Climate Change and Impacts in Africa
According to the United Nations, Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions. But since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like coal, oil, and gas.
The consequences of climate change now include, among others, intense droughts, water scarcity, severe fires, rising sea levels, flooding, melting polar ice, catastrophic storms, and declining biodiversity.
You work for a Non-governmental organization tasked with reporting the state of climate change in Africa at the upcoming African Union Summit. The head of analytics has provided you with IEA-EDGAR CO2 dataset which you will clean, combine and analyze to create a report on the state of climate change in Africa. You will also provide insights on the impact of climate change on African regions (with four countries, one from each African region, as case studies).
Dataset
The dataset, IEA-EDGAR CO2, is a component of the EDGAR (Emissions Database for Global Atmospheric Research) Community GHG database version 7.0 (2022) including or based on data from IEA (2021) Greenhouse Gas Emissions from Energy, www.iea.org/statistics, as modified by the Joint Research Centre. The data source was the EDGARv7.0_GHG website provided by Crippa et. al. (2022) and with DOI.
The dataset contains three sheets - IPCC 2006
, 1PCC 1996
, and TOTALS BY COUNTRY
on the amount of CO2 (a greenhouse gas) generated by countries between 1970 and 2021. You can download the dataset from your workspace or inspect the dataset directly here.
TOTALS BY COUNTRY SHEET
This sheet contains the annual CO2 (kt) produced between 1970 - 2021 in each country. The relevant columns in this sheet are:
Columns | Description |
---|---|
C_group_IM24_sh | The region of the world |
Country_code_A3 | The country code |
Name | The name of the country |
Y_1970 - Y_2021 | The amount of CO2 (kt) from 1970 - 2021 |
IPCC 2006
These sheets contain the amount of CO2 by country and the industry responsible.
Columns | Description |
---|---|
C_group_IM24_sh | The region of the world |
Country_code_A3 | The country code |
Name | The name of the country |
Y_1970 - Y_2021 | The amount of CO2 (kt) from 1970 - 2021 |
ipcc_code_2006_for_standard_report_name | The industry responsible for generating CO2 |
Instructions
The head of analytics in your organization has specifically asked you to do the following:
- Clean and tidy the datasets.
- Create a line plot to show the trend of
CO2
levels across the African regions. - Determine the relationship between time (
Year
) andCO2
levels across the African regions. - Determine if there is a significant difference in the
CO2
levels among the African Regions. - Determine the most common (top 5) industries in each African region.
- Determine the industry responsible for the most amount of CO2 (on average) in each African Region.
- Predict the
CO2
levels (at each African region) in the year 2025. - Determine if
CO2
levels affect annualtemperature
in the selected African countries.
IMPORTANT
-
Make a copy of this workspace.
-
Write your code within the cells provided for you. Each of those cells contain the comment "
#Your code here
". -
Next, run the cells containing the checks. We've asked you not to modify these cells. To pass a check, make sure you create the variables mentioned in the instruction tasks. They (the variables) will be verified for correctness; if the cell outputs nothing your solution passes else the cell will throw an error. We included messages to help you fix these errors.
-
If you're stuck (even after reviewing related DataCamp courses), then uncomment and run the cell which contains the source code of the solution. For example,
print(inspect.getsource(solutions.solution_one))
will display the solution for instruction 1. We advise you to only look at the solution to your current problem. -
Note that workspaces created inside the "I4G 23/24" group are always private to the group and cannot be made public.
-
If after completion you want to showcase your work on your DataCamp portfolio, use "File > Make a copy" to copy over the workspace to your personal account. Then make it public so it shows up on your DataCamp portfolio.
-
We hope you enjoy working on this project as we enjoyed creating it. Cheers!
# Setup
import pandas as pd
import numpy as np
import pingouin
from sklearn.linear_model import LinearRegression
from statsmodels.regression.linear_model import OLS
import seaborn as sns
import matplotlib.pyplot as plt
import inspect
plt.style.use('ggplot')
# The sheet names containing our datasets
sheet_names = ['IPCC 2006', 'TOTALS BY COUNTRY']
# The column names of the dataset starts from rows 11
# Let's skip the first 10 rows
datasets = pd.read_excel('IEA_EDGAR_CO2_1970-2021.xlsx', sheet_name = sheet_names, skiprows = 10)
# we need only the African regions
african_regions = ['Eastern_Africa', 'Western_Africa', 'Southern_Africa', 'Northern_Africa']
ipcc_2006_africa = datasets['IPCC 2006'].query('C_group_IM24_sh in @african_regions')
totals_by_country_africa = datasets['TOTALS BY COUNTRY'].query('C_group_IM24_sh in @african_regions')
totals_by_country_africa
# Read the temperatures datasets containing four African countries
# One from each African Region:
# Nigeria: West Africa
# Ethiopa : East Africa
# Tunisia: North Africa
# Mozambique: South Africa
temperatures = pd.read_csv('temperatures.csv')
temperatures
# The solution code and the test runner
import tests as runner
import solutions
Instruction 1: Clean and tidy the datasets
Tasks
- Rename
C_group_IM24_sh
toRegion
,Country_code_A3
toCode
, andipcc_code_2006_for_standard_report_name
toIndustry
in the corresponding African datasets. - Drop
IPCC_annex
,ipcc_code_2006_for_standard_report
, andSubstance
from the corresponding datasets. - Melt
Y_1970
toY_2021
into a two columnsYear
andCO2
. Drop rows whereCO2
is missing. - Convert
Year
toint
type.
Hints
- Use
df.rename()
method to rename columns. - The
df.drop()
method can be used to drop columns. - You might find
df.melt()
orpd.melt()
useful. - The
df.column.astype(int)
can be used to convert to a column to an integer type.
# Renaming the columns in ipcc_2006_africa
ipcc_2006_africa.rename(columns={'C_group_IM24_sh':'Region','Country_code_A3':'Code','ipcc_code_2006_for_standard_report_name':'Industry'},inplace=True)
# Renaming the columns in totals_by_country_africa
totals_by_country_africa.rename(columns={'C_group_IM24_sh':'Region','Country_code_A3':'Code','ipcc_code_2006_for_standard_report_name':'Industry'},inplace=True)
# Droping the columns 'IPCC_annex','ipcc_code_2006_for_standard_report','Substance' in ipcc_2006_africa
ipcc_2006_africa.drop(columns=['IPCC_annex','ipcc_code_2006_for_standard_report','Substance'],inplace=True)
# Droping the columns 'Substance','IPCC_annex' in totals_by_country_africa
totals_by_country_africa.drop(columns=['Substance','IPCC_annex'],inplace=True)
# Selecting columns not to melt
ids = ['Region','Code','Name','Industry','fossil_bio']
# Melting the year columns
ipcc_2006_africa =ipcc_2006_africa.melt(id_vars=ids,value_vars=ipcc_2006_africa.columns.drop(ids),var_name='Year',value_name='CO2')
# Removing 'Y_' from the entries in the year column
ipcc_2006_africa['Year'] = ipcc_2006_africa['Year'].str.replace('Y_',"").astype(int)
ipcc_2006_africa.dropna(subset=['CO2'],inplace=True)
ids2 = ['Region','Code','Name']
totals_by_country_africa =totals_by_country_africa.melt(id_vars=ids2,value_vars=totals_by_country_africa.columns.drop(ids2),var_name='Year',value_name='CO2')
totals_by_country_africa['Year'] = totals_by_country_africa['Year'].str.replace('Y_',"").astype(int)
totals_by_country_africa.dropna(subset=['CO2'],inplace=True)
print(ipcc_2006_africa, totals_by_country_africa)
# Uncomment and run to view solution one
#print(inspect.getsource(solutions.solution_one))
Instruction 2: Show the trend of CO2
levels across the African regions
CO2
levels across the African regionsTasks
- Using
totals_by_country_africa
, create a line plot ofCO2
vs.Year
in eachRegion
to show the trend of CO2 levels by year.
Hints
- Use
sns.lineplot()
to create a line plot. - Your plot should be similar to the one shown below.
#using lineplot from seaborn
sns.lineplot(data=totals_by_country_africa,x='Year',y='CO2',hue='Region',ci=0)
plt.title('CO2 levels accross th African Regions btween 1970 and 2021');
# Uncomment and run to view solution two
# print(inspect.getsource(solutions.solution_two))
Instruction 3: Determine the relationship between time (Year
) and CO2
levels across the African regions
Year
) and CO2
levels across the African regionsTasks
- Using the
totals_by_country_africa
dataset, conduct a Spearman's correlation to determine the relationship between time (Year
) andCO2
within each AfricanRegion
. - Save the results in a variable called
relationship_btw_time_CO2
.
Hints
- Use
df.groupby()
anddf.corr()
methods. - Use the
corr()
method'smethod
parameter to set the correlation type.
# Grouping totals_by_country_africa data by region
grouped_data = totals_by_country_africa.groupby('Region')['Year','CO2']
grouped_data
# Calculating the spearman correlation
relationship_btw_time_CO2 = grouped_data.corr(method='spearman')
relationship_btw_time_CO2
# DO NOT MODIFY THIS CELL
# Run this cell to determine if you've done the above correctly
# If there are no error messages, you are correct :)
# tests
runner.check_task_3(relationship_btw_time_CO2)
# Uncomment and run to view solution three
# print(inspect.getsource(solutions.solution_three))
Instruction 4: Determine if there is a significant difference in the CO2 levels among the African Regions
Tasks
- Using
totals_by_country_africa
, conduct an ANOVA usingpingouin.anova()
on theCO2
byRegion
. Save the results asaov_results
. - Conduct a posthoc test (with Bonferroni correction) using
pingouin.pairwise_tests()
to find the source of the significant difference. Save the results aspw_ttest_result
. - Is it true that the
CO2
levels of theSouthern_Africa
andNorthern_Africa
region do not differ significantly? The previous task should provide you with the answer.