Why do I get different coefficients from Logistic regression in Python and SPSS
The LogisticRegression class from sklearn by default uses an L2 penalty as a regularization mechanism, which I assume is not used in the SPSS implementation of the logistic regression. This has been a discussion point before but the main reason for this is that sklearn is focused on prediction and getting the best prediction results (where regularisation is used to prevent overfitting) instead of fitting the model to the data and finding which factors affect the outcome. You can try setting the penalty argument to 'none' to see if that gives you the same results as in SPSS.