Analysis of Meteorological Data

In this article we’ll basically perform the analysis of Meteorological Data. One type of data that’s easier to find on the net is meteorological data. Many sites provide historical data on many meteorological parameters such as pressure, temperature, humidity, wind,visibility, etc.We’ll work on one such dataset in this article.

The Dataset has hourly temperature recorded for last 10 years starting from 2006–04–01 to 2016–09–09 . It corresponds to Finland, a country in the Northern Europe.

Our main goal is to perform Data Analysis on this Meteorological Dataset and convert our raw information into knowledge.We’ll perform tasks such as data cleaning ,perform analysis & testing the given hypothesis and then report the conclusion.

A null hypothesis is Ho : “Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming”

The Ho 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.

Let’s get started,

  1. Import all the necessary libraries i.e. Pandas , Seaborn , Matplotlib , Numpy in our Jupyter Notebook.

2. Read the CSV file using Pandas inbuilt function read_csv().

Now, Let’s view our dataset —

Information of the dataset , checking the datatype of all columns —

Datatype of Columns of the Dataset

3. We need to now normalize our dataset. Converting Formatted Date column to Date Time.

Let’s view our dataset now -

4. Resampling our dataset —

Resampled Dataset

Here M denotes: Month starting We are averaging the apparent temperature and humidity using mean() function.

5. Let’s now plot the graph for our data using Matplotlib function plt().The graph will display variation in Apparent Temperature and Humidity with Time.

Graph Plotting


Observation —

From the above plot we can easily see that Humidity remained almost constant in these years.Also ,the average apparent temperature is almost same.

6. Now, For retrieving the data of a particular month, say April then —

7. Now, Plotting the variation in Apparent Temperature and Humidity for the month of April every year :



From the graph we can easily see,there is an increase in apparent temperature in year 2009 ,dropped in 2010 ,slight increase in 2011 and significant drop in the year 2015 whereas there is no noticeable change in the average Humidity.

Conclusion —

Increase in Apparent Temperature and Humidity across 10 years indicates an increase due to global warming.

Dataset link —

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