Performing Analysis Of Meteorological Data

Various datasets are available on internet. Weather datasets is one that is easier to find on the internet.There are different sites providing different meteorological parameters such as pressure,temperature,humidity,etc.

The goal is to transform the raw data into useful information and then convert it into knowledge.

Basic tasks are performed on the raw data to perform the analysis such as data cleaning,data normalizing,testing the hypothesis and finally inferring certain information from the Meteorological Dataset.

The dataset has temperature recorded for 10 years starting from 2006-04-01 to 2016-09-09 on hourly basis.

Null Hypothesis- Has the Apparent Temperature and humidity compared monthly across 10 years of data indicate an increase ,reason being Global Warming.

1.First, we will import all the necessary libraries i.e., Pandas, Seaborn, Matplotlib, Numpy,etc.

2. We can read the csv file using pandas libraries read.csv(). We will store the data frame into a variable and view the first five rows using head() function.

(Given below is the output)

3. We need to convert the data into our need  and resample the data and drop the unwanted data.

 

 

 Here are the received outputs for the given commands.

 Data after resampling:-

 4.  Now we plot the graph of the data using Matplotlib function displaying variation in Apparent Temperature and Humidity with time.

Plot-

 

5- Now we plot another graph of different months of the year to get better inference of temperature with humidity over 10 years.

6- Now Lmplot or reg plot is a statistical method for predictive analysis.

 

Plot-

7- A Heatmap or two-dimensional representation of data where the individual values that are contained in a matrix are represented.

 

Plot-

 

  8- Now we plot a dist plot using seaborn library.It lets us see a histogram with a line over it

 

 

Plot-

 

 9- 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.

 

Plot-

 

 Conclusion- 

From this analysis we infer that Global warming is no doubt deteriorating the climate and is affecting various parameters of the environment. 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 same throughout the past years.