There are a lot of time-consuming, tedious and repetitive data-related tasks, including data cleansing, normalization, and visualization. Thanks to Pandas, such tasks can be carried out fairly swiftly, with some automation. In this course, you will be taught how to master the Pandas library in Python programming. As you will learn, Pandas has various uses, ranging from exploring a dataset stored in your computer to extracting it into a dataFrame (a table, for example). With Pandas, you can calculate statistics and perform calculations such as the average, median, standard variation, and the minimum and maximum of each column of the table. This course will highlight how one column of the table relates to another column of the same table. With Pandas, it is also possible to clean the data by removing missing values or even using some criteria and filtering rows and columns.
As you continue, you will learn an essential fact about Pandas. The cleaned data is stored and transformed into a comma-separated value, another file, or another database. All of this is possible because Pandas is an open data analysis and manipulation tool. Furthermore, it is also fast, powerful, and flexible, leading to unescapable automation. The basis of automation in Pandas is Python programming. Why is Python used for Pandas library? Python is a unique language that allows you to work quickly and integrate systems more efficiently. You use Python programming in web development, GUI (graphical user interface) development and software development. When taking this course, you need to have some experience using Python. Without having a good command of this programming language, you should at least know the basics, including tuples, dictionaries and lists. Knowing functions and iterations are highly recommended, along with familiarity with NumPy.
Pandas provides numerous methods that help tremendously in expediting data analysis and the exploration process. This course covers many of them, some of which are profiling and dataFrame. As an open-source Python module, Pandas profiling allows you to carry out exploratory data analysis following a few lines of code. This contributes to saving you the bulk work of visualizing or understanding the relative distribution of each variable. Another prominent method is Pandas DataFrame. Like a spreadsheet in Excel, a dataFrame is a data structure consisting of columns and rows. You commonly use dataFrames in data science, particularly in machine learning. This course will undoubtedly be helpful to students that have an excellent command of Python and are interested in data science. In addition, professionals specializing in data science management with sound knowledge of Python will benefit from this course.
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