def main(result_dict, df, chat_members, running_all_functions):
result_dict['average word length'] = []
if not running_all_functions:
df['is attachment?'] = df['type'].apply(helpers.is_attachment)
df['is link?'] = df['text'].apply(helpers.is_link)
df['word length'] = df['text'].apply(helpers.average_word_length)
for member_name in chat_members:
initialize_result_dict(member_name, df, result_dict)
average_word_length = df[(df['sender'] == member_name)
& (~df['is reaction?'])
& (~df['is attachment?'])
& (~df['is link?'])]['word length'].mean()
if math.isnan(average_word_length):
average_word_length = 0
result_dict['average word length'].append(round(
average_word_length, 1))
def main(result_dict, df, chat_members):
result_dict['messages that are games'] = []
result_dict['% of messages that are games'] = []
result_dict['messages that are game starts'] = []
result_dict['% of game starts that are by this person'] = []
df['is game message?'] = df.apply(
lambda msg: helpers.is_game_message(msg.text, msg.type), axis=1)
df['is game start?'] = df.apply(
lambda msg: helpers.is_game_start(msg.text, msg.type), axis=1)
for member_name in chat_members:
_, non_reaction_messages = initialize_result_dict(
member_name, df, result_dict)
game_messages = len(df[(df['is game message?'])
& (df['sender'] == member_name)])
game_starts = len(df[(df['is game start?'])
& (df['sender'] == member_name)])
result_dict['messages that are games'].append(game_messages)
result_dict['% of messages that are games'].append(
round(
helpers.safe_divide(game_messages, non_reaction_messages) *
100, 2))
result_dict['messages that are game starts'].append(game_starts)
total_game_starts = sum(result_dict['messages that are game starts'])
for i in range(len(result_dict['messages that are game starts'])):
result_dict['% of game starts that are by this person'].append(
round(
helpers.safe_divide(
result_dict['messages that are game starts'][i],
total_game_starts) * 100, 2))
def main(result_dict, df, chat_members, minutes_threshold):
result_dict['conversation starters'] = []
result_dict['% of all conversation starters that are by this person'] = []
df['is conversation starter?'] = df['time'].diff().apply(
lambda diff: helpers.is_conversation_starter(diff, minutes_threshold))
df.iloc[0, df.columns.get_loc('is conversation starter?')] = True
for member_name in chat_members:
initialize_result_dict(member_name, df, result_dict)
conversation_starters = len(df[(df['is conversation starter?'])
& (df['sender'] == member_name)
& (~df['is reaction?'])])
result_dict['conversation starters'].append(conversation_starters)
total_conversation_starters = sum(result_dict['conversation starters'])
for i in range(len(result_dict['conversation starters'])):
result_dict[
'% of all conversation starters that are by this person'].append(
round(
helpers.safe_divide(
result_dict['conversation starters'][i],
total_conversation_starters) * 100, 2))
def main(result_dict, df, chat_members):
result_dict['total messages'] = []
result_dict['% of all messages that are by this person'] = []
for member_name in chat_members:
total_messages, _ = initialize_result_dict(member_name, df,
result_dict)
result_dict['total messages'].append(total_messages)
total_messages = sum(result_dict['total messages'])
for i in range(len(result_dict['total messages'])):
result_dict['% of all messages that are by this person'].append(
round(
helpers.safe_divide(result_dict['total messages'][i],
total_messages) * 100, 2))
def main(result_dict, df, chat_members):
result_dict['messages that are tweets'] = []
result_dict['% of messages that are tweets'] = []
df['is tweet?'] = df['text'].apply(helpers.is_tweet)
for member_name in chat_members:
_, non_reaction_messages = initialize_result_dict(member_name, df, result_dict)
tweet_messages = len(
df[(df['is tweet?']) & (df['sender'] == member_name)]
)
result_dict['messages that are tweets'].append(tweet_messages)
result_dict['% of messages that are tweets'].append(
round(helpers.safe_divide(tweet_messages, non_reaction_messages) * 100, 2)
)
def main(result_dict, df, chat_members):
result_dict['messages that contain emoji'] = []
result_dict['% of messages that include emoji'] = []
df['includes emoji?'] = df['text'].apply(helpers.includes_emoji)
for member_name in chat_members:
_, non_reaction_messages = initialize_result_dict(
member_name, df, result_dict)
emoji_messages = len(df[(df['includes emoji?'])
& (df['sender'] == member_name)])
result_dict['messages that contain emoji'].append(emoji_messages)
result_dict['% of messages that include emoji'].append(
round(
helpers.safe_divide(emoji_messages, non_reaction_messages) *
100, 2))
def main(result_dict, df, chat_members):
result_dict['all caps messages'] = []
result_dict['% of messages that are all caps'] = []
df['is all caps?'] = df['text'].apply(helpers.is_all_caps)
for member_name in chat_members:
_, non_reaction_messages = initialize_result_dict(
member_name, df, result_dict)
all_caps_messages = len(df[(df['is all caps?'])
& (df['sender'] == member_name)])
result_dict['all caps messages'].append(all_caps_messages)
result_dict['% of messages that are all caps'].append(
round(
helpers.safe_divide(all_caps_messages, non_reaction_messages) *
100, 2))
def main(result_dict, df, chat_members):
result_dict['attachment messages'] = []
result_dict['% of messages that are attachments'] = []
df['is attachment?'] = df['type'].apply(helpers.is_attachment)
for member_name in chat_members:
_, non_reaction_messages = initialize_result_dict(
member_name, df, result_dict)
attachment_messages = len(df[(df['is attachment?'])
& (df['sender'] == member_name)])
result_dict['attachment messages'].append(attachment_messages)
result_dict['% of messages that are attachments'].append(
round(
helpers.safe_divide(attachment_messages, non_reaction_messages)
* 100, 2))
def main(result_dict, df, chat_members, phrase, case_sensitive, separate,
regex):
if phrase is None:
raise Exception('Function is phrase but not given a phrase')
result_dict[f'messages that contain {phrase}'] = []
result_dict[f'% of messages that contain {phrase}'] = []
df[f'includes {phrase}?'] = df['text'].apply(
lambda msg: helpers.is_phrase_in(phrase, msg, case_sensitive, separate,
regex))
for member_name in chat_members:
_, non_reaction_messages = initialize_result_dict(
member_name, df, result_dict)
word_messages = len(df[(df[f'includes {phrase}?'])
& (df['sender'] == member_name)])
result_dict[f'messages that contain {phrase}'].append(word_messages)
result_dict[f'% of messages that contain {phrase}'].append(
round(
helpers.safe_divide(word_messages, non_reaction_messages) *
100, 2))
def main(result_dict, df, chat_members, running_all_functions,
minutes_threshold):
result_dict['total # of message series'] = []
result_dict['total messages'] = []
result_dict['average messages per series'] = []
if not running_all_functions:
df['is conversation starter?'] = df['time'].diff().apply(
lambda diff: helpers.is_conversation_starter(
diff, minutes_threshold))
df.iloc[0, df.columns.get_loc('is conversation starter?')] = True
df['is new message series?'] = df['sender'].apply(lambda x: True)
df['is new message series?'] = df['is new message series?'].shift().where(
df['sender'].shift() != df['sender'], False)
df.iloc[0, df.columns.get_loc('is new message series?')] = True
for member_name in chat_members:
total_messages, _ = initialize_result_dict(member_name, df,
result_dict)
message_series = len(df[((df['is new message series?'])
| (df['is conversation starter?']))
& (df['sender'] == member_name)])
result_dict['total # of message series'].append(message_series)
result_dict['total messages'].append(total_messages)
result_dict['average messages per series'].append(
round(helpers.safe_divide(total_messages, message_series), 2))
def main(result_dict, df, chat_members):
result_dict['total messages'] = []
result_dict['non-reaction messages'] = []
result_dict['% of all non-reaction messages that are by this person'] = []
result_dict['reaction messages'] = []
result_dict['% of all reaction messages that are by this person'] = []
result_dict['% of messages that are reactions'] = []
result_dict['reactions'] = []
result_dict['like reacts'] = []
result_dict['% of reactions that are like reacts'] = []
result_dict['love reacts'] = []
result_dict['% of reactions that are love reacts'] = []
result_dict['dislike reacts'] = []
result_dict['% of reactions that are dislike reacts'] = []
result_dict['laugh reacts'] = []
result_dict['% of reactions that are laugh reacts'] = []
result_dict['emphasis reacts'] = []
result_dict['% of reactions that are emphasis reacts'] = []
result_dict['question reacts'] = []
result_dict['% of reactions that are question reacts'] = []
df['reaction action'] = df['text'].apply(helpers.reaction_action)
df['like react action'] = df['text'].apply(helpers.like_react_action)
df['love react action'] = df['text'].apply(helpers.love_react_action)
df['dislike react action'] = df['text'].apply(helpers.dislike_react_action)
df['laugh react action'] = df['text'].apply(helpers.laugh_react_action)
df['emphasis react action'] = df['text'].apply(
helpers.emphasis_react_action)
df['question react action'] = df['text'].apply(
helpers.question_react_action)
for member_name in chat_members:
total_messages, non_reaction_messages = initialize_result_dict(
member_name, df, result_dict)
result_dict['total messages'].append(total_messages)
result_dict['reaction messages'].append(total_messages -
non_reaction_messages)
result_dict['non-reaction messages'].append(non_reaction_messages)
result_dict['% of messages that are reactions'].append(
round(
(1 -
helpers.safe_divide(non_reaction_messages, total_messages)) *
100, 2))
reactions = int(
df[df['sender'] == member_name]['reaction action'].sum())
like_reacts = int(
df[df['sender'] == member_name]['like react action'].sum())
love_reacts = int(
df[df['sender'] == member_name]['love react action'].sum())
dislike_reacts = int(
df[df['sender'] == member_name]['dislike react action'].sum())
laugh_reacts = int(
df[df['sender'] == member_name]['laugh react action'].sum())
emphasis_reacts = int(
df[df['sender'] == member_name]['emphasis react action'].sum())
question_reacts = int(
df[df['sender'] == member_name]['question react action'].sum())
result_dict['reactions'].append(reactions)
result_dict['like reacts'].append(like_reacts)
result_dict['% of reactions that are like reacts'].append(
round(helpers.safe_divide(like_reacts, reactions) * 100, 2))
result_dict['love reacts'].append(love_reacts)
result_dict['% of reactions that are love reacts'].append(
round(helpers.safe_divide(love_reacts, reactions) * 100, 2))
result_dict['dislike reacts'].append(dislike_reacts)
result_dict['% of reactions that are dislike reacts'].append(
round(helpers.safe_divide(dislike_reacts, reactions) * 100, 2))
result_dict['laugh reacts'].append(laugh_reacts)
result_dict['% of reactions that are laugh reacts'].append(
round(helpers.safe_divide(laugh_reacts, reactions) * 100, 2))
result_dict['emphasis reacts'].append(emphasis_reacts)
result_dict['% of reactions that are emphasis reacts'].append(
round(helpers.safe_divide(emphasis_reacts, reactions) * 100, 2))
result_dict['question reacts'].append(question_reacts)
result_dict['% of reactions that are question reacts'].append(
round(helpers.safe_divide(question_reacts, reactions) * 100, 2))
total_non_reaction_messages = sum(result_dict['non-reaction messages'])
total_reaction_messages = sum(result_dict['reaction messages'])
for i in range(len(result_dict['total messages'])):
result_dict[
'% of all non-reaction messages that are by this person'].append(
round(
helpers.safe_divide(
result_dict['non-reaction messages'][i],
total_non_reaction_messages) * 100, 2))
result_dict[
'% of all reaction messages that are by this person'].append(
round(
helpers.safe_divide(result_dict['reaction messages'][i],
total_reaction_messages) * 100, 2))
def main(result_dict, df, chat_members, args):
if not (args.day or args.week or args.month or args.year):
raise Exception('Must give time period length for graph')
message_freqs = {}
if args.graph_individual:
members = []
for member_name in chat_members:
total_messages, _ = initialize_result_dict(
member_name, df, result_dict)
if total_messages > 0:
members.append(member_name)
for member in members:
message_freqs[member] = []
else:
message_freqs['Total Messages'] = []
if args.day:
df['time_period'] = df['time'].apply(helpers.get_day)
time_period_name = 'day'
elif args.week:
df['time_period'] = df['time'].apply(helpers.get_week)
time_period_name = 'week'
elif args.month:
df['time_period'] = df['time'].apply(helpers.get_month)
time_period_name = 'month'
elif args.year:
df['time_period'] = df['time'].apply(helpers.get_year)
time_period_name = 'year'
day_fmt = '%m/%d/%y'
begin_date = datetime.datetime.strptime(df['time_period'].iloc[0], day_fmt)
end_date = datetime.datetime.strptime(df['time_period'].iloc[-1], day_fmt)
time_periods = helpers.get_time_periods(begin_date, end_date, time_period_name)
for time_period in time_periods:
if args.graph_individual:
for member_name in members:
message_freqs[member_name].append(len(
df[(df['time_period'] == time_period) & (df['sender'] == member_name)]
))
else:
message_freqs['Total Messages'].append(len(
df[df['time_period'] == time_period]
))
colors = [
'rgba(31, 120, 180, 1)',
'rgba(51, 160, 44, 1)',
'rgba(227, 26, 28, 1)',
'rgba(255, 127, 0, 1)',
'rgba(106, 61, 154, 1)',
'rgba(177, 89, 40, 1)',
'rgba(166, 206, 227, 1)',
'rgba(178, 223, 138, 1)',
'rgba(251, 154, 153, 1)',
'rgba(253, 191, 111, 1)',
'rgba(202, 178, 214, 1)',
'rgba(255, 255, 153, 1)'
]
result_dict['graphData'] = {}
if args.day or args.week:
result_dict['graphData']['labels'] = time_periods
elif args.month:
result_dict['graphData']['labels'] = [
f'{time_period.split("/")[0]}/{time_period.split("/")[2]}'
for time_period in time_periods
]
elif args.year:
result_dict['graphData']['labels'] = [
f'20{time_period.split("/")[2]}'
for time_period in time_periods
]
result_dict['graphData']['datasets'] = [
{
'label': name,
'data': message_freqs[name],
'fill': False,
'borderColor': colors[i % len(message_freqs)]
}
for i, name in enumerate(message_freqs)
]
