Pandas: add elements to index at even intervals

Question

I have a dataframe that looks like this:

             B
A             
0.00    5.7096
7.33    8.0280
25.82  15.7212
43.63  19.5156
55.24  20.1888

and I want to add rows with the index at regular intervals (say by 10), so that I can then interpolate the column B with method = 'index'. My desired output is this:

             B
A             
0.00    5.7096
7.33    8.0280
10.00      NaN
20.00      NaN
25.82  15.7212
30.00      NaN
40.00      NaN
43.63  19.5156
50.00      NaN
55.24  20.1888
60.00      NaN

I haven't found any reindex option that adds index elements instead of changing them. My best solution is create a new index, append it to the original dataframe, sort and remove duplicates (if any), but I'm pretty sure there is a better solution.

step = 10
idx = pd.DataFrame(index = df.index).reindex([round(i, 0) for i in np.arange(df.index[0], df.index[-1] + step, step)])
df = df.append(idx)
df.sort_index(inplace = True)
df = df[~df.index.duplicated()]

any suggestion? thanks

Answer 1

Effectively do a union by doing an outer join.

df = pd.read_csv(io.StringIO("""A             B
0.00    5.7096
7.33    8.0280
25.82  15.7212
43.63  19.5156
55.24  20.1888"""), sep="\s+").set_index("A")

df = df.join(pd.DataFrame(index=pd.RangeIndex(0,60, 10)), how="outer")

	B
0	5.7096
7.33	8.028
10	nan
20	nan
25.82	15.7212
30	nan
40	nan
43.63	19.5156
50	nan
55.24	20.1888

Answer 2

idx = sorted(set(list(np.arange(70, step=10)) + list(df.index)))
df = df.reindex(idx)

Output:

df Out[59]:

             B
A             
0.00    5.7096
7.33    8.0280
10.00      NaN
20.00      NaN
25.82  15.7212
30.00      NaN
40.00      NaN
43.63  19.5156
50.00      NaN
55.24  20.1888
60.00      NaN