Visualizing my Website Analytics with Python
I recently completed a mini course on data science and machine learning using Python. I learnt how to manipulate, clean, and analyze data using Python libraries such as Pandas, Matplotlib and NumPy and various machine learning algorithms.
A while back, I wrote about GoatCounter, a free and open source web analytics service that I use to track traffic on my website. GoatCounter lets you export the data as .csv file and I decided to explore the data.
I had to clean the dataset and remove null values and disregard visits by bots. The csv format used by GoatCounter is explained here.
Here are the results:
Top 10 posts with the most visits. Operating System Popularity Browser Popularity Popularity of browsers used on various operating systems.
GoatCounter does already give good visualizations of the data but I decided to do it on my own just for applying what I have learned from the course. The graphs above are really basic but the whole process of using pandas and matplotlib to generate these visualisations was interesting.
This is day 34 of #100DaystoOffload