Plotly：如何使锯齿状的线图看起来更好？
Plotly: How to make a jagged line plot look better?
我制作了一张图表，显示一段时间内购买的商品数量。该图对我来说似乎不可读，很难获得正确的视角。我的代码如下：
import numpy as np
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
trace1 = go.Scatter(x=df_temp['Date'],
y=df_temp['Quantity'],
line = dict(color = 'blue'),
opacity = 0.3)
layout = dict(title='Purchases of NC coin',)
fig = dict(data=[trace1], layout=layout)
iplot(fig)
还有我的一些数据：
Id Date Quantity
8 2022-01-16 19:14:56 50814.040553
15 2022-01-12 09:18:01 2563.443420
17 2022-01-11 13:52:38 33055.752836
18 2022-01-11 11:49:54 6483.182959
19 2022-01-11 11:07:48 13005.174783
21 2022-01-11 10:50:20 19605.381370
23 2022-01-11 10:15:30 6561.223602
24 2022-01-11 10:14:44 19762.821100
28 2022-01-07 15:56:50 3307.607665
29 2022-01-07 15:54:30 66868.030051
30 2022-01-07 12:27:07 42683.069577
31 2022-01-07 12:20:51 3423.618394
34 2022-01-05 12:11:57 69607.963793
35 2022-01-05 10:41:48 20370.090947
37 2022-01-05 10:21:22 72415.914082
38 2022-01-05 10:05:04 20687.003754
39 2022-01-05 09:36:53 37410.532342
40 2022-01-05 08:35:06 43815.009603
41 2022-01-04 19:27:27 30581.795021
44 2022-01-03 16:34:41 14290.644375
我的情节现在是这样的：
你有什么想法吗？
在我看来，您有以下三种选择：
1。如果不需要聚合，请使用带 px.bar
的条形图
2。按天汇总并使用线图
3。按天汇总并使用条形图
既然你是专门要求美学，而不是 Plotly 代码，我将使用 Plotly Express 而不是 iplot。你也应该！如果出于某种原因您不能，请告诉我。
完整代码：
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
df_temp = pd.DataFrame({'Id': {0: 8,
1: 15,
2: 17,
3: 18,
4: 19,
5: 21,
6: 23,
7: 24,
8: 28,
9: 29,
10: 30,
11: 31,
12: 34,
13: 35,
14: 37,
15: 38,
16: 39,
17: 40,
18: 41,
19: 44},
'Date': {0: '2022-01-16',
1: '2022-01-12',
2: '2022-01-11',
3: '2022-01-11',
4: '2022-01-11',
5: '2022-01-11',
6: '2022-01-11',
7: '2022-01-11',
8: '2022-01-07',
9: '2022-01-07',
10: '2022-01-07',
11: '2022-01-07',
12: '2022-01-05',
13: '2022-01-05',
14: '2022-01-05',
15: '2022-01-05',
16: '2022-01-05',
17: '2022-01-05',
18: '2022-01-04',
19: '2022-01-03'},
'Time': {0: '19:14:56',
1: '09:18:01',
2: '13:52:38',
3: '11:49:54',
4: '11:07:48',
5: '10:50:20',
6: '10:15:30',
7: '10:14:44',
8: '15:56:50',
9: '15:54:30',
10: '12:27:07',
11: '12:20:51',
12: '12:11:57',
13: '10:41:48',
14: '10:21:22',
15: '10:05:04',
16: '09:36:53',
17: '08:35:06',
18: '19:27:27',
19: '16:34:41'},
'Quantity': {0: 50814.040553,
1: 2563.44342,
2: 33055.752836,
3: 6483.182959,
4: 13005.174783,
5: 19605.38137,
6: 6561.223602,
7: 19762.8211,
8: 3307.607665,
9: 66868.030051,
10: 42683.069577,
11: 3423.618394,
12: 69607.963793,
13: 20370.090947,
14: 72415.914082,
15: 20687.003754,
16: 37410.532342,
17: 43815.009603,
18: 30581.795021,
19: 14290.644375}})
trace1 = go.Scatter(x=df_temp['Date'],
y=df_temp['Quantity'],
line = dict(color = 'blue'),
opacity = 0.3)
layout = dict(title='Purchases of NC coin',)
# build pandas datetime series
df_temp['DateTime'] = pd.to_datetime(df_temp.Date+' '+df_temp.Time)
# # unaggregated barplot
# fig = px.bar(df_temp, x = 'DateTime', y = 'Quantity')
# fig.update_traces(marker_line_color = 'blue')
# fig.update_layout(title='Purchases of NC coin')
# aggregate by day
df_temp = df_temp.groupby(by=[df_temp.DateTime.dt.date]).mean().reset_index()
# # aggregated lineplot
# fig = px.line(df_temp, x = 'DateTime', y = 'Quantity')
# fig.update_traces(marker_line_color = 'blue')
# fig.update_layout(title='Purchases of NC coin')
# aggregated barplot
fig = px.bar(df_temp, x = 'DateTime', y = 'Quantity')
fig.update_traces(marker_line_color = 'blue')
fig.update_layout(title='Purchases of NC coin')
fig.show()
我制作了一张图表，显示一段时间内购买的商品数量。该图对我来说似乎不可读，很难获得正确的视角。我的代码如下：
import numpy as np
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
trace1 = go.Scatter(x=df_temp['Date'],
y=df_temp['Quantity'],
line = dict(color = 'blue'),
opacity = 0.3)
layout = dict(title='Purchases of NC coin',)
fig = dict(data=[trace1], layout=layout)
iplot(fig)
还有我的一些数据：
Id Date Quantity
8 2022-01-16 19:14:56 50814.040553
15 2022-01-12 09:18:01 2563.443420
17 2022-01-11 13:52:38 33055.752836
18 2022-01-11 11:49:54 6483.182959
19 2022-01-11 11:07:48 13005.174783
21 2022-01-11 10:50:20 19605.381370
23 2022-01-11 10:15:30 6561.223602
24 2022-01-11 10:14:44 19762.821100
28 2022-01-07 15:56:50 3307.607665
29 2022-01-07 15:54:30 66868.030051
30 2022-01-07 12:27:07 42683.069577
31 2022-01-07 12:20:51 3423.618394
34 2022-01-05 12:11:57 69607.963793
35 2022-01-05 10:41:48 20370.090947
37 2022-01-05 10:21:22 72415.914082
38 2022-01-05 10:05:04 20687.003754
39 2022-01-05 09:36:53 37410.532342
40 2022-01-05 08:35:06 43815.009603
41 2022-01-04 19:27:27 30581.795021
44 2022-01-03 16:34:41 14290.644375
我的情节现在是这样的：
你有什么想法吗？
在我看来，您有以下三种选择：
1。如果不需要聚合，请使用带 px.bar
的条形图
2。按天汇总并使用线图
3。按天汇总并使用条形图
既然你是专门要求美学，而不是 Plotly 代码，我将使用 Plotly Express 而不是 iplot。你也应该！如果出于某种原因您不能，请告诉我。
完整代码：
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
df_temp = pd.DataFrame({'Id': {0: 8,
1: 15,
2: 17,
3: 18,
4: 19,
5: 21,
6: 23,
7: 24,
8: 28,
9: 29,
10: 30,
11: 31,
12: 34,
13: 35,
14: 37,
15: 38,
16: 39,
17: 40,
18: 41,
19: 44},
'Date': {0: '2022-01-16',
1: '2022-01-12',
2: '2022-01-11',
3: '2022-01-11',
4: '2022-01-11',
5: '2022-01-11',
6: '2022-01-11',
7: '2022-01-11',
8: '2022-01-07',
9: '2022-01-07',
10: '2022-01-07',
11: '2022-01-07',
12: '2022-01-05',
13: '2022-01-05',
14: '2022-01-05',
15: '2022-01-05',
16: '2022-01-05',
17: '2022-01-05',
18: '2022-01-04',
19: '2022-01-03'},
'Time': {0: '19:14:56',
1: '09:18:01',
2: '13:52:38',
3: '11:49:54',
4: '11:07:48',
5: '10:50:20',
6: '10:15:30',
7: '10:14:44',
8: '15:56:50',
9: '15:54:30',
10: '12:27:07',
11: '12:20:51',
12: '12:11:57',
13: '10:41:48',
14: '10:21:22',
15: '10:05:04',
16: '09:36:53',
17: '08:35:06',
18: '19:27:27',
19: '16:34:41'},
'Quantity': {0: 50814.040553,
1: 2563.44342,
2: 33055.752836,
3: 6483.182959,
4: 13005.174783,
5: 19605.38137,
6: 6561.223602,
7: 19762.8211,
8: 3307.607665,
9: 66868.030051,
10: 42683.069577,
11: 3423.618394,
12: 69607.963793,
13: 20370.090947,
14: 72415.914082,
15: 20687.003754,
16: 37410.532342,
17: 43815.009603,
18: 30581.795021,
19: 14290.644375}})
trace1 = go.Scatter(x=df_temp['Date'],
y=df_temp['Quantity'],
line = dict(color = 'blue'),
opacity = 0.3)
layout = dict(title='Purchases of NC coin',)
# build pandas datetime series
df_temp['DateTime'] = pd.to_datetime(df_temp.Date+' '+df_temp.Time)
# # unaggregated barplot
# fig = px.bar(df_temp, x = 'DateTime', y = 'Quantity')
# fig.update_traces(marker_line_color = 'blue')
# fig.update_layout(title='Purchases of NC coin')
# aggregate by day
df_temp = df_temp.groupby(by=[df_temp.DateTime.dt.date]).mean().reset_index()
# # aggregated lineplot
# fig = px.line(df_temp, x = 'DateTime', y = 'Quantity')
# fig.update_traces(marker_line_color = 'blue')
# fig.update_layout(title='Purchases of NC coin')
# aggregated barplot
fig = px.bar(df_temp, x = 'DateTime', y = 'Quantity')
fig.update_traces(marker_line_color = 'blue')
fig.update_layout(title='Purchases of NC coin')
fig.show()