Standardize(x) comes before drop na
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 35/100
Research direction
Start by reproducing the two dmatrix examples from the issue and compare the handling of missing x2 values in drop_na and standardize(x2). Trace the standardize and missing-data processing entry points, then add a regression test showing the expected non-empty design matrix when standardization is used with missing data.
Written by the indexing model from the issue text.
Description
It looks like Standardize(x) comes before drop_na --> therefore the standardization returns all nan
import numpy as np
import pandas as pd
from patsy import dmatrix
df = pd.DataFrame({'x1': np.arange(5), 'x2': [1,2,np.nan,5,6]})
output:
dmatrix("~x1+x2", df)
[[1. 0. 1.]
[1. 1. 2.]
[1. 3. 5.]
[1. 4. 6.]]
dmatrix("~x1+standardize(x2)", df)
DesignMatrix with shape (0, 3)
Intercept x1 standardize(x2)
Terms:
'Intercept' (column 0)
'x1' (column 1)
'standardize(x2)' (column 2)
- Dominant language
- Python
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