Summary  
This chapter explains how to import a Python library and assign it an alias for convenient use.  

General domain of usage  
Data analysis

In order to feel confident and successfully complete this course, we strongly recommend you **complete the following courses** beforehand (just click on them to start):

## Why Use Pandas?

`pandas` is a Python library designed for data processing and analysis. Mastering this library is beneficial for **data scientists** and **data analysts** alike. With `pandas`, you can sort, filter, modify, and visualize your data, as well as calculate useful statistics, allowing you to perform comprehensive analysis.

To utilize this library, you must **import** it.
```python
# Importing `pandas`
import pandas
```

For ease of use, it's common to import the library using an alias.
```python
# Importing `pandas` with an alias
import pandas as pd
```

In this case, `pd` serves as an alias. So, when invoking functions from this library in the future, you would do it as follows:
```python
# Importing `pandas` with an alias
import pandas as pd

# Using a function from `pandas`
pd.function()
```

import unittest
import importlib
import sys


# 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):

    # Test if `pandas` is imported in user_code.py
    def test_pandas_is_imported(self):
        import user_code
        pandas_imported = "pandas" in sys.modules
        condition = pandas_imported
        failure_message = "Expected `pandas` to be imported, but it is not imported."

        _dynamic_test(
            self,
            condition,
            "The `pandas` library is imported.",
            failure_message
        )

    # Test if `pandas` is correctly assigned to `pd`
    def test_pandas_imported_as_pd(self):
        import user_code
        actual_value = getattr(user_code, "pd", None)

        if actual_value is None:
            condition = False
            failure_message = "Expected `pandas` to be imported as `pd`, but `pd` is not declared."
        elif actual_value.__name__ != "pandas":
            condition = False
            failure_message = f"Expected `pandas` to be imported as `pd`, but got `{actual_value.__name__}` imported as `pd`."
        else:
            condition = True
            failure_message = ''

        _dynamic_test(
            self,
            condition,
            "The `pandas` library is correctly imported as `pd`.",
            failure_message
        )


# Run the tests when executing the script
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.

In this section, we'll explore the fundamentals of Series and DataFrame structures. You'll also learn about the distinctions between these two types of structures.

Data can be sourced in various formats, such as CSV, JSON, SQL, HTML, and more. With Pandas, you're not limited to a single format — you can work with data across a multitude of file types. In this chapter, we'll specifically focus on the CSV and TXT formats.

Here, you'll learn how to process raw data by removing extraneous information and managing null values in a dataset.

What is pandas?

Why Use Pandas?

Solution

Awesome!

What is pandas?

Why Use Pandas?

Solution

Awesome!