Summary  
This chapter explains how to use matplotlib’s `plot()` function to create line plots by specifying both x and y data or by passing a single dataset (with auto-generated x-axis 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

Master the foundational skills of data visualization using the object-oriented approach in Matplotlib. Learn to control every aspect of your figures, from axes and legends to colors and styles. Create professional-quality static charts, handle time-series data, and export high-resolution graphics for reports and publications.

Building Line Plots

Applications

Creating a Line Plot

解答