Project Description¶

As electric vehicles become more common, understanding the growth and usage of charging infrastructure is essential for effective planning and development. This project examines electric vehicle charging trends in the United States from 2014 to 2022.

The US Government's Alternative Fuels Data Center maintains records of electric vehicle (EV) charging infrastructure, including charging ports, station locations, and vehicle sales. With the EV market evolving rapidly, analyzing trends in charging facilities and sales is important to inform strategic decisions.

As data scientists, we will explore this data by wrangling and visualizing aggregated yearly records. The data, collected each December, provides a snapshot of EV charging port installations and station locations over roughly ten years, covering both public and private charging environments.

The Data¶

private_ev_charging.csv¶

Variable Description
year Year of data collection
private_ports Number of charging ports owned by private companies in that year
private_station_locations Number of privately owned station locations for EV charging

public_ev_charging.csv¶

Variable Description
year Year of data collection
public_ports Number of charging ports under public ownership in that year
public_station_locations Number of publicly owned station locations for EV charging

ev_sales.csv¶

Variable Description
Vehicle Electric vehicle model
year Year of data collection
sales Number of vehicles sold in the US
In [1]:
# Import required libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the datasets
private_ev_charging = pd.read_csv("private_ev_charging.csv")
public_ev_charging = pd.read_csv("public_ev_charging.csv")
ev_sales = pd.read_csv("ev_sales.csv")

private_ev_charging.head()
Out[1]:
year private_ports private_station_locations
0 2014 3695 1825
1 2015 4150 1962
2 2016 5763 2331
3 2017 6048 2370
4 2018 6812 2489
In [2]:
public_ev_charging.head()
Out[2]:
year public_ports public_station_locations
0 2013 16619 6938
1 2014 22470 9207
2 2015 26532 10710
3 2016 33165 13150
4 2017 45789 16170
In [3]:
ev_sales.head()
Out[3]:
Vehicle year sales
0 Chevy Volt 2011 7671.0
1 Chevy Volt 2012 23461.0
2 Chevy Volt 2013 23094.0
3 Chevy Volt 2014 18805.0
4 Chevy Volt 2015 15393.0
In [6]:
# Perform an outer join to keep only the rows with complete information
df_combined = private_ev_charging.merge(public_ev_charging, on='year', how='outer', indicator=True)
df_temp = df_combined[df_combined['_merge'] == 'both']

# Drop the _merge column as it's no longer needed
df_temp = df_temp.drop(columns=['_merge'])

df_temp.head()
Out[6]:
year private_ports private_station_locations public_ports public_station_locations
0 2014 3695.0 1825.0 22470 9207
1 2015 4150.0 1962.0 26532 10710
2 2016 5763.0 2331.0 33165 13150
3 2017 6048.0 2370.0 45789 16170
4 2018 6812.0 2489.0 56842 19893

How many vehicles were sold in 2018 in total?¶

In [9]:
# Get total sales grouping by each year
ev_total_sales = ev_sales.groupby('year')['sales'].sum().reset_index()
ev_total_sales[ev_total_sales['year'] == 2018]
Out[9]:
year sales
7 2018 361315.0

Plot trends for private ports, public ports, and sales.¶

In [12]:
# Left-join with sales
df_complete = df_temp.merge(ev_total_sales, how='left', on='year')


# Drop any rows with null values
df_complete = df_complete.dropna(subset="sales")

# Create a figure and axis object
fig, ax = plt.subplots()

# Plot each line
sns.lineplot(data=df_complete, x='year', y='private_ports', label='Private Ports')
sns.lineplot(data=df_complete, x='year', y='public_ports', label='Public Ports')
sns.lineplot(data=df_complete, x='year', y='sales', label='Total Sales', linestyle=':')

# Adding titles and labels
ax.set_title('EV Ports and Sales Over Time')
ax.set(xlabel='Year', ylabel='Count')

# Show the legend
ax.legend(loc='upper left')
plt.show()

C:\Users\newbe\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\newbe\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\newbe\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\newbe\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\newbe\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\newbe\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Did vehicle sales and number of private and public ports show the same trend (either increasing or decreasing) between the years 2015 and 2018?¶

In [13]:
# From the above plot we can conclude that vehicle sales and number of private and public ports show the same trend between 2015 and 2018. i.e. increasing