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 […]
Python
Pandas Groupby and Compute Mean
One of most common use of Pandas’ groupby function is to compute some summary statistics on one or more variables in the dataframe. In this post we will see an example of how to compute mean on all numerical variables and a select variable after groupby operation. Let us first load Pandas package. We will […]
Fun with Pandas Groupby, Aggregate, Multi-Index and Unstack
This post is titled as “fun with Pandas Groupby, aggregate, and unstack”, but it addresses some of the pain points I face when doing mundane data-munging activities. Every time I do this I start from scratch and solved them in different ways. The purpose of this post is to record at least a couple of […]
How To Save Pandas Dataframe as Excel File?
In this post, we will see examples of saving a Pandas dataframe as Excel file. Pandas has to_excel() function to write a dataframe into Excel file. Let us load Pandas. We will create two lists and us these to create a dataframe as before. We can create a Pandas dataframe using the two lists to […]
5 Useful Personal Finance Functions from NumPy Financial
Until recently, NumPy in Python had simple, but useful financial functions. These NumPy Financial functions are extremely handy for many personal finance questions, like estimating how much interest you have to pay on a loan and how your money grows with a monthly investing plan with a certain interest rate. Recently, the financial functions are […]