df = pd.read_csv("Data.csv")

df['DATE'] = pd.DatetimeIndex(df['DATE'], format='%M/%D/%Y')

df['Year'] = df['DATE'].dt.year
df['Month'] = df['DATE'].dt.month
df['Day'] = df['DATE'].dt.day

(df
 .assign(MONTH=df['DATE'].dt.strftime('(%m) %B (%y)'))
 .groupby(['NAME', 'MONTH', 'Year'], as_index=False)['SNOW']
 .agg({'AVERAGE': 'mean'})
)

if 'Year' == '2016':
    df = pd.to_csv('average2016.csv', index=False)
else:
    df = pd.to_csv('average2017.csv', index=False)

if df.loc[df['Year'] == 2016]:
   df = pd.to_csv('average2016.csv', index=False)

else:
   df = pd.to_csv('average2017.csv', index=False)

df = pd.Series(['1/1/2016'])

if df.item():