North America evolution of temperature. · Ricardo

North America evolution of temperature.

6-minute read
Data Analysis
Data Analysis • Python

North america change in temperature

In this project, I decide to make a comparison in North America to check the $CO_2$ emittions and the temperature change between the years. In particular, I’m gonna work with United States, Canada and Mexico. I’m gonna check how many $CO_2$ produce every country from 1960 until now, and also check the average temperature.

First, let me import the libraries to use

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import jupyterthemes as jtplot
from jupyterthemes import jtplot
jtplot.style(theme='monokai')

Now, I need to open the datasets.

data1=pd.read_csv('temperature.csv',skiprows=4)
data2=pd.read_csv('API_EN.ATM.CO2E.PC_DS2_en_csv_v2_1927797.csv',skiprows=3)
data3=pd.read_csv('mexico.csv',header=None,skiprows=1)
data4=pd.read_csv('canada.csv',header=None,skiprows=1)

usa_temp=pd.DataFrame(data1)
CO2=pd.DataFrame(data2)
mexico_temp=pd.DataFrame(data3)
canada_temp=pd.DataFrame(data4)

min_year=1960 #It's an arbitrary value

Work with the first data set. The USA temperature. Let’s check the data

usa_temp.head()
Date Value Anomaly
0 196012 51.44 -0.58
1 196112 51.87 -0.15
2 196212 51.9 -0.12
3 196312 52.26 0.24
4 196412 51.67 -0.35

The best thing I can do it’s to change the name of the columns and also quit the column Anomaly, that because isn’t work for my analysis.

usa_temp=usa_temp.rename(columns={'Date':'Year',
                                  'Value':'Temperature'}).drop(columns='Anomaly')
def celsius_to_fah(x):
    return (x*(9/5))+32

def fah_to_celsius(x):
    return (x-32)*(5/9)

for i in range(len(usa_temp)):
    usa_temp['Year'][i]=min_year+i
    usa_temp['Temperature'][i]=fah_to_celsius(usa_temp['Temperature'][i])
usa_temp.head()
Year Temperature
0 1960 10.8
1 1961 11.0389
2 1962 11.0556
3 1963 11.2556
4 1964 10.9278

I want the change of the temperature, I mean i want the $\Delta T$. This differential it can be calculated by $\Delta T=T_i-T_0$, where $T_i$ is the actual temperature and $T_0$ is the initial temperature, at the year 1960. I’m going to create a new column with this $\Delta T$

dT=[]
#dT.append(0)
T0=usa_temp['Temperature'][0]
for i in range(1,len(usa_temp)):
    dT.append(usa_temp['Temperature'][i]-T0)

usa_temp=usa_temp.join(pd.DataFrame({'dT':dT}))
usa_temp.head()
Year Temperature $\Delta T$
0 1960 10.8 0.238889
1 1961 11.0389 0.255556
2 1962 11.0556 0.455556
3 1963 11.2556 0.127778
4 1964 10.9278 0.138889

Now, It’s time to work with the data from Mexico.

mexico_temp=mexico_temp.rename(columns={0:'Temperature',1:'Year',
                                        2:'Statistics',3:'Country',4:'ISO3'})
mexico_temp.head()
Temperature Year Statistics Country ISO3
0 15.5146 1901 Jan Average Mexico MEX
1 15.3109 1901 Feb Average Mexico MEX
2 17.8516 1901 Mar Average Mexico MEX
3 19.6372 1901 Apr Average Mexico MEX
4 22.5504 1901 May Average Mexico MEX

Okey, i’ll manipulate the data to drop some columns and add the $\Delta T$ column

mexico_temp=mexico_temp.drop(columns={'Statistics','Country','ISO3'})
mexico_temp=mexico_temp.where(mexico_temp['Year']>=min_year).dropna()
mexico_temp=mexico_temp.groupby('Year').mean().reset_index()
mexico_temp['Year']=mexico_temp['Year'].astype('int')

#I'm also add the dT column.
dT=[]
#dT.append(0)
T0=mexico_temp['Temperature'][0]
for i in range(len(mexico_temp)):
    dT.append(mexico_temp['Temperature'][i]-T0)
mexico_temp=mexico_temp.join(pd.DataFrame({'dT':dT}))
mexico_temp.head()
Year Temperature $\Delta T$
0 1960 20.509 0
1 1961 20.4919 -0.01705
2 1962 20.838 0.32905
3 1963 20.6835 0.1745
4 1964 20.3534 -0.155592

Now it’s time to clean the Canada data.

canada_temp=canada_temp.rename(columns={0:'Temperature',1:'Year',2:'Statistics',
                                        3:'Country',4:'ISO3'})
canada_temp.head()
Temperature Year Statistics Country ISO3
0 -25.385 1901 Jan Average Canada CAN
1 -23.719 1901 Feb Average Canada CAN
2 -18.934 1901 Mar Average Canada CAN
3 -9.9643 1901 Apr Average Canada CAN
4 0.02289 1901 May Average Canada CAN

Just like i did with Mexico, i am gonna do the same with Canada.

canada_temp=canada_temp.drop(columns={'Statistics','Country','ISO3'})
canada_temp=canada_temp.where(canada_temp['Year']>=min_year).dropna()
canada_temp=canada_temp.groupby('Year').mean().reset_index()
canada_temp['Year']=canada_temp['Year'].astype('int')
#I'm also add the dT column.
dT=[]
#dT.append(0)
T0=canada_temp['Temperature'][0]
for i in range(len(canada_temp)):
    dT.append(canada_temp['Temperature'][i]-T0)
canada_temp=canada_temp.join(pd.DataFrame({'dT':dT}))
canada_temp.head()
Year Temperature $\Delta T$
0 1960 -6.4908 0
1 1961 -7.39057 -0.899773
2 1962 -6.97667 -0.485867
3 1963 -6.79263 -0.301832
4 1964 -7.67968 -1.18888

And for last, it’s time to clean the CO2 data from every coutry.

#Check the data
CO2.head()
Country Name Country Code Indicator Name Indicator Code 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Unnamed: 65
0 Aruba ABW CO2 emissions (metric tons per capita) EN.ATM.CO2E.PC 204.62 208.823 226.118 214.8 207.616 185.204 172.12 210.779 194.887 253.579 281.996 243.87 234.887 258.82 233.489 168.729 360.853 189.163 161.804 170.083 174.698 165.105 182.259 92.381 228.356 266.475 2.86832 7.2352 10.0262 10.6347 7.84745 8.22808 7.89989 8.95204 8.60574 8.81095 8.72675 8.88309 9.24344 9.10519 26.1949 25.934 25.6712 26.4205 26.5173 27.2007 26.9477 27.895 26.2296 25.9153 24.6705 24.5075 13.1577 8.35356 8.41006 8.61037 8.42691 nan nan nan nan nan
1 Afghanistan AFG CO2 emissions (metric tons per capita) EN.ATM.CO2E.PC 0.0460567 0.0535888 0.0737208 0.0741607 0.0861736 0.101285 0.107399 0.12341 0.115142 0.0865099 0.149651 0.165208 0.129996 0.135367 0.154503 0.167612 0.153558 0.181522 0.161894 0.167066 0.131783 0.150615 0.163104 0.201224 0.231961 0.293957 0.267772 0.26923 0.246823 0.233882 0.210643 0.183364 0.0961966 0.0850871 0.0758065 0.0686399 0.0624346 0.0566423 0.0527632 0.0407225 0.0372348 0.0378461 0.0473773 0.0512556 0.0370753 0.051744 0.0624275 0.0838928 0.151721 0.238399 0.289988 0.406424 0.345149 0.280455 0.253728 0.262556 0.245101 nan nan nan nan nan
2 Angola AGO CO2 emissions (metric tons per capita) EN.ATM.CO2E.PC 0.100835 0.0822038 0.210531 0.202737 0.21356 0.205891 0.268941 0.172102 0.289718 0.480234 0.608224 0.564548 0.721246 0.75124 0.720776 0.628569 0.451354 0.469221 0.694737 0.683063 0.640966 0.611135 0.519355 0.551349 0.520983 0.471903 0.451619 0.544085 0.463508 0.437295 0.431744 0.415531 0.410523 0.441721 0.288119 0.787033 0.726233 0.496361 0.475815 0.577083 0.581961 0.574316 0.722959 0.500225 1.00188 0.985736 1.10502 1.20313 1.185 1.23443 1.24409 1.26282 1.36118 1.29508 1.66474 1.24025 1.20286 nan nan nan nan nan
3 Albania ALB CO2 emissions (metric tons per capita) EN.ATM.CO2E.PC 1.25819 1.37419 1.43996 1.18168 1.11174 1.1661 1.33306 1.36375 1.51955 1.55897 1.75324 1.9895 2.51591 2.3039 1.84901 1.91063 2.01358 2.27588 2.53063 2.89821 1.93506 2.69302 2.62486 2.68324 2.69429 2.65802 2.66536 2.41406 2.3316 2.78324 1.67811 1.31221 0.774725 0.72379 0.600204 0.654537 0.636625 0.490365 0.560271 0.960164 0.978175 1.0533 1.22954 1.4127 1.37621 1.4125 1.30258 1.32233 1.48431 1.4956 1.57857 1.80371 1.69797 1.69728 1.90007 1.60265 1.57716 nan nan nan nan nan
4 Andorra AND CO2 emissions (metric tons per capita) EN.ATM.CO2E.PC nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 7.46734 7.18246 6.91205 6.73605 6.4942 6.66205 7.06507 7.23971 7.66078 7.97545 8.01928 7.78695 7.59062 7.31576 7.35862 7.29987 6.74605 6.51939 6.42781 6.12158 6.12259 5.86741 5.91688 5.90178 5.83291 5.96979 6.07237 nan nan nan nan nan

It’s time to manipulate the data and only left the countrys in North America.

#For USA
usa_co2=CO2.where(CO2['Country Code']=='USA').dropna(how='all')
usa_co2=usa_co2.iloc[:,4:61]

#For Mexico
mexico_co2=CO2.where(CO2['Country Code']=='MEX').dropna(how='all')
mexico_co2=mexico_co2.iloc[:,4:61]

#For Canada
canada_co2=CO2.where(CO2['Country Code']=='CAN').dropna(how='all')
canada_co2=canada_co2.iloc[:,4:61]

#I can make just one dataFrame with all the past CO2 emissions.
years=[]; usa_data=[]; mex_data=[]; can_data=[]
for i in range(len(usa_temp)):
    years.append(min_year+i)
    usa_data.append(usa_co2.iloc[0,i])
    mex_data.append(mexico_co2.iloc[0,i])
    can_data.append(canada_co2.iloc[0,i])

co2_emission=pd.DataFrame({'Year':years,
                           'USA':usa_data,
                           'Mexico':mex_data,
                           'Canada':can_data})
co2_emission.head()
Year USA Mexico Canada
0 1960 15.9998 1.67099 10.7708
1 1961 15.6813 1.67596 10.6279
2 1962 16.0139 1.58749 11.1306
3 1963 16.4828 1.60053 11.1321
4 1964 16.9681 1.73666 12.3054

Let’s see a plot of the temperature of the countrys to see how the average temperature fluctuates from it’s initial value in the year 1960.

Canada it’s the country with more fluctuations. EUA had some too. Now, let’s plot the $CO_2$ emissions per country and also the value of $\Delta T$ per year.

This gives us an idea of how important is the $CO_2$ emission for the entire enviroment. Canada had a significant increase of $\Delta T$ over the years. USA had the most fluctuating values for $\Delta T$ and it’s not a surprise, that country had a lot of $CO_2$ emissions, more that the other countries.

That’s all for this post! You can check the jupyter notebook from this problem here.