I have been tutoring maths for almost ten years, through high school, university and now alongside my day job. Since the pandemic began, I have been inundated with requests for maths tuition and ad hoc help to go through topics. This led me to the idea of creating resources to share online to expand my reach and ultimately help more people!

In December 2020, I began to launch my own maths business and brand — Maths with Lily. Maths with Lily is an online maths education business full of accessible tips, tricks, and tutorials, predominantly for high school students, parents, and educators. …

I was initially exposed to Python and its package, Pandas in particular, to try to move away from manipulating Excel files when working as a Data Analyst. I had automated aspects of my monthly workload using VBA, but found my code to run slower than anticipated even after tuning. I wanted to try out something new which was fast and flexible. When I discovered Pandas, this ticked all the boxes and more, turning out to be fast, flexible and fun to learn!

Below you can find five of the top searched column operations on Google people want to learn how to use. …

It’s not often I need to export records from a SQL query. Every time I have done (up until today), I have done the export and then had to manually add in the column headers.

As exporting from SSMS is not a frequent task I am required to undertake, it hasn’t bothered me too much. I was unsure how I could make this change to SSMS settings as I had not taken the time to look into it — SSMS is not something I use particularly regularly as I generally tend to run SQL queries using the Python library SQLAlchemy.

It is close to Christmas and having started to wind down, I finally took the plunge to find out how to add the setting. …

For the purpose of this article and to shake things up a bit, I’ve decided to use a FIFA 21 dataset from Kaggle — you can download this here. From here, I’ll be analysing these player ratings primarily using the Pandas library, to display a number of different simple yet useful plots.

Getting started

Let’s get our data into a dataframe. Initially I hadn’t included the delimiter parameter for the read_csv() method, however it quickly became obvious that this was required when I took a look at the top five rows. I’ve imported matplotlib.pyplot to display my plots throughout.

Jumping rope — or skipping — is something I used to do in the playground at school. I probably stopped when becoming self-conscious or hearing from someone it wasn’t cool to skip.

Fifteen years later, I decided to dust off the cobwebs and ordered myself a rope in the hope that I would be motivated to get outside and give it a go. It came at the perfect time, with England going into another lockdown — I really didn’t have any excuses!

1. The upfront cost is only £2!

There are loads of mega expensive jump ropes out there, but before you know you’re seriously committed and decide to invest, you only need £2 to start. Here is a link to the rope I bought from Decathlon. …

I first started learning Python just over a year ago in my previous role as a Data Analyst. The company I was then working for was all about Excel. Every month I had to clean and combine nearly 100 different files gathered from all corners of the globe — as you can imagine, this was a nightmare! Due to the nature of the client, it was the age old story of a really tight turnaround for the analysis to be delivered, causing a reoccurring stressful bottleneck.

Initially I used VBA to automate some of my monthly work, however I found it fairly rigid if dataset formats varied slightly month-to-month. I also had serious issues with Excel crashing on me, especially as the data volumes grew. I then discovered Python which — quite literally — changed my life! When some of the file formats differed I could then very easily update and amend my Python script accordingly. …

For this article, I will assume you’re working in Anaconda/GitHub/VS Code etc having already imported pandas into your script with the alias pd

1. Import an Excel file

This reads your Excel file into a pandas dataframe (the python equivalent of the tabular structure you’re used to). You’ll want to reuse this dataframe, so we’ll save it to the variable df.

df = pd.read_excel(some_file_path)

2. View your new dataframe

It might seem a bit strange at first that you’re not viewing every single row of data like in an Excel file. …

On starting a new job in the new year 2020, one of the first things I remember was an issue with bank holidays. There was a handful of queries about the first week of January data. This was due to 01 January (a Wednesday) being classed as a working day, meaning various calculations and averages were skewed. It triggered the memory of my colleague that a table within the SQL database needed to be updated for all territories (>20).

I witnessed my colleague spend a significant period of time finding all these bank holidays manually and putting together a SQL query. I recall thinking to myself ‘next year that will be me’. …