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import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
This CSV file is available on Iran`s dataset in World bank and Turkey`s dataset in the same website . I cleaned this data using LibreOffice and kept the important rows to prevent complexity in my code.
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# Importing Iran`s dataset
IRAN_SOURCE_FILE = 'iran_emission_dataset.csv'
iran_csv = pd.read_csv(IRAN_SOURCE_FILE)
iran_csv.head(5)
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# Importing Turkey`s dataset
TURKEY_SOURCE_FILE = 'turkey_emission_dataset.csv'
turkey_csv = pd.read_csv(TURKEY_SOURCE_FILE)
turkey_csv.head(5)
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As I wanted the emission types be my coloumns and the years be the rows, I used transpose() function. Some data was missing for the last three years which I substituded their value by zero.
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iran_csv = iran_csv.transpose()
iran_csv = iran_csv.fillna(0)
iran_csv.columns = iran_csv.ix[0,:]
iran_csv = iran_csv.ix[1:,:]
iran_csv.astype(np.float64)
iran_csv.head(5)
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turkey_csv = turkey_csv.transpose()
turkey_csv = turkey_csv.fillna(0)
turkey_csv.columns = turkey_csv.ix[0,:]
turkey_csv = turkey_csv.ix[1:,:]
turkey_csv.astype(np.float64)
turkey_csv.head(5)
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#Iran (Blue)
sns.distplot(iran_csv.ix[:,1])
#Turkey (Green)
sns.distplot(turkey_csv.ix[:,1])
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SOLID_FUEL_COLUMN_INDEX = 2
a = sns.jointplot(iran_csv.ix[:,SOLID_FUEL_COLUMN_INDEX],
turkey_csv.ix[:,SOLID_FUEL_COLUMN_INDEX]).set_axis_labels(
"IRAN: " + iran_csv.columns[SOLID_FUEL_COLUMN_INDEX],
"TURKEY: " + turkey_csv.columns[SOLID_FUEL_COLUMN_INDEX])
a.savefig("output.png")
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In these graphs I visualised Iran's and Turkey's datasets of CO2 emmision from various sources by going through these steps:
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