Summary
This chapter demonstrates how to group tabular data by multiple columns in a specified order using Pandas’ .groupby() method and then apply an aggregation function like count.

General domain of usage
Airline flight delay analysis

Let's add some information on the `.groupby()` method. You can group by several columns, but the order is crucial in this case. In the previous chapter, we grouped data by the flight number and counted the number of delays. We can make this task complicated by grouping not only by the `'Flight'` column, but also by the column `'Airline'`. Refresh the information on the dataset and then look at this simple example (the output contains only the first 10 rows):

import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/plane', index_col = 0)
data_flights = data[['Flight', 'Delay', 'Airline']].groupby(['Flight', 'Airline']).count()
print(data_flights.head(10))

**Explanation:**

```python
data[['Flight', 'Delay', 'Airline']].groupby(['Flight', 'Airline']).count()
```
- `data[['Flight', 'Delay', 'Airline']]` - columns you will work with, including the columns by which you will group;
- `.groupby(['Flight', 'Airline'])` -  here, `'Flight'` and `'Airline'` are arguments of the function `.groupby()`.  

Pay attention; if you want to group by several columns, put them into the list - the order is crucial. So, in our case, if rows of the data set have the same value in the column `'Flight'`, they will relate to one group. Then inside those groups, the function finds other groups for rows with the same value in the column `'Airline'`. Then, due to the method `.count()` that counts the rows, our function will calculate the number of rows in the column `'Delay'` that have the same value in the column `'Airline'` for each `'Flight'` group.

import unittest
import pandas as pd
import io
import sys


def _dynamic_test(test_case, condition, success_msg, failure_msg):
    if condition:
        test_case._testMethodName = success_msg
        test_case.assertTrue(True, success_msg)
    else:
        test_case._testMethodName = failure_msg
        test_case.fail(failure_msg)


def frames_equal_strict(df1, df2):
    """ÐÐµÑÐµÐ²ÑÑÐºÐ° ÑÑÐ²Ð½Ð¾ÑÑÑ DataFrame Ð· ÑÑÐ°ÑÑÐ²Ð°Ð½Ð½ÑÐ¼ Ð¿Ð¾ÑÑÐ´ÐºÑ ÐºÐ¾Ð»Ð¾Ð½Ð¾Ðº ÑÐ° ÑÐ½Ð´ÐµÐºÑÑ."""
    try:
        return df1.equals(df2)
    except Exception:
        return False


class TestGroupData(unittest.TestCase):
    def test_grouping_average(self):
        """
        1. Group data by 'AirportFrom' and 'DayOfWeek', calculate mean of 'Time'.
        """
        import user_code

        # ÐÑÐ¸Ð³ÑÐ½Ð°Ð»ÑÐ½Ð¸Ð¹ DataFrame
        df = pd.read_csv(
            "https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/plane",
            index_col=0,
        )

        # ÐÑÐ°Ð»Ð¾Ð½Ð½Ðµ ÑÑÑÐµÐ½Ð½Ñ
        ref = df[["AirportFrom", "DayOfWeek", "Time"]].groupby(["AirportFrom", "DayOfWeek"]).mean()

        condition = (
            hasattr(user_code, "data_flights")
            and isinstance(user_code.data_flights, pd.DataFrame)
            and frames_equal_strict(user_code.data_flights, ref)
        )

        _dynamic_test(
            self,
            condition,
            "The data is correctly grouped by 'AirportFrom' and 'DayOfWeek' with mean 'Time' calculated.",
            "The grouping or averaging is incorrect. Check the columns order and groupby parameters."
        )


class TestOutput(unittest.TestCase):
    def test_output_head_10(self):
        """
        2. Output the first 10 rows of the grouped DataFrame using .head(10).
        """
        import user_code
        captured_output = io.StringIO()
        sys.stdout = captured_output
        print(user_code.data_flights.head(10))
        sys.stdout = sys.__stdout__

        output_text = captured_output.getvalue().strip()
        condition = len(output_text) > 0
        _dynamic_test(
            self,
            condition,
            "The first 10 rows of 'data_flights' are printed correctly using .head(10).",
            "The output is missing or incorrect. Use print(data_flights.head(10))."
        )


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


Grouping by Several Columns

Lösung