def rushing(self, player_link, year, **kwargs):
# Set up the gamelog suffix
gamelog_suffix = '/gamelog/%s/' % year
# Modify the player url to point to the gamelog
log_url = player_link[:-4] + gamelog_suffix
# Get html
html = Loader.Loader().load_page(log_url).content.decode()
# ************** generate general stats, these need to be combined later ******************
gen = PlayerParser.PlayerParser().parse_general_info(html)
# parse tables w pandas
df = pd.read_html(html)[0]
# hash the columns to determine which fields are being used
which_cols = hashlib.md5(json.dumps(list(df.columns.levels[0])).encode()).hexdigest()
# Here we make a dict of hashes and their corresponding column parser, this is faster than if/else
options = {'c3695be2dd2fa9307301dccf047b4e86': Rushhash.RushHash().md5c3695be2dd2fa9307301dccf047b4e86,
'7f97f3885d50fcf9b92797810856a89f': Rushhash.RushHash().md57f97f3885d50fcf9b92797810856a89f,
'aa321161d6f3f5230259dbc4ae67299a': Rushhash.RushHash().md5aa321161d6f3f5230259dbc4ae67299a,
'9c11c15180efbf7aec4300fc190cd3a5': Rushhash.RushHash().md59c11c15180efbf7aec4300fc190cd3a5,
'ad9a12e06546e3019128fec57cdc9d0e': Rushhash.RushHash().md5ad9a12e06546e3019128fec57cdc9d0e,
'00f83a7c4b3e891e3c448db700cc9ada': Rushhash.RushHash().md500f83a7c4b3e891e3c448db700cc9ada,
'5980508dab2f61013bd07809c5ca0e41': Rushhash.RushHash().md55980508dab2f61013bd07809c5ca0e41,
'c35b37a5f0f696bfd1576753faffe81c': Rushhash.RushHash().md5c35b37a5f0f696bfd1576753faffe81c,
'aed81e3e77b9842532b5efa73458a259': Rushhash.RushHash().md5aed81e3e77b9842532b5efa73458a259,
'7d21a9a4ab9adde626d633fbd62db5c0': Rushhash.RushHash().md57d21a9a4ab9adde626d633fbd62db5c0,
'91138c3c08c339b71b8323e2bac3aac7': Rushhash.RushHash().md591138c3c08c339b71b8323e2bac3aac7,
'ddcb0610869ff21799f008209ac6d229': Rushhash.RushHash().md5ddcb0610869ff21799f008209ac6d229}
df = options[which_cols](df)
# send df to the common parser
df = self.common(df, year)
# Add the name
df.loc[:, 'Name'] = gen['name']
# Add the players position
df.loc[:, 'Pos'] = gen['position']
df['Throws'] = gen['throws']
df['Height'] = gen['height']
df['Weight'] = gen['weight']
df['DOB_mo'] = gen['bday_mo']
df['DOB_day'] = gen['bday_day']
df['DOB_yr'] = gen['bday_yr']
df['College'] = gen['college']
df = df[['Name', 'Pos', 'Height', 'Weight', 'DOB_mo', 'DOB_day', 'DOB_yr', 'College'] +
Rushhash.RushHash().base[1:] + ['PF', 'PA'] + Rushhash.RushHash().receiving + Rushhash.RushHash().rushing +
Rushhash.RushHash().kick_rt + Rushhash.RushHash().punt_rt + Rushhash.RushHash().scoring2p +
Rushhash.RushHash().scoring]
return df
def defense(self, player_link, year, **kwargs):
# Set up the gamelog suffix
gamelog_suffix = '/gamelog/%s/' % year
# Modify the player url to point to the gamelog
log_url = player_link[:-4] + gamelog_suffix
# Get html
html = Loader.Loader().load_page(log_url).content.decode()
# ************** generate general stats, these need to be combined later ******************
gen = PlayerParser.PlayerParser().parse_general_info(html)
# parse tables w pandas
df = pd.read_html(html)[0]
# hash the columns to determine which fields are being used
which_cols = hashlib.md5(json.dumps(list(df.columns.levels[0])).encode()).hexdigest()
# Here we make a dict of hashes and their corresponding column parser, this is faster than if/else
options = {'0c329a15f241e5c132d0d5c7612032c0': Defhash.DefHash().md50c329a15f241e5c132d0d5c7612032c0,
'58ffdd172c2358c5e5ab2e0a1994252a': Defhash.DefHash().md558ffdd172c2358c5e5ab2e0a1994252a,
'141f3f6945aa9495c6580650649f4b8f': Defhash.DefHash().md5141f3f6945aa9495c6580650649f4b8f,
'109394668745222b0ccbd92bfd0ac4c1': Defhash.DefHash().md5109394668745222b0ccbd92bfd0ac4c1,
'60dfaf4e946c4ae3d47c6d8b430c92a4': Defhash.DefHash().md560dfaf4e946c4ae3d47c6d8b430c92a4,
'fa476dd5c907f86452c016e54b3fe0f8': Defhash.DefHash().md5fa476dd5c907f86452c016e54b3fe0f8}
df = options[which_cols](df)
# send df to the common parser
df = self.common(df, year)
# Add the name
df.loc[:, 'Name'] = gen['name']
# Add the players position
df.loc[:, 'Pos'] = gen['position']
df['Throws'] = gen['throws']
df['Height'] = gen['height']
df['Weight'] = gen['weight']
df['DOB_mo'] = gen['bday_mo']
df['DOB_day'] = gen['bday_day']
df['DOB_yr'] = gen['bday_yr']
df['College'] = gen['college']
df = df[['Name', 'Pos', 'Height', 'Weight', 'DOB_mo', 'DOB_day', 'DOB_yr', 'College'] +
Defhash.DefHash().base[1:] + ['PF', 'PA'] + Defhash.DefHash().punt_rt + Defhash.DefHash().kick_rt +
Defhash.DefHash().scoring + Defhash.DefHash().rush_sk + Defhash.DefHash().def_int]
return df
def kicking(self, player_link, year, **kwargs):
# Set up the gamelog suffix
gamelog_suffix = '/gamelog/%s/' % year
# Modify the player url to point to the gamelog
log_url = player_link[:-4] + gamelog_suffix
# Get html
html = Loader.Loader().load_page(log_url).content.decode()
# ************** generate general stats, these need to be combined later ******************
gen = PlayerParser.PlayerParser().parse_general_info(html)
# parse tables w pandas
df = pd.read_html(html)[0]
# hash the columns to determine which fields are being used
which_cols = hashlib.md5(json.dumps(list(df.columns.levels[0])).encode()).hexdigest()
# Here we make a dict of hashes and their corresponding column parser, this is faster than if/else
options = {'080683052961d92b5efd07588e614700': Kickhash.KickHash().md5080683052961d92b5efd07588e614700,
'c0fe30e42184e7a59c00c04dc917bb87': Kickhash.KickHash().md5c0fe30e42184e7a59c00c04dc917bb87,
'7ad30bf95e287937864b02dca25801bf': Kickhash.KickHash().md57ad30bf95e287937864b02dca25801bf}
df = options[which_cols](df)
# send df to the common parser
df = self.common(df, year)
# Add the name
df.loc[:, 'Name'] = gen['name']
# Add the players position
df.loc[:, 'Pos'] = gen['position']
df['Throws'] = gen['throws']
df['Height'] = gen['height']
df['Weight'] = gen['weight']
df['DOB_mo'] = gen['bday_mo']
df['DOB_day'] = gen['bday_day']
df['DOB_yr'] = gen['bday_yr']
df['College'] = gen['college']
df = df[['Name', 'Pos', 'Height', 'Weight', 'DOB_mo', 'DOB_day', 'DOB_yr', 'College'] +
Kickhash.KickHash().base[1:] + ['PF', 'PA'] + Kickhash.KickHash().scoring]
return df
def passing(self, player_link, year, **kwargs):
# Set up the gamelog suffix
gamelog_suffix = '/gamelog/%s/' % year
# Modify the player url to point to the gamelog
log_url = player_link[:-4] + gamelog_suffix
# Get html
html = Loader.Loader().load_page(log_url).content.decode()
# gent general stats
gen = PlayerParser.PlayerParser().parse_general_info(html)
# parse tables w pandas
df = pd.read_html(html)[0]
# drop first level of cols
df.columns = df.columns.droplevel()
# rename the home column
df = df.rename(columns={df.columns[5]: "Home"})
# There may be many extra blank cols, delet them
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
# send df to the common parser
df = self.common(df, year)
# Add the name
df.loc[:, 'Name'] = gen['name']
# Add the players position
df.loc[:, 'Pos'] = gen['position']
# add additional player info
df['Throws'] = gen['throws']
df['Height'] = gen['height']
df['Weight'] = gen['weight']
df['DOB_mo'] = gen['bday_mo']
df['DOB_day'] = gen['bday_day']
df['DOB_yr'] = gen['bday_yr']
df['College'] = gen['college']
return df
def test_df_name(self, kicking_req):
html = kicking_req.content.decode()
df = PlayerParser.PlayerParser().kicking(html=html)
assert df.iloc[0]['Name'] == 'Greg Zuerlein'
def receiving(self, player_link, year, **kwargs):
# Set up the gamelog suffix
gamelog_suffix = '/gamelog/%s/' % year
# Modify the player url to point to the gamelog
log_url = player_link[:-4] + gamelog_suffix
# Get html
html = Loader.Loader().load_page(log_url).content.decode()
# ************** generate general stats, these need to be combined later ******************
gen = PlayerParser.PlayerParser().parse_general_info(html)
# parse tables w pandas
df = pd.read_html(html)[0]
# hash the columns to determine which fields are being used
which_cols = hashlib.md5(json.dumps(list(df.columns.levels[0])).encode()).hexdigest()
# Here we make a dict of hashes and their corresponding column parser, this is faster than if/else
options = { "b3c4237d9a10de8cfaad61852cb552c4": Rechash.RecHash().md5b3c4237d9a10de8cfaad61852cb552c4,
"bcb96297b50fb2120f475e8e05fbabcd": Rechash.RecHash().md5bcb96297b50fb2120f475e8e05fbabcd,
"4560c290b45e942c16cc6d7811345fce": Rechash.RecHash().md54560c290b45e942c16cc6d7811345fce,
"4c82a489ec5b2c943e78c9018dcbbca1": Rechash.RecHash().md54c82a489ec5b2c943e78c9018dcbbca1,
"e8ffc7202223bb253e92da83b76e9944": Rechash.RecHash().md5e8ffc7202223bb253e92da83b76e9944,
"50fcceaa170b1a1e501e3f40548e403d": Rechash.RecHash().md550fcceaa170b1a1e501e3f40548e403d,
"e160e714b29305ecfecf513cbf84b80f": Rechash.RecHash().md5e160e714b29305ecfecf513cbf84b80f,
"111e8480632f73642d7e20acbdbe6b16": Rechash.RecHash().md5111e8480632f73642d7e20acbdbe6b16,
"adc05c5af0f88775d3605d02c831c0ed": Rechash.RecHash().md5adc05c5af0f88775d3605d02c831c0ed,
"bfbf86ae0485a0a70692ae04124449b9": Rechash.RecHash().md5bfbf86ae0485a0a70692ae04124449b9,
"6b4698269dd34a823cf6b233c6165614": Rechash.RecHash().md56b4698269dd34a823cf6b233c6165614,
"7f97f3885d50fcf9b92797810856a89f": Rechash.RecHash().md57f97f3885d50fcf9b92797810856a89f,
"aa321161d6f3f5230259dbc4ae67299a": Rechash.RecHash().md5aa321161d6f3f5230259dbc4ae67299a,
"1193d47266d4acdcf1b6fca165121100": Rechash.RecHash().md51193d47266d4acdcf1b6fca165121100,
"52589e869a13d76c6d0dbf066cab536f": Rechash.RecHash().md552589e869a13d76c6d0dbf066cab536f,
"d522b9357244c20714a3b21f8f404918": Rechash.RecHash().md5d522b9357244c20714a3b21f8f404918}
df = options[which_cols](df)
# send df to the common parser
df = self.common(df, year)
# Add the name
df.loc[:, 'Name'] = gen['name']
# Add the players position
df.loc[:, 'Pos'] = gen['position']
df['Throws'] = gen['throws']
df['Height'] = gen['height']
df['Weight'] = gen['weight']
df['DOB_mo'] = gen['bday_mo']
df['DOB_day'] = gen['bday_day']
df['DOB_yr'] = gen['bday_yr']
df['College'] = gen['college']
df = df[['Name', 'Pos', 'Height', 'Weight', 'DOB_mo', 'DOB_day', 'DOB_yr', 'College'] +
Rechash.RecHash().base[1:] + ['PF', 'PA'] + Rechash.RecHash().receiving + Rechash.RecHash().rushing +
Rechash.RecHash().kick_rt + Rechash.RecHash().punt_rt + Rechash.RecHash().scoring2p +
Rechash.RecHash().scoring]
return df
def test_df_pos(self, receiving_req):
html = receiving_req.content.decode()
df = PlayerParser.PlayerParser().receiving(html=html)
assert df.iloc[0]['Pos'] == 'WR'
def test_df_name(self, rushing_req):
html = rushing_req.content.decode()
df = PlayerParser.PlayerParser().rushing(html=html)
assert df.iloc[0]['Name'] == "Le'Veon Bell"
def test_df_pass_td(self, passing_req):
html = passing_req.content.decode()
df = PlayerParser.PlayerParser().passing(html=html)
assert df.loc[0:17, 'TD'].sum() == 488
def test_df_name(self, receiving_req):
html = receiving_req.content.decode()
df = PlayerParser.PlayerParser().receiving(html=html)
assert df.iloc[0]['Name'] == 'Antonio Brown'
def test_df_rush(self, passing_req):
html = passing_req.content.decode()
df = PlayerParser.PlayerParser().passing(html=html)
assert df['Rush_Yds'].sum() == 968.0
def test_df_pos(self, passing_req):
html = passing_req.content.decode()
df = PlayerParser.PlayerParser().passing(html=html)
assert df.iloc[0]['Pos'] == 'QB'
def test_df_name(self, passing_req):
html = passing_req.content.decode()
df = PlayerParser.PlayerParser().passing(html=html)
assert df.iloc[0]['Name'] == 'Tom Brady'
def general_parser(html):
return PlayerParser.PlayerParser().parse_general_info(html)
def test_df_name(self, defense_req):
html = defense_req.content.decode()
df = PlayerParser.PlayerParser().defense(html=html)
assert df.iloc[0]['Name'] == 'Demario Davis'
