How to Analyze Your WeChat Friends with Python: Gender, Avatar, Signature & Location Insights

Data Analysis Overview

The article demonstrates a step‑by‑step Python workflow for extracting WeChat friend information and visualizing four dimensions: gender, avatar, signature, and location.

Required Third‑Party Modules

itchat – wrapper for the WeChat web API to obtain friend data.

jieba – Chinese word segmentation for text processing.

matplotlib – charting library for bar, pie, and other plots.

snownlp – sentiment analysis for Chinese text.

PIL – image handling.

numpy – numeric operations, used with the wordcloud module.

wordcloud – generation of word‑cloud images.

TencentYoutuyun – SDK for face detection and image‑tag extraction.

Login and Data Retrieval

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

The friends object is a list of dictionaries; friends[1:] contains the actual contacts, each with fields such as Sex, HeadImgUrl, Signature, Province, and City.

1. Friend Gender Analysis

Gender values are 0 (Unknown), 1 (Male), and 2 (Female). The code extracts these values, counts each category, and draws 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's WeChat Friend Gender Distribution" % friends[0]['NickName'])
    plt.show()

2. Friend Avatar Analysis

Avatars are downloaded via itchat.get_head_img. Tencent Youtu detects whether an avatar contains a face and extracts image tags. Results are shown with a pie chart for face usage and a word cloud for extracted tags.

def analyseHeadImage(friends):
    basePath = os.path.abspath('.')
    baseFolder = basePath + '\\HeadImages\\'
    if not os.path.exists(baseFolder):
        os.makedirs(baseFolder)
    faceApi = FaceAPI()
    use_face, not_use_face = 0, 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
        tags = faceApi.extractTags(imgFile)
        image_tags += ','.join([t['tag_name'] for t in tags])
    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']
    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's WeChat Friend Face Avatar Usage" % friends[0]['NickName'])
    plt.show()
    # Word cloud for tags
    back_coloring = np.array(Image.open('face.jpg'))
    wc = 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)
    wc.generate(image_tags)
    plt.imshow(wc)
    plt.axis('off')
    plt.show()

3. Friend Signature Analysis

Signatures are processed with jieba for keyword extraction (word cloud) and SnowNLP for sentiment scoring. The code builds a combined word cloud and a bar chart of sentiment categories.

def analyseSignature(friends):
    signatures = ''
    emotions = []
    pattern = re.compile("1f\d.+")
    for friend in friends:
        signature = friend['Signature']
        if signature:
            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))
    # Word cloud
    back_coloring = np.array(Image.open('flower.jpg'))
    wc = 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)
    wc.generate(signatures)
    plt.imshow(wc)
    plt.axis('off')
    plt.show()
    # Sentiment bar chart
    count_good = len(list(filter(lambda x: x>0.66, emotions)))
    count_normal = len(list(filter(lambda x: 0.33<=x<=0.66, emotions)))
    count_bad = len(list(filter(lambda x: x<0.33, 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.bar(range(3), values, color='rgb')
    plt.xticks(range(3), labels)
    plt.xlabel(u'情感判断')
    plt.ylabel(u'频数')
    plt.title(u"%s's WeChat Friend Signature Sentiment Analysis" % friends[0]['NickName'])
    plt.show()

4. Friend Location Analysis

Province and City fields are extracted and written to a CSV file, which can be imported into Baidu BDP for map visualization.

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 shows a complete, reproducible pipeline for visualizing personal WeChat friend data, illustrating how simple Python tools can reveal gender ratios, avatar usage patterns, common signature keywords, sentiment distribution, and geographic concentration, while emphasizing that visualization is a means to gain insight rather than an end in itself.