In this analysis, I was tasked to calculate the growth rate of different energy production methods in the United States, and find the two fastest growing renewable energy methods.

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Energy Production Analysis in the USA
In this notebook we will be analyzing growth rates of energy production
in the US. We will most likely want to compare Total Fossil Fuel
Production with the Total Renewable Energy Production, to understand
how the production as increased or decreased over the years.
Additionally, we will calculate growth rate for both.
Data is from 1949-2019. Note: Solar Energy Production started 1984
In [1]:
In [2]:
In [3]:
Out[3]:
Annual
Total
Coal
Production
Natural
Gas (Dry)
Production
Crude Oil
Production
Natural
Gas Plant
Liquids
Production
Total
Fossil
Fuels
Production
Nuclear
Electric
Power
Production
Hydroelectric
Power
Production
G
P
0 1949 11.973882 5.377243 10.683252 0.706102 28.740479 0.0 1.424722
1 1950 14.060135 6.232975 11.446729 0.813366 32.553205 0.0 1.415411
2 1951 14.419325 7.415733 13.036724 0.910336 35.782118 0.0 1.423795
3 1952 12.734313 7.963599 13.281049 0.985701 34.964662 0.0 1.465812
4 1953 12.277746 8.338838 13.671076 1.050702 35.338361 0.0 1.412859
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
xls = pd.ExcelFile('Energy_Production_by_Source.xlsx')
df1 = pd.read_excel(xls, 'Annual Data')
df2 = pd.read_excel(xls, 'Monthly Data')
df1.head()

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In [4]:
In [5]:
Graph Total Fossil Fuels Production since 1984 - 2019
Out[4]:
Annual
Total
Coal
Production
Natural
Gas (Dry)
Production
Crude Oil
Production
Natural
Gas Plant
Liquids
Production
Total
Fossil
Fuels
Production
Nuclear
Electric
Power
Production
Hydroelectric
Power
Production
51 2000 22.735478 19.661529 12.358101 2.551466 57.306574 7.862349 2.811116
52 2001 23.547080 20.165567 12.281566 2.490533 58.484745 8.028853 2.241858
53 2002 22.732237 19.382055 12.160213 2.502206 56.776711 8.145429 2.689017
54 2003 22.093652 19.633304 11.959568 2.296059 55.982583 7.959622 2.792539
55 2004 22.852099 19.074254 11.550086 2.407581 55.884020 8.222774 2.688468
56 2005 23.185189 18.556015 10.974152 2.279946 54.995301 8.160810 2.702942
57 2006 23.789510 19.021706 10.766775 2.298717 55.876707 8.214626 2.869035
58 2007 23.492742 19.786209 10.741447 2.348716 56.369113 8.458589 2.446389
59 2008 23.851368 20.702884 10.613302 2.359299 57.526852 8.426491 2.511108
60 2009 21.623721 21.139450 11.340126 2.508252 56.611549 8.355220 2.668824
61 2010 22.038226 21.805763 11.610472 2.704829 58.159290 8.434433 2.538541
62 2011 22.221407 23.405720 11.997530 2.890075 60.514732 8.268698 3.102852
63 2012 20.676893 24.610065 13.841900 3.162126 62.290983 8.061822 2.628702
64 2013 20.001304 24.859072 15.865054 3.451386 64.176816 8.244433 2.562382
65 2014 20.285705 26.718073 18.607136 4.005085 69.615999 8.337559 2.466577
66 2015 17.946095 28.066882 19.712044 4.475993 70.201013 8.336886 2.321177
67 2016 14.667089 27.576023 18.537316 4.664785 65.445213 8.426753 2.472442
68 2017 15.625377 28.289335 19.575779 4.987096 68.477586 8.418968 2.766967
69 2018 15.363442 31.882148 22.834796 5.726973 75.807359 8.438068 2.663138
70 2019 14.255763 35.258324 25.473071 6.351729 81.338886 8.451852 2.563228
0 0.706102
Name: Natural Gas Plant Liquids Production, dtype: float64
df1.tail(20)
new = df1[df1['Annual Total'] == 1949]
print(new['Natural Gas Plant Liquids Production'])

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In [6]:
Graph Total Renewable Energy Production 1984 - 2019
In [7]:
Graph Solar Energy Production Production since 1984 - 2019
Out[6]: [<matplotlib.lines.Line2D at 0x7f96fb59fe50>]
Out[7]: [<matplotlib.lines.Line2D at 0x7f96fb7ee1d0>]
fossil_fuels = df1['Total Fossil Fuels Production']
fossil_fuels = fossil_fuels[35:]
#figure(figsize=(50,5))
plt.plot(fossil_fuels)
#plt.xticks(range(70), range(1949, 2019, 1))
renewable_energy = df1['Total Renewable Energy Production']
renewable_energy = renewable_energy[35:]
plt.plot(renewable_energy)

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In [8]:
Modular Code - Calculate Growth Rate
In [9]:
Out[8]: [<matplotlib.lines.Line2D at 0x7f96fb8d3290>]
0.777084 0.570368
#1984, starts 1949
solar_energy = df1['Solar Energy Production']
#print(1949-1984)
solar_energy = solar_energy[35:]
plt.plot(solar_energy)
def get_energyproduction_number(year_selected, sector):
'''
Function that inputs year selected, and energy sector from energy produ
Outputs the energy produced (which is in quadrillion BTU) in exact year
'''
year = df1[df1['Annual Total'] == year_selected]
new_sector = year[sector]
new_sector = new_sector.iloc[0]
return new_sector
x = get_energyproduction_number(2017, 'Solar Energy Production')
y = get_energyproduction_number(2016, 'Solar Energy Production')
print(x,y)

5. 12/21/2020 Energy_DS - Jupyter Notebook
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In [10]:
In [11]:
In [12]:
Out[10]: 2.0559089782613755
Out[12]: [<matplotlib.lines.Line2D at 0x7f96fb9b5510>]
def get_growth_rate(year1, year2, sector):
"""
Function that inputs two exacts energy production years
Function outputs the calculation of growth rate from one year to anothe
"""
year1_energy_produced = get_energyproduction_number(year1, sector)
year2_energy_produced = get_energyproduction_number(year2, sector)
growth_rate = (year2_energy_produced - year1_energy_produced)/year1_ene
return growth_rate
get_growth_rate(2000, 2001, 'Total Fossil Fuels Production')
def get_yearly_sector_growth(year1, year2, sector):
"""
Function that inputs sector
Outputs growth rate since inception date
"""
lst = []
while year1 < 2019:
x = get_growth_rate(year1, year2, sector)
lst.append(x)
year1 += 1
year2 += 1
return lst
list = get_yearly_sector_growth(2000, 2001, 'Total Renewable Energy Product
plt.plot(list)

6. 12/21/2020 Energy_DS - Jupyter Notebook
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In [13]:
In [14]:
Out[13]: [-15.403905195088488,
11.02586476117117,
3.6916527276159896,
2.0227962561676858,
2.6084377661265084,
5.874329589127594,
-1.1520376639610785,
10.462989260052472,
6.026325911007182,
9.033916630338478,
11.857279532418646,
-4.446775262625664,
5.985639461905219,
3.7016095833647844,
-0.386545172654705,
7.134584913912513,
7.4168712807755925,
2.790667638568073,
0.002033303778008388]
get_yearly_sector_growth(2000, 2001, 'Total Renewable Energy Production')
def get_yearly_sector_growth(year1, year2, sector):
"""
Function that inputs sector
Outputs growth rate since inception date
"""
lst = []
while year1 < 2018:
x = get_growth_rate(year1, year2, sector)
lst.append(x)
year1 += 1
year2 += 1
return lst

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In [15]:
In [16]:
Out[15]: ([<matplotlib.axis.XTick at 0x7f96fb9e6e90>,
<matplotlib.axis.XTick at 0x7f96fb9e08d0>,
<matplotlib.axis.XTick at 0x7f96fb8feed0>,
<matplotlib.axis.XTick at 0x7f96fba29f90>,
<matplotlib.axis.XTick at 0x7f96fba9c410>,
<matplotlib.axis.XTick at 0x7f96fba9c210>,
<matplotlib.axis.XTick at 0x7f96fba9cd10>,
<matplotlib.axis.XTick at 0x7f96fbaa3390>,
<matplotlib.axis.XTick at 0x7f96fbaa3290>,
<matplotlib.axis.XTick at 0x7f96fbaaa350>,
<matplotlib.axis.XTick at 0x7f96fbaaa650>,
<matplotlib.axis.XTick at 0x7f96fbaaa510>,
<matplotlib.axis.XTick at 0x7f96fbab0310>,
<matplotlib.axis.XTick at 0x7f96fbab0210>,
<matplotlib.axis.XTick at 0x7f96fbab0e10>,
<matplotlib.axis.XTick at 0x7f96fbab0910>,
<matplotlib.axis.XTick at 0x7f96fbaa3990>,
<matplotlib.axis.XTick at 0x7f96fbab8510>],
<a list of 18 Text xticklabel objects>)
total_fossil_fuel_growthrate_year = get_yearly_sector_growth(2000, 2001, 'T
plt.plot(total_fossil_fuel_growthrate_year)
plt.xticks(range(0,18), range(2000,2018,1))
def get_average_growth(year1, year2, sector):
"""
Function that inputs a list of numbers, which are the growth rate of ea
Performs a calculation by summing all growth rates, and then divides th
Function outputs a number which is the avg growth rate.
"""
list_growth_rate = get_yearly_sector_growth(year1, year2, sector)
total_sum = sum(list_growth_rate)
avg_growth = total_sum/len(list_growth_rate)
return avg_growth

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In [17]:
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Conclusion
Clearly, the average growth rate for Total Renewable Energy Production
has been twice the growth rate for Total Fossil Fuels Production from
1984-2019.
Additionally, in the last 20 years, Wind and Solar Energy Production have
been growing the fastest compared to other renewable energies.
Out[17]: 0.8007875658304962
Out[18]: 1.9340090984949878
Out[19]: 17.185792354542038
Out[20]: 0.2935477524487831
Out[21]: 1.3549097265165744
Out[22]: 24.245650340873755
Out[23]: 3.293788801373669
Out[24]: 3.7267783908048235
Out[25]: 4.787257846711986
Out[26]: 2.7995326374860046
get_average_growth(1984, 1985, 'Total Fossil Fuels Production')
get_average_growth(1984, 1985, 'Total Renewable Energy Production')
get_average_growth(2000, 2001, 'Solar Energy Production')
get_average_growth(2000, 2001, 'Hydroelectric Power Production')
get_average_growth(2000, 2001, 'Geothermal Energy Production')
get_average_growth(2000, 2001, 'Wind Energy Production')
get_average_growth(2000, 2001, 'Biomass Energy Production')
get_average_growth(2000, 2001, 'Crude Oil Production')
get_average_growth(2000, 2001, 'Natural Gas Plant Liquids Production')
get_average_growth(2000, 2001, 'Natural Gas (Dry) Production')

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