Post History

I don't think this is possible using the Pandas plotting API. You can use the lower-level Matplotlib API to do just about anything you can imagine, though this means manually replicating some of the things Pandas does for you like staggering the positioning of the bars and adding an x-tick for every bar group. Here's a solution:

```py
def your_bar_plot(df, spacing=4, vlines=[]):
    cols = df.shape[1]
    fig, ax = plt.subplots()

    # Make sure there's a tick for every row in the data frame.
    ax.set_xticks(df.index)

    # Draw the bars, offsetting each one some amount away from the
    # true x position to allow multiple series to share the plot.
    for i, c in enumerate(df):
        x = df.index - 0.5 + (i + (spacing + 1)/2)/(cols + spacing)
        ax.bar(x, df[c], 1/(cols + spacing), label=c)

    # Draw lines separating the groups.
    ax.vlines(vlines, 0, 1,
        transform=ax.get_xaxis_transform(), # [1]
        color='black',
        linewidths=0.8) # [2]
    # [1]: This makes the above 0 and 1 refer to the top and bottom
    # of the plot, regardless of the actual scale used for the data.
    # [2]: 0.8 is the default width used for the axis frame
    # (matplotlib.rcParams['axes.linewidth'], if you prefer).

    ax.legend()
    plt.show()

your_bar_plot(pd.DataFrame(np.random.rand(9, 4), columns=['a', 'b', 'c', 'd']), vlines=[2.5, 5.5])
```