Performing Analysis of Meteorological Data
Goal Behind it
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.
My goal in this Internship (of data analysis) is to transform the raw data into information and then convert it into knowledge.
I am very excited to share with you my internship’s first project. so, let’s start without any delay!
→ Weather Dataset
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 for the month say April starting from 2006 to 2016 and the for the same period have increased or not.
First of all, let’s describe our dataset. Check weather any null value present or not And analyse the datatype of column is perfect or not.
Before moving on visulization, I have to convert formatted date to date-type. then after visulization is possible in better way.
Now, here data is in hourly period of time. so, for better analysis we need to resample our data in monthly
According to below plotting of Humidity vs Apparent temperature, we can clearly say that humidity throughout the last ten year [2006–2016] remain unchanged. But there is sine wave culture seen in temperature which is quiete normal according to as per our three main season.
We need to find whether the for the month of a month say April starting from 2006 to 2016 and the for the same period have increased or not. Below graph shows humidity vs temperature for April month for ten year.
So, Humidity is remain same for all year but for { year 2009, 2011,2015 } there is major difference shown as compare to average temperature ratio.
Below Plot for Humidity for all ten year [2006–2016]
Below plot is for Temperature measurement for all ten year [2006–2016]
I have also analyse the graph of humidity vs temperature for all 12 month.
Which as shown below Number vice:
(1) January: We can clearly see that how huge change in temperature is created where humidity is similar throughout year.
(2) February: We can clearly see that how huge change in temperature is created and humidity is unchanged.
(3) March:
(4) April:
(5) May:
(6) June:
(7) July:
(8) August:
(9) September:
(10) October:
(11) November:
(12) December:
Conclusion:
According to January-December graph visualisation, April to August month’s temperature change is very less. Its almost same for all year.But there is no change in humidity for last 10 year[2006–2016] for a perticular month.Again september to march, there is very huge change in temperature but humidity is remain unchanged.
GithHub Link with description : Meteorological Data Analysis.ipynb
I am thankful to mentors at Suvenconsultants for providing awesome problem statements and giving many of us a Coding Internship Exprience. Thank you Suvenconsultants.
