<jupyter_start><jupyter_text>Close to a 100K charging units over 42K locations   Charging units per location<jupyter_code># Mean number of charging units per location
print('Mean # of units per location: ', df[df['EV Level2 EVSE Num'] > 0 ]['EV Level2 EVSE Num'].mean())
print('STD # of units per location: ', df[df['EV Level2 EVSE Num'] > 0 ]['EV Level2 EVSE Num'].std())
print('Max # of units per location: ', df[df['EV Level2 EVSE Num'] > 0 ]['EV Level2 EVSE Num'].max())
print('min # of units per location: ', df[df['EV Level2 EVSE Num'] > 0 ]['EV Level2 EVSE Num'].min())
df[df['EV Level2 EVSE Num'] > 0 ]['EV Level2 EVSE Num'].value_counts(normalize = True).head(7)
df[df['EV Level2 EVSE Num'] > 0 ]['EV Level2 EVSE Num'].value_counts().head(12)<jupyter_output><empty_output><jupyter_text>95.11% of all Level 2 locations have less then 4 units.  - 62% have 2 units - 23% have 1 unit- 4.2% have 3 units- 4.9% have 4 units<jupyter_code>temp = df[(df['EV Level2 EVSE Num'] > 0 ) & (df['EV Level2 EVSE Num'] < 12)]
sns.histplot(data = temp, x = 'EV Level2 EVSE Num', bins = 12)
temp = df[(df['EV Level2 EVSE Num'] > 0 ) & (df['EV Level2 EVSE Num'] > 12) & (df['EV Level2 EVSE Num'] < 50) ]
#temp['EV Level2 EVSE Num'].plot(kind = 'hist')
sns.histplot(data = temp, x = 'EV Level2 EVSE Num', bins = 0)
temp = df[(df['EV Level2 EVSE Num'] > 0 ) & (df['EV Level2 EVSE Num'] > 50)  ]
sns.histplot(data = temp, x = 'EV Level2 EVSE Num', bins = 50)
df[(df['EV Level2 EVSE Num'] > 0 ) & (df['EV Level2 EVSE Num'] > 50)  ]['EV Level2 EVSE Num'].plot(kind = 'hist', bins = 50)
sns.violinplot(data = df[df['EV Level2 EVSE Num'] > 0 ], x = 'EV Level2 EVSE Num')
#Locations per state
df[df['EV Level2 EVSE Num'] > 0 ]['State'].value_counts(normalize = True).head(7)
#Locations per state
df[df['EV Level2 EVSE Num'] > 0 ]['State'].value_counts(normalize = True).head(7).sum()
# Top 18 States % minus .29.8% for CA 
df[df['EV Level2 EVSE Num'] > 0 ]['State'].value_counts(normalize = True).head(8).sum() - .298<jupyter_output><empty_output><jupyter_text>------ CA has 29.8% of the EV Charging in the country.  - The top 7 States account for 57% of the EV Charging in the US.  - It takes the next top 8 states to equal CA % of the market<jupyter_code>#Locations per state
df[df['EV Level2 EVSE Num'] > 0 ]['State'].value_counts().plot(kind = 'bar', figsize = (11,7));
# Locations per state
df[ ((df['EV Level2 EVSE Num'] > 0) & (df['State'] != 'CA' ))]['State'].value_counts().plot(kind = 'bar', figsize = (13,7));<jupyter_output><empty_output><jupyter_text>???    There appears to be 3 shelfs in the diagram, NY - CO, MD - NJ, and MN - AK.       Is there a statistical phenomenon for this?  Does this show different slopes.   If there are different slopes what does this mean? CA, FL, TX, MA, WA, GA, CO, MD all have over 1000 locations<jupyter_code># Locatons per state w/o CA in Seaborn
sns.histplot( data = df[ ((df['EV Level2 EVSE Num'] > 0) & (df['State'] != 'CA' ))],
           x = 'State');<jupyter_output><empty_output>
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