Performing Analysis of Meteorological Data
Analysis of influence of Global Warming on temperature and humidity
Objective
To perform Data analysis over the apparent changes occurred in the temperature and humidity monthly for over 10 years and to know whether it indicate an increase due to Global Warming. We posed the null hypothesis as follows:
“ Has the Apparent Temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming.”
It means that 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.
Dataset
The dataset used to perform the analysis is Weather dataset. 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.
(Source URL: Kaggle)
Code
Now let’s jump to the code. The code is available in GitHub.
Importing Libraries
Loading the dataset
Data Cleaning
To perform the analysis we are only focusing on the necessary columns
Converting the date column to standard Python Datetime format
Formatting the column name
Setting index as date
We choose the columns of temperature and humidity and then resampling the data from hourly to monthly
Visualization
Initially plotting the yearly variation of apparent temperature and humidity using Plotly library.
Plotting the yearly variation in apparent temperature and humidity for each month
Hypothesis Testing
By performing t- test, it tends to reject the null hypothesis .
Conclusion
From this analysis we infer that there are either sharp rise in temperatures or sharp falls over the 10 yrs. Hence we can conclude that Global Warming has caused a major difference and unreliability in temperature predictability and also taking humidity into consideration we can say that it has almost remained same throughout the past years.
I am thankful to mentors at https://internship.suvenconsultants.com for providing awesome problem statements and giving many of us a Coding Internship Experience. Thank you www.suvenconsultants.com.