Calculating the correlation and covariance matrix between columns of a time array is a common task in data analysis. In Julia, there are several ways to achieve this. In this article, we
Counting the occurrences of columns in a 2D array can be a useful operation in various data analysis tasks. In Julia, we can achieve this using different approaches. Let’s explore three different
When creating a plot in Julia, it is often necessary to include a legend to provide additional information about the data being displayed. In some cases, the legend can become quite long
When working with arrays in Julia, it is often necessary to reshape them in different ways to suit our needs. One common task is to reshape a 2D array by pairs of
When working with data in Julia, it is common to encounter situations where you need to transform string columns in a dataframe using regex match. In this article, we will explore three
When working with data in Julia, it is often necessary to add columns based on unique values. This can be done in several ways, each with its own advantages and disadvantages. In
When working with dataframes in Julia, it is common to encounter situations where you need to remove certain columns from the dataframe. In this article, we will explore three different ways to
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 can be useful to give columns nicknames for easier reference and readability. In this article, we will explore three different ways to accomplish this task.
When working with dataframes in Julia, it is often necessary to convert the type of a column to a symbol in order to subset the dataframe. This can be achieved in multiple
