Summary  
This chapter covers how to import an external Python library and assign it an alias to simplify calling its functions.  

General domain of usage  
Data processing and analysis

To feel confident and successfully complete this course, you should complete the following course first:

## 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.

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.

What Is pandas?

Why Use Pandas?

解答