Output data from scikit learn logistic regression I have run a logistic regression using scikit learn in python.  I know want to output the results to put into a csv and then load into Tableau.  To do that I need to combine the y_test, y_actual, and X_test data.  I was wondering if there's a way to output the y_test, y_actual, and X_test data?  I know I can use to_csv, but when I try that I can extract each of these data and concat together, but I'm afraid its not matching correctly since there's no identifying to join on.  

Alternatively, is there a way to keep a unique id (uid) with the logistic regression so then it's easy to see what the regression predicts for a specific person?
Each line is treated independently at prediction, so you can be sure that the data is kept in the same order.

For simplicity, you can keep your data in a pandas dataframe. Here's a short working example:



import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=100, n_features=10)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

clf = LogisticRegression()
clf.fit(X_train, y_train)

data_test = pd.DataFrame(data=X_test, columns=['f{}'.format(i) for i in range(1, 11)])
data_test['y_test'] = y_test
data_test['y_pred'] = clf.predict(X_test)