Plotly：如何将数据框中的分组文本元素显示为多行悬停信息？
Plotly: How to show grouped text elements from a dataframe as multiline hoverinfos?
我有一个像这样的数据框：
df =
time_id gt_class num_missed_base num_missed_feature num_objects_base num_objects_feature
5G21A6P00L4100023:1566617404450336 CAR 11 4 27 30
5G21A6P00L4100023:1566617404450336 BICYCLE 4 6 27 30
5G21A6P00L4100023:1566617404450336 PERSON 2 3 27 30
5G21A6P00L4100023:1566617404450336 TRUCK 1 0 27 30
5G21A6P00L4100023:1566617428450689 CAR 25 14 60 67
5G21A6P00L4100023:1566617428450689 PERSON 7 6 60 67
5G21A6P00L4100023:1566617515950900 BICYCLE 1 1 59 65
5G21A6P00L4100023:1566617515950900 CAR 20 9 59 65
5G21A6P00L4100023:1566617515950900 PERSON 10 2 59 65
5G21A6P00L4100037:1567169649450046 CAR 8 0 29 32
5G21A6P00L4100037:1567169649450046 PERSON 1 0 29 32
5G21A6P00L4100037:1567169649450046 TRUCK 1 0 29 32
在每个 time_id 它显示了基础模型中遗漏了多少对象 num_missed_base，特征模型中遗漏了多少对象 num_missed_feature，以及当时存在的对象数量在 num_objects_base、num_objects_feature
的基础和功能中
我需要使用 time_id 的（plotly.graph_objs 和 FigureWidget）绘制一个散点图，这样当用户将鼠标悬停在每个点上时（每个点代表一个唯一的 time_id）它显示 time_id == 5G21A6P00L4100023:1566617404450336 的以下内容：
下面代码中的 hover_text 应该是什么？
import plotly.graph_objs as go
hover_text = ????
df_agg = df.groupby("time_id").sum().reset_index()
error_trace = go.Scattergl(
x=df_agg["num_missed_base"].tolist(),
y=df_agg["num_missed_feature"].tolist(),
text=hover_text,
mode="markers",
marker=dict(cmax=50, cmin=-50, opacity=0.3),
)
pandas 专业人士一定能够使下面的代码片段更加优雅和高效。但我的解决方法也能完成这项工作。主要挑战是将您的源数据框转换为这样的分组版本：
time_id gt_class num_missed_base base_str num_missed_feature feature_str
0 5G21A6P00L4100023:1566617404450336 CAR,BICYCLE,PERSON,TRUCK 18 11,4,2,1 13 11,4,2,1
1 5G21A6P00L4100023:1566617428450689 CAR,PERSON 32 25,7 20 25,7
2 5G21A6P00L4100023:1566617515950900 BICYCLE,CAR,PERSON 31 1,20,10 12 1,20,10
3 5G21A6P00L4100037:1567169649450046 CAR,PERSON,TRUCK 10 8,1,1 0 8,1,1
坏消息是这还远远不够。好消息是下面的代码片段将处理所有问题并为您提供此图：
你在这里看到的是一个图表，它对每个时间戳的相关数据进行分组，这样你就可以看到所有 classes 的总和，例如 num_missed_feature，和 hoverinfo 中每个基础 class 的数字。通过进一步调整，我也许也可以包括总和。但这就是我现在的全部时间了。
完整代码：
import pandas as pd
import re
import plotly.graph_objects as go
smpl = {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
'columns': ['time_id',
'gt_class',
'num_missed_base',
'num_missed_feature',
'num_objects_base',
'num_objects_feature'],
'data': [['5G21A6P00L4100023:1566617404450336', 'CAR', 11, 4, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'BICYCLE', 4, 6, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'PERSON', 2, 3, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'TRUCK', 1, 0, 27, 30],
['5G21A6P00L4100023:1566617428450689', 'CAR', 25, 14, 60, 67],
['5G21A6P00L4100023:1566617428450689', 'PERSON', 7, 6, 60, 67],
['5G21A6P00L4100023:1566617515950900', 'BICYCLE', 1, 1, 59, 65],
['5G21A6P00L4100023:1566617515950900', 'CAR', 20, 9, 59, 65],
['5G21A6P00L4100023:1566617515950900', 'PERSON', 10, 2, 59, 65],
['5G21A6P00L4100037:1567169649450046', 'CAR', 8, 0, 29, 32],
['5G21A6P00L4100037:1567169649450046', 'PERSON', 1, 0, 29, 32],
['5G21A6P00L4100037:1567169649450046', 'TRUCK', 1, 0, 29, 32]]}
df = pd.DataFrame(index=smpl['index'], columns = smpl['columns'], data=smpl['data'])
df['base_str'] = df['num_missed_base'].astype(str)
df['feature_str'] = df['num_missed_base'].astype(str)
df2=df.groupby(['time_id'], as_index = False).agg({'gt_class': ','.join,
'num_missed_base':sum,
'base_str':','.join,
'num_missed_feature':sum,
'feature_str':','.join,})
col_elem=[]
row_elem=[]
for i in df2.index:
gt_class = df2['gt_class'].loc[i].split(',')
base_str = df2['base_str'].loc[i].split(',')
for j, elem in enumerate(gt_class):
new_elem = elem+": "+base_str[j]
row_elem.append(new_elem)
col_elem.append(row_elem)
row_elem=[]
df2['hover']=col_elem
df2['hover'] = df2['hover'].astype(str)
df2['hover2'] = df2['hover'].map(lambda x: x.lstrip('[]').rstrip(']'))
#df2['hover2'].apply(lambda x: x.str.replace(',','.'))
df2['hover2']=df2['hover2'].replace("'",'', regex=True)
df2['hover2']=df2['hover2'].replace(',','<br>', regex=True)
# plotly
fig = go.Figure()
fig.add_traces(go.Scatter(x=df2['num_missed_base'], y=df2['num_missed_feature'],
mode='markers', marker=dict(color='red',
line=dict(color='black', width=1),
size=14),
#hovertext=df2["hover"],
hovertext=df2['hover2'],
hoverinfo="text",
))
fig.update_xaxes(showspikes=True, linecolor='black', title='Base',
spikecolor='black', spikethickness=0.5, spikedash='solid')
fig.update_yaxes(showspikes=True, linecolor='black', title = 'Feature',
spikecolor='black', spikethickness=0.5, spikedash='solid')
fig.update_layout(
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)'
)
fig.show()
根据@vestland 的回答，我想到了这个：
smpl = {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
'columns': ['time_id',
'gt_class',
'num_missed_base',
'num_missed_feature',
'num_objects_base',
'num_objects_feature'],
'data': [['5G21A6P00L4100023:1566617404450336', 'CAR', 11, 4, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'BICYCLE', 4, 6, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'PERSON', 2, 3, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'TRUCK', 1, 0, 27, 30],
['5G21A6P00L4100023:1566617428450689', 'CAR', 25, 14, 60, 67],
['5G21A6P00L4100023:1566617428450689', 'PERSON', 7, 6, 60, 67],
['5G21A6P00L4100023:1566617515950900', 'BICYCLE', 1, 1, 59, 65],
['5G21A6P00L4100023:1566617515950900', 'CAR', 20, 9, 59, 65],
['5G21A6P00L4100023:1566617515950900', 'PERSON', 10, 2, 59, 65],
['5G21A6P00L4100037:1567169649450046', 'CAR', 8, 0, 29, 32],
['5G21A6P00L4100037:1567169649450046', 'PERSON', 1, 0, 29, 32],
['5G21A6P00L4100037:1567169649450046', 'TRUCK', 1, 0, 29, 32]]}
df = pd.DataFrame(index=smpl['index'], columns = smpl['columns'], data=smpl['data'])
def func(row):
return ','.join(row.tolist())
def multi_column1(row):
l = []
for n in row.index:
x = df.loc[n, 'gt_class']
y = df.loc[n, 'num_missed_base']
z = df.loc[n, 'num_missed_feature']
w = '{} : [base = {}, feature = {}]'.format(x, y, z)
l.append(w)
return l
if "hover_text" not in df.columns:
df.insert(0, "hover_text", range(len(df)))
df = df.groupby('time_id').agg({'gt_class':func, 'num_missed_base': sum, 'num_missed_feature': sum, 'hover_text': multi_column1})
df.reset_index(inplace=True)
df['hover_text'] = df['hover_text'].astype(str)
df['hover_text'] = df['hover_text'].map(lambda x: x.lstrip('[]').rstrip(']'))
df['hover_text'] = df['hover_text'].replace("'",'', regex=True)
df['hover_text'] = df['hover_text'].replace('],',']<br>', regex=True)
# plotly
fig = go.Figure()
fig.add_traces(go.Scatter(x=df['num_missed_base'], y=df['num_missed_feature'],
mode='markers', marker=dict(color='red',
line=dict(color='black', width=1),
size=14),
#hovertext=df2["hover"],
hovertext=df['hover_text'],
hoverinfo="text",
))
fig.update_xaxes(showspikes=True, linecolor='black', title='Base',
spikecolor='black', spikethickness=0.5, spikedash='solid')
fig.update_yaxes(showspikes=True, linecolor='black', title = 'Feature',
spikecolor='black', spikethickness=0.5, spikedash='solid')
fig.update_layout(
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)'
)
fig.show()
我有一个像这样的数据框：
df =
time_id gt_class num_missed_base num_missed_feature num_objects_base num_objects_feature
5G21A6P00L4100023:1566617404450336 CAR 11 4 27 30
5G21A6P00L4100023:1566617404450336 BICYCLE 4 6 27 30
5G21A6P00L4100023:1566617404450336 PERSON 2 3 27 30
5G21A6P00L4100023:1566617404450336 TRUCK 1 0 27 30
5G21A6P00L4100023:1566617428450689 CAR 25 14 60 67
5G21A6P00L4100023:1566617428450689 PERSON 7 6 60 67
5G21A6P00L4100023:1566617515950900 BICYCLE 1 1 59 65
5G21A6P00L4100023:1566617515950900 CAR 20 9 59 65
5G21A6P00L4100023:1566617515950900 PERSON 10 2 59 65
5G21A6P00L4100037:1567169649450046 CAR 8 0 29 32
5G21A6P00L4100037:1567169649450046 PERSON 1 0 29 32
5G21A6P00L4100037:1567169649450046 TRUCK 1 0 29 32
在每个 time_id 它显示了基础模型中遗漏了多少对象 num_missed_base，特征模型中遗漏了多少对象 num_missed_feature，以及当时存在的对象数量在 num_objects_base、num_objects_feature
我需要使用 time_id 的（plotly.graph_objs 和 FigureWidget）绘制一个散点图，这样当用户将鼠标悬停在每个点上时（每个点代表一个唯一的 time_id）它显示 time_id == 5G21A6P00L4100023:1566617404450336 的以下内容：
下面代码中的 hover_text 应该是什么？
import plotly.graph_objs as go
hover_text = ????
df_agg = df.groupby("time_id").sum().reset_index()
error_trace = go.Scattergl(
x=df_agg["num_missed_base"].tolist(),
y=df_agg["num_missed_feature"].tolist(),
text=hover_text,
mode="markers",
marker=dict(cmax=50, cmin=-50, opacity=0.3),
)
pandas 专业人士一定能够使下面的代码片段更加优雅和高效。但我的解决方法也能完成这项工作。主要挑战是将您的源数据框转换为这样的分组版本：
time_id gt_class num_missed_base base_str num_missed_feature feature_str
0 5G21A6P00L4100023:1566617404450336 CAR,BICYCLE,PERSON,TRUCK 18 11,4,2,1 13 11,4,2,1
1 5G21A6P00L4100023:1566617428450689 CAR,PERSON 32 25,7 20 25,7
2 5G21A6P00L4100023:1566617515950900 BICYCLE,CAR,PERSON 31 1,20,10 12 1,20,10
3 5G21A6P00L4100037:1567169649450046 CAR,PERSON,TRUCK 10 8,1,1 0 8,1,1
坏消息是这还远远不够。好消息是下面的代码片段将处理所有问题并为您提供此图：
你在这里看到的是一个图表，它对每个时间戳的相关数据进行分组，这样你就可以看到所有 classes 的总和，例如 num_missed_feature，和 hoverinfo 中每个基础 class 的数字。通过进一步调整，我也许也可以包括总和。但这就是我现在的全部时间了。
完整代码：
import pandas as pd
import re
import plotly.graph_objects as go
smpl = {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
'columns': ['time_id',
'gt_class',
'num_missed_base',
'num_missed_feature',
'num_objects_base',
'num_objects_feature'],
'data': [['5G21A6P00L4100023:1566617404450336', 'CAR', 11, 4, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'BICYCLE', 4, 6, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'PERSON', 2, 3, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'TRUCK', 1, 0, 27, 30],
['5G21A6P00L4100023:1566617428450689', 'CAR', 25, 14, 60, 67],
['5G21A6P00L4100023:1566617428450689', 'PERSON', 7, 6, 60, 67],
['5G21A6P00L4100023:1566617515950900', 'BICYCLE', 1, 1, 59, 65],
['5G21A6P00L4100023:1566617515950900', 'CAR', 20, 9, 59, 65],
['5G21A6P00L4100023:1566617515950900', 'PERSON', 10, 2, 59, 65],
['5G21A6P00L4100037:1567169649450046', 'CAR', 8, 0, 29, 32],
['5G21A6P00L4100037:1567169649450046', 'PERSON', 1, 0, 29, 32],
['5G21A6P00L4100037:1567169649450046', 'TRUCK', 1, 0, 29, 32]]}
df = pd.DataFrame(index=smpl['index'], columns = smpl['columns'], data=smpl['data'])
df['base_str'] = df['num_missed_base'].astype(str)
df['feature_str'] = df['num_missed_base'].astype(str)
df2=df.groupby(['time_id'], as_index = False).agg({'gt_class': ','.join,
'num_missed_base':sum,
'base_str':','.join,
'num_missed_feature':sum,
'feature_str':','.join,})
col_elem=[]
row_elem=[]
for i in df2.index:
gt_class = df2['gt_class'].loc[i].split(',')
base_str = df2['base_str'].loc[i].split(',')
for j, elem in enumerate(gt_class):
new_elem = elem+": "+base_str[j]
row_elem.append(new_elem)
col_elem.append(row_elem)
row_elem=[]
df2['hover']=col_elem
df2['hover'] = df2['hover'].astype(str)
df2['hover2'] = df2['hover'].map(lambda x: x.lstrip('[]').rstrip(']'))
#df2['hover2'].apply(lambda x: x.str.replace(',','.'))
df2['hover2']=df2['hover2'].replace("'",'', regex=True)
df2['hover2']=df2['hover2'].replace(',','<br>', regex=True)
# plotly
fig = go.Figure()
fig.add_traces(go.Scatter(x=df2['num_missed_base'], y=df2['num_missed_feature'],
mode='markers', marker=dict(color='red',
line=dict(color='black', width=1),
size=14),
#hovertext=df2["hover"],
hovertext=df2['hover2'],
hoverinfo="text",
))
fig.update_xaxes(showspikes=True, linecolor='black', title='Base',
spikecolor='black', spikethickness=0.5, spikedash='solid')
fig.update_yaxes(showspikes=True, linecolor='black', title = 'Feature',
spikecolor='black', spikethickness=0.5, spikedash='solid')
fig.update_layout(
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)'
)
fig.show()
根据@vestland 的回答，我想到了这个：
smpl = {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
'columns': ['time_id',
'gt_class',
'num_missed_base',
'num_missed_feature',
'num_objects_base',
'num_objects_feature'],
'data': [['5G21A6P00L4100023:1566617404450336', 'CAR', 11, 4, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'BICYCLE', 4, 6, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'PERSON', 2, 3, 27, 30],
['5G21A6P00L4100023:1566617404450336', 'TRUCK', 1, 0, 27, 30],
['5G21A6P00L4100023:1566617428450689', 'CAR', 25, 14, 60, 67],
['5G21A6P00L4100023:1566617428450689', 'PERSON', 7, 6, 60, 67],
['5G21A6P00L4100023:1566617515950900', 'BICYCLE', 1, 1, 59, 65],
['5G21A6P00L4100023:1566617515950900', 'CAR', 20, 9, 59, 65],
['5G21A6P00L4100023:1566617515950900', 'PERSON', 10, 2, 59, 65],
['5G21A6P00L4100037:1567169649450046', 'CAR', 8, 0, 29, 32],
['5G21A6P00L4100037:1567169649450046', 'PERSON', 1, 0, 29, 32],
['5G21A6P00L4100037:1567169649450046', 'TRUCK', 1, 0, 29, 32]]}
df = pd.DataFrame(index=smpl['index'], columns = smpl['columns'], data=smpl['data'])
def func(row):
return ','.join(row.tolist())
def multi_column1(row):
l = []
for n in row.index:
x = df.loc[n, 'gt_class']
y = df.loc[n, 'num_missed_base']
z = df.loc[n, 'num_missed_feature']
w = '{} : [base = {}, feature = {}]'.format(x, y, z)
l.append(w)
return l
if "hover_text" not in df.columns:
df.insert(0, "hover_text", range(len(df)))
df = df.groupby('time_id').agg({'gt_class':func, 'num_missed_base': sum, 'num_missed_feature': sum, 'hover_text': multi_column1})
df.reset_index(inplace=True)
df['hover_text'] = df['hover_text'].astype(str)
df['hover_text'] = df['hover_text'].map(lambda x: x.lstrip('[]').rstrip(']'))
df['hover_text'] = df['hover_text'].replace("'",'', regex=True)
df['hover_text'] = df['hover_text'].replace('],',']<br>', regex=True)
# plotly
fig = go.Figure()
fig.add_traces(go.Scatter(x=df['num_missed_base'], y=df['num_missed_feature'],
mode='markers', marker=dict(color='red',
line=dict(color='black', width=1),
size=14),
#hovertext=df2["hover"],
hovertext=df['hover_text'],
hoverinfo="text",
))
fig.update_xaxes(showspikes=True, linecolor='black', title='Base',
spikecolor='black', spikethickness=0.5, spikedash='solid')
fig.update_yaxes(showspikes=True, linecolor='black', title = 'Feature',
spikecolor='black', spikethickness=0.5, spikedash='solid')
fig.update_layout(
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)'
)
fig.show()