绘图下拉选择不会正确更新绘图 [英] Plotly dropdown selection does not update plots correctly
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问题描述
我想创建一个可以选择保存独立 HTML 文件的交互式绘图，因此我不想使用破折号.
I want to create an interactive plot with selection possibility to save a standalone HTML file, thus I do not want to use dash.
目标是显示房价趋势，并可以选择地区和房间数量.问题是，一旦我更改了选择，所有图仍然处于活动状态而没有正确更新.有人可以帮助解决这个问题吗?我用我自己的语言(立陶宛语)进行了绘图.为方便起见，我翻译了代码中的变量名称.
The goal is to display housing price trends with the possibility to make a selection for region and number of rooms. The problem is that once I change the selection, all plots are still active without correctly updating. Could someone help to solve this issue? I have made the plotting in my own language (Lithuanian). I translated the variable names in the code for convenience.
当前输出:
示例输入数据
df = pd.DataFrame({'Region_title': {0: 'Kaunas',
1: 'Kaunas',
2: 'Kaunas',
3: 'Kaunas',
4: 'Kaunas',
5: 'Kaunas',
6: 'Vilnius',
7: 'Vilnius',
8: 'Vilnius',
9: 'Vilnius',
10: 'Vilnius',
11: 'Vilnius'},
'Room_number': {0: 1,
1: 2,
2: 1,
3: 2,
4: 1,
5: 2,
6: 1,
7: 2,
8: 1,
9: 2,
10: 1,
11: 2},
'Year_quarter': {0: '2010-01',
1: '2010-01',
2: '2014-01',
3: '2014-01',
4: '2019-01',
5: '2019-01',
6: '2010-01',
7: '2010-01',
8: '2014-01',
9: '2014-01',
10: '2019-01',
11: '2019-01'},
'Price': {0: 100,
1: 200,
2: 300,
3: 400,
4: 500,
5: 600,
6: 300,
7: 500,
8: 700,
9: 900,
10: 1100,
11: 1300}})
import plotly.offline as pyo
import plotly.graph_objs as go
import numpy as np
def compare_elements_with_single_value(all_list, value):
all_comparison = []
for elem in all_list:
comp = elem == value
all_comparison.append(comp)
return(all_comparison) #e.g. [False, False, ...., True]
df = df.sort_values(["Region_title", "Room_number", "Year_quarter"])
fig = go.Figure()
region_list = df['Region_title'].unique()
room_number_list = df['Room number'].unique()
all_list = []
for region in region_list:
for room_number in room_number_list:
all_list.append(str(region) + " Room number " + str(room_number))
for region in region_list:
for room_number in room_number_list:
region_room_number = str(region) + " Room number " + str(room_number)
fig.add_trace(go.Scatter(
x = df[(df['Region_title'] == region)
& (df['Room_number'] == room_number)]['Year_quarter'],
y = df[(df['Region_title'] == region)
& (df['Room_number'] == room_number)]['price'],
text = "Average price",
hoverinfo = "x+y",
name = region_room_number,
mode ='lines+markers',
marker={'size': 10, 'opacity': 0.5, 'line': {'width': 0.5, 'color': 'white'
}}))
updatemenu= []
buttons=[]
for region in region_list:
for room_number in room_number_list:
region_room_number = str(region) + " Room number " + str(room_number)
buttons.append(dict(method='restyle',
label = str(region) + " Room_number " + str(room_number),
args = [{'x':[df[(df['Region_title'] == region)
& (df['Room_number'] == room_number)]['Year_quarter']]},
{'y':[df[(df['Region_title'] == region)
& (df['Room_number'] == room_number)]['Price']]},
{'visible': compare_elements_with_single_value(all_list, region_room_number)}
]
))
updatemenu=[]
your_menu=dict()
updatemenu.append(your_menu)
updatemenu[0]['buttons']=buttons
updatemenu[0]['direction']='down'
updatemenu[0]['showactive']=True
fig.update_layout(updatemenus=updatemenu,
showlegend=True,
yaxis_title="EUR / m2")
fig.show()
pyo.plot(fig, filename='Output/Flat price.html')
推荐答案
您是否正在寻找以下内容:
Are you looking for something like:
import plotly.graph_objects as go
import pandas as pd
df = df.sort_values(["Region_title", "Room_number", "Year_quarter"])\
.reset_index(drop=True)
df["dropdown"] = df.apply(lambda x: '{} - Room nbr {}'.format(x['Region_title'], x["Room_number"]),
axis=1)
轨迹和按钮
colors_list = ['#1f77b4', # muted blue
'#ff7f0e', # safety orange
'#2ca02c', # cooked asparagus green
'#d62728', # brick red
'#9467bd', # muted purple
'#8c564b', # chestnut brown
'#e377c2', # raspberry yogurt pink
'#7f7f7f', # middle gray
'#bcbd22', # curry yellow-green
'#17becf' # blue-teal
]
dfs = list(df.groupby('dropdown'))
first_title = dfs[0][0]
traces = []
buttons = []
for i,d in enumerate(dfs):
visible = [False]*4
visible[i] = True
name = d[0]
traces.append(
go.Scatter(x = d[1]["Year_quarter"],
y = d[1]["Price"],
text = "Average price",
hoverinfo = "x+y",
mode = 'lines+markers',
visible = True if i==0 else False,
name = name,
marker = {'color':colors_list[i%len(colors_list)],
'size': 10,
'opacity': 0.5,
'line': {'width': 0.5,
'color': 'white'}
}
))
buttons.append(dict(label=name,
method="update",
args=[{"visible":visible},
{"title":f"Title {name}"}]))
updatemenus = [{'active':0, "buttons":buttons}]
fig = go.Figure(data=traces,
layout=dict(updatemenus=updatemenus))
fig.update_layout(title=first_title, title_x=0.5)
fig.update_yaxes(range=[0, df["Price"].max()*1.2])
fig.show()
这里的想法是通过下拉列表中所需的按钮拆分 df 并使用可见的痕迹进行播放.
Here the idea is to split df by the buttons you want in your dropdown and play with visible traces.
这篇关于绘图下拉选择不会正确更新绘图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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