2019年程序员岗位招聘信息分析
爬取某招聘网站java、python、c/c++，php四种语言在北京，上海，广州，深圳四个一线城市的公开职位发布信息进行分析，数据样本来自前30页的数据，样本大小大概6058个。
一、数据抓取
非常简单，基本上没有发抓取策略
def downloader(city, keyword, page):
'''
:param city:
:param keyword:
:param page:
:return:
'''
url ="https://www.lagou.com/jobs/positionAjax.json?city={}&needAddtionalResult=false"\
.format(quote(city))
data = {
"first":"false",
"pn": page,
"kd": keyword
}
headers = {
"Accept":"application/json, text/javascript, */*; q=0.01",
"Accept-Encoding":"gzip, deflate, br",
"Accept-Language":"zh-CN,zh;q=0.9,en;q=0.8",
"Connection":"keep-alive",
"Content-Length":"26",
"Content-Type":"application/x-www-form-urlencoded; charset=UTF-8",
"Host":"www.lagou.com",
"Cookie":"WEBTJ-ID=20181228093856-167f276e34849d-015bd2bf49274b-6114147a-1327104-167f276e34a334; _ga=GA1.2.651225173.1545961137; _gid=GA1.2.952777220.1545961137; user_trace_token=20181228093740-29e0dba1-0a41-11e9-b14d-525400f775ce; PRE_HOST=www.baidu.com; PRE_SITE=https%3A%2F%2Fwww.baidu.com%2Fs%3Fwd%3D%25E6%258B%2589%25E5%258B%25BE%25E7%25BD%2591%26rsv_spt%3D1%26rsv_iqid%3D0xdc8f964d00002f4f%26issp%3D1%26f%3D8%26rsv_bp%3D1%26rsv_idx%3D2%26ie%3Dutf-8%26rqlang%3Dcn%26tn%3Dbaiduhome_pg%26rsv_enter%3D1%26oq%3D%2525E4%2525B8%252593%2525E8%2525B5%252584%2525E5%25258A%25259E%26rsv_t%3Df7a1d2gJnPyNK%252FsS4vTWJ9EOKhzAsK05aVgqC43iWtqWmiKpIp0u6YQblMkUzbi3KwO7%26inputT%3D8441%26rsv_pq%3D9f44c2a800002af6%26rsv_sug3%3D57%26rsv_sug1%3D62%26rsv_sug7%3D101%26bs%3D%25E4%25B8%2593%25E8%25B5%2584%25E5%258A%259E; LGUID=20181228093740-29e0e252-0a41-11e9-b14d-525400f775ce; LGSID=20181228093745-2cd1a71c-0a41-11e9-b14d-525400f775ce; PRE_UTM=m_cf_cpc_baidu_pc; PRE_LAND=https%3A%2F%2Fwww.lagou.com%2Flp%2Fhtml%2Fcommon.html%3Futm_source%3Dm_cf_cpc_baidu_pc%26m_kw%3Dbaidu_cpc_bj_e110f9_d2162e_%25E6%258B%2589%25E5%258B%25BE%25E7%25BD%2591; JSESSIONID=ABAAABAAAGGABCB3EDF3AFE52B111A35A8BDCCF214C647F; Hm_lvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1545961137,1545961142,1545961149; index_location_city=%E5%8C%97%E4%BA%AC; TG-TRACK-CODE=index_search; SEARCH_ID=832387387eb944a39636c9973cbd41c4; LGRID=20181228093800-3605ba8a-0a41-11e9-ad84-5254005c3644; Hm_lpvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1545961158",
"Origin":"https://www.lagou.com",
"Referer":"https://www.lagou.com/jobs/list_python?labelWords=&fromSearch=true&suginput=",
"User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36",
"X-Anit-Forge-Code":"0",
"X-Anit-Forge-Token":"None",
"X-Requested-With":"XMLHttpRequest"
}
proxies = {
"http":"****",
"https":"****",
}
whileTrue:
try:
response = requests.post(url, data=data, headers=headers, proxies=proxies)
response.encoding ="utf-8"
ifresponse.status_code ==200:
data = json.loads(response.text)
result = jsonpath.jsonpath(data,"$.content.positionResult.result")[0]
with MongodbTools("dataanalysis") as mongo:
lagou = mongo.db["lagou"]
forrow in result:
row["_id"] ="{}".format(row["positionId"])
lagou.update_one({"_id": row["_id"]}, {"$set": row}, upsert=True)
print("update or insert data = {}".format(row["_id"]))
break
except BaseException as e:
print(e)
pass直接保存数据到mongodb中。
对人工智能感兴趣的朋友可以加群705673780，一起学习交流
二、数据分析
1、数据清洗，格式化
importpandasaspd
importnumpyasnp
from matplotlibimportpyplotasplt
from datetimeimportdatetime,timedelta
from pymongoimportMongoClient
importtime
mongo = MongoClient()["dataanalysis"]["lagou"]
values = mongo.find({},{"_id":0,"positionAdvantage":1,"salary":1,"city":1,"positionName":1,"workYear":1,"education":1,"industryField":1,"companySize":1,"financeStage":1,"firstType":1,"secondType":1,"thirdType":1})
values = [rowforrowinvalues]
df = pd.DataFrame(values)
# 格式化公司规模
def length(data,type):
value =data.values
ifnot value:
return0
value = value[0]
ifnot value:
return0
ifvalue.find("以上") !=-1:
iftype ==1:
return2000
else:
return10000
elif value.find("-") !=-1:
t = value.replace("人","").split("-")
iftype ==1:
returnint(t[0])
else:
returnint(t[1])
else:
iftype ==1:
return0
else:
return15
def min_staff(data):
returnlength(data,1)
def max_staff(data):
returnlength(data,2)
df["min_staff"] = df[["companySize"]].apply(min_staff,axis=1)
df["max_staff"] = df[["companySize"]].apply(max_staff,axis=1)
df = df.drop(["companySize"],axis=1)
# 格式化薪资
def salary(data,type):
value =data.values
ifnot value:
return0
value = value[0]
ifnot value:
return0
ifvalue.find("-") !=-1:
t = value.replace("k","").replace("K","").split("-")
iftype ==1:
returnint(t[0])*1000
elif type ==2:
returnint(t[1])*1000
else:
return(int(t[0])*1000+int(t[1])*1000)/2
else:
return0
def min_salary(data):
returnsalary(data,1)
def max_salary(data):
returnsalary(data,2)
def avg_salary(data):
returnsalary(data,3)
df["min_salary"] = df[["salary"]].apply(min_salary,axis=1)
df["max_salary"] = df[["salary"]].apply(max_salary,axis=1)
df["avg_salary"] = df[["salary"]].apply(avg_salary,axis=1)
# 格式化语言
def language(data):
value =data.values
ifnot value:
returnNone
value = value[0]
ifnot value:
returnNone
value = value.upper()
ifvalue.find("PYTHON") !=-1:
return"python"
ifvalue.find("C++") !=-1:
return"c/c++"
ifvalue.find("C") !=-1:
return"c/c++"
ifvalue.find("JAVA") !=-1:
return"java"
ifvalue.find("PHP") !=-1:
return"php"
returnNone
df["language"] = df[["positionName"]].apply(language,axis=1)
df = df.dropna()把薪资，语言，公司规模进行格式化数据，删除为Nan的数据。
2、每个城市地区的平均工资图
total_x = None
total_y = []
total_city = []
for city_name,data in df.groupby(by="city"):
result = data.groupby(by=["language"])["avg_salary"].mean().sort_index()
plt.figure(figsize=(20,8),dpi=80)
_x = result.index
_y = result.values
plt.bar(_x,_y)
total_x = _x
total_y.append(_y)
total_city.append(city_name)
plt.xlabel("语言")
plt.ylabel("平均薪资")
plt.title("{}地区编程语言平均薪资".format(city_name))
plt.grid()
plt
3、平均薪资城市之间的对比
plt.figure(figsize=(20,8),dpi=80)
interval =6
ind = np.array(range(0,len(total_x) * interval,interval))
width =1
forindex inrange(len(total_city)):
plt.bar(ind - (2- index) * width + width/2,total_y[index],label=total_city[index],width=1)
plt.xticks(range(0,len(total_x) * interval,interval),total_x)
plt.xlabel("语言")
plt.ylabel("平均薪资")
plt.title("一线城市编程语言平均薪资")
plt.grid()
plt.legend()
plt可见大帝都的平均工资最高
4、岗位优势的分析
importre
defposition_advantage(data):
value = data.values
ifnotvalue:
return[]
value = value[0]
ifnotvalue:
return[]
value = re.sub(r"[.~]","",value)
returnre.split(r'[,，； ;、+-]',value)
labels = list(set([iforrowindf[["positionAdvantage"]].apply(position_advantage,axis=1).valuesforiinrowifi]))
position_data = pd.DataFrame(np.zeros((df.shape[0],len(labels))).astype(int),columns=labels,index=df.index)
forlabelinlabels:
position_data[label][df["positionAdvantage"].str.contains(label)] =1
result = position_data.sum().sort_values(ascending=False)
size = result[:10].values
size = [rowforrowinsize]
labels = result[:10].index
labels = [rowforrowinlabels]
size.append(result.sum() - sum(size))
labels.append("其它")
explode = [0foriinrange(len(size))]
explode[0] =0.1
plt.figure(figsize=(10,10),dpi=80)
plt.pie(size, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
plt.title("岗位优势百分比")
plt
5、城市之间的岗位优势对比
total_value = []
total_label = []
labels = [rowforrow in result[:10].index]
forindex inrange(len(total_city)):
city = total_city[index]
data = position_data[df["city"] == city]
total_size = data.sum().sum()
total_label.append(city)
total_value.append((data[labels].sum()/total_size*10000).values.tolist())
plt.figure(figsize=(20,8),dpi=80)
interval =8
ind = np.array(range(0,len(labels) * interval,interval))
width =1
forindex inrange(len(total_label)):
plt.bar(ind - (2- index) * width + width/2,total_value[index],label=total_label[index],width=1)
plt.xticks(range(0,len(labels) * interval,interval),labels)
plt.xlabel("福利")
plt.ylabel("占比(*100)")
plt.title("岗位优势占比图")
plt.grid()
plt.legend()
plt
6、工作经验要求占比分析
#工作经验要求占比
forcity_name,dataindf.groupby(by="city"):
result =data.groupby(by=["workYear"])["avg_salary"].count().sort_values()
plt.figure(figsize=(8,8),dpi=80)
_x = result.index
_y = result.values
plt.pie(_y, labels=_x, autopct='%1.1f%%',shadow=True, startangle=90)
plt.title("{}地区编程语言学历要求占比".format(city_name))
plt.grid()
plt
7、学历经验要求占比分析
#学历要求占比
forcity_name,dataindf.groupby(by="city"):
result =data.groupby(by=["education"])["avg_salary"].count().sort_index()
plt.figure(figsize=(8,8),dpi=80)
_x = result.index
_y = result.values
plt.pie(_y, labels=_x, autopct='%1.1f%%',shadow=True, startangle=90)
plt.title("{}地区编程语言学历要求占比".format(city_name))
plt.grid()
plt
8、绘制岗位优势的词云图
# 生成词图
fromscipy.miscimportimread
fromwordcloudimportWordCloud
fromwordcloudimportImageColorGenerator
importmatplotlib.pyplotasplt
fromosimportpath
cloud = WordCloud(
#设置字体，不指定就会出现乱码，文件名不支持中文
font_path="C:/simfang.ttf",
#font_path=path.join(d,'simsun.ttc'),
#设置背景色，默认为黑，可根据需要自定义为颜色
background_color='black',
#词云形状，
#mask=color_mask,
#允许最大词汇
max_words=400,
#最大号字体，如果不指定则为图像高度
max_font_size=100,
#画布宽度和高度，如果设置了msak则不会生效
width=1200,
height =800,
margin =2,
#词语水平摆放的频率，默认为0.9.即竖直摆放的频率为0.1
prefer_horizontal =0.8
)
result = position_data.sum().sort_values(ascending=False)
_labels = [rowforrowinresult.index]
_frequency = [rowforrowinresult.values]
_data = { _labels[index]:_frequency[index]forindexinrange(len(_labels))}
wc = cloud.generate_from_frequencies(_data)
wc.to_file("cloud.jpg")#保存图片
#显示词云图片
plt.imshow(wc)
#不现实坐标轴
plt.axis('off')
plt
对人工智能感兴趣的朋友可以加群705673780，一起学习交流