out)

```{code-cell} ipython3

realwage.stack(future_stack=True).head()

realwage.stack().head()

```

We can also pass in an argument to select the level we would like to

stack

```{code-cell} ipython3

realwage.stack(level='Country', future_stack=True).head() # future_stack=True is required until pandas>3.0

realwage.stack(level='Country').head()

```

Using a `DatetimeIndex` makes it easy to select a particular time

`MultiIndex` creates a cross-section of our panel data

```{code-cell} ipython3

realwage.loc['2015'].stack(level=(1, 2), future_stack=True).transpose().head() # future_stack=True is required until pandas>3.0

realwage.loc['2015'].stack(level=(1, 2)).transpose().head()

```

For the rest of lecture, we will work with a dataframe of the hourly

We can also specify a level of the `MultiIndex` (in the column axis)

to aggregate over.

In the case of `groupby` we need to use `.T` to transpose the columns into rows as `pandas` has deprecated the use of `axis=1` in the `groupby` method.

In the case of `groupby`, we need to use `.T` to transpose the columns into rows, as `pandas` has removed support for `axis=1` in the `groupby` method.

```{code-cell} ipython3

merged.T.groupby(level='Continent').mean().head()

summary statistics

```{code-cell} ipython3

merged.stack(future_stack=True).describe()

merged.stack().describe()

```

This is a simplified way to use `groupby`.