elcome To the project on data analysis where we will be exploring a Weather dataset on various parameters and try to extract the best out of it.
This Project is part of one of my internships so I am writing a blog to learn, explore, and get your reviews on this.
In this small yet interesting project, we will be performing various tasks like data cleaning, data resampling, hypothesis testing, Data visualization in different ways.
So, Metrological data is a weather dataset. many sites provide the historic dataset on many meteorological parameters such as pressure, temperature, humidity, wind speed, visibility. The dataset we have taken is easily available on Kaggle and you can simply download it from
Let's get started with our analysis and present our results.
Step-1) Import all the required libraries
1) Numpy:- for mathematical calculation on a single and multidimensional array
2) Pandas:- for data manipulation
3) Matplotlib:- data visualization library
4) seaborn:- data visualization library in support with Matplotlib.
#Import Libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as pk
Step-2) Load the dataset
#Read Dataset
df =pd.read_csv("weatherHistory.csv")
#Head function
df.head(10)
Step-3) Have a basic look over data and preliminary statical analysis of data
Conclusion:
There is No change in average humidity. The year 2009 can see an increase in average apparent temperature, then a fall in 2010, then a slight increase in 2011, then a significant drop in 2015, and then an increase in 2016.
