Shikong/WeChatMsg
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delete third_party numpy&pandas

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DzhiWang 2023-12-18 23:55:50 +08:00
parent 86f0f1ec78
commit df2a1f47bd
1 changed files with 16 additions and 13 deletions
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29
app/analysis/analysis.py
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@ -1,7 +1,5 @@
from collections import Counter from collections import Counter
import snownlp import snownlp
import numpy as np
import pandas as pd
from PyQt5.QtCore import QFile, QTextStream, QIODevice from PyQt5.QtCore import QFile, QTextStream, QIODevice
@ -203,20 +201,25 @@ def hour_count(wxid, is_Annual_report=False, year='2023'):
def emotion_chart(wxid, is_Annual_report=False, year='2023'): def emotion_chart(wxid, is_Annual_report=False, year='2023'):
txt_messages = msg_db.get_txt_messages_by_days(wxid, is_Annual_report, year) txt_messages = msg_db.get_txt_messages_by_days(wxid, is_Annual_report, year)
df = pd.DataFrame(txt_messages, columns=['message', 'date']) date_emotions = {}
d = df.groupby('date') for message, date in txt_messages:
if date not in date_emotions:
date_emotions[date] = {'sentiments': [], 'count': 0}
val = snownlp.SnowNLP(message).sentiments
date_emotions[date]['sentiments'].append(val)
date_emotions[date]['count'] += 1
dates = [] dates = []
emotions = [] emotions = []
for date, messages in d:
for date, data in date_emotions.items():
dates.append(date) dates.append(date)
s = 0 average_sentiment = sum(data['sentiments']) / data['count']
for msg in messages: emotions.append(round(average_sentiment, 3) * 100)
val = snownlp.SnowNLP(msg).sentiments
s += val # 四舍五入到小数点一位
emotions.append(s / len(messages)) emotions = [round(e, 1) for e in emotions]
emotions = np.array(emotions)
emotions = np.around(emotions, 3) * 100
emotions = np.around(emotions, 1)
max_ = max(emotions) max_ = max(emotions)
min_ = min(emotions) min_ = min(emotions)