In this blog we are going to analyze 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).
“Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming” following is the Hypothesis for the analysis.
The Hypothesis means we need to find whether the average Apparent temperature for the month of a month say April starting from 2006 to 2016 and the average humidity for the same period have increased or not. This monthly analysis has to be done for all 12 months over the 10 year period. So you are basically resampling your data from hourly to monthly, then comparing the same month over the 10 year period. Support your analysis by appropriate visualizations using matplotlib and / or seaborn library.
Step 1: Importing of libraries and Dataset.
Step 2: Looking at the dataset.
Here is a small preview of how our data-set looks:
Step 3: Cleaning Dataset
Step 4: Plotting of Data
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.
