To view the first few rows of a dataset in Python, we can utilize the `head()` function. This function accepts an integer as its argument, which specifies the number of rows to display (by default, it shows first 5 rows). Let's take a look at the first 10 rows of our dataset:
```python
frame = pd.read_csv('file.csv')
# Extracting the first 10 rows
first_lines = frame.head(10)
```

If we want to see the last few rows of a dataframe, we can use the `tail()` function. It works similarly to the `head()` function:
```python
frame = pd.read_csv('file.csv')
# Extracting the last 10 rows
last_lines = frame.tail(10)
```

Another useful function for exploring dataframes is `sample()`. This function fetches random records from a dataframe. By default, it retrieves a single random record unless specified otherwise.
```python
frame = pd.read_csv('file.csv')
# Extracting 10 random rows
random_lines = frame.sample(10)
```

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.

Pandas First Steps

Viewing the Data

Task