Performing Analysis of Meteorological Data

The Null Hypothesis is “Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming”

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.

One type of data that’s easier to find on the net is Weather data. Many sites provide historical data on many meteorological parameters such as pressure, temperature, humidity, wind_speed,visibility, etc. so we will take our dataset from Kaggle : https://www.kaggle.com/muthuj7/weather-dataset

so first we are going to import all the required libraries we need for this analysis:

Then we are going to read the csv file and view it

after which you have to drop all the not required features and view the data frame.

Now we have to make the “Formatted Date” feature as index of the Data Frame and then resample our data from hourly to monthly.

and then we are going to visualize our data to understand and get to a conclusion regarding our Hypothesis.

Now we will visualize the variation of temperature and humidity throughout the years (2006 to 2016).

and now we are going to visualize variation of temperature and humidity for the month of April.

Conclusion:

Global warming has a great affect on the Weather Condition of Finland .
Humidity remain same through the year or time there is extreme change in humidity. In 2009 there was extreme increase in Apparent Temperature.
Apparent Temperature didn’t had any drastic changes but did had few spikes in it.