python3 报错ValueError:not enough values to unpack(expected 2,got 0)

[weixin_39266539]

出错代码
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))

报错提示
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)

完整代码如下
#!/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的微信好友性别组成' % 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'使用人脸头像',u'不使用人脸头像']
counts = [use_face,not_use_face]
colors = ['red','yellowgreen','lightskyblue']
plt.figure(figsize=(8,5), dpi=80)
plt.axes(aspect=1)
plt.pie(counts, #性别统计结果
labels=labels, #性别展示标签
colors=colors, #饼图区域配色
labeldistance = 1.1, #标签距离圆点距离
autopct = '%3.1f%%', #饼图区域文本格式
shadow = False, #饼图是否显示阴影
startangle = 90, #饼图起始角度
pctdistance = 0.6 #饼图区域文本距离圆点距离
)
plt.legend(loc='upper right',)
plt.title(u'%s的微信好友使用人脸头像情况' % 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'负面消极',u'中性',u'正面积极']
values = (count_bad,count_normal,count_good)
plt.rcParams['font.sans-serif'] = ['simHei']
plt.rcParams['axes.unicode_minus'] = False
plt.xlabel(u'情感判断')
plt.ylabel(u'频数')
plt.xticks(range(3),labels)
plt.legend(loc='upper right',)
plt.bar(range(3), values, color = 'rgb')
plt.title(u'%s的微信好友签名信息情感分析' % 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)

[oyljerry]

freqs拿到的数据是不是不符合

[欢乐的小猪]

for k,v in freqs: 改为 for k, v in freqs.items():

[简明教程]

字典的遍历需要先转换成序列。参考 - Python 循环

[crifan]

 for k,v in freqs:

中，是对dict类型的freqs去遍历获取值，其等价于：

 for k,v in freqs.keys():

所以很明显，for中多出个v，应该改为：

 for k in freqs.keys():

但很明显，你还想要每个value，所以应该改为：

 for k, v in freqs.items():

等价于：

  for eachItem in freqs.items():
     eachKey, eachValue = eachItem

就不会报错了。

更详细的解释，参考我的帖子：
【已解决】python 3中dict取值报错：not enough values to unpack (expected 3, got 2)