Summary  
This chapter explains how to remove rows or columns from a DataFrame by using the drop method with index, columns, and axis parameters.

General domain of usage  
Data cleaning

At times, certain columns may not provide valuable information, making it advantageous to remove them. The `pandas` library offers the `drop()` method for this purpose.

```python
drop(index, columns, axis)
```

- `index`: specifies the row indexes to be deleted (used when `axis=0`);
- `columns`: identifies the column names to be deleted (used when `axis=1`);
- `axis`: choose whether to remove labels from the **rows** (`0`) or **columns** (`1`). The default is `0`.

Start by examining the `DataFrame`:


import pandas as pd 

countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : [None, None, 'Europe', None, 'Europe', None, 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(countries_data)
print(countries)

You can see that the `'continent'` column contains many missing values, making it less informative. Remove this column.


import pandas

countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : [None, None, 'Europe', None, 'Europe', None, 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pandas.DataFrame(countries_data)
countries = countries.drop(columns = ['continent'],axis=1)
print(countries)

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

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

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

    def test_capital_column_removed(self):
        import user_code
        try:
            condition = 'capital' not in user_code.audi_cars.columns
            failure_message = "Expected `'capital'` column to be removed from `audi_cars`, but it still exists."

        except AttributeError:
            condition = False
            if not getattr(user_code, 'audi_cars', None):
                failure_message = "The `audi_cars` variable is not declared."
            else:
                failure_message = "`audi_cars` is not a `DataFrame`."

        _dynamic_test(
            self,
            condition,
            "The `'capital'` column is removed from `audi_cars`.",
            failure_message
        )

    def test_audi_cars_columns_correct(self):
        import user_code
        expected_columns = ['fueltype', 'model', 'year']
        try:
            actual_columns = sorted(list(user_code.audi_cars.columns))
            condition = actual_columns == expected_columns
            failure_message = f"Expected `audi_cars` to have columns `{expected_columns}`, but got `{actual_columns}`."

        except AttributeError:
            condition = False
            if not getattr(user_code, 'audi_cars', None):
                failure_message = "The `audi_cars` variable is not declared."
            else:
                failure_message = "`audi_cars` is not a `DataFrame`."

        _dynamic_test(
            self,
            condition,
            f"`audi_cars` has the correct columns.",
            failure_message
        )

    def test_audi_cars_is_correct(self):
        import user_code

        cars_data = {
            'model': ['Audi A1', 'Audi A6', 'Audi A4', 'Audi A3', 'Audi A1'],
            'year': [2017, 2016, 2017, 2019, 2016],
            'fueltype': ['petrol', 'diesel', 'diesel', 'petrol', 'petrol']}

        expected_df = pd.DataFrame(cars_data)

        variable = 'audi_cars'
        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 is an extremely user-friendly library for data analysis. It's also designed to handle large datasets, using data structures like DataFrame and Series. This makes it an invaluable tool for Data Science. In this guide, you'll get acquainted with a range of statistical functions, including how to find correlations, modes, medians, and maximum and minimum values within a dataset. You'll also learn how to handle missing values and manipulate specific values, as well as how to remove them.

Dive into the fundamentals of the pandas library. Learn to create and manipulate Series and DataFrames, explore their structure, and build confidence in performing essential data operations.

Master how to load and manage data from CSV and TXT files. Gain practical experience importing datasets and preparing them for further analysis using pandas tools.

Learn to explore, clean, and summarize data effectively. Understand how to handle missing values, extract insights, and perform basic statistical operations with pandas.

Deleting a Row/Column

解答