3. Boxplot & Pairplot

# Make a boxplot of each petal and sepal measurement

import matplotlib.pyplot as plt

data.boxplot(column=["petal_width", "petal_length", "sepal_width", "sepal_length"],by="species", rot=90)

plt.show()

data.boxplot(by='species');

plt.show()

# Create a single boxplot where the features are separated in the x-axis and species are colored with different hues

# First we have to reshape the data so there is only a single measurement in each column

plot_data = (data

             .set_index('species')

             .stack()

             .to_frame()

             .reset_index()

             .rename(columns={0:'size', 'level_1':'measurement'})

print(plot_data.head())

species measurement size

0 setosa sepal_length 5.1

1 setosa sepal_width 3.5

2 setosa petal_length 1.4

3 setosa petal_width 0.2

4 setosa sepal_length 4.9

# Plot the dataframe from above using Seaborn

import seaborn as sns

import matplotlib.pyplot as plt

sns.set_style('white')

sns.set_context('notebook')

sns.set_palette('dark')

f = plt.figure(figsize=(6,4))

sns.boxplot(x='measurement', y='size',

            hue='species', data=plot_data);

plt.show()

# Create a pairplot with Seaborn to examine the correlation between each of the measurements

import seaborn as sns

import matplotlib.pyplot as plt

sns.set_context('talk')

sns.pairplot(data, hue='species');

plt.show()

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