In this tutorial, we will see examples of using count() function from dplyr to explore variables in a dataframe. One of the first things to do after loading a data is to perform simple exploratory data analysis. One typically starts data exploration with a quick look at the data with functions like glimpse() or head(). […]
How To Get A Peek at Dataframe in R
Getting a quick look at the dataframe to understand the variables we have or data types is an important aspect of data analysis. If you are used to working with excel, your first impulse is to open the data in excel. However, getting a look at the data programmatically in R has many advantages including […]
Pandas explode(): Convert list-like column elements to separate rows
Panads explode() function is one of the coolest functions to help split a list like column elements into separate rows. Often while working with real data you might have a column where each element can be list-like. By list-like, we mean it is of the form that can be easily converted into a list. Let […]
Pandas Melt: Reshape Wide to Tidy with identifiers
Pandas melt() function is a versatile function to reshape Pandas dataframe. Earlier, we saw how to use Pandas melt() function to reshape a wide dataframe into long tidy dataframe, with a simple use case. Often while reshaping dataframe, you might want to reshape part of the columns in your data and keep one or more […]
Principal Component Analysis with Penguins Data in Python
Who does not love PCA with Penguins in Python. Sorry, could not resist saying this :). If you are tired of seeing Iris data for introducing all things Machine Learning, Data Science algorithms and Data Visualization examples, you are in for much needed treat in the form of Penguins. Thanks to Alison Horst, who has […]