Making pyplot.hist() first and last bins include outliers

Question

pyplot.hist() documentation specifies that when setting a range for a histogram \"lower and upper outliers are ignored\".

Is it possible to make th

Answer 1

No. Looking at matplotlib.axes.Axes.hist and the direct use of numpy.histogram I'm fairly confident in saying that there is no smarter solution than using clip (other than extending the bins that you histogram with).

I'd encourage you to look at the source of matplotlib.axes.Axes.hist (it's just Python code, though admittedly hist is slightly more complex than most of the Axes methods) - it is the best way to verify this kind of question.

HTH

Answer 2

I was also struggling with this, and didn't want to use .clip() because it could be misleading, so I wrote a little function (borrowing heavily from this) to indicate that the upper and lower bins contained outliers:

def outlier_aware_hist(data, lower=None, upper=None):
    if not lower or lower < data.min():
        lower = data.min()
        lower_outliers = False
    else:
        lower_outliers = True

    if not upper or upper > data.max():
        upper = data.max()
        upper_outliers = False
    else:
        upper_outliers = True

    n, bins, patches = plt.hist(data, range=(lower, upper), bins='auto')

    if lower_outliers:
        n_lower_outliers = (data < lower).sum()
        patches[0].set_height(patches[0].get_height() + n_lower_outliers)
        patches[0].set_facecolor('c')
        patches[0].set_label('Lower outliers: ({:.2f}, {:.2f})'.format(data.min(), lower))

    if upper_outliers:
        n_upper_outliers = (data > upper).sum()
        patches[-1].set_height(patches[-1].get_height() + n_upper_outliers)
        patches[-1].set_facecolor('m')
        patches[-1].set_label('Upper outliers: ({:.2f}, {:.2f})'.format(upper, data.max()))

    if lower_outliers or upper_outliers:
        plt.legend()

You can also combine it with an automatic outlier detector (borrowed from here) like so:

def mad(data):
    median = np.median(data)
    diff = np.abs(data - median)
    mad = np.median(diff)
    return mad

def calculate_bounds(data, z_thresh=3.5):
    MAD = mad(data)
    median = np.median(data)
    const = z_thresh * MAD / 0.6745
    return (median - const, median + const)

outlier_aware_hist(data, *calculate_bounds(data))