3 2 2 1 0
1.15 1 2 0 1 0
1.20 0 0 0 1 -1
1.25 1 1 3 -5 2
1.30 -3 4 2 6 0
...
So I want that the index starts by 0 and increments by 0.05 until the original start index of 1.10 is reached. How to do this?
>Solution :
Use a reindex with help of numpy.arange:
step = 0.05
out = df.reindex(np.arange(0, df.index.max()+step, step), fill_value=0)
Or if you don’t want to touch the existing DataFrame and only add the missing prior indices (which is more in the spirit of "the index starts by 0 and increments by 0.05 until the original start index of 1.10 is reached"), create the top DataFrame and concat:
step = 0.05
out = pd.concat([pd.DataFrame(0, columns=df.columns,
index=np.arange(0, df.index.min(), step)),
df])
Output:
A B C D E
0.00 0 0 0 0 0
0.05 0 0 0 0 0
0.10 0 0 0 0 0
0.15 0 0 0 0 0
...
1.00 0 0 0 0 0
1.05 0 0 0 0 0
1.10 3 2 2 1 0
1.15 0 0 0 0 0
1.20 0 0 0 0 0
1.25 1 1 3 -5 2
1.30 -3 4 2 6 0