Meteorogical Data Analysis

 Hello In this blog we are going to Analyse the data from the Weather data-set of Finland , a country in the Northern Europe. You can find the data-set on Kaggle (https://www.kaggle.com/muthuj7/weather-dataset).We are going to use Libraries like Numpy , Pandas and the Matplotlib  of Python.

Following is the Hypothesis of the Analysis : “Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming.”

Lets Start by Importing Libraries,

                                                         Libraries required for analysis

                                                                  Importing Data-set

Output of this looks like,

                                                                  Initial 5 Entries 

                                                                          Last 5 Entries

Libraries required for visualization

Matplotlib and Seaborn act as the backbone of data visualization through Python. Matplotlib is a Python library used for plotting graphs with the help of other libraries like Numpy and Pandas.

Pandas head() method is used to return top n (5 by default) rows of a data frame or series.

first 5 entries

Pandas Series.isnull() function detect missing values in the given series object. It return a boolean same-sized object indicating if the values are NA. Missing values gets mapped to True and non-missing value gets mapped to False.

Output Data

Data Information

Now we need to drop the unwanted data, convert the data in to our need and resample our data :

Here is how the data looks after resampling:

Data-set

First 5 entries of resampled data-set

Now let us plot our data in a line graph,

As we can see, both the peaks and the troughs are almost same throughout the period of 10 years. Here is a plot of the average temperature and humidity of the month of April over 10 years.

Input

Final Representation of A .Temp Vs Humidity

We can clearly see that there is a sharp rise in temperature in the year of 2009 whereas there is a fall in temperature in the year of 2015. Hence we can conclude that global warming has caused an uncertainty in temperature over the past 10 years while the average humidity as remained constant throughout the 10 years.