How can I print the outliers of a Boxplot in Python?

IIUC:
There are (n) points beyond the whiskers (upper and lower) and you wish to print (display) the values of these points, from your dataset.

Background:

Generally, outliers can be visualised as the values outside the upper and lower whiskers of a box plot. The upper and lower whiskers can be defined in a number of ways.

One method is:

Lower: Q1 - k * IQR

Upper: Q3 + k * IQR

where k is (generally) defined as 1.5, and the IQR (inner quartile range) is defined as:

IQR = Q3 - Q1 = qn(0.75) - qn(0.25)

Example:

The following dataset can be visualised as:

array([ 65.46329369,  91.64897781,  96.85666088,  60.18189851,
        30.53996122,  55.12666144,  63.00161253,  29.97804178,
        ...,
        47.98458963,  37.69556267,  44.26758617,  58.60869412,
        150.        , 155.        , 160.        , 165.        ,
        170.        , 175.        ])

To extract the outliers (we'll focus on the upper outliers), we first need to know the IQRs, which can be found using:

pandas.Series(data).describe()

Output:

count    106.000000
mean      62.569111
std       29.698729
min        0.000000
25%       46.934198
50%       57.002615
75%       69.516237
max      175.000000

Determine the approximate whisker values:

iqr = (69.516 - 46.934)
upper = 69.516 + (iqr*1.5)
lower = 46.934 - (iqr*1.5)

>>> iqr, upper, lower
>>> (22.582, 103.389, 13.06)

Extract (examine) the values of the upper outliers:

data[data > upper]

>>> array([150., 155., 160., 165., 170., 175.])