CODEWITHSUNDEEP
pythonnumpymatplotlibscipy
Yuxiang Wang
Yuxiang Wang

Reputation: 8433

Obtaining values used in boxplot, using python and matplotlib

I can draw a boxplot from data:

import numpy as np
import matplotlib.pyplot as plt

data = np.random.rand(100)
plt.boxplot(data)

Then, the box will range from the 25th-percentile to 75th-percentile, and the whisker will range from the smallest value to the largest value between (25th-percentile - 1.5*IQR, 75th-percentile + 1.5*IQR), where the IQR denotes the inter-quartile range. (Of course, the value 1.5 is customizable).

Now I want to know the values used in the boxplot, i.e. the median, upper and lower quartile, the upper whisker end point and the lower whisker end point. While the former three are easy to obtain by using np.median() and np.percentile(), the end point of the whiskers will require some verbose coding:

median = np.median(data)
upper_quartile = np.percentile(data, 75)
lower_quartile = np.percentile(data, 25)

iqr = upper_quartile - lower_quartile
upper_whisker = data[data<=upper_quartile+1.5*iqr].max()
lower_whisker = data[data>=lower_quartile-1.5*iqr].min()

I was wondering, while this is acceptable, would there be a neater way to do this? It seems that the values should be ready to pull-out from the boxplot, as it's already drawn.

Upvotes: 39

Views: 74191

Answers (4)

Jupiter
Jupiter

Reputation: 1

You can find the values from the dataframe series. For instance, to show the median value as an annotation in the plot.

For example, assume a dataframe with two series col1 (categorical) and col2 (continuous). We want to boxplot col2 as a function of the values of col1:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

d = {'col1': ['B','A','A','B','B','A'], 'col2':[1,20,30,40,60,70]}
df = pd.DataFrame(data=d)
df['col1']= df['col1'].astype("category")

fig, axes = plt.subplots(figsize=(10, 10),nrows=1, ncols=1, sharey=True)
i='col2'
j='col1'
df.boxplot(ax=axes,column=[i], by=j, grid=True)

for value,cat in enumerate(df[j].cat.categories):
    series=df[df[j]==cat]
    median=series[i].describe()['50%']
    median=np.round(median,1)
    axes.annotate(median,(value+1+0.25,median),fontsize=24, color='blue')

plt.show()

Upvotes: 0

Mandy Yan
Mandy Yan

Reputation: 1

upper_whisker = data[data<=upper_quartile+1.5*iqr].max()
lower_whisker = data[data>=lower_quartile-1.5*iqr].min()

equal to

upper_whisker = data.max()
lower_whisker = data.min()

if you just want to get the real data points in the dataset. But statistically speaking, the whisker values are upper_quantile+1.5IQR and lower_quantile-1.5IQR

Upvotes: -1

t_warsop
t_warsop

Reputation: 1280

I've had this recently and have written a function to extract the boxplot values from the boxplot as a pandas dataframe.

The function is:

def get_box_plot_data(labels, bp):
    rows_list = []

    for i in range(len(labels)):
        dict1 = {}
        dict1['label'] = labels[i]
        dict1['lower_whisker'] = bp['whiskers'][i*2].get_ydata()[1]
        dict1['lower_quartile'] = bp['boxes'][i].get_ydata()[1]
        dict1['median'] = bp['medians'][i].get_ydata()[1]
        dict1['upper_quartile'] = bp['boxes'][i].get_ydata()[2]
        dict1['upper_whisker'] = bp['whiskers'][(i*2)+1].get_ydata()[1]
        rows_list.append(dict1)

    return pd.DataFrame(rows_list)

And is called by passing an array of labels (the ones that you would pass to the boxplot plotting function) and the data returned by the boxplot function itself.

For example:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

def get_box_plot_data(labels, bp):
    rows_list = []

    for i in range(len(labels)):
        dict1 = {}
        dict1['label'] = labels[i]
        dict1['lower_whisker'] = bp['whiskers'][i*2].get_ydata()[1]
        dict1['lower_quartile'] = bp['boxes'][i].get_ydata()[1]
        dict1['median'] = bp['medians'][i].get_ydata()[1]
        dict1['upper_quartile'] = bp['boxes'][i].get_ydata()[2]
        dict1['upper_whisker'] = bp['whiskers'][(i*2)+1].get_ydata()[1]
        rows_list.append(dict1)

    return pd.DataFrame(rows_list)

data1 = np.random.normal(loc = 0, scale = 1, size = 1000)
data2 = np.random.normal(loc = 5, scale = 1, size = 1000)
data3 = np.random.normal(loc = 10, scale = 1, size = 1000)

labels = ['data1', 'data2', 'data3']
bp = plt.boxplot([data1, data2, data3], labels=labels)
print(get_box_plot_data(labels, bp))
plt.show()

Outputs the following from get_box_plot_data:

   label  lower_whisker  lower_quartile    median  upper_quartile  upper_whisker
0  data1      -2.491652       -0.587869  0.047543        0.696750       2.559301
1  data2       2.351567        4.310068  4.984103        5.665910       7.489808
2  data3       7.227794        9.278931  9.947674       10.661581      12.733275

And produces the following plot: enter image description here

Upvotes: 20

CT Zhu
CT Zhu

Reputation: 54380

Why do you want to do so? what you are doing is already pretty direct.

Yeah, if you want to fetch them for the plot, when the plot is already made, simply use the get_ydata() method.

B = plt.boxplot(data)
[item.get_ydata() for item in B['whiskers']]

It returns an array of the shape (2,) for each whiskers, the second element is the value we want:

[item.get_ydata()[1] for item in B['whiskers']]

Upvotes: 39

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