# Matplotlib Boxplot Python

This tutorial explains how we can create a `boxplot` using the `matplotlib.pyplot.boxplot()` function in Python.

The boxplot helps us gain insights about the data by giving information about the position of `minimum`, `1st quartile`, `median`, `3rd quartile`, and the `maximum` values of the data.

## `boxplot` in Python Matplotlib

``````import matplotlib.pyplot as plt

x=[4,5,6,8,9,10,10,11,11,12,13,14,15,15,15,17,18,19,22,23,25]

plt.boxplot(x)
plt.title("Boxplot Using Matplotlib")
plt.show()
``````

Output: It plots a boxplot from the given data `x`. In the boxplot, the box will extend from `Q1` to `Q3`; and the horizontal line inside the box represents the median of the data. The whiskers in the boxplot extend from `Q3` to `maximum` value in the data and from the `minimum` value of the data to `Q1` of the data.

The data’s minimum value is determined by the value of `Q1–1.5(Q3-Q1)` while the maximum value of the data is determined by the formula `Q3+1.5(Q3-Q1)`.

``````import matplotlib.pyplot as plt

x=[1,4,5,6,8,9,10,10,11,11,12,12,13,14,15,15,15,17,18,18,19,22,23,25,30,33,35]

plt.boxplot(x)
plt.title("Boxplot Using Matplotlib")
plt.show()
``````

Output: It plots the boxplot of the given data `x`. We can also notice two `outliers` at the top of the boxplot represented by circles in the plot.

A data point is plotted as an outlier if either its value is smaller than `Q1–1.5(Q3-Q1)` or greater than `Q3+ 1.5(Q3-Q1)`.

If we pass a 2D array as an argument to the `matplotlib.pyplot.boxplot()` function, the `boxplot()` function makes `boxplot` for each array or the list in the 2D array.

``````import numpy as np
import matplotlib.pyplot as plt

np.random.seed(100)

data_a =np.random.randint(2,15, size=15)
data_b =np.random.randint(5,18, size=20)
data_c =np.random.randint(2,20, size=30)
data_d =np.random.randint(1,30, size=40)

data_2d=[data_a,data_b,data_c,data_d]

plt.boxplot(data_2d)
plt.title("Boxplot Using Matplotlib")
plt.show()
``````

Output: It creates `boxplot` for each Numpy array inside the list `data_2d`. Hence, we get 4 boxplots in a single figure sharing common axes.