Performing Analysis of Meteorological Data

In this blog, we will discuss a data analysis which is based on the following dataset.

“Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming” following is the Hypothesis for the analysis?

The Hypothesis means we need to find whether the average Apparent temperature for the month of a month says April starting from 2006 to 2016 and whether the average humidity for the same period has 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. Support your analysis by appropriate visualizations using matplotlib and/or seaborn library.

Step 1: Importing Libraries 

Step 2: Importing Dataset

Step 3: Dataset Description

Step 4: Data Cleaning

We have to find the null values in the dataset

In this step we will prepare our data for the plotting, we will first drop the unwanted columns (all except temperature and humidity).

Then, we will convert the Timezone to +00:00 UTC .

Step 5: Data Visualization

There can be or maybe Linear Relationship between "Humidity" and "Apparent Temperature (C)" with a negative slope.

Firstly we will plot the whole dataset for all months.

• "Humidity" remains constant from 2006 - 2016
• But "Apparent Temperature (C)" is frequently changing from 2006 - 2016

Now we will plot a graph for a specific month(April).

Conclusion

As we can analyze there isn’t any change in humidity in the past 10 years(2006–2010) for the month of April. whereas the temperature increases sharply in 2009 and drops in 2015 for the rest of the years there isn’t any sharp change in the temperature.

I am thankful to mentors at https://internship.suvenconsultants.com for providing awesome problem statements and giving many of us a Coding Internship Experience. Thank you www.suvenconsultants.com"