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statsmodel.api.Logit: valueerror array must not contain infs or nans

I am trying to apply Logistic Regression in Python using statsmodel.api.Logit. I am running into the error ValueError: array must not contain infs or NaNs.

When I am executing with:

data['intercept'] = 1.0
train_cols = data.columns[1:]
logit = sm.Logit(data['admit'], data[train_cols])
result =, method='bfgs', maxiter=20, full_output=1, disp=1, callback=None)

The data contains more than 15000 columns and 2000 rows. which data['admit'] is the target value and data[train_cols] is the list of features. Can anyone please give me some hints to fix this problem?


  1. By default, Logit does not check your data for un-processable infinitities (np.inf) or NaNs (np.nan). In pandas, the latter normally signifies a missing entry.

    To ignore rows with missing data and proceed with the rest, use missing='drop' like so:

    sm.Logit(data['admit'], data[train_cols], missing='drop')

    See the Logit docs for other options.

    If you do not expect your data to contain any missing entries or infinities, perhaps you loaded it incorrectly. Look at data[data.isnull()] to see where the problem is. (N.B. Read this to see how to make infs register as null.)