出錯代碼
def analyseLocation(friends):
freqs = {}
headers = ['NickName','Province','City']
with open('location.csv','w',encoding='utf-8',newline='',) as csvFile:
writer = csv.DictWriter(csvFile, headers)
writer.writeheader()
for friend in friends[1:]:
row = {}
row['NickName'] = friend['NickName']
row['Province'] = friend['Province']
row['City'] = friend['City']
if(friend['Province']!=None):
if(friend['Province'] not in freqs):
freqs[friend['Province']] = 1
else:
freqs[friend['Province']] = 1
writer.writerow(row)
for k,v in freqs:
print("{0}:{1}".format(k,v))
報錯提示
Traceback (most recent call last):
File "main.py", line 195, in <module>
analyseLocation(friends)
File "main.py", line 64, in analyseLocation
for k,v in freqs:
ValueError: not enough values to unpack (expected 2, got 0)
完整代碼如下
#!/usr/bin/python
#-*- coding: utf-8 -*-
import os
import re
import csv
import time
import json
import jieba
from jieba import analyse
import itchat
import base64
from snownlp import SnowNLP
import requests
import sys
from collections import Counter
import matplotlib.pyplot as plt
from pylab import *
from faceApi import FaceAPI
mpl.rcParams['font.sans-serif'] = ['SimHei']
from PIL import Image
import numpy as np
from wordcloud import WordCloud
def analyseSex(firends):
sexs = list(map(lambda x:x['Sex'],friends[1:]))
counts = Counter(sexs).items()
counts = sorted(counts, key=lambda x:x[0], reverse=False)
counts = list(map(lambda x:x[1],counts))
labels = ['Unknow','Male','Female']
colors = ['red','yellowgreen','lightskyblue']
plt.figure(figsize=(8,5), dpi=80)
plt.axes(aspect=1)
plt.pie(counts,
labels=labels,
colors=colors,
labeldistance = 1.1,
autopct = '%3.1f%%',
shadow = False,
startangle = 90,
pctdistance = 0.6
)
plt.legend(loc='upper right',)
plt.title(u'%s的微信好友性別組成' % friends[0]['NickName'])
plt.show()
def analyseLocation(friends):
freqs = {}
headers = ['NickName','Province','City']
with open('location.csv','w',encoding='utf-8',newline='',) as csvFile:
writer = csv.DictWriter(csvFile, headers)
writer.writeheader()
for friend in friends[1:]:
row = {}
row['NickName'] = friend['NickName']
row['Province'] = friend['Province']
row['City'] = friend['City']
if(friend['Province']!=None):
if(friend['Province'] not in freqs):
freqs[friend['Province']] = 1
else:
freqs[friend['Province']] = 1
writer.writerow(row)
for k,v in freqs:
print("{0}:{1}".format(k,v))
def analyseHeadImage(frineds):
# Init Path
basePath = os.path.abspath('.')
baseFolder = basePath + '\\HeadImages\\'
if(os.path.exists(baseFolder) == False):
os.makedirs(baseFolder)
# Analyse Images
faceApi = FaceAPI()
use_face = 0
not_use_face = 0
image_tags = ''
for index in range(1,len(friends)):
friend = friends[index]
# Save HeadImages
imgFile = baseFolder + '\\Image%s.jpg' % str(index)
imgData = itchat.get_head_img(userName = friend['UserName'])
if(os.path.exists(imgFile) == False):
with open(imgFile,'wb') as file:
file.write(imgData)
# Detect Faces
time.sleep(1)
result = faceApi.detectFace(imgFile)
if result == True:
use_face += 1
else:
not_use_face += 1
# Extract Tags
result = faceApi.extractTags(imgFile)
image_tags += ','.join(list(map(lambda x:x['tag_name'],result)))
labels = [u'使用人臉頭像',u'不使用人臉頭像']
counts = [use_face,not_use_face]
colors = ['red','yellowgreen','lightskyblue']
plt.figure(figsize=(8,5), dpi=80)
plt.axes(aspect=1)
plt.pie(counts, #性別統計結果
labels=labels, #性別展示標簽
colors=colors, #餅圖區域配色
labeldistance = 1.1, #標簽距離圓點距離
autopct = '%3.1f%%', #餅圖區域文本格式
shadow = False, #餅圖是否顯示陰影
startangle = 90, #餅圖起始角度
pctdistance = 0.6 #餅圖區域文本距離圓點距離
)
plt.legend(loc='upper right',)
plt.title(u'%s的微信好友使用人臉頭像情況' % friends[0]['NickName'])
plt.show()
image_tags = image_tags.encode('iso8859-1').decode('utf-8')
back_coloring = np.array(Image.open('face.jpg'))
wordcloud = WordCloud(
font_path='simfang.ttf',
background_color="white",
max_words=1200,
mask=back_coloring,
max_font_size=85,
random_state=75,
width=800,
height=480,
margin=15
)
wordcloud.generate(image_tags)
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
def analyseSignature(friends):
signatures = ''
emotions = []
pattern = re.compile("1f\d.+")
for friend in friends:
signature = friend['Signature']
if(signature != None):
signature = signature.strip().replace('span', '').replace('class', '').replace('emoji', '')
signature = re.sub(r'1f(\d.+)','',signature)
if(len(signature)>0):
nlp = SnowNLP(signature)
emotions.append(nlp.sentiments)
signatures += ' '.join(jieba.analyse.extract_tags(signature,5))
with open('signatures.txt','wt',encoding='utf-8') as file:
file.write(signatures)
# Sinature WordCloud
back_coloring = np.array(Image.open('flower.jpg'))
wordcloud = WordCloud(
font_path='simfang.ttf',
background_color="white",
max_words=1200,
mask=back_coloring,
max_font_size=75,
random_state=45,
width=960,
height=720,
margin=15
)
wordcloud.generate(signatures)
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
wordcloud.to_file('signatures.jpg')
# Signature Emotional Judgment
count_good = len(list(filter(lambda x:x>0.66,emotions)))
count_normal = len(list(filter(lambda x:x>=0.33 and x<=0.66,emotions)))
count_bad = len(list(filter(lambda x:x<0.33,emotions)))
print(count_good * 100/len(emotions))
print(count_normal * 100/len(emotions))
print(count_bad * 100/len(emotions))
labels = [u'負面消極',u'中性',u'正面積極']
values = (count_bad,count_normal,count_good)
plt.rcParams['font.sans-serif'] = ['simHei']
plt.rcParams['axes.unicode_minus'] = False
plt.xlabel(u'情感判斷')
plt.ylabel(u'頻數')
plt.xticks(range(3),labels)
plt.legend(loc='upper right',)
plt.bar(range(3), values, color = 'rgb')
plt.title(u'%s的微信好友簽名資訊情感分析' % friends[0]['NickName'])
plt.show()
# login wechat and extract friends
itchat.auto_login(hotReload = True)
friends = itchat.get_friends(update = True)
analyseLocation(friends)
analyseSex(friends)
analyseSignature(friends)
analyseHeadImage(friends)
uj5u.com熱心網友回復：
freqs拿到的資料是不是不符合uj5u.com熱心網友回復：
for k,v in freqs:改為
for k, v in freqs.items():
uj5u.com熱心網友回復：
字典的遍歷需要先轉換成序列。參考 - Python 回圈uj5u.com熱心網友回復：
for k,v in freqs:
中，是對dict型別的freqs去遍歷獲取值，其等價于：
for k,v in freqs.keys():
所以很明顯，for中多出個v，應該改為：
for k in freqs.keys():
但很明顯，你還想要每個value，所以應該改為：
for k, v in freqs.items():
等價于：
for eachItem in freqs.items():
eachKey, eachValue = eachItem
就不會報錯了。
更詳細的解釋，參考我的帖子：
【已解決】python 3中dict取值報錯：not enough values to unpack (expected 3, got 2)
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