How can I forward fill a dataframe column where the limit of rows filled is based on the value of a cell in another column?

So I am trying to forward fill a column with the limit being the value in another column. This is the code I run and I get this error message.

import pandas as pd
import numpy as np

df = pd.DataFrame()

df['NM'] = [0, 0, 1, np.nan, np.nan, np.nan, 0]

df['length'] = [0, 0, 2, 0, 0, 0, 0]

print(df)

   NM      length
0  0.0       0
1  0.0       0
2  1.0       2
3  NaN       0
4  NaN       0
5  NaN       0
6  0.0       0

df['NM'] = df['NM'].fillna(method='ffill', limit=df['length'])

print(df)

ValueError: Limit must be an integer

The dataframe I want looks like this:

       NM      length
    0  0.0       0
    1  0.0       0
    2  1.0       2
    3  1.0       0
    4  1.0       0
    5  NaN       0
    6  0.0       0

Thanks in advance for any help you can provide!

You can first group the dataframe by the length column before filling. Only issue is that for the first group in your example limit would be 0 which causes an error, so we can make sure it's at least 1 with max. This might cause unexpected results if there are nan values before the first non-zero value in length but from the given data it's not clear if that can happen.

# make groups
m = df.length.gt(0).cumsum()

# fill the column
df["NM"] = df.groupby(m).apply(
                    lambda f: f.NM.fillna(
                    method="ffill", 
                    limit=max(f.length.iloc[0], 1))
).values