Summary  
This chapter explains how to import text files into a pandas DataFrame using `pd.read_csv()`, specifying a custom delimiter with `sep` and handling files without header rows by setting `header=None`.  

General domain of usage  
Data analysis

Per leggere **file di testo** in `pandas`, è possibile utilizzare la stessa funzione, `pd.read_csv()`. Tuttavia, per garantire che il file di testo venga letto correttamente, è fondamentale utilizzare un parametro aggiuntivo chiamato `sep`, che indica il **separatore** o **delimitatore** nel testo.

```python
# Importing pandas library
import pandas as pd

# Reading a text file into a DataFrame
text_data = pd.read_csv('file.txt', sep='\r', header=None)
```

Se il file di testo non contiene una **riga di intestazione** con i nomi delle colonne, impostare il parametro `header` su `None`. In questo modo si informa `pandas` di non considerare la prima riga come nomi delle colonne.

Per utilizzare una nuova riga come separatore in un file, pratica comune nei file di testo, impostare `sep='\r'`. Qui, `'\r'` rappresenta un ritorno a capo, un carattere speciale utilizzato per indicare una nuova riga.

Nota

import unittest
import importlib
import pandas as pd


# 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)


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

    def test_text_data_is_dataframe(self):
        import user_code

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

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

    def test_text_data_is_correct(self):
        import user_code
        row_1 = 'pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.[2] The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals.[3] Its name is a play on the phrase "Python data analysis" itself.[4] Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010.[5]'
        row_2 = "taken from the site:"
        row_3 = "https://en.wikipedia.org/wiki/Pandas_(software)"
        expected_df = pd.DataFrame([row_1, row_2, row_3])

        variable = 'text_data'
        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_first_row_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'first_row'),
            "The `first_row` variable is declared.",
            "Expected `first_row` to be declared."
        )

    def test_first_row_is_series(self):
        import user_code

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

        _dynamic_test(
            self,
            condition,
            "The variable `first_row` is a `Series`.",
            failure_message
        )

    def test_first_row_is_correct(self):
        import user_code
        row_1 = 'pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.[2] The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals.[3] Its name is a play on the phrase "Python data analysis" itself.[4] Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010.[5]'
        row_2 = "taken from the site:"
        row_3 = "https://en.wikipedia.org/wiki/Pandas_(software)"
        expected_df = pd.DataFrame([row_1, row_2, row_3])
        expected_value = expected_df.iloc[0]

        variable = 'first_row'
        actual_value = getattr(user_code, variable, None)
        if actual_value is None:
            condition = False
            failure_message = f"The `{variable}` variable is not declared."
        elif isinstance(actual_value, pd.Series):
            condition = actual_value.equals(expected_value)
            if actual_value.empty:
                failure_message = f"`{variable}` is empty."
            else:
                failure_message = f"Expected `{variable}` to contain `{expected_value.values}`, but got `{actual_value.values}`."
        else:
            condition = False
            failure_message = f"`{variable}` is not a `Series`."

        _dynamic_test(
            self,
            condition,
            f"`first_row` contains the correct values.",
            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.

File Txt

Soluzione

Awesome!

File Txt

Soluzione