Let us create a color dictionary with continent as key and its color as valueĬolor_dict = dict(zip(continents, continent_colors)) Let us say we also want a specific color for each continent already available as Hex Code(#RRGGBB).Ĭontinents = gapminder_().tolist()Ĭontinent_colors= ![]() Let us say want to make a boxplot visualizing distributions of lifeExp variable across the continents from the gapminder data. Now the data frame contains rows corresponding to the year 2007. Let us load the gapminder data from software carpentry website and subset the data to make it a smaller dataframe. Let us see an example of how to make boxplot suing Seaborn such that we use specific color for each box. Often you may want to visualize multiple variables as boxplot such that each group has specific color, not the “palette” options available in Seaborn. It provides a high-level interface for drawing attractive and informative statistical graphics Seaborn is a Python data visualization library based on matplotlib. ![]() In an earlier post, we saw a good example of how to create publication quality boxplots with Pandas and Seaborn. Creating a beautiful plot with Boxplots in Python Pandas is very easy. Boxplots with actual data points are one of the best ways to visualize the distribution of multiple variables at the same time.
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