To handle NaN values while retaining each row of the dataframe, we can utilize the `fillna()` function. This allows us to populate each empty cell with a specific value (like a string or number) rather than eliminating it.

To replace NaN values with the number 0:
```python
import pandas as pd
data_frame = pd.read_csv(file.csv)
new_data_frame = data_frame.fillna(0)
```

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

Filling Null Values

Tarefa

Pandas First Steps

Filling Null Values

Tarefa