Summary
Demonstrates how to use DataFrame methods to preview and explore subsets of data—specifically, printing the first or last N rows with `head()` and `tail()`, and fetching random rows with `sample()`.

General domain of usage
Exploratory data analysis in tabular datasets.

For at se de første rækker af et datasæt, anvendes metoden `head()`. Denne metode accepterer et **heltal** som argument, der angiver, hvor mange rækker der skal vises (som standard vises de **første 5 rækker**). Vis de første 10 rækker af datasættet:

```python
df = pd.read_csv('file.csv')
# Extracting the first 10 rows
first_lines = df.head(10)
```

For at se de **sidste rækker** af en `DataFrame`, anvendes metoden `tail()`. Den fungerer på samme måde som `head()`-metoden:

```python
df = pd.read_csv('file.csv')
# Extracting the last 10 rows
last_lines = df.tail(10)
```

En anden nyttig metode til at udforske DataFrames er `sample()`. Denne metode henter tilfældige poster fra en DataFrame. Som standard hentes **én tilfældig post**, medmindre andet er angivet.
```python
df = pd.read_csv('file.csv')
# Extracting 10 random rows
random_lines = df.sample(10)
```

import unittest
import importlib
import pandas as pd


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)


def get_first_differing_row(expected_df, actual_df):
    expected_list = expected_df.astype(str).apply(tuple, axis=1)
    actual_list = actual_df.astype(str).apply(tuple, axis=1)

    for i, (row1, row2) in enumerate(zip(expected_list, actual_list)):
        if row1 != row2:
            return i, expected_df.iloc[i].values, actual_df.iloc[i].values

    if len(expected_df) > len(actual_df):
        return len(actual_df), expected_df.iloc[len(actual_df)].values, None
    else:
        return len(expected_df), None, actual_df.iloc[len(expected_df)].values


class TestUserCode(unittest.TestCase):

    def test_first_lines_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'first_lines'),
            "The `first_lines` variable is declared.",
            "Expected `first_lines` to be declared."
        )

    def test_first_lines_is_dataframe(self):
        import user_code

        try:
            condition = isinstance(user_code.first_lines, pd.DataFrame)
            failure_message = f"Expected `first_lines` to be a `DataFrame`, but got `{type(user_code.first_lines).__name__}`."
        except AttributeError:
            condition = False
            failure_message = "The `first_lines` variable is not declared."

        _dynamic_test(
            self,
            condition,
            "The variable `first_lines` is a `DataFrame`.",
            failure_message
        )

    def test_first_lines_has_correct_values(self):
        import user_code
        wine_data = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a43d24b6-df61-4e11-9c90-5b36552b3437/wine.csv')
        expected_df = wine_data.head(10)

        variable = 'first_lines'
        actual_df = getattr(user_code, variable, None)
        condition = False
        if actual_df is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_df, pd.DataFrame):
            failure_message = f"`{variable}` is not a `DataFrame`."
        elif actual_df.empty:
            failure_message = f"`{variable}` is empty."
        elif actual_df.equals(expected_df):
            condition = True
            failure_message = None
        else:
            idx, expected_row, actual_row = get_first_differing_row(expected_df, actual_df)
            failure_message = f"Expected `{variable}` to contain `{expected_row}` at row {idx}, but got `{actual_row}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )

    def test_last_lines_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'last_lines'),
            "The `last_lines` variable is declared.",
            "Expected `last_lines` to be declared."
        )

    def test_last_lines_is_dataframe(self):
        import user_code

        try:
            condition = isinstance(user_code.last_lines, pd.DataFrame)
            failure_message = f"Expected `last_lines` to be a `DataFrame`, but got `{type(user_code.last_lines).__name__}`."
        except AttributeError:
            condition = False
            failure_message = "The `last_lines` variable is not declared."

        _dynamic_test(
            self,
            condition,
            "The variable `last_lines` is a `DataFrame`.",
            failure_message
        )

    def test_last_lines_has_correct_values(self):
        import user_code
        wine_data = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a43d24b6-df61-4e11-9c90-5b36552b3437/wine.csv')
        expected_df = wine_data.tail(15)

        variable = 'last_lines'
        actual_df = getattr(user_code, variable, None)
        condition = False
        if actual_df is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_df, pd.DataFrame):
            failure_message = f"`{variable}` is not a `DataFrame`."
        elif actual_df.empty:
            failure_message = f"`{variable}` is empty."
        elif actual_df.equals(expected_df):
            condition = True
            failure_message = None
        else:
            idx, expected_row, actual_row = get_first_differing_row(expected_df, actual_df)
            failure_message = f"Expected `{variable}` to contain `{expected_row}` at row {idx}, but got `{actual_row}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )

    def test_random_rows_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'random_rows'),
            "The `random_rows` variable is declared.",
            "Expected `random_rows` to be declared."
        )

    def test_random_rows_is_dataframe(self):
        import user_code

        try:
            condition = isinstance(user_code.random_rows, pd.DataFrame)
            failure_message = f"Expected `random_rows` to be a `DataFrame`, but got `{type(user_code.random_rows).__name__}`."
        except AttributeError:
            condition = False
            failure_message = "The `random_rows` variable is not declared."

        _dynamic_test(
            self,
            condition,
            "The variable `random_rows` is a `DataFrame`.",
            failure_message
        )

    def test_random_rows_has_correct_values(self):
        import user_code
        import numpy as np
        np.random.seed(5)
        wine_data = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a43d24b6-df61-4e11-9c90-5b36552b3437/wine.csv')
        expected_df = wine_data.sample(12)

        variable = 'random_rows'
        actual_df = getattr(user_code, variable, None)
        condition = False
        if actual_df is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_df, pd.DataFrame):
            failure_message = f"`{variable}` is not a `DataFrame`."
        elif actual_df.empty:
            failure_message = f"`{variable}` is empty."
        elif actual_df.equals(expected_df):
            condition = True
            failure_message = None
        else:
            idx, expected_row, actual_row = get_first_differing_row(expected_df, actual_df)
            failure_message = f"Expected `{variable}` to contain `{expected_row}` at row {idx}, but got `{actual_row}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )


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

test_main.py

Pandas er et yderst brugervenligt bibliotek til dataanalyse. Det er også designet til at håndtere store datasæt ved hjælp af datastrukturer som DataFrame og Series. Dette gør det til et uvurderligt værktøj inden for Data Science. I denne guide bliver du introduceret til en række statistiske funktioner, herunder hvordan man finder korrelationer, typetal, medianer samt maksimum- og minimumsværdier i et datasæt. Du lærer også, hvordan man håndterer manglende værdier og manipulerer specifikke værdier, samt hvordan man fjerner dem.

Dyk ned i grundlæggende elementer i pandas-biblioteket. Opret og manipulér Series og DataFrames, undersøg deres struktur, og opbyg fortrolighed med udførelse af essentielle dataoperationer.

Opnå færdigheder i at indlæse og håndtere data fra CSV- og TXT-filer. Få praktisk erfaring med at importere datasæt og forberede dem til videre analyse ved hjælp af pandas-værktøjer.

Lær at udforske, rense og opsummere data effektivt. Forstå håndtering af manglende værdier, udtrækning af indsigter og udførelse af grundlæggende statistiske operationer med pandas.

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