def Normal(params,chartname="",filename="usage_ana"):
'''
params：list，内嵌元组或列表的列表，格式为：[("2019-2-8",32)]
chartname：str，图表名
filename：str，文件名，存储在output文件夹下
'''
day_counter = []
for i in params:
day_counter.append(i[1])
heatmap = HeatMap(chartname, "", width=1500)
heatmap.add(
"",
params,
is_label_show=True,
tooltip_formatter='{c}',
is_calendar_heatmap=True,
visual_text_color="#000",
visual_range_text=["", ""],
visual_range=[1, int(max(day_counter))],
calendar_cell_size=["auto", 30],
is_visualmap=True,
# calendar_date_range="2015",
calendar_date_range=[params[0][0], params[-1][0]],
visual_orient="horizontal",
visual_pos="center",
visual_top="80%",
is_piecewise=True,
)
heatmap.render(path="../../output/"+filename+".html")
heatmap.render(path="../../output/"+filename+".pdf")
def view_date_hot(self):
"""
需要的数据
self.start_year str() 用于显示在网页和html文件名上
self.data_dict 展示的数据：
[["year-month-day", value],["year-month-day", value],["year-month-day", value]]
"""
print("开始生成 %s年 %s每日销货汇总热力图" % (self.start_year, self.table_name))
heat_map = HeatMap("%s每日销货汇总热力图" % self.table_name,
"%s年" % self.start_year,
width=1200,
page_title="Calendar")
heat_map.add(
"",
self.data_dict,
is_calendar_heatmap=True,
visual_text_color='#000',
visual_range_text=['', ''],
visual_range=[0, max(self.data_dict, key=lambda x: x[1])[1]],
calendar_cell_size=['auto', 30],
is_visualmap=True,
calendar_date_range=str(self.start_year),
visual_pos="5%",
visual_top="75%",
visual_orient='horizontal',
is_toolbox_show=False)
heat_map.render(
path=self.BASE_PATH +
"{}{}_data_hot.html".format(self.start_year, self.html_name))
def test_heatmap_calendar():
import datetime
begin = datetime.date(2017, 1, 1)
end = datetime.date(2017, 12, 31)
data = [[
str(begin + datetime.timedelta(days=i)),
random.randint(1000, 25000)
] for i in range((end - begin).days + 1)]
heatmap = HeatMap("日历热力图示例", "某人 2017 年微信步数情况", width=1100)
heatmap.add(
"",
data,
is_calendar_heatmap=True,
visual_text_color='#000',
visual_range_text=['', ''],
visual_range=[1000, 25000],
calendar_cell_size=['auto', 30],
is_visualmap=True,
calendar_date_range="2017",
visual_orient="horizontal",
visual_pos="center",
visual_top="80%",
is_piecewise=True,
)
heatmap.render()
def heatmap(self, year):
das = {}
for item in self.data:
d = datetime.fromtimestamp(item[2])
if (d.year == year):
das[date(d.year, d.month, d.day)] = das.get(
date(d.year, d.month, d.day), 0) + item[1]
result = [[str(key), das[key]] for key in das]
y_min = min([das[key] for key in das])
y_max = max([das[key] for key in das])
heatmap = HeatMap('说说点赞之日历热力图', title_pos='center', width=1100)
heatmap.add("",
result,
is_calendar_heatmap=True,
visual_text_color='#000',
visual_range_text=["", ""],
visual_range=[y_min, y_max],
calendar_cell_size=["auto", 30],
is_visualmap=True,
calendar_date_range=str(year),
visual_orient="horizontal",
visual_pos='center',
visual_top='80%',
is_piecewise=True)
heatmap.render(self.path + 'heatmap' + str(year) + '_' +
str(int(time.time())) + '.html')
webbrowser.open_new_tab(self.path + 'heatmap' + str(year) + '_' +
str(int(time.time())) + '.html')
self.ins.analysis_pb['value'] = 100
self.ins.analysis_lb.configure(text='完毕')
def test_heatmap_calendar():
import datetime
begin = datetime.date(2017, 1, 1)
end = datetime.date(2017, 12, 31)
data = [
[str(begin + datetime.timedelta(days=i)), random.randint(1000, 25000)]
for i in range((end - begin).days + 1)
]
heatmap = HeatMap("日历热力图示例", "某人 2017 年微信步数情况", width=1100)
heatmap.add(
"",
data,
is_calendar_heatmap=True,
visual_text_color="#000",
visual_range_text=["", ""],
visual_range=[1000, 25000],
calendar_cell_size=["auto", 30],
is_visualmap=True,
calendar_date_range="2017",
visual_orient="horizontal",
visual_pos="center",
visual_top="80%",
is_piecewise=True,
)
heatmap.render()
def test_heatmap_default():
data = [
[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例")
heatmap.add("热力图直角坐标系", X_TIME, Y_WEEK, data, is_visualmap=True,
visual_text_color="#000", visual_orient='horizontal',
xaxis_name='XNAME', yaxis_name='YNAME',
yaxis_name_pos='end', yaxis_name_gap=5)
heatmap.render()
def test_heatmap_default():
data = [
[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例")
heatmap.add("热力图直角坐标系", X_TIME, Y_WEEK, data, is_visualmap=True,
visual_text_color="#000", visual_orient='horizontal',
xaxis_name='XNAME', yaxis_name='YNAME',
yaxis_name_pos='end', yaxis_name_gap=5)
heatmap.render()
def test_heatmap():
import random
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例")
heatmap.add("热力图直角坐标系",
X_TIME,
Y_WEEK,
data,
is_visualmap=True,
visual_text_color="#000",
visual_orient='horizontal')
heatmap.render()
def test_heatmap():
# heatmap_0
import random
x_axis = ["12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
"12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"]
y_aixs = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例")
heatmap.add("热力图直角坐标系", x_axis, y_aixs, data, is_visualmap=True,
visual_text_color="#000", visual_orient='horizontal')
heatmap.show_config()
heatmap.render()
def generate_heatmap():
data = pd.read_excel(input_file_path + '每日销量.xlsx', sheet_name=0)
print(data.head())
x_axis = data['week'].tolist()
data['week'] = data['week'].str.replace('week', '').map(int) - 1
data = data.set_index('week')
y_axis = data.columns.tolist()
data.columns = range(0, 7)
data = data.stack().reset_index()
data.columns = ['week', 'day', 'amount']
heatmap = HeatMap()
heatmap.add('全年产量情况', x_axis, y_axis, data.values.tolist(), is_visualmap=True,
visual_text_color="#000", visual_orient='horizontal')
heatmap.render(output_file_path + 'htmls/全年产量情况热力图.html')
def heatmap_2attr(df, attr1, attr2, func=len, value=None):
if value==None:
df2=df.groupby([attr1, attr2])[attr1].agg([func])
else:
df2=df.groupby([attr1, attr2])[value].agg([func])
df2=df2.reset_index()
x_axis = list(set(df2[attr1]))
y_axis = list(set(df2[attr2]))
data = [[x_axis.index(i), y_axis.index(j), df2.loc[(df2[attr1]==i) & (df2[attr2]==j), func.__name__]]
for i in x_axis for j in y_axis]
print(data)
heatmap = HeatMap()
heatmap.add("热力图直角坐标系", x_axis, y_axis, data, is_visualmap=True,
visual_range=[0, max(df2[func.__name__])],
visual_text_color="#000", visual_orient='horizontal')
heatmap.render()
def reli(t):
x_axis, y_axis, y = [], [], []
for i in t:
x_axis.append(i[0])
Str = ''.join(i[1].split(',')) # 注意这里i[2]是一个对象
y.append(int(int(Str) / 200))
# print(x_axis, y)
y_axis = ["one", "two", "three", "four", "five", "six"]
da, data = [], []
for i in y:
# 生成7个随机数，是他们的和为i
while 1:
n1 = random.randint(0, i)
n2 = random.randint(0, i)
n3 = random.randint(0, i)
n4 = random.randint(0, i)
n5 = random.randint(0, i)
n6 = random.randint(0, i)
su = n1 + n2 + n3 + n4 + n5 + n6
if su == i:
listRandom = n1, n2, n3, n4, n5, n6
break
da.append(listRandom)
for i in range(50):
for j in range(6):
x = [i, j, da[i][j]]
data.append(x)
# print(data)
heatmap = HeatMap()
heatmap.add(
"电影热力图",
x_axis,
y_axis,
data,
is_visualmap=True,
visual_text_color="#000",
visual_orient="horizontal",
)
heatmap.render(pathConfig.HTLMSREPATH + '电影热力图.html')
def test_heatmap():
# heatmap_0
import random
x_axis = [
"12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a",
"11a", "12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p",
"10p", "11p"
]
y_axis = [
"Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday",
"Sunday"
]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例")
heatmap.add("热力图直角坐标系",
x_axis,
y_axis,
data,
is_visualmap=True,
visual_text_color="#000",
visual_orient='horizontal')
heatmap.render()
def picture_heatmap(file_path_hcs):
df_hcs = pd.read_csv(file_path_hcs,
encoding='utf-8',
usecols=['Date', 'AQI'])
data_hcs = []
for index_hcs, col_hcs in df_hcs.iterrows():
data_hcs.append([col_hcs[0], col_hcs[1]])
hm_hcs = HeatMap('广州市2018年的空气质量指数日历热力图——黄彩思')
# 参照https://www.jianshu.com/p/fc8c78e0df98
hm_hcs.add(
'',
data_hcs,
is_calendar_heatmap=True,
is_visualmap=True,
calendar_date_range='2018',
calendar_cell_size=['auto', 30],
is_toolbox_show=False,
visual_range=[10, 200],
visual_split_number=5,
is_piecewise=True,
visual_orient="horizontal",
visual_pos="center",
)
hm_hcs.render('hearmap_AQI.html')
def corr_heatmap(data,report=False,file_name='Pairwise_Heatmap.html'):
# draing correlation heatmap for data
Temp_pairwise_df = data.corr()
Temp_pairwise_df = Temp_pairwise_df.fillna(0)
Temp_pairwise_df = Temp_pairwise_df*100
Temp_pairwise_df = np.round(Temp_pairwise_df,2)
x_axis = list(Temp_pairwise_df.index)
y_axis = list(Temp_pairwise_df.columns)
data = [[x_axis[i],y_axis[j],Temp_pairwise_df[x_axis[i]][y_axis[j]]] for i in range(len(x_axis)) for j in range(len(y_axis))]
heatmap = HeatMap()
heatmap.add("热力图直角坐标系", x_axis, y_axis, data, is_visualmap=True,visual_range=[0,100],
visual_text_color="#000", visual_orient='horizontal',is_label_emphasis=True,label_emphasis_pos='inside',
label_emphasis_textcolor="#000",label_emphasis_textsize=15,tooltip_tragger='item',tooltip_formatter='{c}',
is_datazoom_show=True,datazoom_type='both',visual_top=0.1,is_more_utils=True)
if report == False:
return heatmap
else:
return heatmap.render(file_name)
from pyecharts import HeatMap
import pandas as pd
import numpy as np
sim_frame = pd.read_csv('./data/philosophy.csv', index_col=0, encoding='utf8')
names = list(sim_frame.index)
data_array = np.round(np.array(sim_frame) ** 0.25 * 100, 0).astype(int)
x_len, y_len = data_array.shape
data = [[i, j, data_array[i][j]] for i in range(x_len) for j in range(y_len)]
x_axis = names
y_axis = names
heatmap = HeatMap(title='哲学', subtitle='by @MiracleXYZ')
heatmap.add(
"相似度矩阵", x_axis, y_axis, data, is_visualmap=True,
visual_text_color="#000", visual_orient='vertical',
visual_range=[0, 100],
visual_range_text=['低', '高'],
visual_pos='left',
label_pos='inside'
)
heatmap.render('./data/philosophy_heatmap.html')
# #比较matlab的热力图
from pyecharts import HeatMap
data_matrix = res[:, :, 1]
dataa = [[i, j, data_matrix[i, j]] for i in range(34) for j in range(24)]
heatmap = HeatMap(width=2000, height=750)
heatmap.add(
"话题热力图(观测值)",
range(34),
range(24),
dataa,
is_visualmap=True,
visual_text_color="#000",
visual_orient="horizontal",
)
heatmap.render(path='热力图.html')
#关于北京 天猫双十一这一话题的预测效果
from pyecharts import Line
line = Line('北京-天猫双十一')
x = rX1.data[10, :, 0]
y = data_tensor.data[10, :, 0]
line.add(
'prediction',
list(range(24)),
x,
is_smooth=True,
is_fill=True,
line_opacity=0.2,
area_opacity=0.4,
def test_grid():
# grid_0
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%")
line = Line("折线图示例", title_top="50%")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_top="50%")
bar.grid(line.get_series(), grid_top="60%")
bar.show_config()
bar.render()
# grid_1
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter(width=1200, is_grid=True)
scatter.add("散点图示例", v1, v2, grid_left="60%", legend_pos="70%")
es = EffectScatter()
es.add("动态散点图示例", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_pos="20%")
scatter.grid(es.get_series(), grid_right="60%")
scatter.show_config()
scatter.render()
# grid_2
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%", is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%", grid_left="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%", grid_left="60%", legend_pos="80%")
line = Line("折线图示例")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0], effect_scale=6,
legend_top="50%", legend_pos="20%")
bar.grid(line.get_series(), grid_bottom="60%", grid_right="60%")
bar.grid(scatter.get_series(), grid_top="60%", grid_left="60%")
bar.grid(es.get_series(), grid_top="60%", grid_right="60%")
bar.show_config()
bar.render()
# grid_3
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"],
mark_line=["average"], grid_right="65%")
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例", title_pos="45%")
pie.add("", attr, v1, radius=[30, 55], legend_pos="65%", legend_orient='vertical')
line.grid(pie.get_series(), grid_left="60%")
line.show_config()
line.render()
# grid_4
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"],
mark_line=["average"], grid_right="60%")
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%", grid_right="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
value = [20, 40, 60, 80, 100, 120]
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例", title_pos="60%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)], v1, legend_pos="80%")
line.grid(kline.get_series(), grid_left="55%")
line.show_config()
line.render()
# grid_5
import random
x_axis = ["12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
"12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"]
y_aixs = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例", height=700, is_grid=True)
heatmap.add("热力图直角坐标系", x_axis, y_aixs, data, is_visualmap=True, visual_top="45%",
visual_text_color="#000", visual_orient='horizontal', grid_bottom="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", title_top="52%")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True, legend_top="50%")
heatmap.grid(bar.get_series(), grid_top="60%")
heatmap.show_config()
heatmap.render()
# ----------------------------------------------------------------------------
# heatmap
import random
from pyecharts import HeatMap
x_axis = [
"12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
"12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"]
y_axis = [
"Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap()
heatmap.add("热力图直角坐标系", x_axis, y_axis, data, is_visualmap=True,
visual_text_color="#000", visual_orient='horizontal')
heatmap.render()
def heatmap_2attr(df, attr1, attr2, func=len, value=None):
if value==None:
df2=df.groupby([attr1, attr2])[attr1].agg([func])
else:
df2=df.groupby([attr1, attr2])[value].agg([func])
df2=df2.reset_index()
x_axis = list(set(df2[attr1]))
y_axis = list(set(df2[attr2]))
data = [[x_axis.index(i), y_axis.index(j), df2.loc[(df2[attr1]==i) & (df2[attr2]==j), func.__name__]]
for i in x_axis for j in y_axis]
print(data)
from pyecharts import HeatMap
import numpy
x_axis = [
"12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
"12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"
]
y_aixs = [
"Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday",
"Sunday"
]
data = [[i, j, numpy.random.randint(0, 15)] for i in range(24)
for j in range(7)]
heatmap = HeatMap()
heatmap.add("热力图直角坐标系",
x_axis,
y_aixs,
data,
is_visualmap=True,
visual_text_color="#000",
visual_orient="horizontal",
is_more_utils=True)
heatmap.render(path="heatmap.html")
print(day_order_sort)
heatmap = HeatMap("日历热力图示例", "JDATA B榜每日行为总量", width=2000)
heatmap.add("",
day_order,
is_calendar_heatmap=True,
visual_text_color='#000',
visual_range_text=['', ''],
visual_range=[200, 6000],
calendar_cell_size=['auto', 40],
is_visualmap=True,
calendar_date_range=["2016-9-1", "2017-8-31"],
visual_orient="horizontal",
visual_pos="center",
is_piecewise=True,
visual_split_number=20)
heatmap.render("JData_action_b.html")
# 286 2017-06-14 4808
# 322 2017-07-20 5052
# 341 2017-08-08 5278
# 278 2017-06-06 5471
# 273 2017-06-01 5601
# 102 2016-12-12 5926
# 222 2017-04-11 5996
# 292 2017-06-20 6061
# 288 2017-06-16 6299
# 285 2017-06-13 6519
# 281 2017-06-09 6530
# 284 2017-06-12 6943
# 291 2017-06-19 7059
# 289 2017-06-17 9384
dfv.columns,
data,
xaxis_interval=0,
xaxis_label_textsize=10,
xaxis_name_gap=30,
yaxis_label_textsize=10,
yaxis_name_gap=30,
yaxis_rotate=0,
is_visualmap=True,
visual_range=[
np.nanmin([x[2] for x in data]),
np.nanmax([x[2] for x in data])
],
visual_text_color="#000",
visual_orient='horizontal')
heatmap.render(title + '.html')
##################### step 4 #########################
# box-diagram
# 01 attr column 1
y_attr = 'content_rating'
dfr = pde.r_table(df[y_attr])
print(dfr.level[:5])
x_axis = ['G', 'PG', 'PG-13', 'R', 'Not Rated']
x_axis2 = ["bad", "below average", "average", "good", "very good"]
cut_column = 'imdb_score'
x_attr = cut_column + '_level'
Python 3.6.3 (v3.6.3:2c5fed8, Oct 3 2017, 17:26:49) [MSC v.1900 32 bit (Intel)] on win32
Type "copyright", "credits" or "license()" for more information.
>>> # Part 14 Create Heatmap with Pyecharts
>>> from pyecharts import HeatMap
>>> import random
>>> x_axis=["12a","1a","2a","3a","4a","5a","6a","7a","8a","9a","10a","11a","12p","1p","2p","3p","4p","5p","6p","7p","8p","9p","10p","11p",]
>>> y_axis=["Saturday","Friday","Thursday","Wednesday","Tuesday","Monday","Sunday"]
>>> data=[[i,j,random.randint(0,50)] for i in range(24) for j in range(7)]
>>> heatmap=HeatMap()
>>> heatmap.add("Rectangular Coordinate Heatmap",x_axis,y_axis,data,is_visualmap=True,visual_text_color="#000",visual_orient="horizontal")
<pyecharts.charts.heatmap.HeatMap object at 0x038C1A50>
>>> heatmap.render()
>>>
dataa = [[i, j, data_matrix[i, j]] for i in range(34) for j in range(50)]
x_axis = geo_list
y_axis = hash_list
data = data_matrix
heatmap = HeatMap(width=2000, height=750)
heatmap.add(
"话题热力图(观测值)",
x_axis,
y_axis,
dataa,
is_visualmap=True,
visual_text_color="#000",
visual_orient="horizontal",
)
heatmap.render(path='话题热力图(观测值).html')
# 矩阵补全
X_incomplete = data_matrix
missing_mask = (X_incomplete == 0)
X_incomplete[missing_mask] = np.nan
X_filled_knn = KNN(k=6).fit_transform(X_incomplete)
X_filled_nnm = NuclearNormMinimization(min_value=0).fit_transform(X_incomplete)
#knn预测数据热力图
from pyecharts import HeatMap
dataa = [[i, j, X_filled_knn[i, j]] for i in range(34) for j in range(50)]
x_axis = geo_list
y_axis = hash_list
data = data_matrix
[2, 5, 17.53], [2, 6, 18.56], [2, 7, 17.53], [3, 0, 17.57],
[3, 1, 17.48], [3, 2, 17.54], [3, 3, 80.99],
[3, 4, 18.53], [3, 5, 17.5], [3, 6, 85.1], [3, 7, 17.5], [4, 0, 18.53],
[4, 1, 17.5], [4, 2, 85.1], [4, 3, 17.5], [4, 4, 17.56], [4, 5, 80.84],
[4, 6, 17.53], [4, 7, 17.47], [5, 0, 84.27], [5, 1, 17.53],
[5, 2, 18.56], [5, 3, 17.53], [5, 4, 18.53], [5, 5,
17.5], [5, 6, 85.1],
[5, 7, 17.5], [6, 0, 18.53], [6, 1, 17.5], [6, 2, 85.1], [6, 3, 17.5],
[6, 4, 84.27], [6, 5, 17.53], [6, 6, 18.56], [6, 7, 17.53],
[7, 0, 18.53], [7, 1, 17.5], [7, 2, 85.1], [7, 3, 17.5], [7, 4, 17.56],
[7, 5, 80.84], [7, 6, 17.53], [7, 7, 17.47]]
x_axis = [
"12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
"12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"
]
y_axis = [
"Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday",
"Sunday"
]
heatmap = HeatMap("a heatmap", "this is subtitle")
heatmap.add("GPUP2PBandWidth",
GPUS,
GPUS,
data,
is_visualmap=True,
visual_text_color="#000",
visual_orient='horizontal',
label_emphasis_textcolor='#000')
heatmap.render("./MYheatMap2.html")
names=["Date", "Quality_grade", "AQI", "AQI_rank", "PM"])
v1 = ["{}".format(int(i)) for i in np.array(df['PM'])]
begin = datetime.date(2017, 1, 1)
end = datetime.date(2017, 12, 31)
data = [[str(begin + datetime.timedelta(days=i)), v1[i]]
for i in range((end - begin).days + 1)]
heatmap = HeatMap("2017年天津PM2.5指数日历图",
title_pos='40%',
title_top='10',
width=1200,
height=600)
heatmap.add(
"",
data,
is_calendar_heatmap=True,
visual_text_color="#000",
visual_range_text=["", ""],
visual_range=[0, 300],
calendar_cell_size=["auto", 30],
is_visualmap=True,
calendar_date_range="2017",
visual_orient="horizontal",
visual_pos="26%",
visual_top="70%",
is_piecewise=True,
visual_split_number=6,
)
heatmap.render('2017年天津PM2.5指数日历图.html')
def test_grid():
# grid_0
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%")
line = Line("折线图示例", title_top="50%")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"])
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_top="50%")
bar.grid(line.get_series(), grid_top="60%")
bar.show_config()
bar.render()
# grid_1
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter(width=1200, is_grid=True)
scatter.add("散点图示例", v1, v2, grid_left="60%", legend_pos="70%")
es = EffectScatter()
es.add("动态散点图示例", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_pos="20%")
scatter.grid(es.get_series(), grid_right="60%")
scatter.show_config()
scatter.render()
# grid_2
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%", is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%", grid_left="60%")
bar.add("商家B",
attr,
v2,
is_stack=True,
grid_bottom="60%",
grid_left="60%",
legend_pos="80%")
line = Line("折线图示例")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"])
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6,
legend_top="50%",
legend_pos="20%")
bar.grid(line.get_series(), grid_bottom="60%", grid_right="60%")
bar.grid(scatter.get_series(), grid_top="60%", grid_left="60%")
bar.grid(es.get_series(), grid_top="60%", grid_right="60%")
bar.show_config()
bar.render()
# grid_3
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
grid_right="65%")
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例", title_pos="45%")
pie.add("",
attr,
v1,
radius=[30, 55],
legend_pos="65%",
legend_orient='vertical')
line.grid(pie.get_series(), grid_left="60%")
line.show_config()
line.render()
# grid_4
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温",
attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"],
mark_line=["average"],
grid_right="60%")
line.add("最低气温",
attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"],
mark_line=["average"],
legend_pos="20%",
grid_right="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
value = [20, 40, 60, 80, 100, 120]
v1 = [[2320.26, 2320.26, 2287.3,
2362.94], [2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58,
2440.38], [2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例", title_pos="60%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)],
v1,
legend_pos="80%")
line.grid(kline.get_series(), grid_left="55%")
line.show_config()
line.render()
# grid_5
import random
x_axis = [
"12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a",
"11a", "12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p",
"10p", "11p"
]
y_aixs = [
"Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday",
"Sunday"
]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例", height=700, is_grid=True)
heatmap.add("热力图直角坐标系",
x_axis,
y_aixs,
data,
is_visualmap=True,
visual_top="45%",
visual_text_color="#000",
visual_orient='horizontal',
grid_bottom="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", title_top="52%")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True, legend_top="50%")
heatmap.grid(bar.get_series(), grid_top="60%")
heatmap.show_config()
heatmap.render()
dom = df[['Date', 'PM']]
list1 = []
for i, j in zip(dom['Date'], dom['PM']):
time_list = i.split('/')
time = datetime.date(int(time_list[0]), int(time_list[1]),
int(time_list[2]))
PM = int(j)
list1.append([str(time), str(PM)])
heatmap = HeatMap("2018年北京PM2.5指数日历图",
title_pos='40%',
title_top='10',
width=800,
height=400)
heatmap.add(
"",
list1,
is_calendar_heatmap=True,
visual_text_color="#000",
visual_range_text=["", ""],
visual_range=[0, 300],
calendar_cell_size=["auto", 30],
is_visualmap=True,
calendar_date_range="2018",
visual_orient="horizontal",
visual_pos="26%",
visual_top="70%",
is_piecewise=True,
visual_split_number=5,
)
heatmap.render('2018年北京PM2.5指数日历图.html')