matplotlib
offers the function bar
and barh
to do vertical and horizontal bar plots.
matplotlib
also offers the function boxplot
to do vertical box plots.
And Pandas offers its own function for vertical box plots.
But is there any way in matplotlib or Pandas to get a horizontal box plot?
Horizontal Box plots We can turn the boxplot into a horizontal boxplot by two methods first, we need to switch x and y attributes and pass it to the boxplot( ) method, and the other is to use the orient=”h” option and pass it to the boxplot() method.
Switching x and y. Notice how the orientation of the boxplot only depends on how you declare the numerical variable you expect to analyze. If set as the argument y, the boxplot will be vertical, and if set as x, it will be horizontal.
Python Pandas - Draw a vertical boxplot grouped by a categorical variable with Seaborn. Box Plot in Seaborn is used to draw a box plot to show distributions with respect to categories. To create a vertical Box Plot, use the seaborn. boxplot().
matplotlib's boxplot(..., vert=False)
makes horizontal box plots. The keyword parameter vert=False
can also be passed to DataFrame.boxplot
:
import matplotlib.pyplot as plt import pandas as pd x = [[1.2, 2.3, 3.0, 4.5], [1.1, 2.2, 2.9, 5.0]] df = pd.DataFrame(x, index=['Age of pregnant women', 'Age of pregnant men']) df.T.boxplot(vert=False) plt.subplots_adjust(left=0.25) plt.show()
I see from the comment (below) that the motivation for making a horizontal box plot is that the labels are rather long. Another option in that case might be to rotate the xticklabels:
import matplotlib.pyplot as plt import pandas as pd x = [[1.2, 2.3, 3.0, 4.5], [1.1, 2.2, 2.9, 5.0]] df = pd.DataFrame(x, index=['Age of pregnant women', 'Age of pregnant men']) df.T.boxplot() plt.subplots_adjust(bottom=0.25) plt.xticks(rotation=25) plt.show()
vert=False stands # for "no vertical"
Use by='categorical_feature name' to make box for every level plt.tight_layout() # kills any overlapping plots (not always) Matplotlib and Pandas are really easy when you master them and you can do powerful plots using them.
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