c113.txt

Pandas dataframe.quantile() function return values at
the given quantile over requested axis, a numpy.percentile.


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The IQR is used to measure how spread out the data points in 
a set are from the mean of the data set. The higher the IQR, 
the more spread out the data points; in contrast, the smaller 
the IQR, the more bunched up the data points are around the mean.

The interquartile range is an especially useful measure of 
variability for skewed distributions.

For these distributions, the median is the best measure of central 
tendency because it’s the value exactly in the middle when all values
 are ordered from low to high.

Along with the median, the IQR can give you an overview of where most
 of your values lie and how clustered they are.

The IQR is also useful for datasets with outliers. Because it’s based
 on the middle half of the distribution, it’s less influenced by 
extreme values.


The IQR is a type of resistant measure. The second measure of spread 
or variation is called the standard deviation (SD).

You should use the interquartile range to measure the spread of 
values in a dataset when there are extreme outliers present. 
Conversely, you should use the standard deviation to measure the 
spread of values when there are no extreme outliers present.

df = pd.DataFrame({"A":[1, 5, 3, 4, 2],
                   "B":[3, 2, 4, 3, 4],
                   "C":[2, 2, 7, 3, 4], 
                   "D":[4, 3, 6, 12, 7]})