Summary  
This chapter covers using the `.iloc[]` attribute to perform index-based selection on a DataFrame, including accessing single elements, slicing rows and columns, and selecting subsets via lists of indices.

General domain of usage  
Data analysis with tabular datasets using pandas.

We will learn some new features that `iloc[]` provides. The coolest one is that we can specify indices of both rows and columns. This attribute is similar to `.loc[]`, but the last index of the slicing is exclusive.

Look at the example and the relevant output:

- `data.iloc[1, 2]` - extracts the item located in the dataset's second row and third column. The first index corresponds to the row index, and the second to the column index. Indeed, you can skip one of them;
- `data.iloc[:, 3]` - extracts all values from the rows of the **fourth** column `'IMDb-Rating'`;
- `data.iloc[3, :]` or `data.iloc[3]` - extracts the `4th` row and all relevant columns;
- `data.iloc[:2, 1:4]` - extracts the first two rows and column with the indices `1`, `2`, `3`;
- `data.iloc[[2,4],[1,3]]` - extracts the **rows** with indices `2`,`4` and columns with the indices `1`, `3`.

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)


class TestCSVRead(unittest.TestCase):
    def test_csv_read(self):
        """
        1. Read the CSV file into 'data' DataFrame.
        """
        import user_code
        condition = hasattr(user_code, "data") and isinstance(user_code.data, pd.DataFrame)
        _dynamic_test(
            self,
            condition,
            "The CSV file is read correctly into the 'data' DataFrame.",
            "The CSV file is not read correctly or not assigned to 'data'."
        )


class TestDataExtraction(unittest.TestCase):
    def test_data_extracted_slice(self):
        """
        2. Extract first 50 rows and columns with indices 1 and 4 using iloc.
        """
        import user_code
        df = user_code.data
        ref = df.iloc[:50, [1, 4]]
        condition = (
            hasattr(user_code, "data_extracted")
            and isinstance(user_code.data_extracted, pd.DataFrame)
            and user_code.data_extracted.equals(ref)
        )
        _dynamic_test(
            self,
            condition,
            "The variable 'data_extracted' contains the correct rows and columns slice.",
            "The 'data_extracted' slice is incorrect. Check row and column indices."
        )


class TestOutput(unittest.TestCase):
    def test_output_print(self):
        """
        3. Print the first few rows of data_extracted using .head().
        """
        import user_code
        captured_output = io.StringIO()
        sys.stdout = captured_output
        print(user_code.data_extracted.head())
        sys.stdout = sys.__stdout__

        output_text = captured_output.getvalue().strip()
        condition = len(output_text) > 0
        _dynamic_test(
            self,
            condition,
            "The output displays the first few rows of 'data_extracted' using .head().",
            "The 'data_extracted' variable is not printed correctly using .head()."
        )


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


test_code.py

This course contains a lot of useful functions for a future data analyst. You will learn different ways of extracting data and even set conditions on it. After it, you will be familiar with the methods of grouping data. Also, you will learn how to preprocess data. Each section has its data set so that the course will be gripping.

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows  by indices.

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here. 

Expanding Functionality of the .iloc[] Attribute

Solution

Awesome!

Expanding Functionality of the .iloc[] Attribute

Solution