Performing Analysis of Meteorological Data

Prerequisites

If you already have Jupyter notebook and all the necessary python libraries (numpy , Scikit-Learn, matplotlib ) installed you are ready to get started.

Implementation

Import the necessary python libraries

Start by importing all the helpful libraries will be needing for the analysis.

Load the Dataset

To load the data from a CSV (comma-separated value) file into a Pandas DataFrame we use the “read_csv()” function in Pandas. Here stored the data frame into a variable and viewed the first five rows using head() function.

Data Preparation

Let’s describe our dataset. Check weather any null value present or not and analyse the datatype.

Data Cleaning

Now we need to focus on important factors as Apparent Temperature ( C ) & Humidity so we can use the drop() function to drop the unwanted data and clean the dataset.

Data Formatting

Before starting with analysis of the data , we need to normalize the dataset by converting the Formatted Date column to Date Time object. It can be easily done using Pandas function to_datetime().

Resampling the data

Since we have hourly data, we need to resample it to monthly. Resampling is a convenient method for frequency conversion. Object must have a datetime like index.

Plotting the variation in Apparent Temperature and Humidity with time

We will be using Seaborn to plot the variation in apparent temperature and Humidity with time.

Data Analysis

As we can see, both the peaks and the troughs are almost same throughout the period of 10 years. Let’s retrieve the data of a particular month from every year and check whether the average Apparent temperature & the average humidity for the same period have increased or not.

Data Visualization

Use the code given below to plot the graphs for each month

Conclusion

As for our Hypothesis,

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Vaishnavi Saravanan

Vaishnavi Saravanan

Masters Student | AI Learner | A Keen Reader & Listener | Active Blogger