Pythonic way of detecting outliers in one dimensional observation data using Matplotlib

To detect outliers in one dimensional observation data, we can take the following Steps −

  • Create spread, center, flier_high and flier_low.

  • Using the above data (Step 1), we can calculate data.

  • Use the suplots() method to create a figure and a set of subplots, i.e., fig1 and ax1.

  • Set the title of ax1.

  • Now using the boxplot() method and data, make a box and a whisker plot. Beyond the whiskers, data are considered outliers and are plotted as individual points.

  • To display the figure, use the show() method.


from matplotlib import pyplot as plt
import numpy as np
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True
spread = np.random.rand(50) * 100
center = np.ones(25) * 50
flier_high = np.random.rand(10) * 100 + 100
flier_low = np.random.rand(10) * -100
data = np.concatenate((spread, center, flier_high, flier_low))
fig1, ax1 = plt.subplots()
ax1.set_title('Basic Plot')


Updated on: 09-Apr-2021


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