Performing Analysis of Meteorological Data

 

Analysis of Meteorological data

In this blog, we are going to analyze the data from the Weather data-set of Finland, a country in Northern Europe. You can find the data-set on Kaggle (https://www.kaggle.com/muthuj7/weather-dataset). We are going to use the numpy, pandas, and the matplotlib libraries of Python.

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

Let us start by importing the required libraries and our data-set:

Libraries required for analysis

Importing our data-set

Here is a small preview of how our data-set looks:

First 5 entries of our data-set

Now we need to drop the unwanted data, convert the data into our need, and resample our data :

Here is how the data looks after resampling:

First 5 entries of resampled data-set

Now let us plot our data in a line graph

As we can see, both the peaks and the troughs are almost the same throughout the period of 10 years. Here is a plot of the average temperature and humidity of the month of April for over 10 years.

We can clearly see that there is a sharp rise in temperature in the year 2009, whereas there is a fall in temperature in the year 2015. Hence we can conclude that global warming has caused uncertainty in temperature over the past 10 years while the average humidity has remained constant throughout the 10 years.

Dataset:

(https://www.kaggle.com/muthuj7/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 Northern Europe.

The hypothesis to be tested:

• Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming

conclusion:

• No change in average humidity over the ten years from 2006 to 2016. An increase in average apparent temperature can be seen in the year 2009; then again, it dropped in 2010, then a slight increase in 2011, then a significant drop was observed in 2015, and again it increased in 2016.

Prerequisites:

• Python 3 installed
• Make sure the necessary packages and libraries are installed.

Instructions to run:

• First, clone the project
• Go to the directory where you have cloned it.
• Run the following:
• python weather.py

Contributors:

Vikash Patel

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.