Warning: We are not climate scientists, some things may be inaccurate. This is simply just a study on a subject we are interested in, allowing us to go deeper into the subject while at the same time improving our graphing and prediction skills. All my sources are at the bottom of the notebook.
1. Importing Packages¶
| ⚡ Description: Importing Packages ⚡ |
|---|
| In this section we will be importing libraries used throughout our analysis and modelling which will allow us to call functions that are not part of your main python program, and briefly discuss them. |
In [1]:
# Libraries for Anaysis
import numpy as np
import pandas as pd
from scipy import stats
from scipy.stats import norm
import math
# Libraries for Plotting Analysis
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Setting Plot Design
sns.set_style("darkgrid", {"axes.facecolor": "#eff2f5", 'grid.color': '#c0ccd8',
'patch.edgecolor': '#B0B0B0', 'font.sans-serif': 'Verdana'})
sns.set_palette('Dark2_r')
plt.rc('font', size=19)
plt.rc('axes', titlesize=25)
plt.rc('axes', labelsize=20)
plt.rc('xtick', labelsize=17)
plt.rc('ytick', labelsize=17)
plt.rc('figure', titlesize=24)
# Extra Plotting Tools Required for Bar Chart Race
import matplotlib.ticker as ticker
import matplotlib.animation as animation
from IPython.display import HTML
import missingno as msno
# For standardizing features
from sklearn.preprocessing import StandardScaler
# Mute warnings
import warnings
warnings.filterwarnings('ignore')
2. Loading the Data¶
| ⚡ Description: Loading the data ⚡ |
|---|
| In this section we will be loading the data from the CSV and EXCEL files into Pandas DataFrames. |
In [2]:
# Load Base Data
df = pd.read_csv("data/Our_CO2emission_Analysis_Data.csv")
In [3]:
df.columns
Out[3]:
Index(['Unnamed: 0', 'Country', 'e_type', 'Year', 'e_con', 'e_prod', 'GDP',
'Population', 'ei_capita', 'ei_gdp', 'pop_growth', 'pop_density',
'Manuf_GDP', 'Agric_GDP', 'Deforestation', 'emission_per_cap',
'CO2_emission'],
dtype='object')
In [3]:
# View first 5 rows of Loaded Base Data
df.head()
Out[3]:
| Unnamed: 0 | Country | e_type | Year | e_con | e_prod | GDP | Population | ei_capita | ei_gdp | pop_growth | pop_density | Manuf_GDP | Agric_GDP | Deforestation | emission_per_cap | CO2_emission | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | World | all | 1988 | 345.56 | 347.41 | 42106.6 | 4927545.08 | 70.13 | 8.21 | 1.77 | 39.285 | NaN | 2233.75513 | NaN | 0.004295 | 21163.84 |
| 1 | 1 | World | coal | 1988 | 96.87 | 98.48 | 42106.6 | 4927545.08 | 70.13 | 8.21 | 1.77 | 39.285 | NaN | 2233.75513 | NaN | 0.001812 | 8930.92 |
| 2 | 2 | World | nat_gas | 1988 | 71.01 | 71.85 | 42106.6 | 4927545.08 | 70.13 | 8.21 | 1.77 | 39.285 | NaN | 2233.75513 | NaN | 0.000725 | 3571.68 |
| 3 | 3 | World | pet/oth | 1988 | 133.45 | 132.49 | 42106.6 | 4927545.08 | 70.13 | 8.21 | 1.77 | 39.285 | NaN | 2233.75513 | NaN | 0.001758 | 8661.24 |
| 4 | 4 | World | nuclear | 1988 | 19.23 | 19.23 | 42106.6 | 4927545.08 | 70.13 | 8.21 | 1.77 | 39.285 | NaN | 2233.75513 | NaN | 0.000000 | 0.00 |
In [3]:
# Drop Unamed Column
df = df.drop('Unnamed: 0', axis=1)
Column descriptions:
- Country - Country in question
- Energy_type - Type of energy source
- Year - Year the data was recorded
- Energy_consumption - Amount of Consumption for the specific energy source, measured (quad Btu)
- Energy_production - Amount of Production for the specific energy source, measured (quad Btu)
- GDP - Countries GDP at purchasing power parities, measured (Billion 2015$ PPP)
- Population - Population of specific Country, measured (Mperson)
- Energy_intensity_per_capita - Energy intensity is a measure of the energy inefficiency of an economy. It is calculated as units of energy per unit of capita (capita = individual person), measured (MMBtu/person)
- Energy_intensity_by_GDP- Energy intensity is a measure of the energy inefficiency of an economy. It is calculated as units of energy per unit of GDP, measred (1000 Btu/2015$ GDP PPP)
- CO2_emission - The amount of C02 emitted, measured (MMtonnes CO2)
- Rate of population change - To see if a possible change in population of a place will result in change in CO2 emission & to What extent
- Population density - Does the density of a population have any effect on CO2 Emission?
- GDP splits - Example, % for agriculture vs manufacturing; Hypothetically, GDP increase due to agricultural/Green activities should oppose the direct correlation of rise in GDP to CO2 Emission
- Rate of Deforestation - As a result of our research on why the Dip in CO2 Emission of the World occurred in 2009 and the sudden rise in 2010 when Energy Type, Pop, and GDP were Constant....
- Emission per Capita - Also: probing into the theory that a unit increase in Population is directly impacting on the increase in CO2 Emission, we opted to getting a column which represents per capita emission for each country per energy type which will be plotted against the co2 emission and resulting graph compared with the graph of countries/population of highest emitters. The idea is if the comparism correlates, then our Hypothesis theory of increase in pop is directly propotional to increase in CO2 Emission, is 100% valid, if not; To be modified with extra clause.
3. Exploratory Data Analysis (EDA)¶
| ⚡ Description: Exploratory data analysis ⚡ |
|---|
| In this section, we will be carrying out the initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.you are required to perform an in-depth analysis of all the variables in the DataFrame. |
In [5]:
# Let's Visualize our missing values
msno.matrix(df, color=(0.1, 0.4, 0.5))
Out[5]:
<AxesSubplot:>
In [6]:
'''
As Temporary Defeat to dealing with our missing values
We Opted to dropping all rows with missing values
AS Base Analysis
'''
df.dropna(subset=['Deforestation', 'Agric_GDP', 'Manuf_GDP', 'pop_density', 'pop_growth'], inplace=True)
In [7]:
# Let's Visualize our missing values
msno.matrix(df, color=(0.1, 0.4, 0.5))
Out[7]:
<AxesSubplot:>
In [8]:
# More Information as concerning df
df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 27450 entries, 1950 to 35153 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Country 27450 non-null object 1 e_type 27450 non-null object 2 Year 27450 non-null int64 3 e_con 27450 non-null float64 4 e_prod 27450 non-null float64 5 GDP 27450 non-null float64 6 Population 27450 non-null float64 7 ei_capita 27450 non-null float64 8 ei_gdp 27450 non-null float64 9 pop_growth 27450 non-null float64 10 pop_density 27450 non-null float64 11 Manuf_GDP 27450 non-null float64 12 Agric_GDP 27450 non-null float64 13 Deforestation 27450 non-null float64 14 emission_per_cap 27450 non-null float64 15 CO2_emission 27450 non-null float64 dtypes: float64(13), int64(1), object(2) memory usage: 3.6+ MB
So We've dropped from 35154 Observations to 27510, which to a certain extent, representative for our base analysis
In [9]:
# Used to compare for Max Values in Dataset
df[df['Country'] == 'World'].max()
Out[9]:
Country World e_type ren/oth Year 2016 e_con 572.16 e_prod 568.56 GDP 115567.88 Population 7464042.85 ei_capita 77.8 ei_gdp 6.36 pop_growth 1.427 pop_density 57.2 Manuf_GDP 18685.014838 Agric_GDP 4791.444305 Deforestation 40666193.560053 emission_per_cap 0.004774 CO2_emission 34894.26 dtype: object
In [10]:
# Descriptive statistics of the whole Dataframe
round(df.describe(), 3)
Out[10]:
| Year | e_con | e_prod | GDP | Population | ei_capita | ei_gdp | pop_growth | pop_density | Manuf_GDP | Agric_GDP | Deforestation | emission_per_cap | CO2_emission | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 27450.000 | 27450.000 | 27450.000 | 27450.000 | 27450.000 | 27450.000 | 27450.000 | 27450.000 | 27450.000 | 27450.000 | 27450.000 | 2.745000e+04 | 27450.000 | 27450.000 |
| mean | 2005.446 | 1.505 | 1.519 | 826.836 | 64034.329 | 83.579 | 4.418 | 1.540 | 178.986 | 137.950 | 40.785 | 3.950679e+05 | 0.002 | 89.633 |
| std | 8.351 | 15.480 | 15.211 | 5931.430 | 457125.183 | 117.500 | 4.110 | 1.470 | 559.028 | 989.154 | 240.220 | 2.747431e+06 | 0.004 | 980.732 |
| min | 1990.000 | -0.040 | 0.000 | 0.130 | 40.260 | 0.330 | 0.000 | -6.766 | 1.406 | 0.001 | 0.008 | 0.000000e+00 | -0.000 | -0.010 |
| 25% | 1999.000 | 0.000 | 0.000 | 13.850 | 2316.570 | 10.040 | 2.370 | 0.557 | 28.453 | 1.310 | 1.226 | 4.902860e+03 | 0.000 | 0.000 |
| 50% | 2006.000 | 0.020 | 0.000 | 54.140 | 7750.000 | 39.000 | 3.660 | 1.426 | 71.594 | 6.509 | 4.818 | 2.931996e+04 | 0.000 | 0.100 |
| 75% | 2013.000 | 0.220 | 0.130 | 288.390 | 23288.570 | 111.850 | 5.390 | 2.456 | 147.139 | 43.143 | 16.724 | 1.535988e+05 | 0.001 | 8.400 |
| max | 2019.000 | 572.160 | 568.560 | 115567.880 | 7464042.850 | 1115.450 | 166.910 | 17.512 | 8044.526 | 18685.015 | 4791.444 | 4.066619e+07 | 0.056 | 34894.260 |
As We can see; the dataset
- Spans between the period of 1990 to 2019, amounting to 29 years spreed.
- A Total of 27,450 observations with no null values present.
- The barest minimum CO2 Emission was at -0.0 due to the fact that there wasn't energy-produced(0.0), as compared to the maximum amount of CO2 Emission at 34894.0 owing to the fact that energy was produced at that level(569.0).
- Also, One can see from the above information that as Population increases, the GDP also increases.
- Finally, The Standard Deviation signifies that all the numbers in the dataset don't share the same value and will require standardization prior to Modelling with it.
- It appears Over 75% of our has an Energy-Consumption & Energy-Production value of (0.0), This is due the large variation between the individual Countries (having most representations between 0.0 to about 0.2) and the World which is the sum of all energy_consumed for the all countries for that Year
Researching the unique Max Values:
- Our World data (Which is a sum of all Countries event per year): account for the highest
CO2_Emission,Deforestation,Agric_GDP,Manuf_GDP,Population,GDP,e_prod & e_con, which is expected - Singapore One of the most densely populated country following Macau: accounting for the Highest Population density of
8044.526000in 2019 - Belarus (Obviously one of the countries battling with High Energy Intensity): accounts for energy intensity per GDP of
166.91in 1992 - Qatar One of the leading Energy consumers per capita: accounting for energy intensity per capita of
1115.45in year 2000 - Qatar Qatar - Historical Population Growth Rate Data Saw Highest Peak in 2007: accounting for energy intensity per capita of
17.512in year 2007 of our Dataset, Which proves to be the highest so far
Due to this Unique conditions, we should experience a minute proportion of our dataset skewing as outliers which infact may not be.
In [7]:
# Check for Outliers
df.kurtosis()
Out[7]:
Year -1.124145 e_con 753.757823 e_prod 777.430741 GDP 223.488027 Population 194.205794 ei_capita 12.262635 ei_gdp 558.439920 pop_growth 16.155439 pop_density 118.636729 Manuf_GDP 201.239497 Agric_GDP 197.217857 Deforestation 190.553720 emission_per_cap 36.653357 CO2_emission 646.010606 dtype: float64
As anticipated;
majority of the values of the Kurtosis are greater than 3, it should mean the presence of outliers. High kurtosis in a data set is an indicator that data has heavy tails or outliers.
However Scaling of the dataset is highly recommended.
In [18]:
from plotly import graph_objects as go
from plotly.subplots import make_subplots
from sklearn.preprocessing import MinMaxScaler
In [19]:
min_max = MinMaxScaler(feature_range=(0,1))
min_max.fit(df['CO2_emission'].values.reshape(-1,1))
df['emission_norm'] = min_max.transform(df['CO2_emission'].values.reshape(-1,1))
In [74]:
%%timeit
# plot trace of max emitters and add plot of population
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Bar(
x=df.loc[~(df.Country.isin(['World','United States']))]
.groupby('Country', as_index=False)
.agg({'CO2_emission':'sum','GDP_cap':'mean'})
.sort_values('GDP_cap', ascending=False)
.reset_index(drop=True)
.loc[:10,'Country'],
y=df.loc[~(df.Country.isin(['World','United States']))]
.groupby('Country', as_index=False)
.agg({'CO2_emission':'sum','GDP_cap':'mean'})
.sort_values('GDP_cap', ascending=False)
.reset_index(drop=True)
.loc[:10,'CO2_emission'],
name= 'CO2 emission levels'
), secondary_y=False
)
#plot Population growth average
fig.add_trace(
go.Scatter(
x=df.loc[~(df.Country.isin(['World','United States']))]
.groupby('Country', as_index=False)
.agg({'GDP_cap':'mean','CO2_emission':'sum'})
.sort_values('GDP_cap', ascending=False)
.reset_index(drop=True)
.loc[:20,'Country'],
y=df.loc[~(df.Country.isin(['World','United States']))]
.groupby('Country', as_index=False)
.agg({'GDP_cap':'mean','CO2_emission':'sum'})
.sort_values('GDP_cap', ascending=False)
.reset_index(drop=True) #ddcndk
.loc[:20,'GDP_cap'],
name='GDP per Capita'
), secondary_y=True,
)
fig.update_layout(
# title='Plot of highest emitters: CO2 emission & GDP per capita',
width=800, height=600,
margin=dict(t=0,
r=0,
l=0,
),
legend=dict(xanchor='center',
x=0.75,
bgcolor='rgba(0,0,0,0)',
y=0.9
),
)
fig.update_xaxes(tickangle=60,)
fig.update_yaxes(
secondary_y=False,
# tickangle=60,
showgrid=True
)
fig.update_yaxes(
secondary_y=True,
# tickangle=10,
showgrid=False
)
# fig.show()
83.1 ms ± 27.5 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [86]:
# plot trace of max emitters and add plot of population
fig = make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Bar(
x=df.loc[~(df.Country=='World')]
.groupby('Country', as_index=False)
.agg({'GDP':'mean','CO2_emission':'sum','Manuf_GDP':'mean'})
.sort_values('Manuf_GDP', ascending=False)
.reset_index(drop=True)
.loc[:10,'Country'],
y=df.loc[~(df.Country=='World')]
.groupby('Country', as_index=False)
.agg({'GDP':'mean','CO2_emission':'sum','Manuf_GDP':'mean'})
.sort_values('Manuf_GDP', ascending=False)
.reset_index(drop=True)
.loc[:10,'CO2_emission'],
name= 'CO2 emission levels'
), secondary_y=False
)
#plot Population growth average
fig.add_trace(
go.Scatter(
x=df.loc[~(df.Country=='World')]
.groupby('Country', as_index=False)
.agg({'GDP':'mean','CO2_emission':'sum','Manuf_GDP':'mean'})
.sort_values('Manuf_GDP', ascending=False)
.reset_index(drop=True)
.loc[:10,'Country'],
y=df.loc[~(df.Country=='World')]
.groupby('Country', as_index=False)
.agg({'GDP':'mean','CO2_emission':'sum','Manuf_GDP':'mean'})
.sort_values('Manuf_GDP', ascending=False)
.reset_index(drop=True) #ddcndk
.loc[:10,'Manuf_GDP'],
name='Manufacturing GDP'
), secondary_y=True
)
fig.add_trace(
go.Scatter(
x=df.loc[~(df.Country=='World')]
.groupby('Country', as_index=False)
.agg({'GDP':'mean','CO2_emission':'sum','Manuf_GDP':'mean'})
.sort_values('Manuf_GDP', ascending=False)
.reset_index(drop=True)
.loc[:10,'Country'],
y=df.loc[~(df.Country=='World')]
.groupby('Country', as_index=False)
.agg({'GDP':'mean','CO2_emission':'sum','Manuf_GDP':'mean'})
.sort_values('Manuf_GDP', ascending=False)
.reset_index(drop=True) #ddcndk
.loc[:10,'GDP'],
name='Overall GDP'
), secondary_y=True
)
fig.add_trace(
go.Scatter(
x=df.loc[~(df.Country=='World')]
.groupby('Country', as_index=False)
.agg({'GDP':'mean','Agric_GDP':'mean','Manuf_GDP':'mean'})
.sort_values('Manuf_GDP', ascending=False)
.reset_index(drop=True)
.loc[:10,'Country'],
y=df.loc[~(df.Country=='World')]
.groupby('Country', as_index=False)
.agg({'GDP':'mean','Agric_GDP':'mean','Manuf_GDP':'mean'})
.sort_values('Manuf_GDP', ascending=False)
.reset_index(drop=True) #ddcndk
.loc[:10,'Agric_GDP'],
name='Agricultural GDP'
), secondary_y=True
)
fig.update_layout(
# title='Plot of highest emitters: CO2 emission & Population Growth',
width=1280, height=720,
margin=dict(
t=5,
l=1,
r=1
),
legend=dict(
xanchor='center',
x=0.8,
y=0.9,
bgcolor='rgba(0,0,0,0)'
)
)
fig.update_yaxes(secondary_y=True, showgrid=False)
fig.update_xaxes(tickangle=30)
fig.show()
In [55]:
fig = go.Figure()
fig.add_trace(
go.Bar(
x=df.loc[df['Country']=='India']
.groupby('Year',as_index=False)
.agg({'CO2_emission':'sum'})
.loc[:,'Year'],
y=df.loc[df['Country']=='India']
.groupby('Year',as_index=False)
.agg({'CO2_emission':'sum'})
['CO2_emission'].rolling(10).mean(),
name='CO2_emission levels'
)
)
In [5]:
df['GDP_cap'] = df['GDP'] / df['Population']
In [50]:
fig=make_subplots(specs=[[{'secondary_y':True}]])
fig.add_trace(
go.Bar(
x=df.loc[df['Country']=='India']
.groupby('Year',as_index=False)
.agg({'CO2_emission':'sum'})
.loc[:,'Year'],
y=df.loc[df['Country']=='India']
.groupby('Year',as_index=False)
.agg({'CO2_emission':'sum'})
.loc[:,'CO2_emission'],
name='CO2_emission levels'
), secondary_y=False
)
fig.add_trace(
go.Scatter(
x=df.loc[df['Country']=='India']
.groupby('Year',as_index=False)
.agg({'Population':'mean'})
.loc[:,'Year'],
y=df.loc[df['Country']=='India']
.groupby('Year',as_index=False)
.agg({'Population':'mean'})
.loc[:,'Population'],
name='Population levels'
), secondary_y=True
)
fig.show()
In [25]:
df.loc[~(df.Country=='World')].groupby('Country', as_index=False).agg({'CO2_emission':'sum'}).sort_values('CO2_emission', ascending=False).loc[:,'CO2_emission'].reset_index(drop=True)
Out[25]:
0 275956.48
1 257773.63
2 73142.80
3 63607.54
4 59361.91
...
172 6.56
173 6.24
174 4.64
175 2.36
176 2.20
Name: CO2_emission, Length: 177, dtype: float64
In [12]:
'''
A box plot for the Deforestation feature
Proves the Extent The World Data vary from the individual countries
'''
sns.set(style="whitegrid")
plt.figure(figsize=(13,3))
ax = sns.boxplot(x='Deforestation', data=df, orient="v")
In [13]:
# Visualizing Feature Density Distribution
features = ['GDP', 'e_prod', 'Manuf_GDP', 'Population', 'pop_growth', 'pop_density',
'Deforestation', 'emission_per_cap', 'CO2_emission']
df[features].plot(kind='density', subplots=True, layout=(3, 3), sharex=False, figsize=(15, 10));
As we can see, But for this unique characteristics of the countries & World data earlier discussed, causing a highly Right skewed Dataset, Our Dataset looks pretty Normally Distributed. This Unique values may not necessarily be outliers.
Data is skewed when its distribution curve is asymmetrical (as compared to a normal distribution curve that is perfectly symmetrical) and the skewness for a normal distribution is 0. It degrades the model’s ability (especially regression based models) to describe typical cases as it has to deal with rare cases on extreme values.
We will be opting to Model with Tree-Based Models to Overcome any chances of this unique values being Outliers.¶
In [14]:
'''
A Plot of the univariate distribution of our Target data
Comparing the Entire Dataset & For World Dataset
'''
with plt.rc_context(rc = {'figure.dpi': 250, 'axes.labelsize': 13,
'xtick.labelsize': 10, 'ytick.labelsize': 10,
'legend.title_fontsize': 7, 'axes.titlesize': 15,
'axes.titlepad': 7}):
fig, ax = plt.subplots(1, 2, sharex=True, figsize=(7,3))
# Entire dataset
sns.distplot(df['CO2_emission'], ax=ax[0]).set_title('Target Density of Entire Dataset')
# Dataset only for world
world = df[df['Country']=='World']
sns.distplot(world['CO2_emission'], ax=ax[1]).set_title('Target Density for world')
plt.tight_layout(pad=0.4, w_pad=2, h_pad=2)
plt.show()
If we should have to deal with the possible outliers, We could apply one of the following options;
- log transformation: transform skewed distribution to a normal distribution
- Find & Remove outliers
- Normalize (min-max)
- Adopting TREE-Based Modelling To avoid much distortion of original data
In [15]:
# A Pair Plotplot of relevant feature interactions
# for the world & for ALL Energy Type Category
temp_pp = df[df['Country']=='World'][df['e_type']!='all'] # Isolating only World & Energy Type = All
sns.pairplot(temp_pp, hue='e_type', palette="inferno")
plt.show()
In [16]:
# evaluating Feature correlation Using HeatMap
plt.figure(figsize=(15,10))
sns.heatmap(data=df.iloc[:,3:].corr(), annot=True, fmt='.2f')
plt.show()
There is a very High Correlation between Manuf_GDP, Agric_GDP against GDP, Hence will be dropping GDP Column.
Also Energy Consumption vs Energy Production are very correlated. One maybe Dropped during the course of our Feature Engineeriing.
In [17]:
# A look at our Target_Feature distribution by Energy_Type
temp_dist = df.groupby('e_type').count()['CO2_emission'].reset_index().sort_values(by='CO2_emission',ascending=False)
temp_dist.style.background_gradient(cmap='winter')
percent = temp_dist['CO2_emission']
labels= temp_dist['e_type']
my_pie,_,_ = plt.pie(percent, radius = 1.2, labels=labels, autopct="%.1f%%")
plt.setp(my_pie, width=0.6, edgecolor='white')
plt.show()
3.7 Some Comparative analysis¶
3.7.1 Who produced the most CO2 Emissions over this time period?vsCO2 Emissions per Capita per Country over this time period¶
It is said: On average, 1% increase in population is associated with a 1.28% increase in carbon dioxide emissions and With such magnitude, global emissions are likely to grow substantially over the next decades.---Development Research Group The World Bank. But How true is this???
First: Let's visualize the rate of Population Growth as Represented by this Data
In [18]:
dates = list(set(df.Year)) # Extract the dates (X axis)
max_pop_growth = list(df.groupby("Year")["pop_growth"].max()) # List Max Pop Growth (y axis)
# Plot the line graph
plt.plot(dates, max_pop_growth, color='green')
# Set axis and graph titles
plt.xlabel('Year')
plt.ylabel('Max Pop Growth')
plt.title('Line Graph Showing the population growth of each Year \n')
plt.show()
Plotting Max Pop_Growth across the years; Reveals a massive upsurge between 2006 to 2017 being its peak at 17.512% (This Occured at Qatar in the year 2007), But we see again a steep decline till date which for our Theory is good news Globally.
Again Recall: We created Emission per Capita to probe fully the theory of a unit increase in Population is directly impacting on the increase in CO2 Emission, the column represents per capita emission for each country per energy type which will be plotted alongside the co2 emission of each country and resulting graph compared with the graph of countries/population of highest emitters. The idea is if the comparism correlates, then our Hypothesis theory of increase in pop is directly propotional to increase in CO2 Emission, is 100% valid, if not; The Theory then will require modification by adding the extra clause.
In [19]:
# Making temp dataset of all countries and there total CO2 emissions in the time period
# Specifying all countries != world, as well energy_types=all
cd1 = df[df['Country']!='World'][df['e_type']=='all']
lt = []
for country in cd1['Country'].unique():
total = cd1[cd1['Country']==country]['CO2_emission'].sum(axis=0)
lt.extend([[country, total]])
lts = []
for country in cd1['Country'].unique():
total = cd1[cd1['Country']==country]['emission_per_cap'].sum(axis=0)
lts.extend([[country, total]])
# COMPARISON
# Temporary dataset of all the Countries and there respective total CO2 Emission over the time period
temp_cd = pd.DataFrame(lt, columns=['Country', 'Total_CO2']).sort_values(by='Total_CO2',ascending=False)
# Temporary dataset of all the Countries and there respective total emission_per_capita over the time period
temp_cd2 = pd.DataFrame(lts, columns=['Country', 'emission_per_cap']).sort_values(by='emission_per_cap',ascending=False)
# Plotting the top 20 Emiters
fig = px.bar(temp_cd.head(20), x='Country', y='Total_CO2', title='Top 20 CO2 Emitters (MMtonnes CO2)')
# Plotting the top 20 emission_per_capita
fig2 = px.bar(temp_cd2.head(20), x='Country', y='emission_per_cap',
title='Top 20 Countries with emission_per_capita (MMtonnes CO2 per Pop)')
fig.show(), fig2.show()
Out[19]:
(None, None)
In [20]:
'''
Let's Visualise the comparison using a line graph
'''
temp_td = temp_cd.copy()
# Create a df containing ranked countries, Total_C02 & emission_per_cap
temp_td = pd.merge(temp_td, temp_cd2, how="left", on='Country')
# Seperate country because it's an Object variable
others = temp_td.drop(['Country'], axis=1)
c = temp_td['Country']
# Scale the features to be compared to deal with Range Variation
sc = StandardScaler()
others_scaled = pd.DataFrame(sc.fit_transform(others), columns=others.columns)
# concat both Tables
temp_td_scaled = pd.concat([c, others_scaled], axis=1)
# Plot Multiple line graph
fig4 = px.line(temp_td_scaled.head(30), x='Country', y=['Total_CO2', 'emission_per_cap'], title='Top 20 CO2 Emitters vs Top emitters_per_Capita')
fig4.show()
From the Line Graph above
This might be TRUE for Population against CO2_Emission But Totally FALSE when see the Population vs CO2_Emission_per_Capita. As You can see, We have the Top 20 countries with Highest CO2_Emission_per_Capita being UAE, Singapore, Qatar, Kuwait, Bahrain, Australia, Brunei, Saudi Arabia, Luxembourg, US, with Most dealing as Petroleum Porducing/Refining Nations
And From wikipedia.org :
- The UAE economy is heavily reliant on revenues from petroleum and natural gas
- The economy of Singapore is a highly developed free-market economy (an economy in which all resources are owned by individuals), Hence High Transportation, Heat & Electrical energy demand of which it's major sources are Petroleum Fuels
- Petroleum and natural gas are the cornerstones of Qatar's economy and account for more than 70% of total government revenue, more than 60% of gross domestic product, and roughly 85% of export earnings.
- The economy of Kuwait is a wealthy petroleum-based economy
- The economy of Bahrain is heavily dependent upon oil and gas
- Australia which is Australia is a highly developed country with a mixed-market economy, and Known to be prone to Natural disasters
- [Brunei's economy](Brunei's economy is almost totally dependent on the exploitation of its vast reserves of petroleum and natural gas) is almost totally dependent on the exploitation of its vast reserves of petroleum and natural gas
- The economy of Saudi Arabia is dominated by petroleum and its associated industries
- The economy of Luxembourg is largely dependent on the banking, steel, and industrial sectors
- The United States is a highly developed country with a market economy and has the world's largest nominal GDP and net wealth. Also With a rough estimate of 11,567,000 barrels per day, the United States is the top oil-producing country in the world, as it has been for many years. The US also consumes more oil than any other country worldwide.
Now we see a Country like China with the assumed Highest CO2 Emitting country but if to be divided per head/capita, They prove to be doing alot better than the countries listed above.
In [21]:
'''
FOR WORLD DATA: Plotting CO2 EMission per Year for
Coal, Petroleum, Natural Gas, NUclear & Renewable Energy Types
'''
with plt.rc_context(rc = {'figure.dpi': 250, 'axes.labelsize': 9,
'xtick.labelsize': 10, 'ytick.labelsize': 10,
'legend.title_fontsize': 7, 'axes.titlesize': 12,
'axes.titlepad': 7}):
# Data with only the 'World' values
wd = df[df['Country']=='World']
fig, ax = plt.subplots(1, 1, figsize = (5, 3))
sns.lineplot(data=wd[wd['e_type']!='all'],
x='Year',
y='CO2_emission',
hue='e_type',
lw=3,
).set_title('Yearly CO2_emission for each e_type globally (MMtonnes CO2)')
As We can See; The leading Energy source in terms of CO2 Emission is Coal with a World Total Peak of Over 16,400 (MMtonnes CO2), followed by Petroleum at 11,750 (MMtonnes CO2) and still raising.
We also see Natural Gas Total at about 6,500 (MMtonnes CO2) which is about Half of what Coal & Pertroleum Produces Individually. All as of 2015
3.7.2 Comparing Time Series Beteen Top Emitting Country & Top Emitting_per_Capita Country¶
In [22]:
'''
Let's Visualise the actual CO2 Emission
of Our Top 3 Emitting Countries &
Our Top emitting_per_capita Countries
'''
fig, ax = plt.subplots(2, 3, figsize = (15, 8))
# List of focused Countries
countries = ['China','United States', 'India', 'United Arab Emirates', 'Singapore', 'Qatar' ]
# Average CO2 Emission each year for top 6 emiters
for idx, (country, axes) in enumerate(zip(countries, ax.flatten())):
cd3 = df[df['Country']==country][df['e_type']=='all']
temp_data = cd3.groupby(['Year'])['CO2_emission'].sum().reset_index().sort_values(by='CO2_emission',ascending=False)
plot_ = sns.barplot(ax=axes, data=temp_data, x='Year', y='CO2_emission', palette="YlOrRd")
axes.set_title(country) # Title
# Reducing Density of X-ticks
for ind, label in enumerate(plot_.get_xticklabels()):
if ind % 4 == 0: # every 10th label is kept
label.set_visible(True)
else:
label.set_visible(False)
# Rotating X axis
for tick in axes.get_xticklabels():
tick.set_rotation(45)
### Removing empty figures
else:
[axes.set_visible(False) for axes in ax.flatten()[idx + 1:]]
plt.tight_layout(pad=0.4, w_pad=2, h_pad=2)
plt.show()
The above is a clear Conlusion on the matter showing Though UAE has an emission max at about 300 (MMtonnes CO2) and China at Over 10,000 (MMtonnes CO2), UAE is worst at CO2 Emission as compared to China. Same comparing US & Singapore, India & Qatar
In [23]:
'''
Time Series for Top Emitting Country
Country: China
'''
c_df = df[df['Country']== 'China'][['Year', 'e_type','GDP', 'Population',
'e_con', 'Deforestation', 'emission_per_cap', 'CO2_emission']]
c_df = c_df[c_df['e_type']== 'all'].drop('e_type', axis=1)
# Seperate country because it's an Object variable
ot = c_df.drop(['Year'], axis=1)
c = c_df['Year'].to_list()
# Scale the features to be compared to deal with Range Variation
sc = StandardScaler()
ot_s= pd.DataFrame(sc.fit_transform(ot), columns=ot.columns)
# Join Tables
china_comp = ot_s.copy()
china_comp['Year'] = c
# Plot Multiple line graph
fig5 = px.line(china_comp, x='Year', y=['emission_per_cap', 'CO2_emission',
'GDP', 'Population', 'Deforestation'],
title='Time Series for our Top Emitting Country = CHINA')
fig5.show()
China clearly was not doing well in terms of adpoting greener policies in times past, as we can see that her CO2 emission per capita is racing side-by-side with her actual CO2 Emission and GDP Growth. These was much terrible between 2012-2015, but as we can see China's Economic Strategy in 2016, had the Chinese leaders vowing to confront the overcapacity problem as part of supply-side reforms. Understand, Overcapcity & Population raise may sound similar but doesn't eitherly mean the same thing. Paraphrasing the official statement released after the conference, “The nation will study and formulate a set of policies aimed at resolving industrial overcapacity, as the supply glut has presented a major challenge to growth.”. This move as you can see above, is one of the decisions that lead to the quick switch to better lesser co2 emitting processes even with a continous raise in GDP, Population and Deforestation.
In [24]:
'''
Time Series for Top Emitting_per_Capita Country
Country: UAE (United Arab Emirates)
'''
u_df = df[df['Country']== 'United Arab Emirates'][['Year', 'e_type','GDP', 'Population',
'e_con', 'Deforestation', 'emission_per_cap', 'CO2_emission']]
u_df = u_df[u_df['e_type']=='all'].drop('e_type', axis=1)
# Seperate country because it's an Object variable
ot = u_df.drop(['Year'], axis=1)
c = u_df['Year'].to_list()
# Scale the features to be compared to deal with Range Variation
sc = StandardScaler()
ot_s= pd.DataFrame(sc.fit_transform(ot), columns=ot.columns)
# Join Tables
UAE_comp = ot_s.copy()
UAE_comp['Year'] = c
# Plot Multiple line graph
fig6 = px.line(UAE_comp, x='Year', y=['emission_per_cap', 'CO2_emission',
'GDP', 'Population', 'Deforestation'],
title='Time Series for our Top Emitting_per_Capita Country (UAE)')
fig6.show()
The UAE has remained Busy with her manufacturing sector and of course as a leading country in terms of Petroleum production, and also being the one of the tourist center of the world, it's only normal for we to see the high peak in CO2_emission_per_capita comming from here; But she has got two very good signs as we can see from above graph;
- She has greatly matched reducing the CO2_emission_per_capita even with her every increasing activities, from policies to embracing Technology. See The UAE's response to climate change Article: The UAE monitors the emission of gases that lead to the greenhouse effect and has reduced its per capita carbon emissions. Due to better technology and transition to more natural gas in power plants, emissions of carbon dioxide (CO2) per capita have decreased.
- She has flatten the Deforestation quick raise, proving her committment towards a more greener world. CNN Reports revealing Abu Dhabi is replanting mangroves in the fight against climate change
In [25]:
'''
Comparing Total GDP, Manuf_GDP, & Agric_GDP
Country: WORLD
'''
g_df = df[df['Country']== 'United Arab Emirates'][['Year', 'e_type','GDP', 'Manuf_GDP',
'Agric_GDP', 'emission_per_cap', 'CO2_emission']]
g_df = g_df[g_df['e_type']=='all'].drop('e_type', axis=1)
# Seperate country because it's an Object variable
ot = g_df.drop(['Year'], axis=1)
c = g_df['Year'].to_list()
# Scale the features to be compared to deal with Range Variation
sc = StandardScaler()
ot_s= pd.DataFrame(sc.fit_transform(ot), columns=ot.columns)
# Join Tables
GDP_comp = ot_s.copy()
GDP_comp['Year'] = c
# Plot Multiple line graph
fig7 = px.line(GDP_comp, x='Year', y=['emission_per_cap', 'CO2_emission',
'GDP', 'Manuf_GDP', 'Agric_GDP'],
title='WORLD Comparison of Total GDP, Manuf_GDP, & Agric_GD')
fig7.show()
Remember the Global Economic Recession 2007-2009???
The global financial crisis (GFC) which refers to the period the world experienced extreme stress in global financial markets and banking systems, occurred between mid-2007 and early 2009. This as we can see has been clearly captured by this dataset. If not for this event, it was an upward trajectory for the Manufacturing Industry (Purple Line) in terms of World GDP and CO2 emissions, which is the Contributing sector of GDP that sustained its trajectory resulting in the corresponding rise in CO2 Emissions.
Simply put: Manufacturing GDP Contribution is the primary GDP factor that correlates the direct rise in GDP to CO2 emission.
Also; It's worthy of mentioning that we can see a stip decline in Co2 emission per capita (GLOBALLY) even with the raise in ManuFacturing GDP & CO2 emission; This shows that the world is actually taking a good turn towards lesser CO2 Emissions by adopting safer manufacturing technologies and processes. In Fact, This shows, that while we are gradually migrating to more greener energy sources, we will be seeing a much decline in CO2 Emissions.
It's also great news to see that the Agricultural sector is picking up again globally.
3.7.3 Choropleth of CO2 Emission per Capita of each country (MMtonnes CO2 per capita)¶
In [26]:
cd2 = df[df['Country']!='World'][df['e_type']=='all']
temp_cd5 = cd2[['Country', 'Year', 'emission_per_cap']].groupby(['Country','Year']).sum().reset_index()
px.choropleth(data_frame=temp_cd5, locations="Country", locationmode='country names',
animation_frame= 'Year', color='emission_per_cap',
title="CO2 Emission per Capita of each country from 1990 to 2019 (MMtonnes CO2 Per Capita)")
4. Feature Engineering¶
| ⚡ Description: Feature engineering ⚡ |
|---|
| In this section we will be manipulating — adding, deleting, combinating, and mutating — of your data set if need be, to improve machine learning model training, leading to better performance and greater accuracy. Most of this have been done alongside or remains identified in the EDA phase. So, let's Proceed towards execution |
In [27]:
df.head()
Out[27]:
| Country | e_type | Year | e_con | e_prod | GDP | Population | ei_capita | ei_gdp | pop_growth | pop_density | Manuf_GDP | Agric_GDP | Deforestation | emission_per_cap | CO2_emission | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1950 | Antigua and Barbuda | all | 1990 | 0.01 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | 101.0988 | 0.006397 | 0.4 |
| 1951 | Antigua and Barbuda | coal | 1990 | 0.00 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | 101.0988 | 0.000000 | 0.0 |
| 1952 | Antigua and Barbuda | nat_gas | 1990 | 0.00 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | 101.0988 | 0.000000 | 0.0 |
| 1953 | Antigua and Barbuda | pet/oth | 1990 | 0.01 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | 101.0988 | 0.006397 | 0.4 |
| 1954 | Antigua and Barbuda | nuclear | 1990 | 0.00 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | 101.0988 | 0.000000 | 0.0 |
In [28]:
# Checking missing values/ features
for var in df:
print(f'{var}: {df[var].isnull().sum()}')
Country: 0 e_type: 0 Year: 0 e_con: 0 e_prod: 0 GDP: 0 Population: 0 ei_capita: 0 ei_gdp: 0 pop_growth: 0 pop_density: 0 Manuf_GDP: 0 Agric_GDP: 0 Deforestation: 0 emission_per_cap: 0 CO2_emission: 0
In [58]:
# feature Selection SEE HEATMAP
wf = df.copy()
#drop rows with World Data
wf = wf[wf.Country != 'World'] # Drop all world data; Acting like outliers to Model (Got XGB RMSE 47 from 55)
wf = wf[wf['CO2_emission'] >= 0] # Removing all NEGATIVE Prediction; Obvious error in reading (Got XGB RMSE 27.8 from 47)
wf = wf[wf.e_type != 'all'] # Drop all Energy Type = ALL; Acting like outliers to Model (Got XGB RMSE 18.267 from 27.8)
# Drop Country and Year
all_df = wf.drop(['Country', 'Year'], axis=1)
In [45]:
pd_s = x.loc[(x.Country=='Kenya')&(x['Year']==x['Year'].max())&(x['e_type']==5)]
In [39]:
pd_s.values
Out[39]:
array([['Kenya'],
[5],
[2019],
[0.33],
[0.08],
[211.47],
[52587.9],
[6.31],
[1.57],
[2.273],
[92.374],
[16.6574919],
[44.768199],
['Africa'],
[36111.933],
[0.0003565458974402857],
[18.75]], dtype=object)
In [47]:
(pd_s.values)
Out[47]:
array([['Kenya', 5, 2019, 0.33, 0.08, 211.47, 52587.9, 6.31, 1.57, 2.273,
92.374, 16.6574919, 44.768199, 'Africa', 36111.933,
0.0003565458974402857, 18.75]], dtype=object)
In [49]:
s = "A man, a plan, a canal: Panama"
l = 0
r = len(s) - 1
while l < r:
while not s[l].isalnum():
l += 1
while not s[r].isalnum():
r -= 1
if s[l] != s[r]:
print(s[l],s[r])
print(False)
print(True)
A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False 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A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False 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A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False 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A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False A a False
In [25]:
pd_s = pd.DataFrame(
[{
0: 345,
'dhf': 45,
'dfeo': 34
}]
)
In [ ]:
In [27]:
pd_s['dhf'].values
Out[27]:
array([45], dtype=int64)
Observation:¶
The following were observed after several iteration process in the modelling stage leading to the following being dropped.
- All world Data was dropped as the appeard as Outliers to our Models
- Dropped a Couple of Roles with NEGATIVE CO2 Emission values which is obviously an outlier/wrong reading
- Dropped all observations with Energy Type = All as firstly, there is no such energy type, and secondly, it caused some level of distortion in which the relevant features will poss to accurate prediction.
In [3]:
x = pd.read_feather('data/220714_full_dataset.feather')
In [4]:
# engineer existing features
# List Energy Types
# energy_types = all_df['e_type'].unique().tolist()
# Encoding The Energy Types
x["e_type"].replace({"all": 5, "coal": 4, "nat_gas": 2,
"pet/oth": 3, "nuclear": 1, "ren/oth": 0}, inplace=True)
In [8]:
x.to_feather('220801_data.feather')
In [5]:
# Review
x.head()
Out[5]:
| Country | e_type | Year | e_con | e_prod | GDP | Population | ei_capita | ei_gdp | pop_growth | pop_density | Manuf_GDP | Agric_GDP | Continent | Deforestation | emission_per_cap | CO2_emission | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Antigua and Barbuda | 5 | 1990 | 0.01 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | North America | 101.0988 | 0.006397 | 0.4 |
| 1 | Antigua and Barbuda | 4 | 1990 | 0.00 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | North America | 101.0988 | 0.000000 | 0.0 |
| 2 | Antigua and Barbuda | 2 | 1990 | 0.00 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | North America | 101.0988 | 0.000000 | 0.0 |
| 3 | Antigua and Barbuda | 3 | 1990 | 0.01 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | North America | 101.0988 | 0.006397 | 0.4 |
| 4 | Antigua and Barbuda | 1 | 1990 | 0.00 | 0.0 | 0.98 | 62.53 | 95.45 | 6.07 | 0.845 | 142.12 | 0.024676 | 0.017454 | North America | 101.0988 | 0.000000 | 0.0 |
In [62]:
# Check shape if we have sufficient rep. data to model & Test with.
all_df.shape
Out[62]:
(22779, 14)
In [63]:
"""
Extract & Save Data as CSV
Unhash: To Run
"""
all_df.to_csv('data/Our_CO2emission_Modelling_Data.csv')
In [ ]: