Performing Analysis of Meteorological Data

https://internship.suvenconsultants.com for providing awesome problem statements and giving many of us a Coding Internship Experience. @www.suvenconsultants.com" 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. Our goal is to transform the raw data into information and then convert that information into knowledge. We will be performing some basic tasks to perform our analysis such as Data cleaning Data notmalising Testing the hypothesis The Null Hypothesis H0 is “Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming” The H0means 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. The first thing we’ll need to do is load in the libraries and dataset we’ll be using.

The first thing to do when you get a new dataset takes a look at some of it. This lets you see that it all read incorrectly and gives an idea of what’s going on with the data. In this case, let’s see if there are any missing values, which will be repre sented with NaN or None. How many missing data points do we have? Ok, now we know that we do have some missing values. Let’s see how many we have in each column.

That seems like a lot! It might be helpful to see what percentage of the values in our dataset were missing to give us a better sense of the scale of this problem:

Focus on Important factors and exclude others. Here we need Apparent Temperature ( C ), Humidity & Formatted Date so we can resample our data.

Now we need to normalize the dataset in order to do our analysis so, we need to convert the Formatted Date column to Date Time. It can be easily done using the Pandas function to_datetime(). preview of how the data looks after resampling :

“M” Month starting We are averaging the apparent temperature and humidity using the mean() function. Let’s plot the graph for our data using Matplotlib function plot().

The plot will be displaying variation in Apparent Temperature and Humidity with Time.

This method is used to plot data and a linear regression model fit. There are a number of mutually exclusive options for estimating the regression model.

Now Lets plot another graph to get a better inference. Temperature with humidity of the month of may over 10 yrs.

It is quite evident that there is a sharp rise in temp past 2010 whereas there is a fall over 2014. But let’s look at some more graphical visualization so get a better belief.

Conclusion Global warming is deteriorating the climate and is affecting various parameters of the environment. Hence from this analysis, we infer that there is either a 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 also taking humidity into consideration we can say that it has almost remained the same throughout the past years.