How to get boxplot data for matplotlib boxplots

Question

I need to get the statistical data which were generated to draw a box plot in Pandas(using dataframe to create boxplots). i.e. Quartile1,Quartile2,Quartile3, lower whisker value, upper whisker value and outliers. I tried the following query to draw the boxplot.

import pandas as pd
df = pd.DataFrame(np.random.rand(100, 5), columns=['A', 'B', 'C', 'D', 'E'])
pd.DataFrame.boxplot(df,return_type = 'both')

Is there a way to do it instead of manually calculating the values?

Glottal

Answer 1

One option is to use the y data from the plots - probably most useful for the outliers (fliers)

_, bp = pd.DataFrame.boxplot(df, return_type='both')

outliers = [flier.get_ydata() for flier in bp["fliers"]]
boxes = [box.get_ydata() for box in bp["boxes"]]
medians = [median.get_ydata() for median in bp["medians"]]
whiskers = [whiskers.get_ydata() for whiskers in bp["whiskers"]]

But it's probably more straightforward to get the other values (including IQR) using either

quantiles = df.quantile([0.01, 0.25, 0.5, 0.75, 0.99])

or, as suggested by WoodChopper

stats = df.describe()

Cattycornered

Answer 2

• To get the boxplot data, use matplotlib.cbook.boxplot_stats, which returns a list of dictionaries of statistics used to draw a series of box and whisker plots using matplotlib.axes.Axes.bxp
  • To get the boxplot statistics, pass an array to boxplot_stats.
    • This is not specific to pandas.
• The default plot engine for pandas, is matplotlib, so using boxplot_stats will return the correct metrics for pandas.DataFrame.plot.box.
• Pass the numeric columns of interest, to boxplot_stats, as an array, using df.values.
• There can be no NaN values in the columns.
• Tested in python 3.11.4, pandas 2.1.0, matplotlib 3.7.2

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.cbook import boxplot_stats
import numpy as np

# test dataframe
np.random.seed(346)
df = pd.DataFrame(np.random.rand(100, 5), columns=['A', 'B', 'C', 'D', 'E'])

# plot the dataframe as needed
ax = df.plot.box(figsize=(8, 6), showmeans=True, grid=True)

• Extract the boxplot metrics by passing an array to boxplot_stats
  • boxplot_stats(df) or boxplot_stats(df.values) will work.
  • The dicts are in the same order as the column arrays from df.
  • This data had no outliers, fliers, because it was generated with numpy.random.

# create a dict of dicts with the column names as the keyword for each dict of statistics
stats = dict(zip(df.columns, boxplot_stats(df)))

print(stats)
[out]:
{'A': {'cihi': 0.6008396701195271,
       'cilo': 0.45316512285356997,
       'fliers': array([], dtype=float64),
       'iqr': 0.47030110594253877,
       'mean': 0.49412631128104645,
       'med': 0.5270023964865486,
       'q1': 0.2603486498337239,
       'q3': 0.7306497557762627,
       'whishi': 0.9941975539538199,
       'whislo': 0.00892072823759571},
 'B': {'cihi': 0.5460977498205477,
       'cilo': 0.39283808760835964,
       'fliers': array([], dtype=float64),
       'iqr': 0.4880880962171596,
       'mean': 0.47578540593013985,
       'med': 0.4694679187144537,
       'q1': 0.2466015651284032,
       'q3': 0.7346896613455628,
       'whishi': 0.9906905357196321,
       'whislo': 0.002613905425137064},
 'C': {'cihi': 0.6327876179340386,
       'cilo': 0.47317829117336885,
       'fliers': array([], dtype=float64),
       'iqr': 0.5083099578365278,
       'mean': 0.5202481643792808,
       'med': 0.5529829545537037,
       'q1': 0.24608370844800756,
       'q3': 0.7543936662845353,
       'whishi': 0.9968264819096214,
       'whislo': 0.008450848029956215},
 'D': {'cihi': 0.5429786764060252,
       'cilo': 0.40089287519667627,
       'fliers': array([], dtype=float64),
       'iqr': 0.4525025516221303,
       'mean': 0.4948030963370377,
       'med': 0.4719357758013507,
       'q1': 0.279181107815125,
       'q3': 0.7316836594372553,
       'whishi': 0.9836196084903415,
       'whislo': 0.019864664399723786},
 'E': {'cihi': 0.5413819754851169,
       'cilo': 0.3838462046931251,
       'fliers': array([], dtype=float64),
       'iqr': 0.5017062764076173,
       'mean': 0.4922357500877824,
       'med': 0.462614090089121,
       'q1': 0.2490034171367362,
       'q3': 0.7507096935443536,
       'whishi': 0.9984043081918205,
       'whislo': 0.0036707224412856343}}

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