def test_custom_template_for_chart():
data = [{
'name': '衬衫',
'value': 5
}, {
'name': '羊毛衫',
'value': 20
}, {
'name': '雪纺衫',
'value': 36
}]
configure(echarts_template_dir='.')
online()
data1 = {'衬衫': '34', '羊毛衫': 45, '雪纺衫': 40}
names, values = Bar.cast(data)
names1, values1 = Bar.cast(data1)
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", names, values, is_stack=True)
bar.add("商家B", names1, values1, is_stack=True)
bar.render(path='new_version_bar.html')
with codecs.open('new_version_bar.html', 'r', 'utf-8') as f:
actual_content = f.read()
assert "</html>" in actual_content
def draw_DateBar(comments):
print("正在处理观众评论数量与日期的关系......")
time = pd.to_datetime(comments['startTime']) # 获取评论时间并转换为标准日期格式
time = time.apply(lambda x: judgeTime(x, startTime_tag)
) # 将2019.2.4号之前的数据汇总到2.4 统一标识为电影上映前影评数据
timeData = []
for t in time:
if pd.isnull(t) == False: # 获取评论日期（删除具体时间）并记录
t = str(t) # 转换为字符串以便分割
date = t.split(' ')[0]
timeData.append(date)
data = Counter(timeData).most_common() # 记录相应日期对应的评论数
data = sorted(data, key=lambda data: data[0]) # 使用lambda表达式对数据按日期进行排序
bar = Bar('《流浪地球》观众评论数量与日期的关系', '数据来源：猫眼电影 数据分析：16124278-王浩',
**style_others.init_style) # 初始化柱状图
attr, value = bar.cast(data) # 传值
bar.add('',
attr,
value,
is_visualmap=True,
visual_range=[0, 43000],
visual_text_color='black',
is_more_utils=True,
is_label_show=True) # 加入数据和其它参数
bar.render('./output/观众评论日期-柱状图.html') # 渲染
print("观众评论数量与日期的关系已完成!!!")
def create_money(data, title):
page = Page()
style = Style(width=1000, height=500, background_color='#c4ccd3')
money_chart = Bar(title, **style.init_style)
attr, value = money_chart.cast(data)
money_chart.add("", attr, value, is_label_show=True)
page.add(money_chart)
return page
def test_custom_template_for_chart():
data = [
{"name": "衬衫", "value": 5},
{"name": "羊毛衫", "value": 20},
{"name": "雪纺衫", "value": 36},
]
configure(echarts_template_dir=".")
data1 = {"衬衫": "34", "羊毛衫": 45, "雪纺衫": 40}
names, values = Bar.cast(data)
names1, values1 = Bar.cast(data1)
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", names, values, is_stack=True)
bar.add("商家B", names1, values1, is_stack=True)
bar.render(path="new_version_bar.html")
with codecs.open("new_version_bar.html", "r", "utf-8") as f:
actual_content = f.read()
assert "</html>" in actual_content
def render():
global comment_text
with open('jay.csv', mode='r', encoding='utf-8') as f:
rows = f.readlines()
for row in rows[1:]:
if row.count(',') != 3:
continue
elements = row.split(',')
user = elements[0]
date = elements[1]
if '2019' not in date:
continue
like = elements[2]
comment = elements[3]
if '2019-09-14' in date:
dates.append('2019-09-14')
elif '2019-09-15' in date:
dates.append('2019-09-15')
elif '2019-09-16 0' in date or '2019-09-16 1' in date or '2019-09-16 20' in date or '2019-09-16 21' in date:
dates.append('2019-09-16 0-21')
elif '2019-09-18' in date:
continue
else:
dates.append(date)
comment_text += comment
with open("comment_text.txt", "w", encoding='utf-8') as f:
f.write(comment_text)
date_data = Counter(dates).most_common()
# 按日期进行排序
date_data = sorted(date_data)
# print(data)
# 根据评分数据生成柱状图
bar = Bar('评价人数走势图',
'数据来源：不正经程序员-采集自QQ音乐',
title_pos='center',
width=800,
height=600)
attr, value = bar.cast(date_data)
bar.add('',
attr,
value,
is_visualmap=False,
visual_range=[0, 3500],
visual_text_color='#fff',
is_more_utils=True,
xaxis_interval=0,
xaxis_rotate=30,
is_label_show=True,
xaxis_label_textsize=8,
label_text_size=8)
bar.render('picture\评价人数走势图.html')
def render():
cities = []
with open('comments.txt', mode='r', encoding='utf-8') as f:
rows = f.readlines()
for row in rows:
print(row)
if row.strip() != '' and row.find('$'):
city = row.split('$')[1]
if city != '': # 去掉城市名为空的值
cities.append(city)
# 对城市数据和坐标文件中的地名进行处理
handle(cities)
# 统计每个城市出现的次数
# data = []
# for city in set(cities):
# data.append((city, cities.count(city)))
data = Counter(cities).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
style = Style(title_color="#fff",
title_pos="center",
width=1200,
height=600,
background_color="#404a59")
geo = Geo('《毒液》观众位置分布', '数据来源：猫眼-Ryan采集', **style.init_style)
attr, value = geo.cast(data)
geo.add('',
attr,
value,
visual_range=[0, 3500],
visual_text_color='#fff',
symbol_size=15,
is_visualmap=True,
is_piecewise=True,
visual_split_number=10)
geo.render('粉丝位置分布-地理坐标图.html')
data_top20 = Counter(cities).most_common(20)
bar = Bar('《毒液》观众来源排行TOP20',
'数据来源：猫眼-Ryan采集',
title_pos='center',
width=1200,
height=600)
attr, value = bar.cast(data_top20)
bar.add('',
attr,
value,
is_visualmap=True,
visual_range=[0, 3500],
visual_text_color='#fff',
is_more_utils=True,
is_label_show=True)
bar.render('观众来源排行-柱状图.html')
def bartest(self):
from pyecharts import Bar
bar = Bar('等级分布条形图', self.ftitle)
# 利用自带的解析方法把列表解析成2个关联列表
key, value = bar.cast(self.bardata)
# is_datazoom_show = 参数是用来显示下滑筛选块的，默认假
# is_label_show = 参数用来显示数值，默认假
bar.add('等级分布', key, value, is_datazoom_show=False, is_label_show=True)
# bar.show_config()
bar.render(r"pic\等级分布条形图.html")
print('成功输出Bar测试网页')
def render():
city_counts = df.groupby("cityName").size()
new_citys = handle(city_counts.index)
tuple_city = list(city_counts.items())
attr_values = []
for item in tuple_city:
#print(item[0],end=' ')
if item[0] in new_citys:
attr_values.append(item)
# 定义样式
style = Style(title_color='#fff',
title_pos='center',
width=1200,
height=600,
background_color='#404a59',
subtitle_color='#fff')
#
# 根据城市数据生成地理坐标图
geo = Geo('《无双》评星人位置分布', '图表来源：CSDN博客-梦想橡皮擦', **style.init_style)
attr, value = geo.cast(attr_values)
geo.add('',
attr,
value,
visual_range=[0, 2500],
type="scatter",
visual_text_color='#fff',
symbol_size=10,
is_visualmap=True,
visual_split_number=10)
geo.render('评星人位置分布-地理坐标图.html')
# 根据城市数据生成柱状图
city_sorted = city_counts.sort_values(ascending=False).head(20)
bar = Bar("《无双》评星人来源排行TOP20", "CSDN博客-梦想橡皮擦", **style.init_style)
attr, value = bar.cast(list(city_sorted.items()))
bar.add("",
attr,
value,
is_visualmap=True,
visual_range=[0, 2500],
visual_text_color='#fff',
label_color='#fff',
xaxis_label_textcolor='#fff',
yaxis_label_textcolor='#fff',
is_more_utils=True,
is_label_show=True)
bar.render("评星人来源排行-柱状图.html")
def _salary_show(self):
bar = Bar("薪资统计", title_pos='center', width=1200, height=500)
attr, value = bar.cast(self.g.salary_data)
bar.add("",
attr,
value,
xaxis_interval=0,
xaxis_rotate=30,
yaxis_rotate=30,
is_more_utils=True)
return bar.render_embed()
def render():
# 获取所有城市信息
cities = []
with open('comments.txt', mode='r', encoding='utf-8') as f:
rows = f.readlines()
for row in rows:
try:
city = row.split(',')[2]
except:
continue
if city != '':
cities.append(city)
# 对城市数据和坐标文件中的地名进行处理
handle(cities)
# 统计每个城市出现的次数
# data = []
# for city in set(cities):
# data.append((city, cities.count(city)))
data = Counter(cities).most_common()
# 根据城市数据生成地理坐标图
geo = Geo(
"电影《哪吒》用户位置分布及热度",
"数据来源：猫眼",
title_color="#FFF",
title_pos="center",
width=1200,
height=600,
background_color="#404a59",
)
attr, value = geo.cast(data)
geo.add(
"",
attr,
value,
visual_range=[0, 6500],
visual_text_color="#fff",
symbol_size=15,
is_visualmap=True,
)
geo.render('粉丝位置分布.html')
# 根据城市数据生成柱状图
cities_top20 = Counter(cities).most_common(20) # 返回出现次数最多的20条
bar = Bar("《哪吒》粉丝来源排行榜",
'数据来源：猫眼',
title_pos='center',
width=1200,
height=600)
attr, value = bar.cast(cities_top20)
bar.add("", attr, value)
bar.render('粉丝来源排行榜-柱状图.html')
def caculate_mi_list(dir_path):
files = common.get_filelist(dir_path)
res = _caculate_mi_list(files[3], files[0:10])
data_a, data_b = Bar.cast(res)
print data_a
print data_b
bar = Line("甜甜的实验", "二哥出品")
bar.use_theme('vintage')
bar.add("数据", data_a, data_b)
bar.render()
return
def render():
# 获取所有城市
cities = []
with open('friends.json', mode='r') as f:
read_data = f.read()
json_data = json.loads(read_data)
for temp in json_data:
city = str(temp['City'])
if city != '': # 去掉城市名为空的值
cities.append(city)
# 对城市数据和坐标文件中的地名进行处理
# handle(cities)
# 统计每个城市出现的次数
data = Counter(cities).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
print(data)
# 根据城市数据生成地理坐标图
geo = Geo('好友位置分布',
'',
title_color='#fff',
title_pos='center',
width=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
# print(attr, value)
# geo.add('', attr, value, visual_range=[0, 500],
# visual_text_color='#fff', symbol_size=15,
# is_visualmap=True, is_piecewise=True)
try:
geo.add('',
attr,
value,
visual_range=[0, 500],
visual_text_color='#fff',
symbol_size=15,
is_visualmap=True,
is_piecewise=True)
except ValueError:
pass
geo.render('好友位置分布.html')
# 根据城市数据生成柱状图
data_top20 = Counter(cities).most_common(20) # 返回出现次数最多的20条
bar = Bar('好友所在城市TOP20', '', title_pos='center', width=1200, height=600)
attr, value = bar.cast(data_top20)
bar.add('',
attr,
value,
is_visualmap=True,
visual_text_color='#fff',
is_more_utils=True,
is_label_show=True)
bar.render('好友所在城市TOP20.html')
def data_bar():
data = []
with open('haiwangtop20.txt', 'r', encoding='utf-8') as f:
lines = f.readlines()
for item in lines:
cityinfo = item.split()
print(cityinfo[0], cityinfo[1])
data.append((cityinfo[0], cityinfo[1]))
bar = Bar("《海王》评论数前20")
attr, value = bar.cast(data)
bar.add("海王》评论数前20", attr, value,is_label_show=True,xaxis_interval=0)
bar.render('top20.html')
def chart():
data = {
'张三': 123.3,
'李四': 66.8,
'王五': 86.4,
'杨六': 77.9,
'ji': 46.3,
'pp': 110.4
}
bar = Bar()
v1, v2 = bar.cast(data)
bar.add('weight', v1, v2)
return render_template('simple_chart.html', chart=bar)
def gender():
cities = []
with open('E:/spiderproject/maoyanMovies_comments/comments.txt',
'r',
encoding='utf-8') as f:
rows = f.readlines()
try:
for row in rows:
city = row.split(',')[1]
if city != '':
cities.append(city)
#print(city)
except Exception as e:
print(e)
handle(cities)
data = Counter(cities).most_common()
style = Style(title_color='#fff',
title_pos='center',
width=1200,
height=600,
background_color='#404a59')
geo = Geo('《毒液》观众位置分布', '数据来源：猫眼-Ryan采集', **style.init_style)
attr, value = geo.cast(data)
geo.add('',
attr,
value,
visual_range=[0, 1000],
visual_text_color='#fff',
symbol_size=15,
is_visualmap=True,
is_piecewise=False,
visual_split_number=10)
geo.render('观众位置分布-地理坐标图.html')
data_top20 = Counter(cities).most_common(20)
bar = Bar('《毒液》观众来源排行TOP20',
'数据来源：猫眼-Ryan采集',
title_pos='center',
width=1200,
height=600)
attr, value = bar.cast(data_top20)
bar.add('',
attr,
value,
is_visualmap=True,
visual_range=[0, 3500],
visual_text_color='#fff',
is_more_utils=True,
is_label_show=True)
bar.render('观众来源排行-柱状图.html')
def test_custom_template_for_chart():
data = [{
'name': '衬衫',
'value': 5
}, {
'name': '羊毛衫',
'value': 20
}, {
'name': '雪纺衫',
'value': 36
}]
configure(echarts_template_dir='.')
data1 = {'衬衫': '34', '羊毛衫': 45, '雪纺衫': 40}
names, values = Bar.cast(data)
names1, values1 = Bar.cast(data1)
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", names, values, is_stack=True)
bar.add("商家B", names1, values1, is_stack=True)
bar.render(path='new_version_bar.html')
with codecs.open('new_version_bar.html', 'r', 'utf-8') as f:
actual_content = f.read()
assert "</html>" in actual_content
def render():
# 获取所有城市
cities = []
with open('friends.txt', mode='r', encoding='utf-8') as f:
rows = f.readlines()
for row in rows:
city = row.split(',')[4]
if city != '': # 去掉城市名为空的值
cities.append(city)
# 对城市数据和坐标文件中的地名进行处理
handle(cities)
# 统计每个城市出现的次数
data = Counter(cities).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
print(data)
# 根据城市数据生成地理坐标图
geo = Geo('好友位置分布',
'',
title_color='#fff',
title_pos='center',
width=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
geo.add('',
attr,
value,
visual_range=[0, 500],
visual_text_color='#fff',
symbol_size=15,
is_visualmap=True,
is_piecewise=True)
geo.render('好友位置分布.html')
# 根据城市数据生成柱状图
data_top20 = Counter(cities).most_common(20) # 返回出现次数最多的20条
bar = Bar('好友所在城市TOP20', '', title_pos='center', width=1200, height=600)
attr, value = bar.cast(data_top20)
bar.add('',
attr,
value,
is_visualmap=True,
visual_text_color='#fff',
is_more_utils=True,
is_label_show=True)
bar.render('好友所在城市TOP20.html')
def draw_bar(comments):
print("正在处理观众地域排行榜单......")
data_top20 = Counter(comments['cityName']).most_common(20) # 筛选出数据量前二十的城市
bar = Bar('《流浪地球》观众地域排行榜单', '数据来源：猫眼电影 数据分析：16124278-王浩',
**style_others.init_style) # 初始化柱状图
attr, value = bar.cast(data_top20) # 传值
bar.add('',
attr,
value,
is_visualmap=True,
visual_range=[0, 16000],
visual_text_color='black',
is_more_utils=True,
is_label_show=True) # 加入数据与其它参数
bar.render('./output/观众地域排行榜单-柱状图.html') # 渲染
print("观众地域排行榜单已完成!!!")
def render():
# 获取所有城市
""" 安装地图文件包
pip install echarts-china-provinces-pypkg # 中国省、市、县、区地图
pip install echarts-china-cities-pypkg
pip install echarts-china-counties-pypkg
pip install echarts-china-misc-pypkg
pip install echarts-countries-pypkg # 全球国家地图
pip install echarts-united-kingdom-pypkg
"""
# 导入Counter类，用于统计值出现的次数
from collections import Counter
# 导入Geo组件，用于生成地理坐标类图
from pyecharts import Geo
# 导入Bar组件，用于生成柱状图
from pyecharts import Bar
cities = []
with open(friends_data, mode='r', encoding='utf-8') as f:
rows = f.readlines()
for row in rows:
city = row.split(',')[4]
if city != '': # 去掉城市名为空的值
cities.append(city)
# 对城市数据和坐标文件中的地名进行处理
handle(cities)
# 统计每个城市出现的次数
data = Counter(cities).most_common() # 使用Counter类统计出现的次数，并转换为元组列表
# 根据城市数据生成地理坐标图
geo = Geo('好友位置分布', '', title_color='#fff', title_pos='center', width=1200, height=600,
background_color='#404a59')
attr, value = geo.cast(data)
geo.add('', attr, value, visual_range=[0, 100],
visual_text_color='#fff', symbol_size=15,
is_visualmap=True, is_piecewise=True)
geo.render(current_dir+'/好友位置分布.html')
# 根据城市数据生成柱状图
data_top20 = Counter(cities).most_common(20) # 返回出现次数最多的20条
bar = Bar('好友所在城市TOP20', '', title_pos='center', width=1200, height=600)
attr, value = bar.cast(data_top20)
bar.add('', attr, value, is_visualmap=True, visual_text_color='#fff', is_more_utils=True,
is_label_show=True)
bar.render(current_dir+'/好友所在城市TOP20.html')
def draw_TimeBar(comments):
time = comments['startTime']
timeData = []
for t in time:
if pd.isnull(t) == False:
time = t.split(' ')[1]
hour = time.split(':')[0]
timeData.append(hour)
data = Counter(timeData).most_common()
data = sorted(data, key=lambda data : data[0])
bar = Bar('《流浪地球》观众评论数量与时间的关系', '数据来源：猫眼电影 数据分析：公众号【谭某人】', title_pos='center', width=1200, height=600)
attr, value = bar.cast(data)
bar.add('', attr, value, is_visualmap=True, visual_range=[0, 3500], visual_text_color='#fff', is_more_utils=True, is_label_show=True)
bar.render('./观众评论时间-柱状图.html')
print("观众评论数量与时间的关系已完成")
def draw_bar(comments):
data_top20 = Counter(comments['cityName']).most_common(20)
bar = Bar('《毒液》观众来源排行TOP20',
'数据来源：猫眼-Ryan采集',
title_pos='center',
width=1200,
height=600)
attr, value = bar.cast(data_top20)
bar.add('',
attr,
value,
is_visualmap=True,
visual_range=[0, 3500],
visual_text_color='#fff',
is_more_utils=True,
is_label_show=True)
bar.render('./观众来源排行-柱状图.html')
print("success")
def draw_bar(comments):
data_top20 = Counter(comments['cityName']).most_common(20)
bar = Bar('《Avengers:Endgame》观众地域排行榜单',
'数据来源：猫眼电影',
title_pos='center',
width=1450,
height=725)
attr, value = bar.cast(data_top20)
bar.add('',
attr,
value,
is_visualmap=True,
visual_range=[0, 4500],
visual_text_color='#fff',
is_more_utils=True,
is_label_show=True)
bar.render('./观众地域排行榜单.html')
print("观众地域排行榜单已完成")
def hot_city():
data = []
cities = []
city_counts = {}
with open('data/comments.txt', 'r', encoding='utf-8') as f:
rows = f.readlines()
try:
for row in rows:
if row != '':
city = row.split(',')[1]
if city != '':
cities.append(city)
if not city_counts.get(city):
city_counts[city] = 1
else:
city_counts[city] += 1
except Exception as e:
print(e)
for k, v in city_counts.items():
data.append((k, v))
# print(data)
# geo = Geo('Audiences distribution', 'Data Source: m.maoyan.com', title_color="#fff", title_pos="center", width=1000,
# height=600, background_color='#404a59')
# attr, value = geo.cast(data)
# geo.add('', attr, value, visual_range=[0, 1000], visual_text_color='#fff', symbol_size=15, is_visualmap=True,
# is_piecewise=False, visual_split_number=10)
# geo.render('output/audiences-distribution.html')
top20_cities = Counter(cities).most_common(20)
bar = Bar('Top20 cities of audiences',
'Data Source: m.maoyan.com',
title_pos='center',
width=1200,
height=600)
attr, value = bar.cast(top20_cities)
bar.add('',
attr,
value,
is_visualmap=True,
visual_range=[0, 3500],
visual_text_color='#fff',
is_more_utils=True,
is_label_show=True)
bar.render('output/Audiences-source.html')
def location():
cities = []
with open('wechatfriends.txt', mode='r', encoding='utf-8') as f:
rows2 = f.readlines()
for i in rows2:
city = i.split(',')[3]
if '\u4e00' <= city <= '\u9fa5': # 获取好友地区为中文的城市
cities.append(city)
data = Counter(cities).most_common()
# for i in range(len())
print(data)
geo = Geo('好友位置分布',
'',
title_color='#fff',
title_pos='center',
width=1400,
height=800,
background_color='#404a59')
# geo.add_coordinate('江北', 106.57,29.60)
attr, value = geo.cast(data)
geo.add('',
attr,
value,
visual_range=[0, 40],
visual_text_color='#fff',
symbol_size=10,
is_visualmap=True,
is_piecewise=True,
visual_split_number=8)
geo.render('好友位置分布.html')
# 生成柱状图
data_top20 = Counter(cities).most_common(20)
bar = Bar('好友所在城市top20', '', title_pos='center', width=1200, height=600)
attr, value = bar.cast(data_top20)
bar.add('',
attr,
value,
is_visualmap=True,
visual_text_color='#fff',
is_more_utils=True,
is_label_show=True)
bar.render('好友所在城市top20.html')
def create_city(cities):
fc = []
for v in cities:
if v != '' and v != '海淀' and v != '延边':
fc.append(v)
# 统计每个城市出现的次数
data = Counter(fc).most_common(5) # 使用Counter类统计出现的次数，并转换为元组列表
print(data)
# 根据城市数据生成地理坐标图
geo = Geo('好友位置分布',
'',
title_color='#fff',
title_pos='center',
width=1200,
height=600,
background_color='#404a59')
attr, value = geo.cast(data)
print(attr)
print(value)
geo.add('',
attr,
value,
visual_text_color='#fff',
symbol_size=15,
is_visualmap=True,
is_piecewise=True)
geo.render('好友位置分布.html')
# 根据城市数据生成柱状图
data_top10 = Counter(fc).most_common(10) # 返回出现次数最多的20条
bar = Bar('好友所在城市TOP10', '', title_pos='center', width=1200, height=600)
attr, value = bar.cast(data_top10)
bar.add('',
attr,
value,
is_visualmap=True,
visual_text_color='#fff',
is_more_utils=True,
is_label_show=True)
bar.render('好友所在城市TOP10.html')
def create_Bar_charts(data, title):
'''柱状图'''
page = Page()
style = Style(
width=800,
height=600,
title_pos="center",
)
chart = Bar(title, **style.init_style)
attr, value = chart.cast(data)
chart.add('',
attr,
value,
mark_line=["average"],
mark_point=["max", "min"],
is_datazoom_show=True,
datazoom_range=[50, 80])
page.add(chart)
return page
def test_base_cast_dict():
adict = {"key": 1, "value": 2}
keys, values = Bar.cast(adict)
eq_(keys, ["key", "value"])
eq_(values, [1, 2])
def test_base_cast_records():
records = [{"key": 1}, {"value": 2}]
keys, values = Bar.cast(records)
eq_(keys, ["key", "value"])
eq_(values, [1, 2])
from pyecharts import Style
# 定义样式
style = Style(title_color='#fff',
title_pos='center',
width=1000,
height=600,
background_color='#404a59',
subtitle_color='#fff')
# 根据城市数据生成柱状图
city_counts = ct_datas.groupby("城市").size()
city_sorted = city_counts.sort_values(ascending=False).head(20)
bar = Bar("《流浪地球》评星人来源排行TOP20", "蜗牛行天下", **style.init_style)
attr, value = bar.cast(list(city_sorted.items()))
bar.add("",
attr,
value,
is_visualmap=True,
visual_range=[0, 2500],
visual_text_color='#fff',
label_color='#fff',
xaxis_label_textcolor='#fff',
yaxis_label_textcolor='#fff',
is_more_utils=True,
is_label_show=True)
# 保存可视化图片文件
bar.render("评星人来源排行-柱状图.html")
# 图像可视化
@itchat.msg_register(TEXT, isGroupChat=True)
def statistics(msg):
if msg['FromUserName'] == groupID:
print('{}发言了:{}'.format(msg['ActualNickName'], msg['Text']))
talkCounter[msg['ActualNickName']] += 1
print(talkCounter)
if __name__ == '__main__':s
itchat.auto_login(hotReload=True)
groupName = '啊哈'
groupID = itchat.search_chatrooms(name=groupName)[0]['UserName']
talkCounter = Counter()
beginTime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
itchat.run()
endTime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
duration = '统计自 {} 到 {} 时间段内'.format(beginTime, endTime)
bar = Bar(title='{}群活跃度表'.format(groupName), subtitle=duration, width=1200, height=600, title_pos='left')
attr, value = bar.cast(talkCounter.most_common(100))
bar.add(
'', attr, value,
xaxis_name='昵称',
yaxis_name='发言活跃度',
is_label_show = True,
is_datazoom_show = True,
datazoom_type = 'both',
xaxis_name_pos = 'end'
)
bar.render('./{}发言活跃度.html'.format(groupName))
# 导入Counter类，用于统计值出现的次数
from collections import Counter
from pyecharts import Bar
# 获取备注名
remarkNames = []
with open('friends.txt', mode='r', encoding='utf-8') as f:
rows = f.readlines()
for row in rows:
remarkName = row.split(',')[1]
if remarkName != '':
remarkNames.append(remarkName)
# 设置分词
words = [
x for x in jieba.cut(str(remarkNames), cut_all=False)
if x not in ['-', ',', '(', ')', '（', '）', ' ', "'"]
] # 排除短横线、逗号、空格、单引号
data_top10 = Counter(words).most_common(10) # 返回出现次数最多的10条
print(data_top10)
bar = Bar('好友分类TOP10', '', title_pos='center', width=1200, height=600)
attr, value = bar.cast(data_top10)
bar.add('',
attr,
value,
visual_range=[0, 200],
is_visualmap=True,
is_label_show=True)
bar.render('好友分类TOP10.html')
def test_base_cast_dict():
adict = {"key": 1, "value": 2}
keys, values = Bar.cast(adict)
eq_(keys, ["key", "value"])
eq_(values, [1, 2])
def test_base_cast_records():
records = [{"key": 1}, {"value": 2}]
keys, values = Bar.cast(records)
eq_(keys, ["key", "value"])
eq_(values, [1, 2])
