Performing Analysis Of Meteorological Data
In This blog we are going to perform data analysis over weather data. The link for dataset is given here. Our goal in this Internship (of data analysis) is to transform the raw data into information and then convert it into knowledge.

This is my first project of Suven Consultants & Technology Pvt. Ltd data analytics internship.

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.

Goal

Following are the goal of this project -

Perform data cleaning.

Perform analysis for 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 H0 means 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.

Implementation :-

This is the implementation of analysis in python.

Step 1 : Importing Libraries and dataset.

importing libraries

Step 2 : Check the datatypes of our dataset.

datatypes

Step 3 : Describe the dataset to gain more insights.

describing dataset

Step 4 : Perform data cleaning.

removing unwanted data

Step 5 : Resample our Formatted Date.

resampling convert Formatted date to datetime and set this column as a index in the dataset.

resampling

Step 6 : Plotting of data.

Plot of variation in Apparent Temperature and Humidity with time.

Firstly we will plot the whole dataset for all months.

yearly graph

Step 7 : plot graph for a specific month(April).

Graph for April month

Step 8 : Bargraph plot.

Plotting the variation in Apparent Temperature and Average Humidity of each month over the years.

barplot of each month in year

Step 9 : Plot of Apparent Temperature v/s Humidity with time(in year).

year wise graph

Step 9 : Monthly analysis of all 12 months over the 10 year period.

january

february

March

April

May

June

July

August

September

October

November

December

Step 10 : Histogram.

Step 11 : Relplot.

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 also taking humidity into consideration we can say that it has almost remained same throughout the past years.

According to Null Hypothesis (H0) both increases due to Global Warming is proven wrong here, and thus null hypothesis failed.

Thank you.
['Suryansh Pandey']
['Pandas', 'Matplotlib', 'Python', 'Seaborn', 'Data Analytics']