Summary  
This chapter covers generating a countplot to visualize the frequency of categorical data by tallying each category’s occurrences using a plotting library.

General domain of usage  
Data visualization

Ein **Countplot** ist ein Diagramm, das eine kategorische Liste entgegennimmt und Säulen zurückgibt, die die Anzahl der Listeneinträge für jede Kategorie darstellen.

# Importing the `seaborn` library
import seaborn as sns
# Importing `matplotlib.pyplot`
import matplotlib.pyplot as plt

# Data list
data_list = ['apple', 'milk', 'milk', 'milk', 'apple', 'milk', 'milk', 'milk', 'fish']

# Creating the countplot
sns.countplot(x=data_list, palette='mako')

# Showing the plot
plt.show()

Um einen Countplot zu initialisieren, muss mindestens ein Parameter angegeben werden: `x` (die Spalte, in der die Werte gezählt werden sollen).

Sie können auch `y` anstelle von `x` verwenden, um die Ausrichtung des Diagramms zu ändern.

Hinweis

Da `seaborn` auf `matplotlib` basiert, können Sie eine spezielle Bibliothek `matplotlib.pyplot` und deren Funktion `plt.show()` verwenden, um das Diagramm anzuzeigen.

import unittest
import importlib
import sys
from unittest.mock import patch

# Helper function to dynamically generate test names and assertions
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):

    # Test if seaborn is imported with alias sns
    def test_seaborn_import(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'sns'),
            "The `seaborn` library is imported as `sns`.",
            "Expected `seaborn` to be imported as `sns`."
        )

    # Test if matplotlib.pyplot is imported with alias plt
    def test_matplotlib_import(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'plt'),
            "The `matplotlib.pyplot` library is imported as `plt`.",
            "Expected `matplotlib.pyplot` to be imported as `plt`."
        )

    # Test if guests_list contains the correct data
    def test_guests_list_correct(self):
        import user_code
        expected_list = ['Girl', 'Boy', 'Boy', 'Boy', 'Girl', 'Boy', 'Boy', 'Boy', 'Girl', 'Boy', 'Girl', 'Girl', 'Girl', 'Girl', 'Girl', 'Girl']
        
        # Check if variable exists first
        if not hasattr(user_code, 'guests_list'):
             _dynamic_test(self, False, "", "The `guests_list` variable is not declared.")
             return

        _dynamic_test(
            self,
            user_code.guests_list == expected_list,
            "The `guests_list` contains the correct data.",
            f"Expected `guests_list` to be `{expected_list}`, but got `{user_code.guests_list}`."
        )

    # Test if countplot function is used
    def test_countplot_used(self):
        # We patch seaborn.countplot to intercept the call
        with patch('seaborn.countplot') as mock_countplot:
            # We also patch plt.show to prevent the plot window from actually popping up during tests
            with patch('matplotlib.pyplot.show'):
                # Reload user_code to force the script to run again with our mocks active
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
        
        _dynamic_test(
            self,
            mock_countplot.called,
            "The `sns.countplot()` function is used.",
            "Expected `sns.countplot()` function to be used to create the plot."
        )

    # Test if the correct argument is passed to countplot
    def test_countplot_argument(self):
        with patch('seaborn.countplot') as mock_countplot:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
            
            # Check arguments passed to countplot
            # kwargs retrieves named arguments like x=...
            passed_x = None
            if mock_countplot.called:
                args, kwargs = mock_countplot.call_args
                passed_x = kwargs.get('x')

            import user_code
            # We compare the actual list object passed to the function
            condition = passed_x == user_code.guests_list

            _dynamic_test(
                self,
                condition,
                "The `sns.countplot()` uses `guests_list` for the `x` parameter.",
                "Expected `sns.countplot()` to be called with `x=guests_list`."
            )

    # Test if plt.show() is used
    def test_show_used(self):
        with patch('seaborn.countplot'):
            with patch('matplotlib.pyplot.show') as mock_show:
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code

                _dynamic_test(
                    self,
                    mock_show.called,
                    "The `plt.show()` function is used.",
                    "Expected `plt.show()` to be used to display the plot."
                )

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

test_code.py

Entdecken Sie die Möglichkeiten der statistischen Datenvisualisierung zur Aufdeckung von Mustern und Zusammenhängen. Nutzen Sie Seaborn, um aussagekräftige Verteilungsdiagramme, Heatmaps und kategoriale Diagramme mit minimalem Codeaufwand zu erstellen. Erfahren Sie, wie ästhetische Designs und Farbpaletten angewendet werden, um komplexe statistische Daten zugänglich und leicht interpretierbar zu machen.

Zählen Kategorialer Daten

Lösung

Zählen Kategorialer Daten

Lösung