I have a time-series of dates and interest rates on a pandas dataframe. Below is the list:
dates rates
0 3/1/2018 0.014553
1 3/8/2018 0.014951
2 4/2/2018 0.016987
3 5/1/2018 0.018719
4 6/1/2018 0.020044
5 9/4/2018 0.021602
6 12/3/2018 0.022361
7 3/1/2019 0.023080
8 6/3/2019 0.023726
9 9/3/2019 0.024333
10 12/2/2019 0.024811
11 3/2/2020 0.025234
12 3/1/2021 0.026456
13 3/1/2022 0.027126
14 3/1/2023 0.027541
15 3/1/2024 0.027898
16 3/3/2025 0.028206
17 3/2/2026 0.028486
18 3/1/2027 0.028748
19 3/1/2028 0.028998
20 3/1/2030 0.029444
21 3/1/2033 0.029850
22 3/1/2038 0.030126
23 3/2/2043 0.030019
24 3/2/2048 0.029778
I'd like to interpolate a rate for any date (for example - 03/21/2021) that falls b/n the min and max dates.
And I'd like to achieve this using the interpolate method of pandas. How can I do it?
I will recommend numpy.interp, here I am convert date type to numeric
np.interp(pd.to_numeric(pd.Series(pd.to_datetime('03/21/2021'))).values,pd.to_numeric(df['dates']).values,df['rates'].values)
Out[425]: array([0.02649271])