Course Content

Ultimate Visualization with Python

1. Matplotlib Introduction

Plotting Applications Why Use Matplotlib?Creating a Canvas Basic Plotting

2. Creating Commonly Used Plots

Line Plot Multiple Line Plots Scatter Plot Bar Chart Stacked Bar Charts Grouped Bar Charts

3. Plots Customization

Adding Title to the Plot Adding Legend Axes Customization Colors and Transparency Customizing Grid Subplots

4. More Statistical Plots

Histogram Box Plot Pie Chart

5. Plotting with Seaborn

Seaborn Introduction Countplot Plots Decoration KDE Plot Joint Plot Pair Plot Heatmap

Ultimate Visualization with Python

Line Plot

Congratulations on completing the first section! Since you have already created a plot with a single point on it, it's time to create a line plot.

Applications

Line plot is used to depict the relationship between two variables (e.g. x, y) using straight lines. More formally, it shows the relationship between continuous or ordinal variables in a continuous data point manner. Moreover, it can show how a certain variable changes with time.

Creating a Line Plot

We'll use a function from pyplot that we're already familiar with to create line plots: plot(). Let’s have a look at an example of a line plot which shows a quadratic relationship between two variables:

Code Description

import matplotlib.pyplot as plt

import numpy as np

First, we import pyplot with plt alias and numpy with np alias.

data_linear = np.arange(0, 6)

Then comes a more interesting part where we create a numpy array with numbers from 0 to 5 inclusive using np.arange() function.

data_squared = data_linear ** 2

Next, using numpy broadcasting (vectorization), we square every element of the previously created data_linear array.

plt.plot(data_linear, data_squared, '-o')

To create a line plot, we use data_linear for x-values and data_squared for y-values. We format the plot with '-o', where '-' signifies solid lines ('-' is the default value) and 'o' represents markers. '--o', for example, will create dashed lines with markers.

plt.show()

Finally, we display the line plot.

In fact, this code can even be further simplified. Have a look at another example:

Here we only used one array data_squared for plotting. But how does matplotlib understand which values are used for x-axis and y-axis?

Note
If only one array (pandas Series object) is specified, its indices will be used for x-axis and values for y-axis.

The indices in this example are numbers from 0 to 5 including (just integer indices of a usual array of size 6).

Task

Use the correct function for creating a line plot.
Pass in the correct order x_data (x-axis) and y_data (y-axis) as the first two arguments.
Pass the rightmost argument such that the plot will have 'o' markers and dashed lines.

Everything was clear?

Section 2. Chapter 1

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