In this article, we are going to make a summary of the different functions that are used in Pandas to perform Indexing, Selection, and Filtering.
Join the DZone community and get the full member experience. We begin with the second post of our training saga with Pandas. In this article, we are going to make a summary of the different functions that are used in Pandas to perform Indexing, Selection, and Filtering. Before we start, we are going to visualize ahead of our didactic dataset that we are going to follow to show the examples. It is a well-known dataset that contains wine information. As an introduction, we are going to explain some functions that can be very useful when obtaining a broader view of the state of our dataset. We will start with info() function, which offers us insights about the number of columns, name of every column, We start with the info() function that provides us with information about the number and names of columns, the number of non-null elements, and the data type of each column. We continue with the dtypes attribute that shows us exclusively the data type of each column. The following function provides us with information on numerous statistical calculations that help us understand the distribution of our dataset. Here we are going to take a deep dive into explaining the two main indexing and selection pandas functions: ‘iloc’ and ‘loc’ +.
