I have the following data frame:
OBJECTID 2017 2018 2019 2020 2021 1.0 NaN NaN 7569.183179 7738.162829 7907.142480 2.0 NaN NaN 766.591146 783.861122 801.131099 3.0 NaN NaN 8492.215747 8686.747704 8881.279662 4.0 NaN NaN 40760.327825 41196.877473 41633.427120 5.0 NaN NaN 6741.819674 6788.981231 6836.142788
I am trying to apply a spline interpolation on each row to get the values for 2017 and 2018 using the following code:
years = list(range(2017,2022)) df[years] = df[years].interpolate(method="spline", order =1, limit_direction="both", axis=1)
However, I get the following error: ValueError: Index column must be numeric or datetime type when using spline method other than linear. Try setting a numeric or datetime index column before interpolating.
The dataframe in this question is just a subset of a much larger dataset I am using. All of the examples I have seen do the spline interpolation down each column, but I can’t seem to get it work across each row. I feel like it’s a simple solution and I’m just missing it. Could someone please help?
Advertisement
Answer
It appears to be because the dtype
of the index (really columns for axis=1
) is probably object
in your case since the index contains a string column name also. Even though you are grabbing a slice of the columns that contains only integer years the overall index dtype
remains the same – object
. Then it looks like interpolate
looks at the dtype
and punts when it sees a dtype
of object
.
Example – even though the years are stored as integers the overall dtype
is object
:
df.columns Index(['OBJECTID', 2017, 2018, 2019, 2020, 2021], dtype='object')
If we did this:
df.drop(columns=['OBJECTID'], inplace=True) df.columns = df.columns.astype('uint64') df.columns UInt64Index([2017, 2018, 2019, 2020, 2021], dtype='uint64')
Then the axis=1
interpolation works:
years = list(range(2017,2022)) df[years] = df[years].interpolate(method="spline", order =1, limit_direction="both", axis=1) 2017 2018 2019 2020 2021 0 7231.223878 7400.203528 7569.183179 7738.162829 7907.142480 1 732.051193 749.321169 766.591146 783.861122 801.131099 2 8103.151832 8297.683789 8492.215747 8686.747704 8881.279662 3 39887.228530 40323.778178 40760.327825 41196.877473 41633.427120 4 6647.496560 6694.658117 6741.819674 6788.981231 6836.142788
Dropping the OBJECTID
was done to illustrate what is going on.