Python First Steps to Perform Data Analysis

A lot has been said about the “of the moment” programming language in recent times. Easy, intuitive, powerful: these are just some of the business owners phone numbers found to describe the Python language and its numerous features, in the face of a visible growth in its use. Throughout the months of August and September 2021, I participated in the Python Programming course taught at. Harve and was able to see with my own eyes what it was really about. Having been able to understand why it was so popular. During the course we learn from the most basic – such as installing Python on your machine or basic concepts of programming logic. To slightly more complex subjects, such as web development, for example.


When I came across the proposal I imagined for the final project. I initially found it difficult to decide what exactly I would analyze, since at the moment. I don’t work in an area that involves data manipulation such as spreadsheets. for example. Thus, after some research I found the website. Which is an API that generates random data in different file formats, including the CSV format. Within the platform, it is possible to shape the database you intend to generate. Inserting the name of the columns in the “Field Name” field and the type of data in each column through the “Type” field . It is worth remembering that the choice of column names is arbitrary, but the choice of data contained in each column is based on a series of data that the API provides, such as: dates, names, credit card numbers, URLs, email addresses, among others.

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Once you have the data to be analyzed, it is time to visualize it in its raw form and, if necessary, treat it so that it can be manipulated through programming. That is, this step is basically where the data is standardized so that it can be suitable for Python. The first step is to import the Pandas DV Leads, giving it a “nickname” (in this case, “pd”). Then, we insert a data frame that will read the CSV file and perform the spreadsheet preview. To do this, use the following commands. When viewing the data frame, you can get an idea of ​​the information contained in it. However, visualizing a thousand lines manually would probably be a boring and tiring task. So, an interesting command to start the analysis might be the following:

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