alternatives to pivot very large table pandas - pandas

Question

I have a dataframe of 25M x 3 cols of format:
import pandas as pd
import numpy as np
d={'ID':['A1','A1','A2','A2','A2'], 'date':['Jan 1','Jan7','Jan4','Jan5','Jan12'],'value':[10,12,3,5,2]}
df=pd.DataFrame(data=d)
df
ID date value
0 A1 Jan 1 10
1 A1 Jan7 12
2 A2 Jan4 3
3 A2 Jan5 5
4 A2 Jan12 2
...
An
And want to pivot it using:
df['date'] = pd.to_datetime(df['date'], format='%b%d')
(df.pivot(index='date', columns='ID',values='value')
.asfreq('D')
.interpolate()
.bfill()
.reset_index()
)
df.index = df.index.strftime('%b%d')
This works for 500k rows
df3=(df.iloc[:500000,:].pivot(index='date', columns='ID',values='value')
.resample('M').mean()
.interpolate()
.bfill()
.reset_index()
)
, but when I used my full data set, or >1M rows, it fails with:
ValueError: Unstacked DataFrame is too big, causing int32 overflow
Are there any suggestions on how I can get this to run to completion?
A further computation is performed on the wide table:
N=19/df2.iloc[0]
df2.mul(N.tolist(),axis=1).sum(1)