Your wechat nickname may be betraying you

Contents of this article ：

One 、 Preface

Hello everyone , The first thing to say is , This is a technical article , It's also an article not Technical papers , What we share today is , When I get the wechat applet The English name is Of 3500 Multiple wechat user nicknames 、 After age , The results are as follows ...

Two 、Let’s get it

1. Access to basic information

1. visit The English name is The basic user information interface , obtain The English name is User's wechat name （NickName）、 Number of visits （Count）、 Aggregate data set （ResponseData）, And put the wechat name in the file .

#  Get all the number of users and related information 
def get_json():
    #  Gain entry 
    search_url = ' English name user interface , Welcome to scan the code and use the English name , Create an English name that best suits you '
    #  send out http request , Get request page 
    search_response = requests.get(search_url)
    #  Set encoding 
    search_response.encoding = 'UTF-8'
    #  Turn the page into json Code format 
    search_json = search_response.json()
    #  Get the data we need , It's a list format 
    our_data = search_json['ResponseData']
    list_len = len(our_data)
    print(' The total number of users is ：' + str(list_len))
    user_visit_numbers = 0
    data_research = 0
    NickName = []
    for x in our_data:
        user_visit_numbers= x['Count'] + user_visit_numbers
        if x['NickName'] == '':
            data_research += 1
        NickName.append(x['NickName'])
    print(" The number of wechat name acquisition failures ："+str(data_research))
    print(NickName)
    name = [' Wechat name ']
    file_test = pd.DataFrame(columns=name, data=NickName)
    file_test.to_csv(r'I:/data.csv', encoding='utf-8',index=False)
    print(' Total visits ：' + str(user_visit_numbers))

Running results ：

 The total number of users is ：3549
 The number of wechat name acquisition failures ：0
 Total visits ：4573

2. Read all wechat names , data classification

(1) Read wechat name

#  Read the file , Take out the wechat name 
def get_name():
    NickName = []
    with open('I:/data.csv','r',encoding='utf8') as file :
        i = 0
        for line in file:
            if i == 0:   #  Remove the meter head 
                i = 1
                continue
            line = line.strip()    #  Remove line breaks 
            NickName.append(line)
    return NickName

​(2) The data are divided into the following six categories

Chinese name	Variable name	data type
All Chinese nicknames	ch_name	list
All English Nicknames	en_name	list
Chinese and digital mixed nicknames	ch_di_name	list
Include picture emoticon nicknames	img_name	list
Other nicknames	other_name	list

# ch ：Chinese
ch_name_number = 0
ch_name = []
# en ：English
en_name_number = 0
en_name = []
# di : digtal
di_name_number = 0
di_name = []
# img : image
img_name_number = 0
img_name = []
# ch_di : Chinese and digtal
ch_di_name = []
# other : other
oth_name_number = 0
oth_name = []

(3) Data classification judgment

#  Nicknames are judged in Chinese 
def is_all_ch(keyword):
    for c in keyword:
        #  Contains common Chinese characters 
        if not ('\u4e00' <= c <= '\u9fa5'):
            return False
    return True

#  Nicknames are judged in English 
def is_all_en(keyword):
    #  It can't be all spaces or the first place is spaces 
    if all(ord(c) == 32 for c in keyword) or keyword[0] == ' ':
        return False
    #  Allow spaces and English to coexist ( for example ：Xist A)
    if not all(65 < ord(c) < 128 or ord(c) == 32 for c in keyword):
        return False
    return True

#  Nickname full number judgment 
def is_all_di(keyword):
    for uchar in keyword:
        if not (uchar >= '\u0030' and uchar <= u'\u0039'):
            return False
    return True

#  Nicknames contain emoticons to judge 
def have_img(keyword):
    #  Here is one of most of the pictures unicode Encoding set 
    #  View details ：https://en.wikipedia.org/wiki/Emoji
    img_re = re.compile(u'['
                      u'\U0001F300-\U0001F64F'
                      u'\U0001F680-\U0001F6FF'
                      u'\u2600-\u2B55]+',
                      re.UNICODE)
    if img_re.findall(keyword) :
        return True
    return False

#  chinese + Digital nickname judgment 
def is_ch_di(keyword):
    for c in keyword:
        if not ('\u4e00' <= c <= '\u9fa5') and not (c >= '\u0030' and c <= u'\u0039'):
            return False
    return True

(4) Data classification and calculation of various quantities

list_name = get_name()
print(" All in all ："+str(len(list_name))+" Wechat name ")
for i in range(len(list_name)):
    result = classification_name(list_name[i])
    if result == 'ch':  #  chinese 
        ch_name_number +=1
        ch_name.append(list_name[i])
    if result == 'en':  #  english 
        en_name_number +=1
        en_name.append(list_name[i])
    if result == 'di':  #  Numbers 
        di_name_number +=1
        di_name.append(list_name[i])
    if result == 'img': #  Facial expression 
        img_name_number +=1
        img_name.append(list_name[i])
    if result == 'ch_di': #  Chinese and numbers 
        ch_di_name_number +=1
        ch_di_name.append(list_name[i])
    if result == 'other': #  other 
        oth_name_number +=1
        oth_name.append(list_name[i])

print(" Number of pure Chinese nicknames ："+ str(ch_name_number))
# print(ch_name)
print(" Number of pure English Nicknames ："+ str(en_name_number))
#print(en_name)
print(" Number of pure digital nicknames ："+ str(di_name_number))
# print(di_name)
print(" Including the number of emoticons ："+ str(img_name_number))
# print(img_name)
print(" Number of Chinese and digital mixed nicknames ："+ str(ch_di_name_number))
print(ch_di_name)
print(" Number of other nicknames ："+ str(oth_name_number))
# print(oth_name)

Running results ：

 All in all ：3549 Wechat name 
 Number of pure Chinese nicknames ：1514
 Number of pure English Nicknames ：569
 Number of pure digital nicknames ：9
 Including the number of emoticons ：400
 Number of Chinese and digital mixed nicknames ：19
 Number of other nicknames ：1038

3. Get user paintings ( Only get the age of the user )

3. visit The English name is User portrait interface , Get close to 30 God Active users and new user The age of

#  Get the age of the user 
def get_data():
    #  obtain token, And deal with 
    t = get_token().strip('"')
    #  And then we'll deal with the token Values and other parameters as post Parameter value of the method , Call user portrait api
    post_user_api = " https://api.weixin.qq.com/datacube/getweanalysisappiduserportrait?access_token="
    post_user_url = post_user_api + t
    #  Access to get profile data  ( Data in the past month )
    data = json.dumps({
    "begin_date" : "2018-07-21",
    "end_date" : "2018-08-19"})
    #  pick up information 
    user_portrait_data = get_info(post_user_url, data)
    #  Period of time 
    ref_date = user_portrait_data['ref_date']
    #  new user 
    visit_uv_new = user_portrait_data['visit_uv_new']
      Active users 
    visit_uv = user_portrait_data['visit_uv']
    #  age group 
    print(ref_date )
    print((visit_uv_new['ages']))
    print((visit_uv['ages']))

Running results ：

# id ：  Is the age group number  name ： Age group name  value :  Number of people in this age group 
20180721-20180819
[{
   'id': 0, 'name': ' Unknown ', 'value': 6}, {
   'id': 1, 'name': '17 Under the age of ', 'value': 18}, {
   'id': 2, 'name': '18-24 year ', 'value': 118}, {
   'id': 3, 'name': '25-29 year ', 'value': 75}, {
   'id': 4, 'name': '30-39 year ', 'value': 81}, {
   'id': 5, 'name': '40-49 year ', 'value': 14}, {
   'id': 6, 'name': '50 Years of age or older ', 'value': 7}]
[{
   'id': 0, 'name': ' Unknown ', 'value': 6}, {
   'id': 1, 'name': '17 Under the age of ', 'value': 20}, {
   'id': 2, 'name': '18-24 year ', 'value': 147}, {
   'id': 3, 'name': '25-29 year ', 'value': 88}, {
   'id': 4, 'name': '30-39 year ', 'value': 95}, {
   'id': 5, 'name': '40-49 year ', 'value': 20}, {
   'id': 6, 'name': '50 Years of age or older ', 'value': 10}]

3、 ... and 、 Have some fun , Data cleaning 、 analysis

1. Visual analysis of wechat name type data

Core code ：

# 1. Wechat name classification ： Rose pie 
from pyecharts import Pie
#  Data obtained from the above code 
attr = [" Pure Chinese nickname ", " Pure English nickname ", " Pure digital nicknames ", " Include emoticons nicknames ", " Chinese and digital mixed nicknames ", " Other nicknames "]
v1 = [1514, 569, 9, 400, 19, 1038]
pie = Pie(" Wechat name classification pie chart ", title_pos='center', width=900)
pie.add(
    " Proportion ",
    attr,
    v1,
    center=[50, 50],
    is_random=True,
    radius=[30, 75],
    rosetype="area",
    is_legend_show=False,
    is_label_show=True,
)
pie.render("render_01.html")

Running effect ：

It can be seen from it that , Wechat nicknames account for the most in Chinese , occupy 42.66%, Second, other nicknames ( Mix Chinese and English 、 Characters and so on ), occupy 29.25%, The bigger class is the pure English nickname , occupy 16.03%, And include emoticon pack nicknames , occupy 11.27%, For example, pure digital nicknames and Chinese digital mixed nicknames are relatively less , The most common Chinese and digital mixed nicknames are Organization name / full name + Contact information , Some marketing numbers are often used , By contrast , Most people still like to use pure Chinese for nicknames , It embodies a kind of cultural feelings , I also introduced myself briefly , For example, my wechat name is cousin , This is my nickname in junior high school , Friends say the old watch , It's not necessarily about relatives , Maybe I'm talking about , Ha ha ha .

2. Wechat user age visualization analysis

Core code ：

# 2. User age ： Rose pie 
from pyecharts import Pie
#  Data obtained from the above code 
attr = [" Unknown ", "17 Under the age of ", "18-24 year ", "25-29 year ", "30-39 year ", "40-49 year ","50 Years of age or older "]
v1 = [12, 38, 265, 163, 176, 34,17]
pie = Pie(" Wechat user age pie chart ", title_pos='center', width=900)
pie.add(
    " Proportion ",
    attr,
    v1,
    center=[50, 50],
    is_random=True,
    radius=[30, 75],
    rosetype="area",
    is_legend_show=False,
    is_label_show=True,
)
pie.render("render_02.html")

Running effect ：

It can be seen from it that , In the age group ,18-24 year Of 95-00 after Most of them , achieve 37.59%, Next is 30-39 year Of 80-90 after , Proportion to 24.97%, Next to it is 25-29 year Of 90-95 after , Percentage 23.12%, Other age groups can be roughly divided into two categories ： Partial children and partial old people , In all 10.21%, I personally think the reason for the small number of such people is ： The child 、 The old people seldom play wechat , Not to mention wechat apps , The role of wechat for children is Play a game （ Login account ）, For the old man , Wechat is mainly used for Chat , It's more complicated , The use of small programs may be more complicated for the elderly , also Lack of necessity .

3. Word cloud analysis wechat name which words 、 Expression packs are more popular

(1) Continue to use pyecharts Generate word cloud
Core code ：

#  Data cleaning , Generate word cloud 
def split_word(test_str):
    test_str = re.sub('[,,.. \r\n]', '', test_str)
    # jieba  words 
    segment = jieba.lcut(test_str)
    words_df = pd.DataFrame({
   'segment': segment})
    # quoting=3  Express stopwords.txt All the contents in are not quoted 
    stopwords = pd.read_csv(r"H:\PyCoding\ Analysis_wx_name\stopwords.txt", index_col=False, quoting=3, sep="\t", names=['stopword'], encoding='utf-8')
    words_df = words_df[~words_df.segment.isin(stopwords.stopword)]
    words_stat = words_df.groupby(by=['segment'])['segment'].agg({
   " Count ": numpy.size})
    words_stat = words_stat.reset_index().sort_values(by=[" Count "], ascending=False)
    test = words_stat.head(200).values
    codes = [test[i][0] for i in range(0,len(test))]
    counts = [test[i][1] for i in range(0,len(test))]
    wordcloud = WordCloud(width=1300, height=620)
    wordcloud.add(" Wechat nickname ", codes, counts, word_size_range=[20, 100])
    wordcloud.render('render_03.html')

Running effect ：

(2) Use wordcloud+matplotlib Generate a higher level cloud of words
Core code ：

#  Let's talk about it next time matplotlib Drawing Visualization , That's interesting 
#  call get_name Function to get all wechat names 
text = get_name()
#  call jiebaclearText function , Data cleaning ( This function is the same as the above wordcutting idea )
text1=jiebaclearText(text)
# Create a cloud of words 
bg = plt.imread(r"G:\small_pig.jpg")
# Generate the word cloud 
wc=WordCloud(
    background_color="wathet", # Set the background to white , Default is black 
    mask=bg,      #  Set the content range of word cloud ( All areas except the white area of the specified image will cover the content of the word cloud )
    margin=10,               # Set the edge of the picture 
    max_font_size=70,   # Maximum font size displayed 
    random_state=20,    # Return one... For each word PIL Color 
    font_path='G:\simkai.ttf'   # Chinese processing , Use the font provided by the system 
    #  You can download this font here ：http://www.font5.com.cn/font_download.php?id=534&part=1245067666
    ).generate(text1)
# Set the font for the picture 
my_font=fm.FontProperties(fname='G:\simkai.ttf')
#  Picture background 
bg_color = ImageColorGenerator(bg)
#  Start drawing 
plt.imshow(wc.recolor(color_func=bg_color))
#  Remove the axis for the cloud image 
plt.axis("off")
#  Save the cloud 
wc.to_file("render_04.png")

The outline of the word cloud ：

Running effect ：

Because the second method can't parse emoticons , So there's no expression , In addition, these two methods show almost the same content .
Through a cloud of words , We can see at a glance that you use the most , Apart from Chinese , It's the emoticon , Do you have any WeChat friends like this Red lips , Mine seems to have , Ha ha ha ~ When we just look at Chinese in the word cloud , Discovery image The sun 、 The sun 、 smile 、 lovely 、 Happy 、 Love 、 future And other positive words are more popular , It also shows our inner positivity 、 optimistic , Of course, it's like Lily 、 xu 、 Chen Wait for the name part , We also use a lot of nicknames , There is no lack of a resemblance sad 、 cool This is a cool word .

Four 、 Emotional analysis through nicknames ( The text comes from Zhihu netizens )

01 | Wechat nickname is all Chinese

Wechat nicknames in Chinese can be divided into two categories ： Your real name and other nicknames .

People who use their own names as wechat nicknames directly , Character is mostly straight to the kind of , Treat people more candidly .

Their wechat is generally used for social contacts and daily work , Usually not casually add unfamiliar people , I'm not afraid to divulge personal information even with my real name , An inappropriate metaphor ： Don't do the wrong thing , Not afraid of ghosts knocking at the door , Ha ha ha .

For other nicknames , Most of them have their own opinions , Maybe a nickname is a kind of expectation for the future , Maybe a nickname is an attitude to life , Or some nonsense , Cool words .( guess )

02 | Wechat nickname is all English

Out of personal preferences or work needs , Some people will give themselves an easy to remember 、 It's called Shunkou's English name , such as Sam、Abby、Jason, And often when I introduce myself , Let's call ourselves by English name .

For them , English name is equivalent to your second name , Use it as wechat name , It's not much different from the original name .

Some people try to avoid the common English names , Take something smaller , They are more concerned about improving their “ Forced case ”, Like to be innovative , The pursuit of fashion and avant-garde .

03 | Wechat nickname with emoticons

There are many girls who will add various emoticons to their wechat names , May be a love , A rose , A star , Or the system itself emoji expression .

They think this is a special decoration , Can make your name different from others .

Such a girl , Most of them have delicate and careful thinking 、 Romantic life sentiment , And a young girl's heart .

04 | Wechat nicknames are professional

Generally speaking , I will take the initiative to put a letter in front of my wechat name “A” Of , Most of them are wechat merchants or agents who advertise in the circle of friends all day .

More formal , Using all of these “ Company name + full name ” In the form of , This kind of people are basically salesmen or real estate agents ……

others , I will change my name suffix from time to time according to my different working stages .

Meet a friend who works as a human resource in a real estate company , In order to enjoy the holidays , She will change her wechat name to “ΧΧΧ On leave ”, In order to remind those who still privately mail her to ask about their work on holidays .

There are also some people coming in the opposite direction , In order to show that they are particularly positive , Change the wechat name to “ΧΧΧ Working overtime ”……emmm Mainly for the boss .

05 | Wechat nickname with idol name

Needless to say , These are all typical star chasers , And most of them are girls , For example, Mrs. Wu Yifan , Cai Xukun's secret girlfriend , Hu Ge's little wife …… No accident , Their heads are usually their idols .

They usually call idols on Weibo call, Friends circle will also send a lot of relevant recommendations , If someone praises his love beans , They will feel that they have met a confidant ; By contraries , If someone says they love beans , They'll pull black right away ……

Bear in mind , In front of the Star chaser , Don't give in easily , Tell her love beans what to do ……

06 | Wechat nickname is four words

Carefully observe the wechat names of elders , They will find that they especially like to use four words as nicknames .

What these four words have in common , It is a kind of quiet atmosphere of time ：“ Life is like tea ”、“ Flowers are fragrant ”、“ As good as water ”、“ People are still ”“ clear ”……

Young people mark themselves with unique wechat names , The older aunts and uncles just want to place a pure ideal of life .

---

It's said that names are the second face of people . Good wechat name , It tends to make a better impression .

What's the meaning of your wechat name ？ Let's talk in the comment area .

5、 ... and 、 appendix ： Reference documents

（1） Wechat applet api Help document
（2） Wikipedia ： Detailed explanation of the coding of expression
（3）Wordcloud Official documents
（4） It's written by AO Jiao's Paramecium Wordcloud Meaning of each parameter
（5） You know To whom Written ： Wechat name , Exposed what kind of person you are

Scan the code to generate an English name that suits you best

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