I used Python to climb my wechat friends. They are like this

With the popularity of wechat , More and more people are using wechat . Wechat has gradually changed from a simple social software to a lifestyle , People's daily communication needs wechat , Work communication also needs wechat . Every friend in wechat , All represent the different roles people play in society .

Today's article will be based on Python Data analysis of wechat friends , The dimensions selected here mainly include ： Gender 、 Head portrait 、 Signature 、 Location , The results are mainly presented in the form of charts and word clouds , among , For text information, we will use word frequency analysis and emotion analysis . As the saying goes ： A good workman does his work well , We must sharpen our tools first . Before officially starting this article , Briefly introduce the third-party module used in this article ：

• itchat： Wechat web page interface encapsulation Python edition , In this article, it is used to obtain wechat friend information .
• jieba： Stuttering participle Python edition , In this paper, it is used to segment text information .
• matplotlib：Python Chart drawing module in , In this paper, it is used to draw column chart and pie chart
• snownlp： One Python Chinese word segmentation module in , In this paper, it is used to make emotional judgment on text information .
• PIL：Python Image processing module in , In this paper, it is used to process pictures .
• numpy：Python in Numerical calculation module , In this paper, we cooperate with wordcloud Module USES .
• wordcloud：Python Word cloud module in , In this paper, it is used to draw word cloud pictures .
• TencentYoutuyun： Provided by Tencent Youtu Python edition SDK , In this paper, it is used to recognize face and extract picture label information .

The above modules can be passed pip install , Detailed instructions on the use of each module , Please refer to your own documents .

1. Data analysis

The premise of analyzing wechat friend data is to obtain friend information , By using itchat This module , All this will become very simple , We can do this through the following two lines of code ：

itchat.auto_login(hotReload = True) 
friends = itchat.get_friends(update = True)
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It's the same as logging in to the web version of wechat , We can log in by scanning QR code with our mobile phone , Back here friends Object is a collection , The first element is the current user . therefore , In the following data analysis process , We always take friends[1:] As raw input data , Each element in the collection is a dictionary structure , Take myself for example , You can notice that there are Sex、City、Province、HeadImgUrl、Signature These four fields , The following analysis starts with these four fields ：

2 Friend gender

Analyze friends' gender , We first need to get the gender information of all our friends , Here we will the information of each friend Sex Field extraction , Then count out Male、Female and Unkonw Number of , We assemble these three values into a list , You can use matplotlib The module draws a pie chart to , Its code implementation is as follows ：

def analyseSex(firends): 
  sexs = list(map(lambda x:x['Sex'],friends[1:])) 
 counts = list(map(lambda x:x[1],Counter(sexs).items())) 
 labels = ['Unknow','Male','Female'] 
 colors = ['red','yellowgreen','lightskyblue'] 
 plt.figure(figsize=(8,5), dpi=80) 
 plt.axes(aspect=1) 
 plt.pie(counts, # Gender statistics  
   labels=labels, # Gender display label  
   colors=colors, # Pie chart area color matching  
   labeldistance = 1.1, # The distance between the label and the dot  
   autopct = '%3.1f%%', # Pie chart area text format  
   shadow = False, # Whether the pie chart shows shadows  
   startangle = 90, # The starting angle of the pie chart  
   pctdistance = 0.6 # The distance between the text in the pie chart area and the dot  
 ) 
 plt.legend(loc='upper right',) 
 plt.title(u'%s Gender composition of wechat friends ' % friends[0]['NickName']) 
 plt.show()
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Here is a brief explanation of this code , The values of wechat gender fields are Unkonw、Male and Female Three , The corresponding values are 0、1、2. adopt Collection Module Counter() These three different values are statistically analyzed , Its items() Method returns a collection of tuples .

The first dimensional element of the tuple represents the key , namely 0、1、2, The second element of the tuple represents the number , And the set of tuples is sorted , That is, press the key 0、1、2 The order of , So pass map() Method can get the number of these three different values , We pass it on to matplotlib Draw it , The percentages of these three different values are determined by matplotlib calculated . The picture below is matplotlib Draw the gender distribution map of friends ：

3 My friend's picture

Analyze friends' avatars , Analyze from two aspects , First of all , Among these friends' avatars , What is the proportion of friends who use facial avatars ; second , From these friends' avatars , What valuable keywords can be extracted .

It needs to be based on HeadImgUrl Field download avatar to local , Then through the... Provided by Tencent Youtu Face recognition dependent API Interface , Detect whether there are faces in the avatar picture and extract the labels in the picture . among , The former is a subtotal , We use pie charts to present the results ; The latter is to analyze the text , We use word clouds to present the results . The key code is as follows ：

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' Use face faces ',u' Don't use face avatars '] 
 counts = [use_face,not_use_face] 
 colors = ['red','yellowgreen','lightskyblue'] 
 plt.figure(figsize=(8,5), dpi=80) 
 plt.axes(aspect=1) 
 plt.pie(counts, # Gender statistics  
   labels=labels, # Gender display label  
   colors=colors, # Pie chart area color matching  
   labeldistance = 1.1, # The distance between the label and the dot  
   autopct = '%3.1f%%', # Pie chart area text format  
   shadow = False, # Whether the pie chart shows shadows  
   startangle = 90, # The starting angle of the pie chart  
   pctdistance = 0.6 # The distance between the text in the pie chart area and the dot  
 ) 
 plt.legend(loc='upper right',) 
 plt.title(u'%s Your wechat friends use facial avatars ' % 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=75, 
  random_state=45, 
  width=800, 
  height=480, 
  margin=15
 )  
 wordcloud.generate(image_tags) 
 plt.imshow(wordcloud) 
 plt.axis("off") 
 plt.show()
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Here we will create a new... In the current directory HeadImages Catalog , Used to store the avatars of all friends , Then we'll use a name here FaceApi class , This class is created by Tencent Youtu SDK Come in a package ,** Face detection and image label recognition are called here respectively API Interface ,** The former will count ” Use face faces ” and ” Don't use face avatars ” The number of your friends , The latter will accumulate the tags extracted from each avatar . The analysis results are shown in the figure below ：

It can be noted that , Among all wechat friends , It's close to 1/4 Our wechat friends use facial avatars , And there is proximity 3/4 My wechat friends don't have faces , This shows that among all wechat friends ” Level of appearance “ Confident people , It only accounts for... Of the total number of friends 25%, Or say 75% Our wechat friends are mainly on the low side , I don't like making wechat avatars with face avatars .

** secondly , Considering that Tencent Youtu can't really identify ” Face ”,** Here we extract the tags in the friends' avatars again , To help us understand the keywords in the avatar of wechat friends , The analysis results are shown in the figure ：

Through word cloud , We can find out ： In the signature words of wechat friends , Keywords with relatively high frequency are ：** The girl 、 tree 、 House 、 Text 、 Screenshot 、 cartoon 、 A group photo 、 sky 、 The sea .** This shows that among my wechat friends , Wechat avatars selected by friends mainly include daily 、 tourism 、 scenery 、 Screenshot four sources .

The style of wechat avatar selected by friends is mainly cartoon , The common elements in the wechat avatar selected by friends are sky 、 The sea 、 House 、 tree . By observing the avatars of all your friends , I found that among my wechat friends , Using personal photos as wechat avatars are 15 people , Using network pictures as wechat avatars are 53 people , Using animation pictures as wechat avatars are 25 people , Using group photos as wechat avatars are 3 people , Using children's photos as wechat avatars are 5 people , Some people who use landscape pictures as wechat avatars are 13 people , Some people who use girls' photos as wechat avatars are 18 people , It is basically consistent with the analysis results of image label extraction .

4 Friend signature

Analyze friends' signatures , Signature is the most abundant text information in friend information , According to the usual human ” Label ” Methodology of , Signature can analyze a person's state in a certain period of time , Just like people laugh when they are happy 、 Sad will cry , Cry and laugh , It shows that people are happy and sad .

Here we deal with signatures in two ways , The first is to use stuttering word segmentation to generate word cloud , The purpose is to understand the keywords in the friend signature , Which keyword appears relatively frequently ;** The second is to use SnowNLP Analyze the emotional tendencies in friends' signatures ,** That is, friends' signatures are generally positive 、 Negative or neutral , What are their respective proportions . Extract here Signature Field can be , The core code is as follows ：

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))) 
 labels = [u' Negative negative ',u' Neutral ',u' Positive '] 
 values = (count_bad,count_normal,count_good) 
 plt.rcParams['font.sans-serif'] = ['simHei'] 
 plt.rcParams['axes.unicode_minus'] = False
 plt.xlabel(u' Emotional judgment ') 
 plt.ylabel(u' frequency ') 
 plt.xticks(range(3),labels) 
 plt.legend(loc='upper right',) 
 plt.bar(range(3), values, color = 'rgb') 
 plt.title(u'%s Wechat friend signature information emotional analysis ' % friends[0]['NickName']) 
 plt.show()
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Through word cloud , We can find out ： In the signature information of wechat friends , Keywords with relatively high frequency are ： Strive 、 Grow up 、 happy 、 happy 、 life 、 Happiness 、 life 、 distance 、 time 、 take a walk .

Through the following histogram , We can find out ： In the signature information of wechat friends , Positive emotional judgment accounts for about 55.56%, Neutral emotional judgment accounts for about 32.10%, Negative emotional judgments account for about 12.35%. This result is basically consistent with the result we show through word cloud , This shows that in the signature information of wechat friends , There are about 87.66% Signature information for , It conveys a positive attitude .

5 Friend location

Analyze friend locations , Mainly by extracting Province and City These two fields .Python Map visualization in mainly through Basemap modular , This module needs to download map information from foreign websites , It's very inconvenient to use .

Baidu ECharts Used more at the front end , Although the community provides pyecharts project , But I noticed that because of the change of policy , at present Echarts The ability to export maps is no longer supported , Therefore, the customization of maps is still a problem , The mainstream technical scheme is to configure the equipment of provinces and cities all over the country JSON data .

What I'm using here is BDP Personal Edition , This is a zero programming scheme , We go through Python Export a CSV file , Then upload it to BDP in , You can create a visual map by simply dragging , It can't be simpler , Here we just show the generation CSV Part of the code ：

def analyseLocation(friends): 
 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'] 
   writer.writerow(row)
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The picture below is BDP The geographical distribution map of wechat friends generated in , You can find ： My wechat friends are mainly concentrated in Ningxia and Shaanxi .

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6 summary

This article is another attempt of my data analysis , Mainly from gender 、 Head portrait 、 Signature 、 Position has four dimensions , A simple data analysis of wechat friends , The results are mainly presented in the form of charts and word clouds . In a word ,” Data visualization is a means, not an end ”, The important thing is not that we made these pictures here , But the phenomenon reflected in these pictures , What essential enlightenment can we get , I hope this article can inspire you .

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