郭敬明五年电影最动人之作 | Python爬取《悲伤逆流成河》 大禹编程 今天

# 获取数据并存储




def

 

get_data


(self,json_comment)

:


    json_response = json_comment[

"cmts"

]  

# 列表


    list_info = []

    

for

 data 

in

 json_response:

        cityName = data[

"cityName"

]

        content = data[

"content"

]

        

if

 

"gender"

 

in

 data:

            gender = data[

"gender"

]

        

else

:

            gender = 



        nickName = data[

"nickName"

]

        userLevel = data[

"userLevel"

]

        score = data[

"score"

]

        list_one = [self.time,nickName,gender,cityName,userLevel,score,content]

        list_info.append(list_one)

    self.file_do(list_info)

# 存储文件




def

 

file_do


(list_info)

:


    

# 获取文件大小


    file_size = os.path.getsize(

r'G:\maoyan\maoyan.csv'

)

    

if

 file_size == 


:

        

# 表头


        name = [

'评论日期'

, 

'评论者昵称'

, 

'性别'

, 

'所在城市'

,

'猫眼等级'

,

'评分'

,

'评论内容'

]

        

# 建立DataFrame对象


        file_test = pd.DataFrame(columns=name, data=list_info)

        

# 数据写入


        file_test.to_csv(

r'G:\maoyan\maoyan.csv'

, encoding=

'gbk'

, index=

False

)

    

else

:

        

with

 open(

r'G:\maoyan\maoyan.csv'

, 

'a+'

, newline=

''

) 

as

 file_test:

            

# 追加到文件后面


            writer = csv.writer(file_test)

            

# 写入文件


            writer.writerows(list_info)

def

 

read_csv


()

:


    content = 

''


    

# 读取文件内容


    

with

 open(

r'G:\maoyan\maoyan.csv'

, 

'r'

, encoding=

'utf_8_sig'

, newline=

''

) 

as

 file_test:

        

# 读文件


        reader = csv.reader(file_test)

        i = 



        

for

 row 

in

 reader:

            

if

 i != 


:

                time.append(row[


])

                nickName.append(row[

1

])

                gender.append(row[

2

])

                cityName.append(row[

3

])

                userLevel.append(row[

4

])

                score.append(row[

5

])

                content = content + row[

6

]

                

# print(row)


            i = i + 

1


        print(

'一共有：'

 + str(i - 

1

) + 

'条数据'

)

        

return

 content

一共有：15195条数据

# 评论者性别分布可视化




def

 

sex_distribution


(gender)

:


    

# print(gender)


    

from

 pyecharts 

import

 Pie

    list_num = []

    list_num.append(gender.count(

'0'

)) 

# 未知


    list_num.append(gender.count(

'1'

)) 

# 男


    list_num.append(gender.count(

'2'

)) 

# 女


    attr = [

"其他"

,

"男"

,

"女"

]

    pie = Pie(

"性别饼图"

)

    pie.add(

""

, attr, list_num, is_label_show=

True

)

    pie.render(

"H:\PyCoding\spider_maoyan\picture\sex_pie.html"

)

# 评论者所在城市分布可视化




def

 

city_distribution


(cityName)

:


    city_list = list(set(cityName))

    city_dict = {city_list[i]:


 

for

 i 

in

 range(len(city_list))}

    

for

 i 

in

 range(len(city_list)):

        city_dict[city_list[i]] = cityName.count(city_list[i])

    

# 根据数量(字典的键值)排序


    sort_dict = sorted(city_dict.items(), key=

lambda

 d: d[

1

], reverse=

True

)

    city_name = []

    city_num = []

    

for

 i 

in

 range(len(sort_dict)):

        city_name.append(sort_dict[i][


])

        city_num.append(sort_dict[i][

1

])


    

import

 random

    

from

 pyecharts 

import

 Bar

    bar = Bar(

"评论者城市分布"

)

    bar.add(

""

, city_name, city_num, is_label_show=

True

, is_datazoom_show=

True

)

    bar.render(

"H:\PyCoding\spider_maoyan\picture\city_bar.html"

)



# 地图可视化




def

 

render_city


(cities)

:


     点击阅读原文查看该函数完整代码

# 每日评论总数可视化分析




def

 

time_num_visualization


(time)

:


    

from

 pyecharts 

import

 Line

    time_list = list(set(time))

    time_dict = {time_list[i]: 


 

for

 i 

in

 range(len(time_list))}

    time_num = []

    

for

 i 

in

 range(len(time_list)):

        time_dict[time_list[i]] = time.count(time_list[i])

    

# 根据数量(字典的键值)排序


    sort_dict = sorted(time_dict.items(), key=

lambda

 d: d[


], reverse=

False

)

    time_name = []

    time_num = []

    print(sort_dict)

    

for

 i 

in

 range(len(sort_dict)):

        time_name.append(sort_dict[i][


])

        time_num.append(sort_dict[i][

1

])


    line = Line(

"评论数量日期折线图"

)

    line.add(

        

"日期-评论数"

,

        time_name,

        time_num,

        is_fill=

True

,

        area_color=

"#000"

,

        area_opacity=

0.3

,

        is_smooth=

True

,

    )

    line.render(

"H:\PyCoding\spider_maoyan\picture\c_num_line.html"

)

# 评论者猫眼等级、评分可视化




def

 

level_score_visualization


(userLevel,score)

:


    

from

 pyecharts 

import

 Pie

    userLevel_list = list(set(userLevel))

    userLevel_num = []

    

for

 i 

in

 range(len(userLevel_list)):

        userLevel_num.append(userLevel.count(userLevel_list[i]))


    score_list = list(set(score))

    score_num = []

    

for

 i 

in

 range(len(score_list)):

        score_num.append(score.count(score_list[i]))


    pie01 = Pie(

"等级环状饼图"

, title_pos=

'center'

, width=

900

)

    pie01.add(

        

"等级"

,

        userLevel_list,

        userLevel_num,

        radius=[

40

, 

75

],

        label_text_color=

None

,

        is_label_show=

True

,

        legend_orient=

"vertical"

,

        legend_pos=

"left"

,

    )

    pie01.render(

"H:\PyCoding\spider_maoyan\picture\level_pie.html"

)

    pie02 = Pie(

"评分玫瑰饼图"

, title_pos=

'center'

, width=

900

)

    pie02.add(

        

"评分"

,

        score_list,

        score_num,

        center=[

50

, 

50

],

        is_random=

True

,

        radius=[

30

, 

75

],

        rosetype=

"area"

,

        is_legend_show=

False

,

        is_label_show=

True

,

    )

    pie02.render(

"H:\PyCoding\spider_maoyan\picture\score_pie.html"

)