from pyecharts.charts import Line
import pyecharts.options as opts
line = Line()
datax = [str(x) for x in range(1995, 2010)] # 横坐标必须是字符串
datay = [
0.32, 0.32, 0.32, 0.32, 0.33, 0.33, 0.34, 0.37, 0.37, 0.37, 0.37, 0.39,
0.41, 0.42, 0.44
]
line.add_xaxis(datax)
line.add_yaxis('Price', datay, is_step=True)
line.set_global_opts(title_opts=opts.TitleOpts(title='美国邮费阶梯图'),
yaxis_opts=opts.AxisOpts(min_=0.3, max_=0.45))
line.render('step.html')
LOGGER = logging.getLogger(__name__)
if __name__ == '__main__':
df = pd.read_csv('C:/git/machine_learning/machine_learning/data/资料01_商铺数据(2)(9).csv')
df1 = df[df['comment'].str.contains('条')]
df1['comment'] = df1['comment'].str.split(' ').str[0]
df1['comment'] = df1['comment'].astype('int')
df1 = df[df['price'].str.contains('￥')]
df1['price'] = df1['price'].str.split('￥').str[-1]
df1['price'] = df1['price'].astype('float')
df1 = df1[df1['price'] > 50]
import tushare as ts
df = ts.get_day_all()
df = df.round(2)
df2 = ts.get_hist_data('600848')
x = df2.index.tolist()[:20]
y = df2[['open', 'close', 'low', 'high']]
y1 = df2['open'].iloc[:20]
ma5 = df2['ma5']
ma10 = df2['ma10']
ma20 = df2['ma20']
line = Line("我的第一个图表", "这里是副标题", width="800px", height="400px")
line.add('开盘价', x, y1)
line.render('C:/git/machine_learning/machine_learning/data/1.html')
from pyecharts.charts import Line
from pyecharts import options
# 准备数据
cuntry = ['美国', '巴西', '印度', '俄罗斯']
data1 = [10000000, 600000, 5000000, 300000]
# 实例化图表对象
line = Line()
# 关联数据
line.add_xaxis(cuntry)
line.add_yaxis(series_name='感染人数', y_axis=data1, is_smooth=True)
# is_smooth=True曲线
# 配置
# 全局设置
line.set_global_opts(title_opts=options.TitleOpts(title='世界严重感染国家'),
toolbox_opts=options.ToolboxOpts())
# 系列设置
line.set_series_opts(markline_opts=options.MarkLineOpts(data=[
options.MarkLineItem(name='平均值', type_='average'),
]))
line.render('templates/折线图.html')
# Confirmed_diagnosis_data=year_rate
# 2. 创建图表对象
line = Line()
# 3. 关联数据
line.add_xaxis(newdate) # x轴
line.add_yaxis(
"30MA",
thirty,
# 设置折线是否平滑
is_smooth=False,
is_hover_animation=True,
is_symbol_show=False)
# 4. 设置
line.set_series_opts(markline_opts=options.MarkLineOpts(
# 设置平均值的标记线
data=[
options.MarkPointItem(type_="min", name="最小值"),
# 设置最大值的标记线
options.MarkPointItem(type_="max", name="最大值")
]))
line.set_global_opts(
xaxis_opts=options.AxisOpts(axislabel_opts=options.LabelOpts(rotate=-30)),
yaxis_opts=options.AxisOpts(name='每日年化收益率'),
title_opts=options.TitleOpts(title="每日年化收益率变化曲线", subtitle="副标题"))
# 5. 数据渲染
line.render("plot_changqi_zhexian.html")
def draw_line_picture(xaxis_data, data, to_file, unit, svg_name, stack,
y_name):
"""
多条线，是否堆叠要确认下
:param label_right:
:param y_name: y轴名称
:param stack: boolean, 是否要堆叠
:param xaxis_data: x轴
:param data: {series_name, data}
:param svg_name: svg文件
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Line(init_opts=opts.InitOpts(
width="800px", height="600px", bg_color='white')).add_xaxis(
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
)
for series_name, y_axis in data.items():
if stack:
bar = bar.add_yaxis(
series_name=series_name,
is_smooth=True,
symbol="circle",
# symbol_size=8,
stack='1',
is_symbol_show=False,
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
# linestyle_opts=opts.LineStyleOpts(width=2),
areastyle_opts=opts.AreaStyleOpts(opacity=1))
else:
bar = bar.add_yaxis(
series_name=series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
bar = bar.set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=y_name,
name_textstyle_opts=opts.TextStyleOpts(font_size=25),
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=25),
),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}", font_size=25, interval=0), ),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
legend_opts=opts.LegendOpts(
pos_left="12%",
pos_top="10%",
orient="vertical",
# backgroundColor='rgb(255, 255, 255)',
item_width=40,
item_height=20,
textstyle_opts=opts.TextStyleOpts(font_size=25)),
)
make_snapshot(snapshot, bar.render(to_file), svg_name) # 生成svg图片
def process_prepare_data(singer_id):
# 找出歌手所有歌曲
all_songs = db.scan(0, match="song:*", count=10000)
target_songs = []
total_score = 0
emotion_list = []
words_count = Counter()
maker_count = Counter()
for song in all_songs[1]:
sid = db.hget(song, "singer_id")
if sid.decode("utf-8") == str(singer_id):
target_songs.append(song)
# 取得所有歌曲并排序
sorted_songs = [(song, int(db.hget(song, "comment_count").decode("utf-8")))
for song in target_songs]
sorted_songs.sort(key=lambda x: x[1], reverse=True)
# log("最热度的前20首歌曲为：{}".format(
# [db.hget(song[0], "name").decode("utf-8").replace("\n", "") for song in sorted_songs[:20]]))
# log("最冷门的前20首歌曲为：{}".format(
# [db.hget(song[0], "name").decode("utf-8").replace("\n", "") for song in sorted_songs[-20:]]))
for song in sorted_songs:
# 歌词处理 song: (song_id, comment_count)
text, maker_info = merge_lyric_text(song[0])
# 歌词的情绪分析
song_name = db.hget(song[0], "name").decode("utf-8")
song_score = db.get(b'score:' + song[0])
if song_score is None:
song_score = process_emotion(text)
db.set(b'score:' + song[0], song_score)
log("歌曲 {} 未发现情绪分，已计算后存入Redis".format(song_name.replace("\n", "")))
total_score += float(song_score)
emotion_list.append((song_name, float(song_score)))
# log("正在处理第{}首歌曲，{}的情绪分为{:.2f}".format(sorted_songs.index(song) + 1, song_name.replace("\n", ""),
# float(song_score)))
for line in maker_info.split("\n"):
if "作词" in line:
line = SnowNLP(line).han
if "：" in line:
maker_count[line.split("：")[1].strip()] += 1
else:
maker_count[line.split(":")[1].strip()] += 1
# 词频统计
words_count.update(process_frequency(text))
maker_analysis(maker_info)
# ----图表部分----
# 歌词情绪分析
emotion_bar = Bar()
emotion_x_data = []
emotion_y_data = []
for item in sorted(emotion_list, key=lambda x: x[1], reverse=True)[:20]:
emotion_x_data.append(item[0])
emotion_y_data.append(round(item[1], 3))
emotion_bar.add_xaxis(emotion_x_data)
emotion_bar.add_yaxis("情绪分值", emotion_y_data)
emotion_bar.set_global_opts(title_opts=opts.TitleOpts(
title="歌词情绪好的前20首歌曲"))
emotion_bar.render("[歌手id-{}]歌词情绪好的前20首歌曲.html".format(singer_id))
# 作词人统计
maker_pie = Pie()
maker_data = []
for name, times in maker_count.most_common(10):
maker_data.append((name, times))
maker_pie.add("出现次数", maker_data)
maker_pie.set_global_opts(title_opts=opts.TitleOpts(title="合作次数最多的作词人前十名",
pos_top="8%"),
legend_opts=opts.LegendOpts(pos_left="15%"))
maker_pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{d}%"))
maker_pie.render("[歌手id-{}]合作次数最多的作词人前十名.html".format(singer_id))
# 歌词高频词语
words_bar = Bar()
word_x_data = []
word_y_data = []
for word, count in words_count.most_common(20):
word_x_data.append(word)
word_y_data.append(count)
words_bar.add_xaxis(word_x_data)
words_bar.add_yaxis("出现次数", word_y_data, category_gap="25%")
words_bar.set_global_opts(title_opts=opts.TitleOpts(title="歌词中高频出现的前20个词"))
words_bar.render("[歌手id-{}]歌词中重复出现的前20个词.html".format(singer_id))
# 评论热门歌曲TOP30
hot_line = Line()
x_data = []
y_data = []
for song in sorted_songs[:20]:
x_data.append(db.hget(song[0], "name"))
y_data.append(song[1])
hot_line.add_xaxis(x_data)
hot_line.add_yaxis("评论数", y_data)
hot_line.set_global_opts(title_opts=opts.TitleOpts(title="评论最火热的前20首歌曲"))
hot_line.render("[歌手id-{}]热门歌曲TOP20.html".format(singer_id))
# 评论冷门歌曲TOP30
# cool_line = Line()
# x_data = []
# y_data = []
# for song in sorted_songs[-20:]:
# x_data.append(db.hget(song[0], "name"))
# y_data.append(song[1])
# cool_line.add_xaxis(x_data)
# cool_line.add_yaxis("评论数", y_data)
# cool_line.set_global_opts(title_opts=opts.TitleOpts(title="评论冷清歌曲前20首"))
# cool_line.render("[歌手id-{}]冷门歌曲TOP20.html".format(singer_id))
return
from pyecharts.charts import Line
X = [28.9] * 10
n = len(X)
y = []
for i in range(n):
y.append(i + 1)
bar = Line()
bar.add_xaxis(y)
bar.add_yaxis("thickness", X)
# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件
# 也可以传入路径参数，如 bar.render("mycharts.html")
bar.render()
border_color="#B44038", #颜色
border_width=10)) #图元的大小
line.add_yaxis(
"湖北新增确诊病例",
hubei_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#4E87ED'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#4E87ED',
border_color="#4E87ED",
border_width=3))
line.add_yaxis(
"其他省份新增病例",
other_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#F1A846'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#F1A846',
border_color="#F1A846",
border_width=3))
line.set_global_opts(
title_opts=opts.TitleOpts(title="新增确诊病例", subtitle='数据来源：丁香园'),
yaxis_opts=opts.AxisOpts(
max_=16000,
min_=1,
type_="log", #坐标轴配置项
splitline_opts=opts.SplitLineOpts(is_show=True), #分割线配置项
axisline_opts=opts.AxisLineOpts(is_show=True))) #坐标轴刻度线配置项
#line.render(path='/home/aistudio/data/新增确诊趋势图.html')
line.render(path='D:/pycharm/projects/data/新增确诊趋势图.html')
chart_line_data = data_pld_zh_1.reset_index()
# print(chart_line_data.info())
chart_line_data = chart_line_data.replace(org_dict)
ln = Line(init_opts=opts.InitOpts(
width='1220px',
height='760px',
))
ln.add_xaxis(headers)
for row in chart_line_data.index:
ln.add_yaxis(chart_line_data.iloc[row, 0],
chart_line_data.iloc[row, 1:].to_list())
ln.set_global_opts(title_opts=opts.TitleOpts(title="蒙商银行2021年各分行月度存款偏离度情况:"),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="10%",
pos_right="2%"))
ln.render("line_base1.html")
# ----------------------------------- 分行偏离度 -----------------------------------
sql_pld_fh = " SELECT I.INDIC_KEY,I.INDIC_NAME,I.ORG_NUM," \
"CONCAT(YEAR(I.STAT_DT),\'年\',MONTH(I.STAT_DT),\'月\') STAT_DT ," \
"ROUND(I.IND_VAL,4) IND_VAL " \
" FROM t09_rm_indic I WHERE 1=1 AND I.CURR_CD = "+p_curr_cd+" AND I.INDIC_KEY = \'YS_JG_164\' " \
" AND I.PERIOD = \'M\' AND I.STAT_DT >= " + p_stat_dt + " " \
" AND I.ORG_NUM IN (SELECT O.ORG_NUM FROM t09_report_org O WHERE O.ORG_LEVEL = \'2\' AND O.ORG_TYPE=\'YW\') "
data_pld_fh = pd.read_sql(sql_pld_fh, engine)\
.rename(columns={'STAT_DT': '月份', 'IND_VAL': '存款偏离度', 'ORG_NUM': '机构'})
data_pld_fh = data_pld_fh.replace(org_dict)
sns.set_theme(style="whitegrid")
ax = sns.stripplot(data=data_pld_fh, x='月份', y='存款偏离度', hue='机构')
plt.rcParams['font.sans-serif'] = ['STXIHEI']
plt.rcParams['axes.unicode_minus'] = False
total_latency)
print('\n', case_name[:-1], " QPS stddev: ", qps_std_dev,
", latency stddev: ", latency_std_dev)
else:
xx = line.find(".lua")
if xx >= 0:
case_name = line
else:
num_thds = line.find("Number of threads: ")
if num_thds >= 0:
num_threads = str(int(line[num_thds + 18:]))
print(line)
finally:
f.close()
lp.set_global_opts(
title_opts=opts.TitleOpts(title="QPS", subtitle="DML only"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
lp.render("sysbench-" + product_name + "-QPS.html")
lp_latency.set_global_opts(
title_opts=opts.TitleOpts(title="Latency", subtitle="DML only"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
lp_latency.render("sysbench-" + product_name + "-latency.html")
X = data[['Num', 'x2', 'x4', 'temp']].values
x_train, x_test, train_Y, test_Y = train_test_split(X,Y,\
test_size=0.2,random_state=44)
net, scaler_X, scaler_Y = net_eval(data)
y_predict = scaler_Y.inverse_transform(net.predict(\
scaler_X.transform(x_test)))
line = Line()
line.add_xaxis(range(1, len(test_Y) + 1))
line.add_yaxis('samples',test_Y.reshape(-1,),\
label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('predict',y_predict,\
label_opts=opts.LabelOpts(is_show=False))
line.set_global_opts(title_opts=opts.TitleOpts(title="line demo"))
line.render('./html/line.html')
# 3D surface
target_feature = (1, 2)
pdp, axes = partial_dependence(net,\
scaler_X.transform(x_train),\
target_feature,\
grid_resolution=30)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].T
names = ['Num', 'x2', 'x4', 'temp']
fig = plt.figure()
ax = Axes3D(fig)
surf = ax.plot_surface(XX,
YY,
password='******',
database='mydb',
charset='utf8')
cur = conn.cursor()
sql = "insert into mydb.summary_cony(qz,ys,sw,zy,create_time)\
values ({},{},{},{},{});".format(
'"' + str(getattr(row, 'qz')) + '"',
'"' + str(getattr(row, 'ys')) + '"',
'"' + str(getattr(row, 'sw')) + '"',
'"' + str(getattr(row, 'zy')) + '"',
'"' + str(getattr(row, 'create_time')) + '"',
)
# cur.execute(sql)
print(sql)
cur.execute(sql)
# print(row)
cur.close()
# commit 提交
conn.commit()
# 关闭MySQL链接
conn.close()
###画Line
a = Line()
a.add_xaxis(['2019-01-01'])
a.add_yaxis("全国确诊", qg_qz, is_smooth=True)
a.add_yaxis("全国死亡", qg_sw, is_smooth=True)
a.set_global_opts(title_opts=opts.TitleOpts(title="Line-smooth"))
a.render('ceshi.html')
color='#B44038', #图元样式配置项
border_color="#B44038", #颜色
border_width=10)) #图元的大小
line.add_yaxis(
"湖北新增确诊病例",
hubei_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#4E87ED'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#4E87ED',
border_color="#4E87ED",
border_width=3))
line.add_yaxis(
"其他省份新增病例",
other_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#F1A846'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#F1A846',
border_color="#F1A846",
border_width=3))
line.set_global_opts(
title_opts=opts.TitleOpts(title="新增确诊病例", subtitle='数据来源：丁香园'),
yaxis_opts=opts.AxisOpts(
max_=16000,
min_=1,
type_="log", #坐标轴配置项
splitline_opts=opts.SplitLineOpts(is_show=True), #分割线配置项
axisline_opts=opts.AxisLineOpts(is_show=True))) #坐标轴刻度线配置项
line.render(path='data/新增确诊趋势图.html')
# 看评论量走势和时间的关系。
df['时间'] = df.评论时间.str.split('-').str[1] + '-' + df.评论时间.str.split('-').str[2]
# print(df.head())
# 下面用冒号：分割‘时间’这栏的数据就能得到我们需要的日期和小时的数据
df['时间'] = df.时间.str.split(':').str[0]
time_num = df.时间.value_counts().sort_index()
time_num[:5]
print(time_num[:5])
# 用下面两行代码设定横坐标为时间，纵坐标表示评论数量。
# 产生数据
x1_line1 = time_num.index.values.astype('str').tolist()
y1_line1 = time_num.values.tolist()
# 绘制面积图
line1 = Line(init_opts=opts.InitOpts(width='1350px', height='750px'))
line1.add_xaxis(x1_line1)
line1.add_yaxis('',
y1_line1,
areastyle_opts=opts.AreaStyleOpts(opacity=0.3),
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_='max', name='最大值')]))
line1.set_global_opts(
title_opts=opts.TitleOpts('各个时段评论人数'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate='30')))
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts())
line1.render(
) # replace the file:///C:/Users/Maggie/Desktop/Udemy-desktop/xiaoxiangxueyuan/pythonCases/xiaoxiang_projects/python_in_16_days/render.html
# #从5月20日8:30发布MV后，评论数量逐渐上升，12点左右有一个快速增加，达到高峰。后续随着时间的推移，评论人数逐渐减少，趋于平缓。
color='#B44038', #图元样式配置项
border_color="#B44038", #颜色
border_width=10)) #图元的大小
line.add_yaxis(
"湖北新增确诊病例",
hubei_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#4E87ED'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#4E87ED',
border_color="#4E87ED",
border_width=3))
line.add_yaxis(
"其他省份新增病例",
other_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#F1A846'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#F1A846',
border_color="#F1A846",
border_width=3))
line.set_global_opts(
title_opts=opts.TitleOpts(title="新增确诊病例", subtitle='数据来源：丁香园'),
yaxis_opts=opts.AxisOpts(
max_=16000,
min_=1,
type_="log", #坐标轴配置项
splitline_opts=opts.SplitLineOpts(is_show=True), #分割线配置项
axisline_opts=opts.AxisLineOpts(is_show=True))) #坐标轴刻度线配置项
line.render(path='/home/aistudio/data/新增确诊趋势图.html')
# -*- coding: utf-8 -*-
from pyecharts.charts import Line
from requests import get
from time import time
from json import loads
from operator import itemgetter
data = loads(
get(f"https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5&callback=&_="
f"{int(time() * 1000)}").json()['data'])["chinaDayList"][-15:]
line = Line()
line.add_xaxis([*map(itemgetter("date"), data)])
line.add_yaxis("确诊", [*map(lambda x: int(x["confirm"]), data)])
line.add_yaxis("疑似", [*map(lambda x: int(x["suspect"]), data)])
line.add_yaxis("治愈", [*map(lambda x: int(x["heal"]), data)])
line.add_yaxis("死亡", [*map(lambda x: int(x["dead"]), data)])
line.render("疫情最新动态.html")
# 时间排序
time = df_all.日期.value_counts()
time.sort_index(inplace=True)
from pyecharts.charts import Line
# 绘制时间走势图
line1 = Line(init_opts=opts.InitOpts(width='1350px', height='750px'))
line1.add_xaxis(time.index.tolist())
line1.add_yaxis('评论热度',
time.values.tolist(),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False))
line1.set_global_opts(title_opts=opts.TitleOpts(title="时间走势图"),
toolbox_opts=opts.ToolboxOpts())
line1.render('评论时间走势图.html')
# 国内城市top10
city_top10 = df_all.城市处理.value_counts()[:12]
city_top10.drop('国外', inplace=True)
city_top10.drop('未知', inplace=True)
from pyecharts.charts import Bar
# 条形图
bar1 = Bar(init_opts=opts.InitOpts(width='1350px', height='750px'))
bar1.add_xaxis(city_top10.index.tolist())
bar1.add_yaxis("城市", city_top10.values.tolist())
bar1.set_global_opts(title_opts=opts.TitleOpts(title="评论者Top10城市分布"),
toolbox_opts=opts.ToolboxOpts())
bar1.render('评论者Top10城市分布条形图.html')
yaxis3d_opts=opts.Axis3DOpts(Faker.week, type_='category'),
zaxis3d_opts=opts.Axis3DOpts(type_='value'))
bar3d.set_global_opts(
visualmap_opts=opts.VisualMapOpts(max_=20), # 设定最大值边界
title_opts=opts.TitleOpts(title='Bar3D Sample'))
bar3d.render(path='pyecharts-bar3d.html')
# 折线图，面积图
line = Line()
line.add_xaxis(Faker.choose())
line.add_yaxis('Sample A', Faker.values(), is_smooth=True)
line.add_yaxis('Sample B',
Faker.values(),
areastyle_opts=opts.AreaStyleOpts(opacity=0.3, color='blue'))
line.set_global_opts(title_opts=opts.TitleOpts(title='Line Sample'))
line.render(path='pyecharts-line.html')
# 饼图
pie = Pie()
data = [list(z) for z in zip(Faker.choose(), Faker.values())] # 创建有二个元素列表的列表
pie.add(
'',
data,
radius=['40%', '80%'], # 内部中空的饼图，指定内环和外环半径
rosetype='area') # 通过半径大小区分的玫瑰饼图
pie.set_global_opts(title_opts=opts.TitleOpts(title='Pie Sample'))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter='{b}:{c}')) # 标签显示格式
pie.render(path='pyecharts-pie.html')
# 特效散点图
effect_scatter = EffectScatter()
dataX = []
dataY = []
for row in results:
# 此处不可以用索引访问：row[0]
dataX.append(row["日期"])
dataY.append(row["订单量"])
return dataX, dataY
except:
print("错误：数据查询操作失败")
finally:
connection.close()
# 执行主函数
if __name__ == '__main__':
print(order_sum_query())
dataX, dataY = order_sum_query()
line = Line()
line.add_xaxis(dataX)
line.add_yaxis("订单量", dataY, is_smooth=True)
line.set_global_opts(title_opts=opts.TitleOpts(title="日订单量历史数据趋势图"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category",
boundary_gap=False))
line.render('line.html')
from pyecharts.charts import Line
from pyecharts.faker import Faker #虚构的数据
from pyecharts import options as opts #配置
from pyecharts.charts import Bar #导入bar图
from pyecharts.globals import ThemeType
import random
line = Line()
line.add_xaxis(Faker.choose())
line.add_yaxis('商家A', Faker.values(), is_smooth=True)
line.add_yaxis('商家B', Faker.values())
line.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-基本示例", subtitle="我是副标题"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=30)), #旋转角度
)
line.render()
from pyecharts.charts import Line
from pyecharts.faker import Faker
from pyecharts import options as opts
line = Line()
# 设置数据
# 添加x轴，Faker.choose(): 使用faker的随机数据生成x轴标签
line.add_xaxis(Faker.provinces)
# 添加y轴，Faker.values(): 使用faker的随机数据生成y轴数值
line.add_yaxis('数据1', Faker.values())
line.set_global_opts(title_opts=opts.TitleOpts(title='Line 基本示例'))
line.render("line.html")
def query2controller(request):
# 给定的省份和城市，城市可选，不指定城市的话可以按照省份进行数据筛选
province, city = '北京', '北京'
cursor = connection.cursor()
# 第一步获取时间跨度
cursor.execute(
'SELECT MIN("Date") AS "Min", MAX("Date") AS "Max" '
'FROM "Data"'
)
fetchResult = cursor.fetchone();
minYear, maxYear = int(fetchResult[0][0:4]), int(fetchResult[1][0:4]) # 上一步得到的结果格式为'YYYYMMDD'，因此需要将年份提取出来并转换为int
years = []
# 建立一个字典，keys预先设置好
typeDict = {}.fromkeys(('CO', 'NO2', 'O3', 'PM10', 'PM2.5', 'SO2'))
# 查询每年的数据
for year in range(minYear, maxYear + 1):
years.append(str(year))
curYear = str(year) + '%'
# 查询粒度为省份
# cursor.execute(
# 'SELECT d."Type" AS "Type", ROUND(AVG(d."Value")::NUMERIC, 2) AS "Value" '
# 'FROM "Data" d, '
# '(SELECT m."ID" AS "ID" '
# 'FROM "Monitors" m, '
# '(SELECT c."name" AS "Name" '
# 'FROM "Provinces" p, "Cities" c '
# 'WHERE p."name" = %s AND p."province" = c."province") tmptable0 '
# 'WHERE m."City" = tmptable0."Name") tmptable1 '
# 'WHERE d."Monitor" = tmptable1."ID" AND d."Date" LIKE %s AND d."Type" <> %s '
# 'GROUP BY d."Type" ',
# [province, curYear, 'AQI']
# )
# 查询粒度为城市
cursor.execute(
'SELECT d."Type" AS "Type", ROUND(AVG(d."Value")::NUMERIC, 2) AS "Value" '
'FROM "Data" d, '
'(SELECT m."ID" AS "ID" '
'FROM "Monitors" m, '
'(SELECT c."name" AS "Name" '
'FROM "Provinces" p, "Cities" c '
'WHERE p."name" = %s AND p."province" = c."province") tmptable0 '
'WHERE m."City" = %s) tmptable1 '
'WHERE d."Monitor" = tmptable1."ID" AND d."Date" LIKE %s AND d."Type" <> %s '
'GROUP BY d."Type" ',
[province, city, curYear, 'AQI']
)
fetchResult = cursor.fetchall()
# 对查询结果的数据进行格式转换，因为每一年的查询结果中包含当年的各项指标的值，但是需要的是把各年的所有同类指标放在一起，因此需要在上面建立字典
for data in fetchResult:
key, value = data[0], float(data[1])
if typeDict[key] is None:
typeDict[key] = [value]
else:
typeDict[key].append(value)
# 创建一个折线图表
line = Line()
line.add_xaxis(xaxis_data = years) # 添加横坐标，每个横坐标值的类型必须是str
# 从字典中提取数据然后添加纵坐标，具体的参数查看官方文档
for key in typeDict.keys():
line.add_yaxis(
series_name = key,
y_axis = typeDict.get(key),
label_opts = opts.LabelOpts(is_show = False)
)
# 对折线图进行全局设置
line.set_global_opts(
title_opts = opts.TitleOpts(title="{}年~{}年{}\n空气质量指数".format(minYear, maxYear, province)), # 如果显示某个城市的数据的话参数中的province改为city
tooltip_opts = opts.TooltipOpts(trigger = "axis"),
yaxis_opts = opts.AxisOpts(
type_ = "value",
axistick_opts = opts.AxisTickOpts(is_show = True),
splitline_opts = opts.SplitLineOpts(is_show = True),
),
xaxis_opts = opts.AxisOpts(type_ = "category", boundary_gap = False),
)
line.render('templates/line.html') # 将查询结果的图表存为单独的一个html文件
return render(request, 'HomePage.html') # 在返回的这个html中包含上面的查询结果
def draw_line_with_two_y(xaxis_data, y1_series_name, y1_axis, y2_series_name,
y2_axis, y3_series_name, y3_axis, y1_name, y2_name,
to_file, unit, svg_name):
"""
左边是 两条线； 右边是 一条线。
:param svg_name: svg文件
:param xaxis_data: 横轴
:param y1_series_name: 左边第一条线的系列名
:param y1_axis: 左边第一条线的数值
:param y2_series_name: 左边第二条线的系列名
:param y2_axis: 左边第二条线的数值
:param y3_series_name: 右边第一条线的系列名
:param y3_axis: 右边第一条线的数值
:param y1_name: 左边的纵轴名称
:param y2_name: 右边的纵轴名称
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Line(init_opts=opts.InitOpts(
width="800px", height="600px", bg_color='white'
)).add_xaxis(
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
).add_yaxis(
series_name=y1_series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y1_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name=y2_series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y2_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name=y3_series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y3_axis,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).extend_axis(yaxis=opts.AxisOpts(
type_="value",
name=y2_name,
# min_=0,
# max_=25,
position="right",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=15),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)),
name_textstyle_opts=opts.TextStyleOpts(font_size=15))).set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=y1_name,
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}",
font_size=15),
name_textstyle_opts=opts.TextStyleOpts(font_size=15)),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}", font_size=15), ),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
legend_opts=opts.LegendOpts(
item_width=25,
item_height=10,
textstyle_opts=opts.TextStyleOpts(font_size=15)),
)
make_snapshot(snapshot, bar.render(to_file), svg_name) # 生成svg图片
def handle_time_list(time_list):
"""
处理float格式的时间数据为 年/月/日 小时:分钟
time_list: 时间列表
return: 处理好的时间数据迭代器
"""
new_time_list = [xlrd.xldate_as_datetime(i, 0).strftime(r'%Y/%m/%d %H:%M') for i in time_list]
return new_time_list
data = xlrd.open_workbook('C:\\Users\\Administrator\\Desktop\\新建文件夹\\国——月\\12月.xlsx')
table = data.sheets()[0]
PM2_5 = table.col_values(0)
PM10 = table.col_values(1)
CO = table.col_values(2)
NO2 = table.col_values(3)
SO2 = table.col_values(4)
O3 = table.col_values(5)
time = table.col_values(6)
line = Line()
line.add_xaxis(handle_time_list(time[1:]))
line.add_yaxis('PM2.5', PM2_5[1:])
line.add_yaxis('PM10', PM10[1:])
line.add_yaxis('CO', CO[1:])
line.add_yaxis('NO2', NO2[1:])
line.add_yaxis('SO2', SO2[1:])
line.add_yaxis('O3', O3[1:])
line.render('./china/12.html')
# make_snapshot(snapshot, line.render(), 'test.png')
# -*- encoding: utf-8 -*-
# Project :zs
# Time :2019/7/22 下午7:32
# BY :FormatFa
from pyecharts.charts import Bar,Line
b = Bar()
b.add_xaxis(['one','two','three'])
b.add_yaxis('s1',[1,2,3])
l = Line()
l.add_xaxis(['one','two','three','four'])
l.add_yaxis('s2',[1,2,3])
l.overlap(b)
print(b)
print(l.dump_options())
l.render('test.html')
color='#B44038', #图元样式配置项
border_color="#B44038", #颜色
border_width=10)) #图元的大小
line.add_yaxis(
"湖北新增确诊病例",
hubei_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#4E87ED'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#4E87ED',
border_color="#4E87ED",
border_width=3))
line.add_yaxis(
"其他省份新增病例",
other_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#F1A846'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#F1A846',
border_color="#F1A846",
border_width=3))
line.set_global_opts(
title_opts=opts.TitleOpts(title="新增确诊病例" + today, subtitle='数据来源：丁香园'),
yaxis_opts=opts.AxisOpts(
max_=16000,
min_=1,
type_="log", #坐标轴配置项
splitline_opts=opts.SplitLineOpts(is_show=True), #分割线配置项
axisline_opts=opts.AxisLineOpts(is_show=True))) #坐标轴刻度线配置项
line.render(path='/Users/liuhui/Desktop/COVID19/html/新增确诊趋势图' + today +
'.html')
# encoding: utf-8
from opendatatools import aqi
from pyecharts.charts import Line
import pandas as pd
if __name__ == '__main__':
df_aqi = aqi.get_daily_aqi_onecity('北京市')
df_aqi.set_index('date', inplace=True)
df_aqi.sort_index(ascending=True, inplace=True)
df_aqi = df_aqi[df_aqi.index >= "2018-01-01"]
axis_x = df_aqi.index
axis_y = df_aqi['aqi']
line = Line("北京AQI趋势图")
line.add("aqi curve for beijing", axis_x, axis_y, mark_point=["average"])
line.render("aqi_bj_curve.html")
hubei_statis = statistics__data['湖北']
# 湖北以外的新增趋势
other_statis = [all_statis[i] - hubei_statis[i] for i in range(len(dateId))]
line = Line()
line.add_xaxis(dateId)
line.add_yaxis("全国新增确诊病例", #图例
all_statis, #数据
is_smooth=True, #是否平滑曲线
linestyle_opts=opts.LineStyleOpts(width=4, color='#B44038'),#线样式配置项
itemstyle_opts=opts.ItemStyleOpts(color='#B44038', #图元样式配置项
border_color="#B44038", #颜色
border_width=10)) #图元的大小
line.add_yaxis("湖北新增确诊病例", hubei_statis, is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#4E87ED'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#4E87ED',
border_color="#4E87ED",
border_width=3))
line.add_yaxis("其他省份新增病例", other_statis, is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#F1A846'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#F1A846',
border_color="#F1A846",
border_width=3))
line.set_global_opts(title_opts=opts.TitleOpts(title="新增确诊病例", subtitle='数据来源：丁香园'),
yaxis_opts=opts.AxisOpts(max_=16000, min_=1, type_="log", #坐标轴配置项
splitline_opts=opts.SplitLineOpts(is_show=True),#分割线配置项
axisline_opts=opts.AxisLineOpts(is_show=True)))#坐标轴刻度线配置项
line.render(path='html/新增确诊趋势图.html')
x = [e for e in year_com_last['year']]
y = [int(e) for e in year_com_last['mean']]
line = Line()
line.add_xaxis(x)
line.add_yaxis('Delhi', y)
line.set_series_opts(
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值'),
]
),
label_opts=opts.LabelOpts(is_show=False),
)
line.set_global_opts(
title_opts=opts.TitleOpts(
title='2015-2020印度德里PM2.5年度走势图',
pos_left='center',
),
legend_opts=opts.LegendOpts(
pos_left='left',
orient='vertical',
)
)
line.render('line_PM2.5_year.html')
dateAll_info['date'] = dateAll['date']
dateAll_list.append(dateAll_info)
dateAll_df = pd.DataFrame(dateAll_list)
dateAll_df = dateAll_df.select_dtypes(exclude=['bool'])
dateAll_df.to_csv("g:/sp/国外历史数据.csv")
dateAll_confirm = dateAll_df.confirm.tolist()
dateAll_heal = dateAll_df.heal.tolist()
dateAll_date = dateAll_df.date.tolist()
line = Line() # 海外疫情历史趋势
x = dateAll_date
y1 = dateAll_confirm
y2 = dateAll_heal
line.add_xaxis(x)
line.add_yaxis('累计确诊', y1)
line.add_yaxis('累计治愈', y2)
line.render("g:/sp/海外疫情历史趋势.html")
# 获取国家名称和数据
print(data_dict['globalStatis'])
areaAll_list = list()
for areaAll in data_dict.get('foreignList'):
areaAll_info = areaAll
areaAll_list.append(areaAll_info)
areaAll_df = pd.DataFrame(areaAll_list)
areaAll_df.drop(['children'], axis=1, inplace=True)
areaAll_df.to_csv("g:/sp/国外地区疫情数据.csv")
areaAll_name = areaAll_df.name.tolist()
areaAll_confirm = areaAll_df.confirm.tolist()
areaAll_dead = areaAll_df.dead.tolist()
areaAll_heal = areaAll_df.heal.tolist()