I am doing a Linear Regression using Statsmodels in a Jupyter notebook. The data is in a DataFrame called "train\_base", where the id column identifies every unique subject of my database. Train\_base is like this:

```
id     y     x0     x1     x2
a123   20     8      1      3
b789   33     8      3      2
d782   77     9      6      5      

```

The main chunk of code is shown below. Note that I am using another base called "test\_base" to make predictions, and this base also has the same structure as "train\_base", except for the "y" column:

```
results = smf.ols('y ~ x0 + x1 + x2', data=train_base).fit()
predictions = results.predict(test_base)
predictions.head()

```

The predictions are like this:

```
0   -0.054789
1   -0.036042
2   -0.043962
3   -0.135725
4   -0.409129
dtype: float64

```

It seems to me that the first column shown in the predictions is the index of the original train\_base (am I correct?). Since I need to identify the predicted value for each individual of my test base, what do I have to do to have the "id" column in the predictions?