Explore Weather Trends!
Current predictions of climate change may significantly underestimate the speed and severity of global warming. Many changes have been unprecedented over the years and continuing to an exponential extent. In this report we will analyze the database obtained from the udacity’s workspace. We will also learn about working with tables and plotting a Moving Average Line chart using tools such as SQL and Python.
Comparing London’s and Dublin’s average temperature with the Global average temperature, sounds fun, lets get on with it.
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites
Understanding of Python and MySQL required. What things you need to install the software and how to install them.
- Python3 Click here to redirect to Python installation page.
- MYSQL Click here to redirect to MYSQL installation page.
- Jupyter Notebook Click here to redirect to MYSQL installation page.
- Pandas Click here to redirect to MYSQL installation page.
- Matplotlib (Click here) to redirect to Matplotlib installation page.
Dependencies
INSTALLATION
Spyder
Resource -> (https://docs.spyder-ide.org/installation.html)
Install on GNU/Linux
Please refer to the Requirements to see what other packages you might need.
sudo apt install spyder3
Install on Arch Linux Install on arch using AUR package here
Install on Fedora Install on Fedora. Refer here
sudo dnf install python3-spyder ### Matplotlib Matplotlib and its dependencies are available as wheel packages for macOS, Windows and Linux distributions:
python -m pip install -U pip
python -m pip install -U matplotlib
Pandas
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive.
pip install pandas
Now lets jump into the next section
Code
MYSQL
The CSV fies were extracted using the Udacity’s native SQL Workspace:
SELECT ∗ FROM city data;
Joining and creating a columns
ALTER TABLE
global data RENAME COLUMN
avg temp to global avg temp;
ALTER TABLE
city data RENAM E COLUMN
avg temp to london avg temp;
ALTER TABLE
city data ADD COLUMN
dublin avg temp;
Now creating a inner join such that the created table can be used for plotting Moving Average line chart.
SELECT global data.year,
global data.global avg temp, london avg temp
FROM global data INNER JOIN
city data ON global data.year = city data.year
WHERE city like 'London';
Export your file to CSV.
CSV Analysis using Pandas
To accomplish the task of plotting the line chart using pandas for correlation of data from the table and and declaring a function such that different moving averages can be calculated with ease. dropna() from Pandas module is used to reduce the noise from the CSV file.
import pandas as pd
import matplotlib.pyplot as plt
temp = pd.read_csv("city_analysis.csv")
def MeanFunction(check, input):
output = input.rolling(window = check, center=False, on = "year").mean().dropna()
return output
Moving Average is calculated only after cleaning the CSV files from error to avoid sudden fluctuations in the line chart.
average_span = 7
moving_avg = MeanFunction(average_span, temp)
plt.plot(moving_avg['year'], moving_avg['london_avg_temp'], label='London')
plt.plot(moving_avg['year'], moving_avg['dublin_avg_temp'], label='Dublin')
plt.plot(moving_avg['year'], moving_avg['global_avg_temp'], label='Global')
plt.legend()
plt.xlabel("Year (C.E.)")
plt.ylabel("Temperature (°C)")
plt.title("Temperature in London verus Dublin versus Global values ({} year moving average)".format(average_span))
plt.show()
Graphs
It is clearly seen that the average temperature have been rising ever since the temperature were being recorded. Global warming to be blamed on this part.
A 10 year moving Average was taken because it provided a better understanding of the line chart and gave wide range of values where the comparison can be made.
Similarly, 50 years moving Average of London’s versus Dublin’s versus global average temperature as shown in below in line chart.
Analysis
The overall trends tends to give us an idea that global warming a very serious issue and it should be taken care of.
The trends from 1950-present has seen drastic change in terms of global average temperature as well as London’s and Dublin’s average temperature. The world is getting hotter place to live in. The temperature gives us an idea that these figure will increase exponentially until several strict measures are not taken into account. The trend has been consistently increasing with each passing decade and will increase or the climate change will soon plunder the planet
Contributors
Raunak Tripathi
Feel free to reach me @ Linkedin