We are going to analyze the data from the weather data-set. The dataset has hourly temperature recorded for last 10 years starting from 2006-04-01 00:00:00.000 +0200 to 2016-09-09 23:00:00.000 +0200. It corresponds to Finland, a country in the Northern Europe. You can download the dataset from Kaggle (link: https://www.kaggle.com/muthuj7/weather-dataset). We are going to use the numpy, pandas and the matplotlib libraries of Python.
We perform data cleaning and analysis for testing the Hypothesis has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming.
Import Packages and Load the Data:
Here is a preview of how our data-set looks:
We have to convert the "Formatted Date" column which is in object form to "Datetime" format so that we can work further on that format.
To 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, visualize, variation in Apparent Temperature and Humidity
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.
Now group by the 'Formatted Date' in separate 'month' and 'year'.
Now let us plot our data in a line graph, visualize whether the average Apparent temperature for the year 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.
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.
Thus from the above visualization it is clear that there is a mark able change in the Average Apparent Temperature due to Global Warming. The humidity remains approximately constant throughout the time span.
