I am performing a linear interpolation with a Pandas Series but it seems that it keeps replacing the NaN with the same value and I can't figure out why..
# To check where are located the missing values :
d1[d1.isna().any(axis=1)]
volume price
datetime
2018-05-23 09:00:00 NaN 0.0
2018-05-22 11:00:00 NaN 0.0
2018-05-21 12:00:00 NaN 0.0
2018-05-21 10:00:00 NaN 0.0
2018-05-21 09:00:00 NaN 0.0
2018-05-18 09:00:00 NaN 0.0
d1["price"].astype(float).interpolate(method="time")
datetime
2018-05-23 10:00:00 23.825000
2018-05-23 09:00:00 22.425000
2018-05-22 17:00:00 24.041000
...
2018-05-22 12:00:00 23.975000
2018-05-22 11:00:00 22.425000
2018-05-22 10:00:00 24.000000
...
2018-05-21 12:00:00 22.425000
2018-05-21 11:00:00 23.200000
2018-05-21 10:00:00 22.425000
2018-05-21 09:00:00 22.425000
...
2018-05-18 10:00:00 23.425000
2018-05-18 09:00:00 22.425000
2018-05-17 17:00:00 23.516000
The missing values are always replaced with 22.45, which is neither the mean nor the median of the Series. Could someone help ? Thanks !
EDIT : A glimpse of the initial dataframe :
price volume
datetime
2018-05-23 16:00:00 23.936667 70.0
2018-05-23 15:00:00 24.040000 5.0
2018-05-23 14:00:00 23.971875 185.0
2018-05-23 13:00:00 23.811111 250.0
2018-05-23 12:00:00 23.800000 240.0
2018-05-23 11:00:00 23.816667 140.0
2018-05-23 10:00:00 23.825000 10.0
2018-05-23 09:00:00 NaN 0.0
2018-05-22 17:00:00 24.041000 260.0
2018-05-22 16:00:00 24.062857 150.0
2018-05-22 15:00:00 24.031818 1525.0
2018-05-22 14:00:00 24.079167 165.0
2018-05-22 13:00:00 23.950000 5.0
2018-05-22 12:00:00 23.975000 375.0
2018-05-22 11:00:00 NaN 0.0
2018-05-22 10:00:00 24.000000 30.0
2018-05-22 09:00:00 24.000000 30.0
