Performing Analysis of Meteorological Data

In this blog, we’ll see how we can analyze the meteorological data using python libraries: pandas and NumPy. Similarly, we’ll visualize our analysis using another python visualization library: Matplotlib.

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, windspeed, visibility, etc. Let’s use the weather dataset from Kaggle.

Source URL: https://www.kaggle.com/muthuj7/weather-dataset

The dataset has hourly temperature recorded for last 10 years starting from 2006–04–0100:00:00.000 +0200 to 2016–09–09 23:00:00.000 +0200. It corresponds to Finland, a country in the Northern Europe.

Here in this article, we are going the find out “Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming”

We’ve to evaluate the average Apparent temperature and average humidity on monthly basis for 10 years, from year 2006 to 2016.

• Apparent temperature: Apparent temperature is the temperature equivalent perceived by humans, caused by the combined effects of air temperature, relative humidity and wind speed.
• Humidity: Humidity is the concentration of water vapor present in the air.

Let’s proceed with our code:

From all the above analysis and graphs we can now tell that ,on average, the apparent temperature and humidity are fluctuating year by year and it becomes very uncertain to predict.

Remember the Hypothesis: ‘Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming.’