They show the distribution through the thickness of the violin instead of only the summary statistics. Violin plots are very useful alternatives to boxplots. The single points outside this range indicate any outliers in the data. The other two lines in the middle are medians of quartile 2 and 3 which show how much the values vary from the median. The bottom and top most lines towards the ends of the box plot are the medians of quartile 1 and 4 which basically show the minimum and maximum of the distribution. The middle line is the median value and is the point where the data is centered around. Here we can see that each attribute has its individual boxplot.Ī box plot is based on a 5 number summary which are each displayed as different lines. import pandas as pd from matplotlib import pyplot as plt import seaborn as sns df = pd.read_csv('Pokemon.csv', index_col = 0, encoding='unicode-escape') df.head() You can find the CSV file to this tutorial here. Let’s first import the required Python libraries and our dataset. Using pip: pip install pandas pip install matplotlib pip install seaborn Using conda: conda install pandas conda install matplotlib conda install seaborn Make sure you have the necessary libraries installed in your system: ![]() If not, you can refer to the following articles on the same: It lets you plot striking charts in a much simpler way.įor better understanding of this article, you will need to know the basics of pandas as well as matplotlib. ![]() Seaborn works well with dataframes while Matplotlib doesn’t. It provides a large number of high-level interfaces to Matplotlib. Seaborn is a powerful Python library which was created for enhancing data visualizations.
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