Make a Class(updating) using pandas data frame

See revised code below. This should give you the output you're looking for. If you need clarification on any of the code do let me know - but it's quite self explanatory.

import pandas as pdclass logdata:    def __init__(self, size):        self.size = size        self.df = pd.DataFrame(data = None,                                columns = ['Timestamp','Label','Occurance', 'Confidence'],                              )    def insertdf(self, x, timestamp):        # default values        occurance = 1        confidence = 100        self.df = self.df.append(pd.Series({            'Timestamp': timestamp,             'Label': x,             'Occurance': occurance,             'Confidence': confidence        }), ignore_index=True)        self.df.sort_index(inplace=True, ascending=False)        self.del_row()        # Calculate the confidence and occurances of labels        if self.df.shape[0] > 1:            occurance = self.get_occurance()            confidence = self.get_confidence(occurance)            self.df['Occurance'] = self.df.Label.apply(lambda x: occurance[x])            self.df['Confidence'] = self.df.Label.apply(lambda x: confidence[x])        return self.df    def get_occurance(self):        # group by label and count        occ = self.df.groupby('Label').Timestamp.count().rename('Occurance').astype(int)        return occ    def get_confidence(self, occurance):        conf = ((occurance / sum(occurance)).rename('Confidence') * 100).astype(int)        return conf    def del_row(self):        if self.df.shape[0] > int(self.size):            self.df = self.df.head(self.size)    def get_result(self):        return self.df.loc[self.df['Confidence'].idxmax()]