Summary  
This chapter explains how to create a one-dimensional Pandas Series from a Python list using the pd.Series() constructor and printing its index-value structure.

General domain of usage  
Data analysis

Pandas offers two primary data structures: `Series` and `DataFrame`. The `Series` is the simpler of the two, resembling a **one-dimensional** array or a list. You can create a `Series` object using the `pd.Series()` constructor by passing a **list** as its argument.

import pandas as pd
numbers = [17, 43, 74, 20]
series = pd.Series(numbers)

print(series)

You use the `pd.Series()` constructor to create a `Series` object that contains a **list of numbers**.


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)


class TestUserCode(unittest.TestCase):

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

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

    def test_numbers_series_is_pandas_series(self):
        import user_code

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

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

    def test_states_series_is_pandas_series(self):
        import user_code

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

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

    def test_numbers_series_has_correct_values(self):
        import user_code
        expected_value = pd.Series([2, 4, 8, 16])

        variable = 'numbers_series'
        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"`{variable}` contains the correct values.",
            failure_message
        )

    def test_states_series_has_correct_values(self):
        import user_code
        expected_value = pd.Series(["New York", "California", "Chicago", "Alaska"])

        variable = 'states_series'
        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"`{variable}` contains the correct values.",
            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.

Series

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