# Box and whiskers plot

We can effortlessly visualize the dispersion and skewness of data using the box and whiskers plot.

``````1
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import seaborn as sns
data = data.dropna()

from matplotlib.pyplot import boxplot
import matplotlib.pyplot as plt

boxplot(data['age'], labels = ['age'])
plt.title("Titanic passenger's age - bars and whiskers")
``````

The plot consists of 3 elements:

• The line inside the rectangle indicates the median of data.

• The rectangle shows the interquartile range (IQR). Its lower edge is placed at the 25% percentile (1st quartile). The upper edge is at the 75% percentile (3rd quartile).

• The T-shaped lines are the whiskers. Normally the range of the whiskers shows values which are between the 1st quartile (Q1) and a number (Q1 — IQR1.5). The upper whisker ends at the value = Q3 + IQR1.5.

In case of this plot, the whiskers end at the minimal and the maximal values.

# Outliers

If we limit the whiskers range to 1*IQR we will see another part of the plot. The circles indicate outliers.

``````1
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from matplotlib.pyplot import boxplot
import matplotlib.pyplot as plt

boxplot(data['age'], whis = 1, labels = ['age'])
plt.title("Titanic passenger's age - bars and whiskers")
``````

We can also limit the whiskers to given percentiles. The plot will display value lower than the n-th percentile and larger than k-th percentile as outliers.

``````1
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from matplotlib.pyplot import boxplot
import matplotlib.pyplot as plt

boxplot(data['age'], whis = [5, 95], labels = ['age'])
plt.title("Titanic passenger's age - bars and whiskers")
``````

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You may also like ## Bartosz Mikulski

• MLOps engineer by day
• AI and data engineering consultant by night
• Python and data engineering trainer
• Conference speaker
• Contributed a chapter to the book "97 Things Every Data Engineer Should Know"