data[ 'id' ] = data[ 'players_links' ].str.extract( '(\d+)' )

async function writeData ( )  {const csv = require ( 'csv-parser' )const results = [];fs.createReadStream( 'C:\\Users\\User\\Documents\\GitHub\\Springboard Capstone BoxingPredictionWebApp\\boxingdata\\readdata.csv' ).pipe(csv()).on( 'data' , ( data )=> results.push(data)).on( 'end' , async () => {const cookieJar = await getCookieJar();const promises = [];results.forEach( ( data ) => {promises.push(boxrec.getPersonById(cookieJar,data.id));})const fighters = await Promise .all(promises); fighters.forEach( ( fighter ) => {let data = '' ;for ( const key in fighter.output) {if ( Array .isArray(fighter.output[key])) {data += JSON .stringify(fighter.output[key]) + ',' ;} else if ( typeof fighter.output[key] === 'object' ) {data += JSON .stringify(fighter.output[key]) + ',' ;} else {data += fighter.output[key] + ',' ;}}data = data.replace( /(^,)|(,$)/g , "" );data += '\n' ;fs.appendFile( 'C:\\Users\\User\\Documents\\datatest.csv' ,data, ( err ) => {if (err) throw err;});

df = pd.DataFrame(file_full[file_full.columns[ 0 :]].apply( lambda x: ' ' .join(x.astype(str)),axis= 1 ))
df = pd.DataFrame(df[ 0 ].str.replace( 'date' , 'dateday' ))
#split each fight into a separate column
df_split = pd.DataFrame(df[ 0 ].str.split( 'date' ,expand= True ))
def func (df,col) :return df[col].str.extract( 'day(?P<day>.*?)firstBoxerRating(?P<firstBoxerRating>.*?)firstBoxerWeight(?P<firstBoxerWeight>.*?)judges(?P<JudgeID>.*?)links(?P<Links>.*?)location(?P<location>.*?)metadata(?P<metadata>.*?)numberOfRounds(?P<numberofrounds>.*?)outcome(?P<outcome>.*?)rating(?P<rating>.*?)referee(?P<referee>.*?)secondBoxer(?P<secondBoxer>.*?)secondBoxerLast6(?P<secondBoxerLast6>.*?)secondBoxerRating(?P<secondBoxerRating>.*?)secondBoxerRecord(?P<secondBoxerRecord>.*?)secondBoxerWeight(?P<secondBoxerWeight>.*?)titles(?P<titles>.*?){' )
for i in range( 1 ,len(df_split.columns) -1 ):df_split[[ 'date' +str(i), 'firstBoxerRating' +str(i), 'firstBoxerWeight' +str(i), 'JudgeID' +str(i), 'Links' +str(i),'location' +str(i), 'metadata' +str(i), 'numberofrounds' +str(i), 'outcome' +str(i), 'rating' +str(i),'referee' +str(i), 'secondBoxer' +str(i), 'secondBoxerLast6' +str(i), 'secondBoxerRating' +str(i),'secondBoxerRecord' +str(i), 'secondBoxerWeight' +str(i), 'titles' +str(i)]] = func(df_split,i)

#cleanup first boxer rating
def remove_colon (col_name) :return merged[col_name].str.replace( ':' , '' )
#weights need to be converted to float
def firstweight (col_name) :a = remove_colon(col_name)return pd.to_numeric(a,errors= 'coerce' )
#update first boxer rating columns
boxer_var = list_var( 'firstBoxerRating' )
for i in boxer_var:merged[i] = remove_colon(i)
#update weight
weight_var = list_var( 'firstBoxerWeight' )
for i in weight_var:merged[i] = firstweight(i)

def split_time (col) :return merged[col].str.extract( '(\d*\:\d+)' ,expand= True )
times = list_var( 'metadata' )
for i in times:merged[i] = split_time(i)

opp_names = [ 'secondBoxer' +str(i) for i in range( 1 , 85 )]
outcome_cols = [ 'outcome' +str(i) for i in range( 1 , 85 )]
#remove quotation marks from secondboxer name
data[opp_names] = data[opp_names].astype(str).apply( lambda x: x.str.replace( '"' , '' ))
#get points for each opponent, if negative convert to zero
data[opp_names]=data[opp_names].apply( lambda x : x.map(dict(zip(topten.name,topten.total_points))))
data[opp_names] = data[opp_names].fillna( 0 )
data[opp_names] = data[opp_names].mask(data[opp_names] < 0 , 0 )
data[outcome_cols] = data[outcome_cols].astype(str).apply( lambda x: x.str.replace( '"' , '' ))
#add opp points to total points if outcome was a win
topten[ 'total_points' ] = (data[opp_names].where(data[outcome_cols].eq( 'win ' ).values, 0 ).sum(axis= 1 )/ 5 ) + (topten[ 'total_points' ])

@app.callback(dash.dependencies.Output( 'total-bouts-v-bouts-won' , 'figure' ),[dash.dependencies.Input( 'weight_class' , 'value' ),dash.dependencies.Input( 'gender' , 'value' )])

def update_scatterplot (weight_class,gender) :if weight_class is None or weight_class == []:weight_class = WEIGHT_CLASSif gender is None or gender == []:gender = GENDERweight_df = data[(data[ 'division' ].isin(weight_class))]weight_df = weight_df[(weight_df[ 'sex' ].isin(gender))]return {'data' : [go.Scatter(x=weight_df[ 'bouts_fought' ],y=weight_df[ 'w' ],text=weight_df[ 'name' ],mode= 'markers' ,opacity= 0.5 ,marker={'size' : 14 ,'line' : { 'width' : 0.5 , 'color' : 'blue' }},)],

  dcc.Dropdown(id= 'weight_class' ,options=[{ 'label' : i, 'value' : i} for i in data[ 'division' ].unique()],multi= True),dcc.Dropdown(id= 'gender' ,options=[{ 'label' : i, 'value' :i} for i in fight_outcomes[ 'sex' ].unique()],multi= True),

html.Section(id= 'slideshow' ,children=[html.Div(style={ 'backgroundColor' :colors[ 'background' ],'textAlign' : 'center' },id= 'slideshow-container' ,children=[html.Div(id= 'image' ),dcc.Interval(id= 'interval' ,interval= 3000 ),])])