`pandas` is the go-to library for data analysis and manipulation, and one of its key features is its ability to read and write various file types, including CSV files. Functions like `read_csv()` and `to_csv()` come in handy for dealing with CSV data.

To load a CSV file into a DataFrame, use the following:

# Load the CSV into a dataframe
import pandas as pd
data_frame = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a43d24b6-df61-4e11-9c90-5b36552b3437/Salary+Dataset.csv')
print(data_frame)

Just to clarify, we've imported the CSV file as a DataFrame using the `pd.read_csv()` function. 

> Note
>
> Make sure that the dataset link is wrapped in quotation marks.

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

CSV Files

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Pandas First Steps

CSV Files

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