Basic plotting with Matplotlib and Seaborn

Load packages

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

Load dataset

We going to use the iris dataset.

dat = sns.load_dataset('iris')

We can further see the information on this dataset.

dat.head(5)

##    sepal_length  sepal_width  petal_length  petal_width species
## 0           5.1          3.5           1.4          0.2  setosa
## 1           4.9          3.0           1.4          0.2  setosa
## 2           4.7          3.2           1.3          0.2  setosa
## 3           4.6          3.1           1.5          0.2  setosa
## 4           5.0          3.6           1.4          0.2  setosa

Histogram

Let’s plot the histogram using matplotlib first.

plt.hist(dat['sepal_length'], bins=30)
plt.show()

Notice that this histogram does not has any label. So, to add a label, we need to do this manually.

plt.hist(dat['sepal_length'], bins=30)
plt.xlabel('Sepal length') #x-axis label
plt.ylabel('Frequency') #y-axis label
plt.show()

However, using seaborn, the label is extracted from the variable name, which is pretty convenient.

sns.histplot(dat['sepal_length'], bins=30)
plt.show()

Let’s say we want to plot the histogram according to different levels.

species = ['setosa', 'versicolor', 'virginica']

for i in species:
    subset = dat[dat['species'] == i]
    plt.hist(subset['sepal_length'], label = i)

plt.legend(loc = 'upper right')
plt.xlabel('Sepal length')
plt.ylabel('Frequency')
plt.show()

The codes above are quite long. In seaborn, the histogram above can be generated quite easily.

sns.histplot(x = 'sepal_length', hue = 'species', data = dat)
plt.show()

Boxplot

First, let’s do boxplot using matplotlib.

bp = plt.boxplot(dat['sepal_length'])
plt.xlabel('Sepal length')
plt.show()

If we wanto to do boxplot according to other variable. The codes become a bit complicated especially for beginners.

species = dat.groupby('species')
setosa = species.get_group('setosa')['sepal_length']
versicolor = species.get_group('versicolor')['sepal_length']
virginica = species.get_group('virginica')['sepal_length']

bp = plt.boxplot([setosa, versicolor, virginica], labels = ['setosa', 'versicolor', 'virginica'])
plt.xlabel('Sepal length')
plt.show()

Both plots above are quite easy to do in seaborn. Below are the codes for the basic histogram.

sns.boxplot(dat['sepal_length'])
plt.show()

Next, to plot sepal_length based on species is pretty much straightforward in seaborn.

sns.boxplot(y='sepal_length', hue='species', data=dat)
plt.show()

Scatter plot

Lastly, let’s see the scatter plot using matplotlib.

plt.scatter(x=dat['sepal_length'], y=dat['sepal_width'])
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.show()

We can further extend this plot by categorising it into different species.

# Define the species to colors mapping
species_to_color = {'setosa': 'blue', 'versicolor': 'green', 'virginica': 'red'}
colors = dat['species'].map(species_to_color)

# Create the scatter plot
plt.scatter(x=dat['sepal_length'], y=dat['sepal_width'], c=colors)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.legend(handles=[plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, markersize=10, label=species) for species, color in species_to_color.items()], title='Species')
plt.show()

Now, let’s see the seaborn package. This is the basic scatter plot.

sns.scatterplot(x='sepal_length', y='sepal_width', data=dat)
plt.show()

To extend this plot by categorising it into different species in seaborn is actually quite simple.

sns.scatterplot(x='sepal_length', y='sepal_width', hue='species', data=dat)
plt.show()

Conclusion

In conclusion, matplotlib and seaborn complement each other well. Seaborn is an excellent choice for quick and standard plots, thanks to its high-level interface. On the other hand, matplotlib offers a more extensive range of customization options and is ideal for creating complex and detailed visualizations. Ultimately, choosing between matplotlib and seaborn depends on the specific requirements of the visualization task.