使用 plotly 库绘制数据区域 Python
Area plotting data using plotly library Python
尝试创建与图片 1 类似的聊天，只有一个区别，我想从 1.01 开始填充区域，而不是 0。第二张图片是我正在寻找的快速示例。
chartData = {'Price': [0.965879, 0.964773, 0.96447, 0.961223, 0.958788, 0.956747, 0.958788, 0.959872, 0.959868, 0.960104, 0.961375, 0.962256, 0.963297, 0.963315, 0.964611, 0.964513, 0.963761, 0.963763, 0.963922, 0.963907, 0.963857, 0.963265, 0.963293, 0.963171, 0.96318, 0.963707, 0.964389, 0.964352, 0.963077, 0.961785, 0.959572, 0.958703, 0.959223, 0.95819, 0.952994, 0.95124, 0.950693, 0.950906, 0.95184, 0.951838, 0.953997, 0.960501, 0.960672, 0.960486, 0.960171, 0.960241, 0.959292, 0.959263, 0.959205, 0.95906, 0.954787, 0.954901, 0.954993, 0.955447, 0.955465, 0.955626, 0.953638, 0.952751, 0.951972, 0.950729, 0.950532, 0.952849, 0.952773, 0.952682, 0.952351, 0.948383, 0.94847, 0.948451, 0.95198, 0.952234, 0.951982, 0.952163, 0.952301, 0.952407, 0.955843, 0.956628, 0.957734, 0.957548, 0.95771, 0.956813, 0.958674, 0.958295, 0.954697, 0.953861, 0.955926, 0.953264, 0.951443, 0.950245, 0.949453, 0.949492, 0.948764, 0.946932, 0.949487, 0.950302, 0.950381, 0.949979, 0.948601, 0.949252, 0.949217, 0.949271, 0.947859, 0.947683, 0.947763, 0.947593, 0.948247, 0.9483, 0.948568, 0.947236, 0.946515, 0.946128, 0.946793, 0.946244, 0.951683, 0.951324, 0.950662, 0.949001, 0.947648, 0.946191, 0.946928, 0.933038, 0.92239, 0.923197, 0.925719, 0.937193, 0.93354, 0.932933, 0.932073, 0.931954, 0.932393, 0.931602, 0.932908, 0.932966, 0.933866, 0.931223, 0.929834, 0.933195, 0.936534, 0.935959, 0.932762, 0.931187, 0.937434, 0.937664, 0.936378, 0.934742, 0.934742], 'Date': [1652117700000, 1652118000000, 1652118300000, 1652118600000, 1652118900000, 1652119200000, 1652119500000, 1652119800000, 1652120100000, 1652120400000, 1652120700000, 1652121000000, 1652121300000, 1652121600000, 1652121900000, 1652122200000, 1652122500000, 1652122800000, 1652123100000, 1652123400000, 1652123700000, 1652124000000, 1652124300000, 1652124600000, 1652124900000, 1652125200000, 1652125500000, 1652125800000, 1652126100000, 1652126400000, 1652126700000, 1652127000000, 1652127300000, 1652127600000, 1652127900000, 1652128200000, 1652128500000, 1652128800000, 1652129100000, 1652129400000, 1652129700000, 1652130000000, 1652130300000, 1652130600000, 1652130900000, 1652131200000, 1652131500000, 1652131800000, 1652132100000, 1652132400000, 1652132700000, 1652133000000, 1652133300000, 1652133600000, 1652133900000, 1652134200000, 1652134500000, 1652134800000, 1652135100000, 1652135400000, 1652135700000, 1652136000000, 1652136300000, 1652136600000, 1652136900000, 1652137200000, 1652137500000, 1652137800000, 1652138100000, 1652138400000, 1652138700000, 1652139000000, 1652139300000, 1652139600000, 1652139900000, 1652140200000, 1652140500000, 1652140800000, 1652141100000, 1652141400000, 1652141700000, 1652142000000, 1652142300000, 1652142600000, 1652142900000, 1652143200000, 1652143500000, 1652143800000, 1652144100000, 1652144400000, 1652144700000, 1652145000000, 1652145300000, 1652145600000, 1652145900000, 1652146200000, 1652146500000, 1652146800000, 1652147100000, 1652147400000, 1652147700000, 1652148000000, 1652148300000, 1652148600000, 1652148900000, 1652149200000, 1652149500000, 1652149800000, 1652150100000, 1652150400000, 1652150700000, 1652151000000, 1652151300000, 1652151600000, 1652151900000, 1652152200000, 1652152500000, 1652152800000, 1652153100000, 1652153400000, 1652153700000, 1652154000000, 1652154300000, 1652154600000, 1652154900000, 1652155200000, 1652155500000, 1652155800000, 1652156100000, 1652156400000, 1652156700000, 1652157000000, 1652157300000, 1652157600000, 1652157900000, 1652158200000, 1652158500000, 1652158800000, 1652159100000, 1652159400000, 1652159700000, 1652160000000, 1652160300000, 1652160600000, 1652160636000]}
df: DataFrame = pd.DataFrame.from_dict(chatData).fillna(method="backfill")
df['Date'] = pd.to_datetime(df['Date'], unit='ms')
fig = px.area(df, x='Date', y="Price")
fig.show()
既然你没有提供数据框，我就举一个简单的例子。另外，我很少使用 plotly.express，所以我将向您展示如何使用 plotly.graph_objects 实现这一点。希望您能够使其适应 plotly.express，或将您的代码更改为 plotly.graph_objects。
想法是绘制一条水平线并将其从图表和图例中隐藏起来。比使用这条水平线作为填充区域的参考。
在下面的示例中，您将看到填充是相对于 y=2 处的水平线完成的。
import plotly.graph_objects as go
import numpy as np
x = np.linspace(-10, 10, 200)
y = np.cos(x) + 2
fig = go.Figure([
go.Scatter(x=x, y=2*np.ones_like(x), opacity=0, line_width=0, showlegend=False),
go.Scatter(x=x, y=y, fill="tonexty", opacity=0.5)
])
fig
尝试创建与图片 1 类似的聊天，只有一个区别，我想从 1.01 开始填充区域，而不是 0。第二张图片是我正在寻找的快速示例。
chartData = {'Price': [0.965879, 0.964773, 0.96447, 0.961223, 0.958788, 0.956747, 0.958788, 0.959872, 0.959868, 0.960104, 0.961375, 0.962256, 0.963297, 0.963315, 0.964611, 0.964513, 0.963761, 0.963763, 0.963922, 0.963907, 0.963857, 0.963265, 0.963293, 0.963171, 0.96318, 0.963707, 0.964389, 0.964352, 0.963077, 0.961785, 0.959572, 0.958703, 0.959223, 0.95819, 0.952994, 0.95124, 0.950693, 0.950906, 0.95184, 0.951838, 0.953997, 0.960501, 0.960672, 0.960486, 0.960171, 0.960241, 0.959292, 0.959263, 0.959205, 0.95906, 0.954787, 0.954901, 0.954993, 0.955447, 0.955465, 0.955626, 0.953638, 0.952751, 0.951972, 0.950729, 0.950532, 0.952849, 0.952773, 0.952682, 0.952351, 0.948383, 0.94847, 0.948451, 0.95198, 0.952234, 0.951982, 0.952163, 0.952301, 0.952407, 0.955843, 0.956628, 0.957734, 0.957548, 0.95771, 0.956813, 0.958674, 0.958295, 0.954697, 0.953861, 0.955926, 0.953264, 0.951443, 0.950245, 0.949453, 0.949492, 0.948764, 0.946932, 0.949487, 0.950302, 0.950381, 0.949979, 0.948601, 0.949252, 0.949217, 0.949271, 0.947859, 0.947683, 0.947763, 0.947593, 0.948247, 0.9483, 0.948568, 0.947236, 0.946515, 0.946128, 0.946793, 0.946244, 0.951683, 0.951324, 0.950662, 0.949001, 0.947648, 0.946191, 0.946928, 0.933038, 0.92239, 0.923197, 0.925719, 0.937193, 0.93354, 0.932933, 0.932073, 0.931954, 0.932393, 0.931602, 0.932908, 0.932966, 0.933866, 0.931223, 0.929834, 0.933195, 0.936534, 0.935959, 0.932762, 0.931187, 0.937434, 0.937664, 0.936378, 0.934742, 0.934742], 'Date': [1652117700000, 1652118000000, 1652118300000, 1652118600000, 1652118900000, 1652119200000, 1652119500000, 1652119800000, 1652120100000, 1652120400000, 1652120700000, 1652121000000, 1652121300000, 1652121600000, 1652121900000, 1652122200000, 1652122500000, 1652122800000, 1652123100000, 1652123400000, 1652123700000, 1652124000000, 1652124300000, 1652124600000, 1652124900000, 1652125200000, 1652125500000, 1652125800000, 1652126100000, 1652126400000, 1652126700000, 1652127000000, 1652127300000, 1652127600000, 1652127900000, 1652128200000, 1652128500000, 1652128800000, 1652129100000, 1652129400000, 1652129700000, 1652130000000, 1652130300000, 1652130600000, 1652130900000, 1652131200000, 1652131500000, 1652131800000, 1652132100000, 1652132400000, 1652132700000, 1652133000000, 1652133300000, 1652133600000, 1652133900000, 1652134200000, 1652134500000, 1652134800000, 1652135100000, 1652135400000, 1652135700000, 1652136000000, 1652136300000, 1652136600000, 1652136900000, 1652137200000, 1652137500000, 1652137800000, 1652138100000, 1652138400000, 1652138700000, 1652139000000, 1652139300000, 1652139600000, 1652139900000, 1652140200000, 1652140500000, 1652140800000, 1652141100000, 1652141400000, 1652141700000, 1652142000000, 1652142300000, 1652142600000, 1652142900000, 1652143200000, 1652143500000, 1652143800000, 1652144100000, 1652144400000, 1652144700000, 1652145000000, 1652145300000, 1652145600000, 1652145900000, 1652146200000, 1652146500000, 1652146800000, 1652147100000, 1652147400000, 1652147700000, 1652148000000, 1652148300000, 1652148600000, 1652148900000, 1652149200000, 1652149500000, 1652149800000, 1652150100000, 1652150400000, 1652150700000, 1652151000000, 1652151300000, 1652151600000, 1652151900000, 1652152200000, 1652152500000, 1652152800000, 1652153100000, 1652153400000, 1652153700000, 1652154000000, 1652154300000, 1652154600000, 1652154900000, 1652155200000, 1652155500000, 1652155800000, 1652156100000, 1652156400000, 1652156700000, 1652157000000, 1652157300000, 1652157600000, 1652157900000, 1652158200000, 1652158500000, 1652158800000, 1652159100000, 1652159400000, 1652159700000, 1652160000000, 1652160300000, 1652160600000, 1652160636000]}
df: DataFrame = pd.DataFrame.from_dict(chatData).fillna(method="backfill")
df['Date'] = pd.to_datetime(df['Date'], unit='ms')
fig = px.area(df, x='Date', y="Price")
fig.show()
既然你没有提供数据框，我就举一个简单的例子。另外，我很少使用 plotly.express，所以我将向您展示如何使用 plotly.graph_objects 实现这一点。希望您能够使其适应 plotly.express，或将您的代码更改为 plotly.graph_objects。
想法是绘制一条水平线并将其从图表和图例中隐藏起来。比使用这条水平线作为填充区域的参考。
在下面的示例中，您将看到填充是相对于 y=2 处的水平线完成的。
import plotly.graph_objects as go
import numpy as np
x = np.linspace(-10, 10, 200)
y = np.cos(x) + 2
fig = go.Figure([
go.Scatter(x=x, y=2*np.ones_like(x), opacity=0, line_width=0, showlegend=False),
go.Scatter(x=x, y=y, fill="tonexty", opacity=0.5)
])
fig