Contingency tables and Fisher’s exact test are commonly used in statistical analysis to determine the association between two categorical variables. In Julia, we can use the DataFrames package to create contingency tables
One hot encoding is a common technique used in machine learning to convert categorical variables into a numerical representation that can be used by algorithms. In Julia, we can use the DataFrames
When it comes to working with data in Julia, one of the most popular choices is using dataframes. Dataframes provide a convenient way to organize and manipulate data, but they may not
When working with data in Julia, it is often necessary to convert the data type of a column in a DataFrame. This can be useful when the original data type does not
When working with dataframes in Julia, it is common to encounter situations where you need to perform a left join on two dataframes, but none of the columns are common. In this
When working with dataframes in Julia, it is common to encounter situations where we need to merge two dataframes based on a specific condition. One such condition is when we want to
When working with dataframes in Julia, it is often necessary to store and retrieve data from a database. This can be done using the DBInterface package, which provides a common interface for
When working with dataframes in Julia, it is often necessary to perform stratified and weighted sampling. This article will explore three different ways to achieve this in Julia, each with its own
When working with dataframes in Julia, it is often necessary to perform operations on specific columns or rows. One common task is to calculate the sum of a column or row. In
When working with dataframes in Julia, it is often necessary to perform mathematical operations on specific columns. In this article, we will explore different ways to multiply a dataframe column by a
