Performing Analysis on Meteorological Data

 Is there any change due in weather to global warming in of Finland by using Data analytics ?

In this blog we will discuss about 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.

we need to find whether the average Apparent temperature for the month of a month says April starting from 2006 to 2016 and 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 library.

first of all, we Importing all necessary Python libraries.

Step 1: Importing of libraries and Dataset.

Step 2: Looking at the dataset.

check null value as df.isnull().sum()

isnull() function detect missing values in the given series object. It returns a boolean same-sized object indicating if the values are NA. Missing values get mapped to True and non-missing value gets mapped to False.here not need for data cleaning but We only need Apparent temperature and Average humidity, so we can resample our data. And also we need to resample it monthly. Resampling is a convenient method for frequency conversion. The object must have a DateTime like an index.so set the index.

df=df.set_index(‘Formatted Date’)

x=df[[‘ApparentTemperature©’,’Humidity’]].resample(‘MS’).mean()

Step 3: Cleaning Dataset

Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset . Dropping  unwanted data and converting it according to our requirement.

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

Step 4: Plotting of Data

The purpose of plotting data is to visualize variation or show relationships between variables. We will now plot the line graph to display Average Humidity and Average Apparent Temperature over 10 years(2006–2016). 

In is final step we will plot the data to for the analysis ,

> Firstly we will plot the whole dataset for all months .

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

Conclusion :

                       As we can see in the above images  there are many ups and downs in the temperature. So , We can conclude that global warming has caused an uncertainty in the temperature over  past 10 years while the average humidity has remained constant throughout the 10 years.

Note: This conclusion statement is for only month of April , please refer to the model for all the months.