Sometimes, you may want to rotate your data, if it’s easier to explore in a horizontal format.īy default, Seaborn will infer the orientation of your boxplot based on the data that exists in the dataset. This returns our updated boxplot: Rotating your Seaborn boxplot For example, if we wanted to place the weekend days first, we could write: sns.boxplot(data=df, x='day', y='total_bill', order=) Currently, Seaborn is inferring the order us, but we can also specify a particular order. There may be times when you want to sort your data in different ways. This returns our chart, with a helpful title and some axis labels added: Ordering Seaborn boxplots sns.boxplot(data=df, x='day', y='total_bill') Let’s now add a descriptive title and some axis labels that aren’t based on the dataset. To do this, we use the pyplot module from matplotlib.īy default, Seaborn will infer the column names as the axis labels. We can also use Matplotlib to add some descriptive titles and axis labels to our plot to help guide the interpretation of the data even further. Now our boxplot looks much nicer! Adding titles and axis labels to Seaborn boxplots Sns.boxplot(data=df, x='day', y='total_bill') Let’s apply the darkgrid style and the Set2 palette: sns.set_style('darkgrid') You can learn more about the style function by checking out the official documentation. We can use the sns.set_style() function and the sns.set_palette() function to apply both a style and a palette. Let’s learn how we can apply some style and a different colour palette to the Seaborn boxplot. The default boxplot generated by Seaborn is not the prettiest. This returns the following image: Styling a Seaborn boxplot Let’s see how we’d do this in Python: sns.boxplot(data=df, x='day', y='total_bill') Let’s start by creating a boxplot that breaks the data out by date on the x-axis and shows the total bill on the y-axis. This returns the following dataframe: total_bill tip sex smoker day time size
To demonstrate the sns.boxplot() function, let’s import the libraries we’ll need as well as load a sample dataframe that comes bundled with Seaborn: import pandas as pd Seaborn has an aptly named sns.boxplot() function that is used to create, well, boxplots. Inversely, if the median line is lower in the box, the data is said to be positively skewed. If the line is higher in the interquartile range (the box), the data is said to be negatively skewed. The median line can be very descriptive as well. Outliers are generally classified as being outside 1.5 times the interquartile range. Specifically, boxplots show a five number summary that includes:Īdditionally, boxplots will identify any outliers that exist in the data.
It helps you understand the data in a much clearer way than just seeing a single summary statistic. Changing Whisker Length in Seaborn BoxplotĪ boxplot is a helpful data visualization that illustrates a five different summary statistics for your data.Adding titles and axis labels to Seaborn boxplots.
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