def merge_salaries_stats(salaries, year):
"""
This function is to merge salaries dataset and nba stats dataset for a given year.
Since this function is called for preparing for the regression analysis and we are trying regress next year's salaries on this year's statistics, therefore,
*statistics year = salaries year - 1*
Attributes:
salaries: a preprocessed salaries dataset
year: a salaries year, from 2000 to 2015
Return:
a merged dataframe with salaries and nba stats dataset for the selected year.
"""
try:
salaries_year = salaries[year].dropna(subset=['SALARY'])
stats_year = pd.read_csv(
os.path.dirname(os.path.realpath(__file__)) +
'/../static/data/stats_{}.csv'.format(year - 1)) #load stats data
except:
raise cannotLoadDataError('NBA Stats Data Not Found!')
stats_year = stats_year[stats_year['POS'].isin(
['C', 'PF', 'PG', 'SF', 'SG'])] #check positions
stats_year = stats_year.sort(columns='PLAYER')
stats_year = stats_year.drop_duplicates(subset=['PLAYER', 'POS'])
stats_year = stats_year.set_index(['PLAYER', 'POS'])
stats_year = stats_year.drop('TEAM', 1)
return pd.merge(salaries_year,
stats_year,
right_index=True,
left_index=True)
def salaries_preprocessing():
"""
This function is to preprocess 2000 - 2015 salaries dataset.
Return: a merged dataset containing 2000-2015 salaries data.
"""
s_year = xrange(2000, 2016)
#load 2000-2015 salaries data into a dictionary
try:
dfs = {year:pd.read_csv(os.path.dirname(os.path.realpath(__file__))+"/../static/data/salaries_"+str(year)+".csv").dropna(subset = ['SALARY']) for year in s_year}
except:
raise cannotLoadDataError('Salaries Data Not Found!')
for year in s_year:
dfs[year]['POS'].fillna('N', inplace=True)
dfs[year] = dfs[year].set_index(['PLAYER','POS'])
return pd.concat(dfs, axis=1, join='outer') #merge 2000-2015 dataframes into a dataframe
def salaries_preprocessing():
"""
This function is to preprocess 2000 - 2015 salaries dataset.
Return: a merged dataset containing 2000-2015 salaries data.
"""
s_year = xrange(2000, 2016)
#load 2000-2015 salaries data into a dictionary
try:
dfs = {
year: pd.read_csv(
os.path.dirname(os.path.realpath(__file__)) +
"/../static/data/salaries_" + str(year) +
".csv").dropna(subset=['SALARY'])
for year in s_year
}
except:
raise cannotLoadDataError('Salaries Data Not Found!')
for year in s_year:
dfs[year]['POS'].fillna('N', inplace=True)
dfs[year] = dfs[year].set_index(['PLAYER', 'POS'])
return pd.concat(
dfs, axis=1,
join='outer') #merge 2000-2015 dataframes into a dataframe
def merge_salaries_stats(salaries, year):
"""
This function is to merge salaries dataset and nba stats dataset for a given year.
Since this function is called for preparing for the regression analysis and we are trying regress next year's salaries on this year's statistics, therefore,
*statistics year = salaries year - 1*
Attributes:
salaries: a preprocessed salaries dataset
year: a salaries year, from 2000 to 2015
Return:
a merged dataframe with salaries and nba stats dataset for the selected year.
"""
try:
salaries_year = salaries[year].dropna(subset = ['SALARY'])
stats_year = pd.read_csv(os.path.dirname(os.path.realpath(__file__))+'/../static/data/stats_{}.csv'.format(year-1)) #load stats data
except:
raise cannotLoadDataError('NBA Stats Data Not Found!')
stats_year = stats_year[stats_year['POS'].isin(['C','PF','PG','SF','SG'])] #check positions
stats_year = stats_year.sort(columns='PLAYER')
stats_year = stats_year.drop_duplicates(subset=['PLAYER','POS'])
stats_year = stats_year.set_index(['PLAYER','POS'])
stats_year = stats_year.drop('TEAM',1)
return pd.merge(salaries_year,stats_year,right_index=True, left_index=True)
