Plotting and Data Visualization with Matplotlib

Working with raw data in the form of a CSV (comma-separated value) does not visually tell a story. However, if done right with a visualization library like Matplotlib, your users tend to appreciate you because they can connect the dots easily with visuals.

This article is an introduction to using Matplotlib for plotting and data visualizations.

GitHub Repo

Check the complete source code in this repo.

What is Matplotlib

Matplotlib is a Python plotting library that allows you to turn data into pretty visualizations, also known as plots or figures.

The following reasons are why Matplotlib is necessary for data scientists:

• It is built on NumPy arrays (and Python)


• Integrates directly with Pandas


• Can create basic or advanced plots

Importing Matplotlib

To start with Matplotlib, import it into your Jupyter Notebook like this:

%matplotlib inline

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

• 
  %matplotlib inline: this magic command with the percentage sign in front of matplotlib helps make sure all matplotlib plots and graphs appear within the notebook

The simplest way to create a plot is with:

plt.plot();

Let's add some data to the plot:

x = [1, 2, 3, 4]

y = [11, 22, 33, 44]

plt.plot(x, y);

This code shows the plot values on the plot figure's x and y axes.

The recommended way of plotting a graph is using this method, which should give the same result as the previous method:

fig, ax = plt.subplots()

ax.plot(x, y);

Note: Changing the x and y should return an entirely different graph.

Anatomy of Matplotlib Plot

The representation of a typical workflow of a Matplotlib figure includes:

• A plot axes title


• Legend


• y-axis label


• x-axis label

Let's see an example of the workflow.

# 0. import matplotlib and get it ready for plotting in Jupyter

%matplotlib inline 

import matplotlib.pyplot as plt



# 1. Prepare data 

x = [1, 2, 3, 4]

y = [11, 22, 33, 44]



# 2. Setup plot

fig, ax = plt.subplots(figsize=(10, 10))



# 3. Plot data  

ax.plot(x, y)



# 4. Customize plot

ax.set(title = "Simple plot",

       xlabel = "x-axis",

       ylabel = "y-axis")



# 5. Save & show (you have to save the whole figure)

fig.savefig("images/sample-plot.png")

The code above shows that you can set a title with the ax.set() method and save the plot as a .png file in the images folder.

Creating Figures with NumPy arrays

In this section, you will create different plots like scatter and bar, but there are others like histograms, lines, and subplots.

Copy-paste this code in your notebook:

import numpy as np

x = np.linspace(0, 10, 100)

x[:10]

linspace: returns evenly spaced numbers over a specified interval. Also, the index of x displays only the first ten results.

Plot the data and create a line plot:

fig, ax = plt.subplots()

ax.plot(x, x**2);

You should see something like this:

For a scatter plot, use the same data from above:

fig, ax = plt.subplots()

ax.scatter(x, np.exp(x));

Note: Instead of using .plot() on ax axes, switch to using .scatter().

Working with dictionaries and making a plot:

nut_butter_prices = {"Almond butter": 10, "Peanut butter": 9, "Cashew butter": 5}



fig, ax = plt.subplots()

ax.bar(nut_butter_prices.keys(), nut_butter_prices.values())

ax.set(title = "Teri's Nut Butter Store",

       ylabel = "Price ($)"

      );

Horizontal Bar

Another way of creating a plot is plotting a horizontal bar with .barh.

fig, ax = plt.subplots()

ax.barh(list(nut_butter_prices.keys()), list(nut_butter_prices.values()));

Subplots and Histograms

You can turn a single figure into subplots of four equal parts with this code:

# Subplots option 1

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(

    nrows = 2,

    ncols = 2,

    figsize = (10, 5)

)



# Plot to each different axes

ax1.plot(x, x / 2);

ax2.scatter(np.random.random(10), np.random.random(10));

ax3.bar(nut_butter_prices.keys(), nut_butter_prices.values());

ax4.hist(np.random.randn(1000));

# subplots option 2

fig, ax = plt.subplots(nrows = 2, 

                       ncols = 2, 

                       figsize = (10, 5))



# Plot to each different index

ax[0, 0].plot(x, x/2);

ax[0, 1].scatter(np.random.random(10), np.random.random(10));

ax[1, 0].bar(nut_butter_prices.keys(), nut_butter_prices.values());

ax[1, 1].hist(np.random.randn(1000));

Plotting from Pandas DataFrame

This section will show you how to use the Pandas DataFrame to visualize data using a .csv file.

Download the car sales data

Before using an imported to read and use it, first import the pandas library:

import pandas as pd

Make a DataFrame with this command:

car_sales = pd.read_csv("car_sales.csv")

car_sales

Reading the car sales data is saved in the root directory of the main Python notebook. But if you save it in a folder, you must reference it in the .read_csv() method.

To remove the $ sign and turn it into an integer data type, run this command, which is in regex:

car_sales['Price']=car_sales['Price'].str.replace('$','',regex=False).str.replace(',','',regex=False).astype(float).astype(int)

car_sales

Add a Sale Date Column:

car_sales["Sale Date"] = pd.date_range("1/1/2023", periods=len(car_sales))

car_sales

Add Total Sales Column:

car_sales["Total Sales"] = car_sales["Price"].cumsum()

car_sales

Plot the Total Sales:

car_sales.plot(x = "Sale Date", y = "Total Sales")

Repeat the same process to plot with any column axis just like this:

car_sales.plot(x="Odometer (KM)", y = "Price", kind = "scatter");

In Summary

Matplotlib creates beautiful visualization depending on what you want to achieve, as it is rich with various options to spice up your data and make it visually appealing.

Resources

• Matplotlib Documentation


• Different types of plot


• The lifecycle of a plot


• Pandas DataFrame Visualization