Data Analytics With Python

How to use iloc and loc in pandas Pandas is a popular Python library for data analysis and manipulation. It provides various methods and attributes to access and modify data in different ways. Two of the most commonly used methods are iloc and loc, which allow you to select rows and columns by integer position or by label, respectively. In this blog post, we will explain the difference between iloc and loc, how to use them effectively, and some common pitfalls to avoid. iloc vs loc The iloc method stands for integer location, and it allows you to select rows and columns by their integer position. For example, if you have a DataFrame df with 5 rows and 3 columns, you can use iloc to access the element in the second row and third column as follows: df.iloc[1, 2] Note that iloc uses zero-based indexing, meaning that the first row or column has index 0, the second has index 1, and so on. The loc method stands for label location, and it allows you to select rows and columns by...

Comparing Relationships in Dataframes with Python Comparing Relationships in Dataframes with Python If you work with data, chances are you will need to compare different datasets to each other. One specific task could be to compare the relationships between categorical variables in different datasets. In this blog post, we will use Python and its powerful libraries such as Pandas and NumPy to accomplish this task. What are we trying to achieve? Imagine you have two datasets, a 'base' and a 'comparison' dataset. Each dataset has the same categorical variables, for example 'degree_type' and 'acad_degree'. You want to check if the relationship between these variables is the same in both datasets. For instance, if in the base dataset 'AAS' always corresponds to 'Associates', you want to ensure the same applies to the comparison dataset. How do we do it? We start by creating a function, let's cal...