Plotly - 如何讓這個 plot 更漂亮？

Question

我制作了一張圖表，顯示了一段時間內購買的商品數量。 該圖對我來說似乎難以閱讀，很難獲得正確的視角。 我的代碼如下：

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)

還有我的一些數據：

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 plot 現在看起來像這樣：

你有什么想法？

Answer 1

在我看來，你有三個選擇：

1. 如果不需要聚合，請使用帶有px.bar的條形圖

2. 按天匯總，使用一行 plot

3.按天匯總，使用條形 plot

由於您專門要求美觀，而不是 Plotly 代碼，因此我將使用 Plotly Express 而不是iplot 。 你也應該，如果由於某種原因你不能。 讓我知道。

完整代碼：

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()