Summary
This chapter covers how to generate line plots by passing one or two numeric arrays to a plotting function—optionally with markers—while leveraging automatic axis generation based on array indices.

General domain of usage
Data visualization

## 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

Use the familiar `plot()` function from `pyplot` to create line plots. Here's an example that displays a quadratic relationship between two variables:


import matplotlib.pyplot as plt
import numpy as np

data_linear = np.arange(0, 6)
data_squared = data_linear ** 2

# Creating a line plot with specifying x and y
plt.plot(data_linear, data_squared, '-o')
plt.show()

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

import matplotlib.pyplot as plt
import numpy as np
data_squared = np.arange(0, 6) ** 2
# Creating a line plot using only one array
plt.plot(data_squared, '-o')
plt.show()

When only one array (or `pandas.Series()`) like `data_squared` is passed to the `plot()` function, `matplotlib` uses the array values for the y-axis. The x-axis is automatically generated using the element indices — in this case, the numbers from 0 to 5.


import unittest
from unittest.mock import patch
import importlib
import numpy as np

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestUserCode(unittest.TestCase):

    def test_plot_function_is_used(self):
        with patch('matplotlib.pyplot.plot') as mock_plot:
            importlib.reload(importlib.import_module("user_code"))
            _dynamic_test(
                self,
                mock_plot.called,
                "`plot()` is used to create the line plot.",
                "`plot()` is not used â make sure to call `plt.plot()`."
            )

    def test_plot_arguments_order(self):
        with patch('matplotlib.pyplot.plot') as mock_plot:
            import user_code
            importlib.reload(importlib.import_module("user_code"))
            x_data = getattr(user_code, 'x_data', None)
            y_data = getattr(user_code, 'y_data', None)

            correct_order = False
            if mock_plot.called:
                args, kwargs = mock_plot.call_args
                correct_order = (
                    np.array_equal(args[0], x_data) and
                    np.array_equal(args[1], y_data)
                )

            _dynamic_test(
                self,
                correct_order,
                "`plot()` is called with `x_data` as x-axis and `y_data` as y-axis in correct order.",
                "`plot()` must be called with `x_data` (x-axis) and `y_data` (y-axis) in correct order."
            )

    def test_plot_style_argument(self):
        with patch('matplotlib.pyplot.plot') as mock_plot:
            importlib.reload(importlib.import_module("user_code"))
            correct_style = False
            if mock_plot.called:
                args, kwargs = mock_plot.call_args
                correct_style = len(args) >= 3 and args[2] == '-o'

            _dynamic_test(
                self,
                correct_style,
                "`plot()` is called with correct style ('-o' - markers and lines).",
                "`plot()` is called with incorrect style."
            )

if __name__ == '__main__':
    unittest.main()


test_line.py

Data is everywhere around us, and making sense of it is extremely important. Visualization helps you deal with data by finding certain patterns and insights in it. You will develop a solid foundation of data visualization using Python and its libraries, such as matplotlib and seaborn, to get as much information from data as possible in a neat and concise way.

First, we will discuss plotting applications, Matplotlib library, and its advantages. Afterwards you will create your first simple plot using Matplotlib.

Now you are ready to create various commonly used in practice plots. Without further ado, let's dive in right now!

Plotting and visualization is not only about creating a plot or several plots, it is also about making them clear, understandable and pleasant to look at. Time to learn plots customization in maplotlib, let's do it!

Statistics is essential in working with data, hence why it is extremely important to know how to create various statistical plots. Now it's time for us to explore the most common ones.

Seaborn is another powerful plotting library in Python built on top of matplotlib. Now that you are familiar with matplotlib, it's time to dive into visualization with seaborn.

Line Plot

Applications

Creating a Line Plot

Solution

Awesome!

Line Plot

Applications

Creating a Line Plot

Solution

Awesome!