Performing Analysis Of Meteorological Data

This article serves as a documentation of my first project — Performing Analysis of Meteorological Data for my internship program at Suven Consultants & Technology Pvt. Ltd.

The main objective of this Data Analytics Internship is to transform the raw data into information and then convert it into knowledge. Since weather data is one of the most easily available data on the internet, it serves as a great starting point to understand fundamental data analytics concepts.


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.

Download the weather dataset from this Google drive link.


Transform the raw data into information and then convert it into knowledge. By -

  • Perform data cleaning,

Null Hypothesis (H0)

“Has the apparent temperature & 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. 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.

Implementation -

Step 1: Importing the Necessary Libraries & Data

Step 2: Data cleaning

2.1 Find all Missing values from the Dataset.

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

2.3 Change the format of data for better analysis

Converted the ‘Formatted Date’ column to standard Python datetime format for easier analysis.

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

Step 3: Resample data from hourly to month wise

The data in the dataset is hourly values, we resample the entire dataset to monthly values to meet our analysis requirements.

Step 4: Analysis plots of temperature & humidity over the range of years in the dataset

4.1 Variation in apparent temperature & humidity with time (in years)

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

Conclusion -

  • No change in average humidity observable.