Performing Analysis of Meteorological Data

 As I already had fundamental knowledge of python and its libraries used for data science, I directly started working on the project.

 

Firstly, I imported all the required libraries such as pandas, numpy, seaborn and matplotlib. I then fetched the data form the csv file I downloaded from the mentioned data source via pandas function and converted it to dataframe.

Secondly, I filtered the data by screening for null values and getting data types of all the columns. Then I converted the 'Formatted Date' column into a datetime format so that the kernel doesn't consider it as a number and it can be used later on for resample. I used the set index command to set 'Formatted Date' as the index of the dataframe and then resampled the data to 'MS' which helped me in data reduction.

Thirdly, I used the matplotlib and seaborn libraries to draw graphs which showed a direct comparison between apparent temperature and humidity. At last, I used a pairplot to showcase the relation between different dimensions of the data and concluded that there was no change in average humidity. The year 2009 can see an increase in average apparent temperature, then a fall in 2010, then a slight increase in 2011, then a significant drop in 2015, and then an increase in 2016. 

The github link of my project is --> https://github.com/itskedarpandya/Performing-Analysis-of-Meteorological-Data.git