Data_Analysis_on_Weather_Dataset

Data Analysis on Weather Dataset using Python

Introduction

Data Analysis is one major part that you must master before learning or diving into the machine learning algorithms section because data analysis is a process to explore the data to get a better understanding of data.

Dataset Overview

The dataset we will use is a simple weather dataset which is a time-series dataset that stores the temperature, humidity, wind speed, etc on an hourly basis on different dates in 2012Load Dataset.

Load Dataset

You have the dataset and open the Jupyter Notebook or you can also create a Kaggle notebook over there itself. The first step is to import the necessary libraries and load the dataset into a notebook.

Basic Python Pandas Data Analysis Functions

1. Shape

data.shape

2. Data types

data.dtypes

3. Unique

data['Weather'].unique()

4. Count

data.count()

5. Value counts

data['Weather'].value_counts()

6.information

data.info

7.Describe

data.describe()

Answering Different Data Analysis Problems

Q1)To check if there are null values and drop them

Q2) To find unique instances of weather types

Q3) To rename column named 'Weather' to Weather Condition'

Q4)To find all records from data of when the weather was exactly clear ?

Q5)To find the mean temperature, wind speed and visibility?

Q6)To find the variance of pressure ?

Q7) To Find the Days in which wind speed was more then 30km/h & temperature is greater then 0C?

Q8) To find the date and temperatures for all instances when snow was recorded

Q9) To display a graph of variation of temperature with respect to time ?

Q10) To display a pie chart of percentages of weather conditions (pie chart)

Conclusion

Data analysis is a continuous process that represents how deep and better you represent your analysis to the client so the insights that can be used to drive business decisions are understandable.

References

• Kaggle.com