In this blog we are going to analyse the data from the Weather data-set of Finland, a country in the Northern Europe. You can find the data-set on Kaggle (https://www.kaggle.com/muthuj7/weather-dataset).We are going to use the numpy, pandas and the matplotlib libraries of Python.
Following is the Hypothesis of the Analysis : “Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming.”
Let us start by importing the required libraries and our data-set:
Libraries required for analysis
Importing our data-set
Here is a small preview of how our data-set looks:
First 5 entries of our data-set
Now we need to drop the unwanted data, convert the data in to our need and resample our data :
Here is how the data looks after resampling:
Now let us plot our data in a line graph
As we can see, both the peaks and the troughs are almost same throughout the period of 10 years. Here is a plot of the average temperature and humidity of the month of April over 10 years.
We can clearly see that there is a sharp rise in temperature in the year of 2009 whereas there is a fall in temperature in the year of 2015. Hence we can conclude that global warming has caused an uncertainty in temperature over the past 10 years while the average humidity as remained constant throughout the 10 years.