tidymodels, is one of the new suite of packages for doing machine learning analysis in R with tidy principles from RStudio. Tidymodels, the metapackage, has a core set of packages for statistical/machine learning models like infer, parsnip, recipes, rsample, and dials in addition to the core tidyverse packages dplyr, ggplot2, purr, and broom. In addition […]
Pandas Melt: Reshape Wide Data to Long/Tidy Data
Pandas offers multiple ways to reshape data in wide form to data in tidy or long form. Pandas melt() function is one of the powerful functions to use for reshaping dataframe with Python. In this case, we will see examples of basic use of Pandas melt to reshape wide data containing all numerical variables into […]
Pandas Groupby and Computing Median
One of the common operations of data analysis is group the data by a variable and compute some sumamry statistics on the sub-group of data. In this post, we will see an example of how to use groupby() function in Pandas to group a dataframe into multiple smaller dataframes and compute median on another variable […]
How to Convert Wide Dataframe to Tidy Dataframe with Pandas stack()?
Data in wide form is often easy to read for human eyes. However, you might need data in tidy/long form for data analysis. In Pandas there are a few ways to reshape a dataframe in wide form to a dataframe in long/tidy form. In this post we will see a simple example of converting a […]
Computing Quantile Normalization in Python
When working with high-dimensional data, preprocessing and normalizing the data are key important steps in doing data analysis. Quantile normalization is one such statistical methods that can be useful in analyzing high-dimensional datasets. One of the main goals performing normalization like Quantile normalization is to transform the raw data such that we can remove any […]