Climate Change Overview

You can see how climate changes over the years with the help of plotly interactive map. By pointing at any country and by sliding over the years you can see how the average temperature changes across the globe. Deployed Heroku app can be found here Link to GitHub here

Import libraries first

import numpy as np
import pandas as pd
import plotly as py
import plotly.express as px
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

load the data

you can download the original data from here

df = pd.read_csv('../assets/GlobalLandTemperaturesByCountry.csv')

Look at the data, find the Null values, clean the data

print(df.shape)
df.head()
df.tail()
df.isnull().sum()
df = df.drop('AverageTemperatureUncertainty', axis=1)
df = df.rename(columns={'dt': 'Date', 'AverageTemperature': 'Average_Temp'})
df.isnull().sum()
df = df.dropna()
print(df.shape)
df.head()

Creating Celsius to Farenheit function

def convert_to_farenheit (c):
    return (c*9/5)+32

Round up the result to 2 decimals as well

df['Average_Temp_Farenheit'] = df['Average_Temp_Celsius'].apply(convert_to_farenheit).round(2)
df['Average_Temp_Celsius'] = df['Average_Temp_Celsius'].round(2)

Group the dataframe by Country name and dataset

df_sorted = df.groupby(['Country', 'Date']).sum().reset_index().sort_values(['Date'], ascending=False)
df_sorted.head()

Create the data range

start_date = '2009-12-01'
end_date = '2013-01-01'
mask = (df_sorted['Date'] > start_date) & (df_sorted['Date'] <= end_date)

Create a new sorted dataset

df_2000 = df_sorted[mask]
print(df_2000.shape)
df_2000.head()

Create a dataset for Climate change by timeline

df_2000_by_month = df_2000.groupby(['Date','Country']).sum().reset_index()
df_2000_by_month.head()

Visualization

fig = px.choropleth(df_2000_by_month, locations='Country', locationmode='country names', color='Average_Temp_Farenheit', hover_data=['Average_Temp_Celsius'], hover_name='Country', animation_frame='Date')

fig.update_layout(title_text='Average Temperature Change from 2010-01-01 to 2013-01-01', title_x = 0.5, geo=dict(showframe = False, showcoastlines = False))

fig.show()