def show_all(self, sport, start_date, end_date):
df = self.df_dict[sport].copy(deep=False)
df = self.get_sessions_in_timespan(df, start_date, end_date)
df.reset_index(inplace=True)
df.rename(columns={'index': 'i-th session (0 started)'}, inplace=True)
df = df.sort_values(by='start_time',
ascending=False,
ignore_index=True)
utils.show_dataframe_in_web(df, 'All sessions')
def show_top_time_consuming(self, sport, how_many, start_date, end_date):
df = self.df_dict[sport].copy(deep=False)
df = self.get_sessions_in_timespan(df, start_date, end_date)
df.reset_index(inplace=True)
df.rename(columns={'index': 'i-th session (0 started)'}, inplace=True)
df = df.sort_values(by='duration', ascending=False,
ignore_index=True).head(how_many)
utils.show_dataframe_in_web(
df, f'{how_many} most time consuming sessions')
def show_top_time_in_distance(self, sport, how_many, distance, start_date,
end_date):
column = f'{distance}km_time'
df = self.df_dict[sport].copy(deep=False)
df = self.get_sessions_in_timespan(df, start_date, end_date)
df.reset_index(inplace=True)
df.rename(columns={'index': 'i-th session (0 started)'}, inplace=True)
df = df.sort_values(by=column, ascending=True,
ignore_index=True).head(how_many)
utils.show_dataframe_in_web(df, f'Best {distance}km time sessions')
def show_top_distances(self, sport, how_many, start_date, end_date):
df = self.df_dict[sport].copy(deep=False)
df = self.get_sessions_in_timespan(df, start_date, end_date)
df.reset_index(inplace=True)
df.rename(columns={'index': 'i-th session (0 started)'}, inplace=True)
# Dealing with strings and ints
df = df.replace(const.empty_value, 0)
df = df.sort_values(by='distance', ascending=False,
ignore_index=True).head(how_many)
df = df.replace(0, const.empty_value)
utils.show_dataframe_in_web(df, f'{how_many} longest sessions')
def show_sessions_with_minimum_distance(self, sport, distance, start_date,
end_date):
df = self.df_dict[sport].copy(deep=False)
df = self.get_sessions_in_timespan(df, start_date, end_date)
df.reset_index(inplace=True)
df.rename(columns={'index': 'i-th session (0 started)'}, inplace=True)
df = df[df['distance'] >= distance]
df = df.sort_values(by='start_time',
ascending=False,
ignore_index=True)
utils.show_dataframe_in_web(df, f'Sessions with at least {distance}km')
def show_top_avg_heart_rates(self, sport, how_many, start_date, end_date):
df = self.df_dict[sport].copy(deep=False)
df = self.get_sessions_in_timespan(df, start_date, end_date)
df.reset_index(inplace=True)
df.rename(columns={'index': 'i-th session (0 started)'}, inplace=True)
# Dealing with strings and ints
df = df.replace(const.empty_value, 0)
df = df.sort_values(by=[
'avg_heart_rate', 'avg_heart_rate_as_percentage', 'max_heart_rate',
'max_heart_rate_as_percentage'
],
ascending=False,
ignore_index=True).head(how_many)
df = df.replace(0, const.empty_value)
utils.show_dataframe_in_web(
df, f'{how_many} highest average heart rates in session')
def show_country_stats(self, sport, start_date, end_date):
df = self.df_dict[sport].copy(deep=False)
df = self.get_sessions_in_timespan(df, start_date, end_date)
df.reset_index(inplace=True)
df.rename(columns={'index': '# of sessions'}, inplace=True)
df['duration'] = utils.duration_to_timedelta(df['duration'])
if const.sport_processor[sport.upper(
)] == 'running' or const.sport_processor[
sport.upper()] == 'distance_based' or const.sport_processor[
sport.upper()] == 'cycling':
# Dealing with strings
df = df.replace(
{
'distance': const.empty_value,
'duration': const.empty_value,
'avg_speed': const.empty_value
}, 0)
df = df.groupby(by='country').agg({
'# of sessions': 'count',
'distance': 'sum',
'duration': 'sum',
'avg_speed': 'mean'
})
df = df.sort_values(
by=['# of sessions', 'distance', 'duration', 'avg_speed'],
ascending=False,
ignore_index=False)
else:
df = df.groupby(by='country').agg({
'# of sessions': 'count',
'duration': 'sum'
})
df = df.sort_values(by=['# of sessions', 'duration'],
ascending=False,
ignore_index=False)
df['duration'] = utils.timedelta_to_duration(df['duration'])
utils.show_dataframe_in_web(df, f'Country stats for {sport}')
