#encoding='utf-8'
from selenium import webdriver
import time
import re
import pandas as pd
import os

def close_windows():
    #如果有登錄彈窗，就關(guān)閉
    try:
        time.sleep(0.5)
        if dr.find_element_by_class_name("jconfirm").find_element_by_class_name("closeIcon"):
            dr.find_element_by_class_name("jconfirm").find_element_by_class_name("closeIcon").click()
    except BaseException as e:
        print('close_windows,沒有彈窗',e)


def get_current_region_job(k_index):
    flag = 0
    # page_num_set=0#每區(qū)獲取多少條數(shù)據(jù)，對30取整

    df_empty = pd.DataFrame(columns=['崗位', '地點(diǎn)', '薪資', '工作經(jīng)驗(yàn)', '學(xué)歷', '公司', '技能'])
    while (flag == 0):
        # while (page_num_set151)(flag == 0):#每次只能獲取150條信息
        time.sleep(0.5)
        close_windows()
        job_list = dr.find_elements_by_class_name("job-primary")
        for job in job_list:#獲取當(dāng)前頁的職位30條
            job_name = job.find_element_by_class_name("job-name").text
            # print(job_name)
            job_area = job.find_element_by_class_name("job-area").text
            salary = job.find_element_by_class_name("red").get_attribute("textContent")  # 獲取薪資
            # salary_raw = job.find_element_by_class_name("red").get_attribute("textContent")  # 獲取薪資
            # salary_split = salary_raw.split('·')  # 根據(jù)·分割
            # salary = salary_split[0]  # 只取薪資，去掉多少薪

            # if re.search(r'天', salary):
            #     continue

            experience_education = job.find_element_by_class_name("job-limit").find_element_by_tag_name(
                "p").get_attribute("innerHTML")

            # experience_education_raw = '1-3年em class="vline">/em>本科'
            experience_education_raw = experience_education
            split_str = re.search(r'[a-zA-Z =>/"]{23}', experience_education_raw)  # 搜索分割字符串em class="vline">/em>
            # print(split_str)

            experience_education_replace = re.sub(r'[a-zA-Z =>/"]{23}', ",", experience_education_raw)  # 分割字符串替換為逗號
            # print(experience_education_replace)

            experience_education_list = experience_education_replace.split(',')  # 根據(jù)逗號分割
            # print('experience_education_list:',experience_education_list)

            if len(experience_education_list)!=2:
                print('experience_education_list不是2個，跳過該數(shù)據(jù)',experience_education_list)
                break
            experience = experience_education_list[0]
            education = experience_education_list[1]
            # print(experience)
            # print(education)



            company = job.find_element_by_class_name("company-text").find_element_by_class_name("name").text

            skill_list = job.find_element_by_class_name("tags").find_elements_by_class_name("tag-item")
            skill = []
            for skill_i in skill_list:
                skill_i_text = skill_i.text
                if len(skill_i_text) == 0:
                    continue
                skill.append(skill_i_text)
            # print(job_name)
            # print(skill)

            df_empty.loc[k_index, :] = [job_name, job_area, salary, experience, education, company, skill]
            k_index = k_index + 1
            # page_num_set=page_num_set+1
            print("已經(jīng)讀取數(shù)據(jù){}條".format(k_index))

        close_windows()
        try:#點(diǎn)擊下一頁
            cur_page_num=dr.find_element_by_class_name("page").find_element_by_class_name("cur").text
            # print('cur_page_num',cur_page_num)

            #點(diǎn)擊下一頁
            element = dr.find_element_by_class_name("page").find_element_by_class_name("next")
            dr.execute_script("arguments[0].click();", element)
            time.sleep(1)
            # print('點(diǎn)擊下一頁')

            new_page_num=dr.find_element_by_class_name("page").find_element_by_class_name("cur").text
            # print('new_page_num',new_page_num)

            if cur_page_num==new_page_num:
                flag = 1
                break

        except BaseException as e:
            print('點(diǎn)擊下一頁錯誤',e)
            break

    print(df_empty)
    if os.path.exists("數(shù)據(jù).csv"):#存在追加，不存在創(chuàng)建
        df_empty.to_csv('數(shù)據(jù).csv', mode='a', header=False, index=None, encoding='gb18030')
    else:
        df_empty.to_csv("數(shù)據(jù).csv", index=False, encoding='gb18030')

    return k_index








def main():
    # 打開瀏覽器
    # dr = webdriver.Firefox()
    global dr
    dr = webdriver.Chrome()
    # dr = webdriver.Ie()

    # # 后臺打開瀏覽器
    # option=webdriver.ChromeOptions()
    # option.add_argument('headless')
    # dr = webdriver.Chrome(chrome_options=option)
    # print("打開瀏覽器")

    # 將瀏覽器最大化顯示
    dr.maximize_window()

    # 轉(zhuǎn)到目標(biāo)網(wǎng)址
    # dr.get("https://www.zhipin.com/job_detail/?query=Pythoncity=100010000industry=position=")#全國
    dr.get("https://www.zhipin.com/c101010100/?query=Pythonka=sel-city-101010100")#北京
    print("打開網(wǎng)址")
    time.sleep(5)

    k_index = 0#數(shù)據(jù)條數(shù)、DataFrame索引

    flag_hot_city=0

    for i in range(3,17,1):
        # print('第',i-2,'頁')

        # try:

        # 獲取城市
        close_windows()
        hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
        close_windows()
        # hot_city_list[i].click()#防止彈窗，改為下面兩句
        # element_hot_city_list_first = hot_city_list[i]
        dr.execute_script("arguments[0].click();", hot_city_list[i])

        # 輸出城市名
        close_windows()
        hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
        print('城市：{}'.format(i-2),hot_city_list[i].text)
        time.sleep(0.5)


        # 獲取區(qū)縣
        for j in range(1,50,1):
            # print('第', j , '個區(qū)域')
            # try:

            # close_windows()
            # hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")

            # 在這個for循環(huán)點(diǎn)一下城市，不然識別不到當(dāng)前頁面已經(jīng)更新了
            close_windows()
            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            close_windows()
            # hot_city_list[i].click()#防止彈窗，改為下面
            dr.execute_script("arguments[0].click();", hot_city_list[i])

            #輸出區(qū)縣名稱
            close_windows()
            city_district = dr.find_element_by_class_name("condition-district").find_elements_by_tag_name("a")
            if len(city_district)==j:
                print('遍歷完所有區(qū)縣，沒有不可點(diǎn)擊的，跳轉(zhuǎn)下一個城市')
                break
            print('區(qū)縣：',j, city_district[j].text)
            # city_district_value=city_district[j].text#當(dāng)前頁面的區(qū)縣值


            # 點(diǎn)擊區(qū)縣
            close_windows()
            city_district=  dr.find_element_by_class_name("condition-district").find_elements_by_tag_name("a")
            close_windows()
            # city_district[j].click()]#防止彈窗，改為下面兩句
            # element_city_district = city_district[j]
            dr.execute_script("arguments[0].click();", city_district[j])



            #判斷區(qū)縣是不是點(diǎn)完了
            close_windows()
            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            print('點(diǎn)擊后這里應(yīng)該是區(qū)縣', hot_city_list[1].text)#如果是不限，說明點(diǎn)完了，跳出

            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            print('如果點(diǎn)完了，這里應(yīng)該是不限：',hot_city_list[1].text)

            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            if hot_city_list[1].text == '不限':
                print('當(dāng)前區(qū)縣已經(jīng)點(diǎn)完了，點(diǎn)擊下一個城市')
                flag_hot_city=1
                break


            close_windows()
            k_index = get_current_region_job(k_index)#獲取職位，爬取數(shù)據(jù)


            # 重新點(diǎn)回城市頁面，再次獲取區(qū)縣。但此時多了區(qū)縣，所以i+1
            close_windows()
            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            close_windows()
            # hot_city_list[i+1].click()#防止彈窗，改為下面兩句
            # element_hot_city_list_again = hot_city_list[i+1]
            dr.execute_script("arguments[0].click();", hot_city_list[i+1])



            # except BaseException as e:
            #     print('main的j循環(huán)-獲取區(qū)縣發(fā)生錯誤:', e)
            #     close_windows()

            time.sleep(0.5)


        # except BaseException as e:
        #     print('main的i循環(huán)發(fā)生錯誤:',e)
        #     close_windows()

        time.sleep(0.5)

    # 退出瀏覽器
    dr.quit()
    # p1.close()



if __name__ == '__main__':
    main()

# coding=utf-8
import collections
import wordcloud
import re
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimHei']  # 顯示中文標(biāo)簽
plt.rcParams['axes.unicode_minus'] = False  # 設(shè)置正常顯示符號


def create_dir_not_exist(path):  # 判斷文件夾是否存在,不存在-新建
    if not os.path.exists(path):
        os.mkdir(path)


create_dir_not_exist(r'./image')
create_dir_not_exist(r'./image/city')

data = pd.read_csv('數(shù)據(jù).csv', encoding='gb18030')
data_df = pd.DataFrame(data)
print("\n查看是否有缺失值\n", data_df.isnull().sum())

data_df_del_empty = data_df.dropna(subset=['崗位'], axis=0)
# print("\n刪除缺失值‘崗位'的整行\(zhòng)n",data_df_del_empty)
data_df_del_empty = data_df_del_empty.dropna(subset=['公司'], axis=0)
# print("\n刪除缺失值‘公司'的整行\(zhòng)n",data_df_del_empty)

print("\n查看是否有缺失值\n", data_df_del_empty.isnull().sum())
print('去除缺失值后\n', data_df_del_empty)

data_df_python_keyword = data_df_del_empty.loc[data_df_del_empty['崗位'].str.contains('Python|python')]
# print(data_df_python_keyword)#篩選帶有python的行

# 區(qū)間最小薪資
data_df_python_keyword_salary = data_df_python_keyword['薪資'].str.split('-', expand=True)[0]
print(data_df_python_keyword_salary)  # 區(qū)間最小薪資
# Dataframe新增一列  在第 列新增一列名為' ' 的一列 數(shù)據(jù)
data_df_python_keyword.insert(7, '區(qū)間最小薪資(K)', data_df_python_keyword_salary)
print(data_df_python_keyword)

# 城市地區(qū)
data_df_python_keyword_location_city = data_df_python_keyword['地點(diǎn)'].str.split('·', expand=True)[0]
print(data_df_python_keyword_location_city)  # 北京
data_df_python_keyword_location_district = data_df_python_keyword['地點(diǎn)'].str.split('·', expand=True)[1]
print(data_df_python_keyword_location_district)  # 海淀區(qū)

data_df_python_keyword_location_city_district = []
for city, district in zip(data_df_python_keyword_location_city, data_df_python_keyword_location_district):
    city_district = city + district
    data_df_python_keyword_location_city_district.append(city_district)
print(data_df_python_keyword_location_city_district)  # 北京海淀區(qū)
# Dataframe新增一列  在第 列新增一列名為' ' 的一列 數(shù)據(jù)
data_df_python_keyword.insert(8, '城市地區(qū)', data_df_python_keyword_location_city_district)
print(data_df_python_keyword)

data_df_python_keyword.insert(9, '城市', data_df_python_keyword_location_city)
data_df_python_keyword.insert(10, '地區(qū)', data_df_python_keyword_location_district)
data_df_python_keyword.to_csv("data_df_python_keyword.csv", index=False, encoding='gb18030')

print('-------------------------------------------')


def draw_bar(row_lable, title):
    figsize_x = 10
    figsize_y = 6
    global list1_education, list2_education, df1, df2
    plt.figure(figsize=(figsize_x, figsize_y))
    list1_education = []
    list2_education = []
    for df1, df2 in data_df_python_keyword.groupby(row_lable):
        list1_education.append(df1)
        list2_education.append(len(df2))
    # print(list1_education)
    # print(list2_education)
    # 利用 * 解包方式 將 一個排序好的元組，通過元組生成器再轉(zhuǎn)成list
    # print(*sorted(zip(list2_education,list1_education)))
    # print(sorted(zip(list2_education,list1_education)))
    # 排序，兩個列表對應(yīng)原始排序,按第幾個列表排序，注意先后位置
    list2_education, list1_education = (list(t) for t in zip(*sorted(zip(list2_education, list1_education))))
    plt.bar(list1_education, list2_education)
    plt.title('{}'.format(title))
    plt.savefig('./image/{}分析.jpg'.format(title))
    # plt.show()
    plt.close()


# 學(xué)歷
draw_bar('學(xué)歷', '學(xué)歷')
draw_bar('工作經(jīng)驗(yàn)', '工作經(jīng)驗(yàn)')
draw_bar('區(qū)間最小薪資(K)', '14個熱門城市的薪資分布情況(K)')
# -----------------------------------------
# 根據(jù)城市地區(qū)求均值
list_group_city1 = []
list_group_city2 = []

for df1, df2 in data_df_python_keyword.groupby(data_df_python_keyword['城市地區(qū)']):
    # print(df1)
    # print(df2)
    list_group_city1.append(df1)
    salary_list_district = [int(i) for i in (df2['區(qū)間最小薪資(K)'].values.tolist())]
    district_salary_mean = round(np.mean(salary_list_district), 2)  # 每個區(qū)縣的平均薪資 round(a, 2)保留2位小數(shù)
    list_group_city2.append(district_salary_mean)
    list_group_city2, list_group_city1 = (list(t) for t in
                                          zip(*sorted(zip(list_group_city2, list_group_city1), reverse=False)))
#
# print(list_group_city1)
# print(list_group_city2)

plt.figure(figsize=(10, 50))
plt.barh(list_group_city1, list_group_city2)
# 坐標(biāo)軸上的文字說明
for ax, ay in zip(list_group_city1, list_group_city2):
    # 設(shè)置文字說明 第一、二個參數(shù)：坐標(biāo)軸上的值； 第三個參數(shù)：說明文字；ha:垂直對齊方式；va：水平對齊方式
    plt.text(ay, ax, '%.2f' % ay, ha='center', va='bottom')
plt.title('14個熱門城市的各區(qū)縣招聘工資情況(K)')
plt.savefig('./image/14個熱門城市的各區(qū)縣招聘工資情況(K).jpg')
# plt.show()
plt.close()

# -----------------------------------------
# 根據(jù)城市分組排序，

list_group_city11 = []
list_group_city22 = []
list_group_city33 = []
list_group_city44 = []

for df_city1, df_city2 in data_df_python_keyword.groupby(data_df_python_keyword['城市']):
    # print(df_city1)#市
    # print(df_city2)
    list_group_district2 = []  # 區(qū)縣列表
    district_mean_salary2 = []  # 工資均值列表
    for df_district1, df_district2 in df_city2.groupby(data_df_python_keyword['地區(qū)']):
        # print(df_district1)#區(qū)縣
        # print(df_district2)#工作
        list_group_district2.append(df_district1)  # 記錄區(qū)縣
        salary_list_district2 = [int(i) for i in (df_district2['區(qū)間最小薪資(K)'].values.tolist())]  # 工資列表
        district_salary_mean2 = round(np.mean(salary_list_district2), 2)  # 每個區(qū)縣的平均薪資 round(a, 2)保留2位小數(shù)
        district_mean_salary2.append(district_salary_mean2)  # 記錄區(qū)縣的平均工作的列表

    district_mean_salary2, list_group_district2 = (list(tt) for tt in zip(
        *sorted(zip(district_mean_salary2, list_group_district2), reverse=True)))
    plt.figure(figsize=(10, 6))
    plt.bar(list_group_district2, district_mean_salary2)

    # 坐標(biāo)軸上的文字說明
    for ax, ay in zip(list_group_district2, district_mean_salary2):
        # 設(shè)置文字說明 第一、二個參數(shù)：坐標(biāo)軸上的值； 第三個參數(shù)：說明文字；ha:垂直對齊方式；va：水平對齊方式
        plt.text(ax, ay, '%.2f' % ay, ha='center', va='bottom')

    plt.title('14個熱門城市的各區(qū)縣招聘工資情況_{}(K)'.format(df_city1))
    plt.savefig('./image/city/14個熱門城市的各區(qū)縣招聘工資情況_{}(K).jpg'.format(df_city1))
    # plt.show()
    plt.close()

# ----------------------------------------------------


skill_all = data_df_python_keyword['技能']
print(skill_all)

skill_list = []

for i in skill_all:
    # print(type(i))
    print(i)
    # print(i.split(", | ' | \[ | \]  |  \" | "))
    result = re.split(r'[,\' [, ]  ]', i)
    print(result)
    # if type(i) == list:

    skill_list = skill_list + result
print('++++++++++++++++++++++++++++++++')
# print(skill_list)

list_new = skill_list

# 詞頻統(tǒng)計
word_counts = collections.Counter(list_new)  # 對分詞做詞頻統(tǒng)計
word_counts_top10 = word_counts.most_common(30)  # 獲取前10最高頻的詞
# print (word_counts_top10) # 輸出檢查
# print (word_counts_top10[0][0]) # 輸出檢查

# 生成柱狀圖
list_x = []
list_y = []
for i in word_counts_top10:

    list_x.append(i[0])
    list_y.append(i[1])
print('list_x', list_x[1:])
print('list_y', list_y[1:])
plt.figure(figsize=(30, 5))
plt.bar(list_x[1:], list_y[1:])
plt.savefig('./image/技能棧_詞頻_柱狀圖.png')
# plt.show()
plt.close()

list_new = " ".join(list_new)  # 列表轉(zhuǎn)字符串，以空格間隔
# print(list_new)


wc = wordcloud.WordCloud(
    width=800,
    height=600,
    background_color="#ffffff",  # 設(shè)置背景顏色
    max_words=50,  # 詞的最大數(shù)（默認(rèn)為200）
    max_font_size=60,  # 最大字體尺寸
    min_font_size=10,  # 最小字體尺寸（默認(rèn)為4）
    # colormap='bone',  # string or matplotlib colormap, default="viridis"
    colormap='hsv',  # string or matplotlib colormap, default="viridis"
    random_state=20,  # 設(shè)置有多少種隨機(jī)生成狀態(tài)，即有多少種配色方案
    # mask=plt.imread("mask2.gif"),  # 讀取遮罩圖片！！
    font_path='simhei.ttf'
)
my_wordcloud = wc.generate(list_new)

plt.imshow(my_wordcloud)
plt.axis("off")
# plt.show()
wc.to_file('./image/技能棧_詞云.png')  # 保存圖片文件
plt.close()