Analyzing Your WeChat Friends with Python: Gender, Avatar, Signature & Location

Introduction

With the widespread use of WeChat, many people rely on it for daily communication and work. This article demonstrates how to perform data analysis on WeChat friends using Python, focusing on gender, avatar, signature, and location dimensions, and presenting results with charts and word clouds.

Required Third‑Party Modules

itchat : Python wrapper for the WeChat web interface, used to retrieve friend information.

jieba : Chinese word segmentation library for processing text.

matplotlib : Plotting library for creating bar and pie charts.

snownlp : Chinese sentiment analysis library.

PIL : Image processing library.

numpy : Numerical computation, used together with wordcloud.

wordcloud : Generates word‑cloud images.

TencentYoutuyun : SDK for Tencent Youtu API, used for face detection and image tag extraction.

All modules can be installed via pip.

Fetching Friend Data

Login to WeChat through itchat and obtain the friend list. The first element is the logged‑in user; the actual friends start from friends[1:].

itchat.auto_login(hotReload=True)
friends = itchat.get_friends(update=True)

Gender Analysis

Extract the Sex field, count Male, Female, and Unknown values, and plot a pie chart.

def analyseSex(friends):
    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, 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()

The resulting pie chart shows the gender composition of the friend list.

Avatar Analysis

Download each friend's avatar, use Tencent Youtu to detect whether the image contains a face and to extract image tags. The face‑presence statistics are shown with a pie chart, while the extracted tags are visualized as a word cloud.

def analyseHeadImage(friends):
    basePath = os.path.abspath('.')
    baseFolder = basePath + '\\HeadImages\\'
    if not os.path.exists(baseFolder):
        os.makedirs(baseFolder)
    faceApi = FaceAPI()
    use_face = 0
    not_use_face = 0
    image_tags = ''
    for index in range(1, len(friends)):
        friend = friends[index]
        imgFile = baseFolder + '\\Image%s.jpg' % str(index)
        imgData = itchat.get_head_img(userName=friend['UserName'])
        if not os.path.exists(imgFile):
            with open(imgFile, 'wb') as file:
                file.write(imgData)
        time.sleep(1)
        if faceApi.detectFace(imgFile):
            use_face += 1
        else:
            not_use_face += 1
        result = faceApi.extractTags(imgFile)
        image_tags += ','.join([x['tag_name'] for x in 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, 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的微信好友使用人脸头像情况" % friends[0]['NickName'])
    plt.show()
    # Word cloud for tags
    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()

Signature Analysis

Combine jieba keyword extraction and SnowNLP sentiment analysis on the Signature field. Generate a word cloud of frequent keywords and a bar chart of sentiment distribution.

def analyseSignature(friends):
    signatures = ''
    emotions = []
    for friend in friends:
        signature = friend.get('Signature')
        if signature:
            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)
    # Word cloud
    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()
    # Sentiment bar chart
    count_good = len([x for x in emotions if x > 0.66])
    count_normal = len([x for x in emotions if 0.33 <= x <= 0.66])
    count_bad = len([x for x in emotions if x < 0.33])
    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.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()

Location Analysis

Extract Province and City fields, write them to a CSV file, and then use external tools (e.g., BDP) to create a geographic distribution map.

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 = {
                'NickName': friend['NickName'],
                'Province': friend['Province'],
                'City': friend['City']
            }
            writer.writerow(row)

Conclusion

The article demonstrates a complete workflow for analyzing WeChat friends from four perspectives—gender, avatar, signature, and location—using Python for data collection, processing, and visualization. It emphasizes that visualization is a means to reveal insights rather than an end goal.