Performing Analysis of Meteorological Data
In this blog we will discuss about a data analysis which is based on some dataset .The dataset has hourly temperature recorded for the 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 Northern Europe.
The null hypothesis has the Apparent Temperature and Humidity compared monthly across 10 years of the data indicate an increase due to global warming where Null 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.
So We are only using the Apparent Temperature and humidity Field because that we need to prove our hypothesis.
1.) Importing Of Libraries
2.) Importing and Cleaning Dataset
Here we can see there is a total of 517 Null Values are present and We have a total of 96453 rows in the dataset. In which there are only 514 rows contains null values so we can simply remove those rows which contained null values.
3.) To convert the 'Formatted Date' Column which is in object form to 'Datetime' Format and also to change index as 'Formatted Date' Column.
4.) To Convert the 'Formatted Date' Column into Month wise format from day wise format.
5.) Analysis of Apparent Temperature and Humidity from 2006-2016.
6.) For January Month we can see that Average Apparent Temperature in January is somewhat getting affected and is most affected in 2007 and the avg temp is also decreased by 1 degree from 2006 to 2016.
7.) Here For Month February we know that in the month of February temp remains somewhat low but we can see from the figure that the average app. temperature has risen too much from 2006 to 2016.
8.) As In Month March We can see from the figure that the average app. temperature has risen too much from 2006 to 2016 and thus is affected by the Global Warming.
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12.) In Month July We can see that average app temperature rises in between and is unstable throughout this span but no change in Average app. temperature seen.
14.) In Month September This graph is also unstable and shows the unstability of temperature.
16.) For Month November From the above two graphs we can see that the temperature sharply falls from October(i.e. 12 degrees ) to November(i.e. 3 degrees). And both the graphs are also too unstable showing the affect of Global Warming
17.) For Month December We can see a rise in the Average app. temperature.
18.) From the above visualization it is clear that there is a markable change in the Average Apparent Temperature due to Global Warming. The humidity remains approximately constant throughout the time span.