Global Plastic Pollution Analysis

Global Plastic Pollution Analysis

import numpy as np

import pandas as pd

import seaborn as sns

import warnings

import matplotlib.pyplot as plt

%matplotlib inline

warnings.filterwarnings('ignore')

sns.set_style('darkgrid')

''' reading dataset '''

df = pd.read_csv('per-capita-plastic-waste-vs-gdp-per-capita.csv')

''' displaying first 5 rows '''

df.head()

''' shape of data '''

df.shape

(48168, 7)

''' checking null values in data '''

df.isnull().sum()

Entity 0 Code 2014 Year 0 Per capita plastic waste (kg/person/day) 47982 GDP per capita, PPP (constant 2011 international $) 41761 Total population (Gapminder, HYDE & UN) 1285 Continent 47883 dtype: int64

''' checking percentage of null values in each column '''

for column in df.columns:

    print("{} has {:.2f}% null values: ".format(column, (df[column].isnull().sum() / len(df)) * 100 ))

    print("-" * 100)

''' checking info of data '''

df.info()

''' renaming column names '''

df.rename(columns={'GDP per capita, PPP (constant 2011 international $)': 'GDP per capita in PPP',

                   'Total population (Gapminder, HYDE & UN)': 'Total Population',

                    'Per capita plastic waste (kg/person/day)': 'Waste per person(kg/day)'}, inplace=True)

df.head()

''' removing entities/countries with incomplete/missing data '''

incmp_df_idx = df[(df['Total Population'].isna()) & (df['GDP per capita in PPP'].isna())].index

df.drop(incmp_df_idx, inplace=True)

df.head()

df.shape

(48113, 7)

'''retrieving rows in which year == 2010'''

df_2010 = df[df['Year'] == 2010]

df_2010 = df_2010.drop(columns='Continent')

df_2010.head()

'''retrieving continent name in which year == 2015'''

df_2015 = df[df['Year'] == 2015]

df_2010['Continent'] = df_2015['Continent'].values

df_2015.head()

'''dropping rows with missing Continent values using index'''

missing_idx = df_2010[df_2010['Continent'].isna()].index

df_2010.drop(missing_idx, inplace=True)

''' dropping rows with missing per person waste generation values '''

df_2010 = df_2010[df_2010['Waste per person(kg/day)'].notna()]

wa_g = df_2010.reset_index().drop('index', axis=1)

wa_g.head()

''' reading 2nd file '''

df2 = pd.read_csv('per-capita-mismanaged-plastic-waste-vs-gdp-per-capita.csv')

''' displaying first 5 rows of df2 '''

df2.head()

''' renaming columns'''

df2.rename(columns={'Per capita mismanaged plastic waste': 'Mismanaged waste per person(kg/day)',

                  'GDP per capita, PPP (constant 2011 international $)': 'GDP per capita in PPP',

                  'Total population (Gapminder, HYDE & UN)': 'Total Population'}, inplace=True)

''' dropping Continent column '''

df2.drop('Continent', axis=1, inplace=True)

'''retrieving rows in which year == 2010'''

df2_2010 = df2[df2.Year == 2010]

df2_2010.head()

''' dropping rows with missing mismanaged waste values '''

df2_2010 = df2_2010[df2_2010['Mismanaged waste per person(kg/day)'].isna() != True]

''' reset index '''

w_m = df2_2010.reset_index().drop('index', axis=1)

w_m.head()

''' merging w_m and wa_g '''

df_plastic_waste = pd.merge(wa_g, w_m, how='inner')

''' displaying data '''

df_plastic_waste.head()

''' converting column names into list '''

df_plastic_waste.columns.tolist()

''' column names '''

col_names = ['Entity','Code','Year','Waste per person(kg/day)','Mismanaged waste per person(kg/day)',

           'GDP per capita in PPP','Total Population','Continent']

df_plastic_waste = df_plastic_waste[col_names]

'''rounding the values per person'''

df_plastic_waste.iloc[:, 3:5] = np.around(df_plastic_waste[['Waste per person(kg/day)',

                                                            'Mismanaged waste per person(kg/day)']], decimals=2)

''' changing data type '''

df_plastic_waste['Total Population'] = df_plastic_waste['Total Population'].astype(int)

'''Generating Total waste and Total mismanaged waste by country'''

df_plastic_waste['Total waste(kgs/year)'] = ((df_plastic_waste['Waste per person(kg/day)'] *

                                    df_plastic_waste['Total Population']) * 365)

df_plastic_waste['Total waste mismanaged(kgs/year)'] = ((df_plastic_waste['Mismanaged waste per person(kg/day)'] *

                                    df_plastic_waste['Total Population']) * 365)

df_plastic_waste.head()

''' scatter plot graph '''

plt.figure(1, figsize=(12,8))

plt.scatter(df_plastic_waste['GDP per capita in PPP'], df_plastic_waste['Mismanaged waste per person(kg/day)'])

plt.title('Waste Mismanaged', loc='center', fontsize=15)

plt.ylabel('Mismanaged waste', loc='center', fontsize=15)

plt.xlabel('GDP per capita', fontsize=12)

sns.regplot(x='GDP per capita in PPP', y='Mismanaged waste per person(kg/day)', data=df_plastic_waste,

           scatter_kws={'color': '#34568B'}, line_kws={'color': '#650021'})

plt.show()

''' scatter plot '''

plt.figure(2, figsize=(12,8))

plt.scatter(df_plastic_waste['GDP per capita in PPP'], df_plastic_waste['Waste per person(kg/day)'])

plt.title('Waste Generated by GDP', loc='center', fontsize=14)

plt.ylabel('Waste per person(kg/day)', loc='center', fontsize=12)

plt.xlabel('GDP per capita in PPP', fontsize=12)

sns.regplot(x=df_plastic_waste['GDP per capita in PPP'], y=df_plastic_waste['Waste per person(kg/day)'],

           scatter_kws={'color': '#CD212A'}, line_kws={'color': '#380282'})

plt.show()