Introduction to Data Analysis with Python

We must first comprehend the importance of data analysis. As you may be aware, data is collected everywhere around us, whether manually by scientists or digitally every time you click on a website or use a mobile device. However, data does not equal information. Data analysis, and hence data science, assists us in extracting information and insights from raw data in order to answer our queries. So data analysis is crucial because it allows us to uncover relevant information from data, answer questions, and even anticipate the future or the unknown. Let us look at a problem to better understand it.

Let's imagine we have a colleague who wants to sell her automobile. But she has no idea how much she should charge for her automobile. She intends to sell her automobile for the highest possible price. However, she also wants to establish a reasonable price so that someone will want to buy it. As a result, the price she chooses should reflect the car's value. How can we assist her in determining the greatest price for her car? Let us think as data scientists and describe some of his problems: Is there, for example, data on the costs and attributes of different cars?

What characteristics of automobiles influence their prices? Colour? Brand? Does horsepower influence the selling price?

These are some of the questions we might consider as data analysts or data scientists. We'll need some data to answer these questions. We must recognize the significance of data analysis. As you may be aware, data is collected everywhere around us, whether manually by scientists or digitally every time you click on a web page or use a mobile device. However, data does not equal information. Data analysis, and hence data science, assists us in extracting information and insights from raw data in order to answer our queries. So data analysis is crucial because it allows us to uncover relevant information from data, answer questions, and even anticipate the future or the unknown.

Understanding the Data

This dataset is in CSV format, which uses commas to separate each value, making it relatively straightforward to import into most tools or apps. Each line in the dataset represents a row.

Importing and Understanding Data

Once we have our data in Python, we can execute all of the necessary data analysis operations. The process of loading and reading data into a notebook from numerous sources is known as data acquisition. To read any data using Python's pandas library, two criteria must be considered: format and file location. The format of data is how it is encoded. We can typically distinguish various encoding systems by looking at the file name's ending. Encodings that are often used include csv, json, xlsx, hdf, and others. The (file) path indicates the location of the data. It is usually saved on the computer we are using or online on the internet.