Code Snippets – Coffee Break

July 14, 2025

Improving Query Performance via PostgreSQL Table Partitioning – Part II

Welcome to Part II series of exploring postgresql partitioning. If you haven’t already checked out Part I, it has some pre-requisite information. This helps you find out if partitioning is the right solution for you, as it did for me. Table of Contents Key Information To Know Before Starting Partitioned Table Characteristics In addition, I…

Code Snippets, Engineering

#database, #partitioning, #postgresql, #python, #sql
July 13, 2025

Improving Query Performance via PostgreSQL Table Partitioning – Part I

Intro: At work I created a new set of tables to fulfill the business needs. Unfortunately for the first time around, I couldn’t set aside the time consider maintenance of the tables as it was premature to even consider if the business idea would be successful. So, I decided to work on it another time.…

Code Snippets, Engineering

#database, #partitioning, #postgresql, #sql
April 10, 2016

Finding Out All of your NAN and NAT rows in your Data Frame At Once

Coming into DataFrames, one of my biggest pain points was finding out where the NAN (Not A Number) or NAT (Not A Time) is in my plethora of data. I prefer not to drop these rows or ignore them during crucial calculations. Sometimes I may want to find out exactly where these values are so…

Code Snippets

#pandas, #python
April 10, 2016

Converting All Unix Time Stamp to DateTime in a DataFrame in One Run!

While Data Munging for my Weather Data Analysis blog, I skipped out a bunch of other valuable data which were all related converting to date_time. I managed to come across a solution today while playing with Pandas’ .apply() and .applymap() functions. Here’s how the raw data looked like prior human readable date time conversion. To…

Code Snippets

#pandas, #python

Category: Code Snippets

Improving Query Performance via PostgreSQL Table Partitioning – Part II

Improving Query Performance via PostgreSQL Table Partitioning – Part I

Finding Out All of your NAN and NAT rows in your Data Frame At Once

Converting All Unix Time Stamp to DateTime in a DataFrame in One Run!