Summary  
This chapter introduces methods to preview sections of tabular data by printing the first or last N rows and random samples using head(), tail(), and sample().

General domain of usage  
Data analysis

Per visualizzare le prime righe di un dataset, possiamo utilizzare il metodo `head()`. Questo metodo accetta un **intero** come argomento, che specifica il numero di righe da visualizzare (per impostazione predefinita, mostra le **prime 5 righe**). Diamo un'occhiata alle prime 10 righe del nostro dataset:
```python
df = pd.read_csv('file.csv')
# Extracting the first 10 rows
first_lines = df.head(10)
```

Se vogliamo vedere le **ultime righe** di un DataFrame, possiamo usare il metodo `tail()`. Funziona in modo simile al metodo `head()`:
```python
df = pd.read_csv('file.csv')
# Extracting the last 10 rows
last_lines = df.tail(10)
```

Un altro metodo utile per esplorare i DataFrame è `sample()`. Questo metodo estrae record casuali da un DataFrame. Per impostazione predefinita, recupera un **singolo record casuale** a meno che non sia specificato diversamente.
```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 è una libreria estremamente intuitiva per l'analisi dei dati. È anche progettata per gestire grandi set di dati, utilizzando strutture dati come DataFrame e Series. Questo la rende uno strumento inestimabile per la Data Science. In questa guida, ti familiarizzerai con una gamma di funzioni statistiche, tra cui come trovare correlazioni, modali, mediane e valori massimi e minimi all'interno di un set di dati. Imparerai anche come gestire i valori mancanti e manipolare valori specifici, oltre a come rimuoverli.

In questa sezione, esploreremo i fondamenti delle strutture Series e DataFrame. Imparerai anche le differenze tra questi due tipi di strutture.

I dati possono essere ottenuti in vari formati, come CSV, JSON, SQL, HTML e altri. Con Pandas, non sei limitato a un solo formato: puoi lavorare con dati provenienti da una moltitudine di tipi di file. In questo capitolo, ci concentreremo specificamente sui formati CSV e TXT.

Qui, imparerai come elaborare i dati grezzi rimuovendo le informazioni superflue e gestendo i valori nulli in un dataset.

Visualizzazione dei Dati

Soluzione

Awesome!

Visualizzazione dei Dati

Soluzione

Awesome!