Summary  
Introduces a unified distribution-plotting interface that can generate histograms, KDE plots, or ECDFs and automatically facet them into subplots by category.

General domain of usage  
Data visualization

`displot` (distributionsplot) er "forælder"-funktionen for alle de plottyper, vi har lært indtil nu. Den tilbyder én samlet grænseflade til at oprette histogrammer, KDE-plots og ECDF'er.

## Hvorfor bruge Displot?

Selvom `histplot` og `kdeplot` er gode til enkelte plots, har `displot` en særlig styrke: **facettering**.

Ved at bruge parameteren `col` (kolonne) eller `row` (række) kan `displot` automatisk opdele datasættet i flere side-om-side underplots baseret på en kategori.

### Centrale parametre

* **`kind`**: bestemmer typen af plot;
* `'hist'` (standard);
* `'kde'`;
* `'ecdf'`.
* `col` / `row`: opdeler data i separate underplots arrangeret i kolonner eller rækker.

### Eksempel

Her vises, hvordan du straks kan oprette 3 separate histogrammer for forskellige arter med blot én linje kode.

import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
df = sns.load_dataset('penguins')

# Create a grid of plots
sns.displot(
    data=df,
    x='bill_length_mm',
    col='species',     # Creates 3 subplots (one per species)
    kind='hist',       # Draw histograms
    element='step'
)

plt.show()

import unittest
import importlib
import sys
import pandas as pd
from unittest.mock import patch, MagicMock

# 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):

    def setUp(self):
        self.patcher_csv = patch('pandas.read_csv')
        self.mock_read_csv = self.patcher_csv.start()
        
        # Provide real data structure
        self.mock_df = pd.DataFrame({
            'carat': [0.23, 0.21, 0.25, 0.20, 0.31],
            'cut': ['Ideal', 'Premium', 'Good', 'Premium', 'Good'],
            'color': ['E', 'E', 'J', 'I', 'J']
        })
        self.mock_read_csv.return_value = self.mock_df

    def tearDown(self):
        self.patcher_csv.stop()

    # Test style
    def test_style(self):
        with patch('seaborn.displot'), patch('matplotlib.pyplot.show'):
            with patch('seaborn.set_style') as mock_style:
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
                
                # Check if style was set
                if not mock_style.called:
                    _dynamic_test(self, False, "", "Expected `sns.set_style()` to be called.")
                    return

                args, kwargs = mock_style.call_args
                passed_style = args[0] if args else kwargs.get('style')
                
                _dynamic_test(
                    self,
                    passed_style == 'darkgrid',
                    "The style is set to 'darkgrid'.",
                    f"Expected style to be `'darkgrid'`, but got `{passed_style}`."
                )

    # Test displot call
    def test_displot_called(self):
        with patch('seaborn.displot') as mock_displot:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
            
            _dynamic_test(
                self,
                mock_displot.called,
                "The `sns.displot()` function is used.",
                "Expected `sns.displot()` to be used."
            )

    # Test logic parameters (kind, col, multiple)
    def test_displot_params(self):
        with patch('seaborn.displot') as mock_displot:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
            
            if not mock_displot.called:
                return

            args, kwargs = mock_displot.call_args
            
            # Check faceting and kind
            kind_check = kwargs.get('kind') == 'kde'
            col_check = kwargs.get('col') == 'color'
            multiple_check = kwargs.get('multiple') == 'fill'
            palette_check = kwargs.get('palette') == 'viridis'

            _dynamic_test(
                self,
                kind_check and col_check and multiple_check and palette_check,
                "The `displot` is configured correctly (`kind='kde'`, `col='color'`, `multiple='fill'`).",
                f"Expected `kind='kde'`, `col='color'`, `multiple='fill'`, `palette='viridis'`. Got: {kwargs}"
            )

    # Test basic data parameters
    def test_data_params(self):
        with patch('seaborn.displot') as mock_displot:
            with patch('matplotlib.pyplot.show'):
                if 'user_code' in sys.modules:
                    importlib.reload(sys.modules['user_code'])
                else:
                    import user_code
            
            if not mock_displot.called:
                return

            args, kwargs = mock_displot.call_args
            
            x_check = kwargs.get('x') == 'carat'
            hue_check = kwargs.get('hue') == 'cut'
            
            # We mock the dataframe, so we check if the passed object is the same instance
            data_check = kwargs.get('data') is user_code.df

            _dynamic_test(
                self,
                x_check and hue_check and data_check,
                "The `x`, `hue`, and `data` parameters are set correctly.",
                "Expected `x='carat'`, `hue='cut'`, and `data=df`."
            )

    # Test show
    def test_show_used(self):
        with patch('seaborn.displot'):
            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

Udforsk styrken ved statistisk datavisualisering designet til at afsløre mønstre og sammenhænge. Brug Seaborn til at skabe informative fordelingsdiagrammer, varmekort og kategoriske diagrammer med minimal kode. Lær at anvende æstetiske temaer og farvepaletter, der gør kompleks statistisk data tilgængelig og let at fortolke.

Brug af Distributionsgrænsefladen

Hvorfor bruge Displot?

Centrale parametre

Eksempel

Løsning