Summary  
This chapter covers inspecting a pandas DataFrame’s structure and metadata—retrieving row and column counts and each column’s data type—using the DataFrame.info() method and the len() function.  

General domain of usage  
Data analysis

In `pandas`, there's a handy method named `info()` that provides basic information about a dataset.

import pandas as pd
# It's a dataframe
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a43d24b6-df61-4e11-9c90-5b36552b3437/example.csv')
info = df.info()
print(info)

This method displays the number of rows and columns in the DataFrame, as well as each column's name and data type. For instance, `df` contains **20** rows and **5** columns.

To determine the length of a `DataFrame`, use the `len()` function, as shown in the example below:

```python
length = len(df)
```

import unittest
import importlib
import pandas as pd
from io import StringIO
import re


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

    def test_info_method_is_used(self):
        with open('user_code.py', 'r') as file:
            user_code_source = file.read()

        _dynamic_test(
            self,
            re.search(r'\.info\s*\(', user_code_source),
            "The `info()` method is used.",
            "Expected the `info()` method to be used."
        )


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.

Exploring the Dataset

Solution