Plotly：如何在指定阈值的折线图上显示不同的颜色段？
[英]Plotly: How to display different color segments on a line chart for specified thresholds?
问题描述
I have a multi-line graph that displays percent increase over time.
我有一个多线图，显示随时间增加的百分比。 I'd like to set a threshold in my code to have an upper and lower bound.我想在我的代码中设置一个阈值来设置上限和下限。 If the line falls outside these bounds, I'd like that specific part of the line graph to be a different color than its parent.如果线条超出这些范围，我希望折线图的特定部分与其父项的颜色不同。
This is what I am doing:这就是我正在做的：
import plotly.express as px
import plotly.graph_objects as go
fig = px.line(df14, x = "Date", y = "Percent", color = "POD",
title = "POD Growth in Percentage (US)",
labels = {"Percent": "Percent Growth"})
fig.update_layout(
font_family="Arial",
font_color="black",
title_font_family="Arial",
title_font_color="black",
legend_title_font_color="black" #style the text (legend, title etc)
)
fig.update_xaxes(title_font_family="Arial") #style ance center title
fig.update_layout(
title={
'text': "POD Growth Percentage in US (Line Graph)",
'y':0.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
fig.update_traces(mode='markers+lines') #add dots to line
fig.show()
This is the visual result:这是视觉效果：
Let me zoom in on one line to better explain:让我放大一行以更好地解释：
I would like a threshold set for each POD , and if the line goes above or below this threshold, the color will be different for that part of the line graph.我想为每个 POD 设置一个阈值，如果线条高于或低于此阈值，则折线图该部分的颜色将不同。 For instance:例如：
The upper bound for POD IAD may be 5 and the lower bound for IAD may be 0. Any value that is over 5 , or below 0, show be highlighted a specific color. POD IAD 的上限可以是 5，而 IAD 的下限可以是 0。任何大于 5 或小于 0 的值都将显示突出显示特定颜色。
The upper bound for POD SJC may be 10 and the lower bound for SJC may be 3 Any value that is over 10, or below 3, should be highlighted a specific color. POD SJC 的上限可能是 10，SJC 的下限可能是 3任何大于 10 或小于 3 的值都应以特定颜色突出显示。
I am wanting the thresholds to be for each POD我希望每个 POD 的阈值
Please see below:请参阅以下内容：
The highlighted yellow parts of the line reflect where the line graph has exceeded or decreased a set threshold.线条中突出显示的黄色部分反映了折线图超出或降低了设定阈值的位置。 Is this possible to do this using Plotly?使用 Plotly 可以做到这一点吗？
Here is the raw data example: Updated:这是原始数据示例：更新：
POD Start End Diff Percent Date
IAD 4/1/2019 5/1/2019 160.4279 11.10809 04-01-2019 to 05-01-2019
IAD 5/1/2019 6/1/2019 136.0248 8.476798 05-01-2019 to 06-01-2019
IAD 6/1/2019 7/1/2019 174.0513 9.998946 06-01-2019 to 07-01-2019
IAD 7/1/2019 8/1/2019 112.0424 5.851551 07-01-2019 to 08-01-2019
IAD 8/1/2019 9/1/2019 141.8488 6.998691 08-01-2019 to 09-01-2019
IAD 9/1/2019 10/1/2019 103.5522 4.774984 09-01-2019 to 10-01-2019
IAD 10/1/2019 11/1/2019 125.6087 5.528085 10-01-2019 to 11-01-2019
IAD 11/1/2019 12/1/2019 145.2591 6.058016 11-01-2019 to 12-01-2019
IAD 12/1/2019 1/1/2020 115.5121 4.542251 12-01-2019 to 01-01-2020
IAD 1/1/2020 2/1/2020 185.7191 6.985673 01-01-2020 to 02-01-2020
IAD 2/1/2020 3/1/2020 126.7386 4.455896 02-01-2020 to 03-01-2020
IAD 3/1/2020 4/1/2020 231.3461 7.786734 03-01-2020 to 04-01-2020
IAD 4/1/2020 5/1/2020 97.02587 3.02981 04-01-2020 to 05-01-2020
IAD 5/1/2020 6/1/2020 42.85235 1.298792 05-01-2020 to 06-01-2020
IAD 6/1/2020 7/1/2020 124.666 3.729997 06-01-2020 to 07-01-2020
IAD 7/1/2020 8/1/2020 357.9974 10.32609 07-01-2020 to 08-01-2020
IAD 8/1/2020 9/1/2020 490.9587 12.8358 08-01-2020 to 09-01-2020
IAD 9/1/2020 10/1/2020 204.5478 4.739428 09-01-2020 to 10-01-2020
IAD 10/1/2020 11/1/2020 287.6025 6.362292 10-01-2020 to 11-01-2020
SJCtest 4/1/2019 5/1/2019 12.38486 5.780551 04-01-2019 to 05-01-2019
SJCtest 5/1/2019 6/1/2019 -2.61735 -1.15487 05-01-2019 to 06-01-2019
SJCtest 6/1/2019 7/1/2019 -5.6187 -2.50814 06-01-2019 to 07-01-2019
SJCtest 7/1/2019 8/1/2019 3.204252 1.467153 07-01-2019 to 08-01-2019
SJCtest 8/1/2019 9/1/2019 -25.3782 -11.4521 08-01-2019 to 09-01-2019
SJCtest 9/1/2019 10/1/2019 -10.9717 -5.59137 09-01-2019 to 10-01-2019
Updated Raw Data更新的原始数据
> {'Unnamed: 0': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7,
> 7: 8, 8: 9, 9: 10, 10: 11, 11: 12, 12: 13, 13: 14, 14:
> 15, 15: 16, 16: 17, 17: 18, 18: 19, 19: 21, 20: 22, 21:
> 23, 22: 24, 23: 25, 24: 26, 25: 27, 26: 28, 27: 29, 28:
> 30, 29: 31, 30: 32, 31: 33, 32: 34, 33: 35, 34: 36, 35:
> 37, 36: 38, 37: 40, 38: 41, 39: 42, 40: 43, 41: 44, 42:
> 45, 43: 46, 44: 47, 45: 48, 46: 49, 47: 50, 48: 51, 49:
> 52, 50: 53, 51: 54, 52: 55, 53: 56, 54: 57, 55: 58, 56:
> 60, 57: 61, 58: 62, 59: 63, 60: 64, 61: 65, 62: 66, 63:
> 67, 64: 68, 65: 69, 66: 70, 67: 71, 68: 72, 69: 73, 70:
> 74, 71: 75, 72: 76, 73: 77, 74: 78, 75: 80, 76: 81, 77:
> 82, 78: 83, 79: 84, 80: 85, 81: 86, 82: 87, 83: 88, 84:
> 89, 85: 90, 86: 91, 87: 92, 88: 93, 89: 94, 90: 95, 91:
> 96, 92: 97, 93: 98, 94: 100, 95: 101, 96: 102, 97: 103,
> 98: 104, 99: 105, 100: 106, 101: 107, 102: 108, 103: 109,
> 104: 110, 105: 111, 106: 112, 107: 113, 108: 114, 109: 115,
> 110: 116, 111: 117, 112: 118, 113: 120, 114: 121, 115: 122,
> 116: 123, 117: 124, 118: 125, 119: 126, 120: 127, 121: 128,
> 122: 129, 123: 130, 124: 131, 125: 132, 126: 133, 127: 134,
> 128: 135, 129: 136, 130: 137, 131: 138, 132: 140, 133: 141,
> 134: 142, 135: 143, 136: 144, 137: 145, 138: 146, 139: 147,
> 140: 148, 141: 149, 142: 150, 143: 151, 144: 152, 145: 153,
> 146: 155, 147: 156, 148: 157, 149: 158, 150: 159, 151: 160,
> 152: 161, 153: 162, 154: 163, 155: 164, 156: 165, 157: 166,
> 158: 167, 159: 168, 160: 169, 161: 170, 162: 171, 163: 172,
> 164: 173}, 'POD': {0: 'IAD ', 1: 'IAD ', 2: 'IAD ', 3: 'IAD ',
> 4: 'IAD ', 5: 'IAD ', 6: 'IAD ', 7: 'IAD ', 8: 'IAD ', 9:
> 'IAD ', 10: 'IAD ', 11: 'IAD ', 12: 'IAD ', 13: 'IAD ', 14:
> 'IAD ', 15: 'IAD ', 16: 'IAD ', 17: 'IAD ', 18: 'IAD ', 19:
> 'SJCtest ', 20: 'SJCtest ', 21: 'SJCtest ', 22: 'SJCtest ',
> 23: 'SJCtest ', 24: 'SJCtest ', 25: 'SJCtest ', 26: 'SJCtest ',
> 27: 'SJCtest ', 28: 'SJCtest ', 29: 'SJCtest ', 30: 'SJCtest ',
> 31: 'SJCtest ', 32: 'SJCtest ', 33: 'SJCtest ', 34: 'SJCtest ',
> 35: 'SJCtest ', 36: 'SJCtest ', 37: 'SJCtest2 ', 38: 'SJCtest2
> ', 39: 'SJCtest2 ', 40: 'SJCtest2 ', 41: 'SJCtest2 ', 42:
> 'SJCtest2 ', 43: 'SJCtest2 ', 44: 'SJCtest2 ', 45: 'SJCtest2 ',
> 46: 'SJCtest2 ', 47: 'SJCtest2 ', 48: 'SJCtest2 ', 49: 'SJCtest2
> ', 50: 'SJCtest2 ', 51: 'SJCtest2 ', 52: 'SJCtest2 ', 53:
> 'SJCtest2 ', 54: 'SJCtest2 ', 55: 'SJCtest2 ', 56: 'SJCtest3 ',
> 57: 'SJCtest3 ', 58: 'SJCtest3 ', 59: 'SJCtest3 ', 60: 'SJCtest3
> ', 61: 'SJCtest3 ', 62: 'SJCtest3 ', 63: 'SJCtest3 ', 64:
> 'SJCtest3 ', 65: 'SJCtest3 ', 66: 'SJCtest3 ', 67: 'SJCtest3 ',
> 68: 'SJCtest3 ', 69: 'SJCtest3 ', 70: 'SJCtest3 ', 71: 'SJCtest3
> ', 72: 'SJCtest3 ', 73: 'SJCtest3 ', 74: 'SJCtest3 ', 75:
> 'SJCtest4 ', 76: 'SJCtest4 ', 77: 'SJCtest4 ', 78: 'SJCtest4 ',
> 79: 'SJCtest4 ', 80: 'SJCtest4 ', 81: 'SJCtest4 ', 82: 'SJCtest4
> ', 83: 'SJCtest4 ', 84: 'SJCtest4 ', 85: 'SJCtest4 ', 86:
> 'SJCtest4 ', 87: 'SJCtest4 ', 88: 'SJCtest4 ', 89: 'SJCtest4 ',
> 90: 'SJCtest4 ', 91: 'SJCtest4 ', 92: 'SJCtest4 ', 93: 'SJCtest4
> ', 94: 'SJCtest5 ', 95: 'SJCtest5 ', 96: 'SJCtest5 ', 97:
> 'SJCtest5 ', 98: 'SJCtest5 ', 99: 'SJCtest5 ', 100: 'SJCtest5 ',
> 101: 'SJCtest5 ', 102: 'SJCtest5 ', 103: 'SJCtest5 ', 104:
> 'SJCtest5 ', 105: 'SJCtest5 ', 106: 'SJCtest5 ', 107: 'SJCtest5
> ', 108: 'SJCtest5 ', 109: 'SJCtest5 ', 110: 'SJCtest5 ', 111:
> 'SJCtest5 ', 112: 'SJCtest5 ', 113: 'SJCtest6 ', 114: 'SJCtest6
> ', 115: 'SJCtest6 ', 116: 'SJCtest6 ', 117: 'SJCtest6 ', 118:
> 'SJCtest6 ', 119: 'SJCtest6 ', 120: 'SJCtest6 ', 121: 'SJCtest6
> ', 122: 'SJCtest6 ', 123: 'SJCtest6 ', 124: 'SJCtest6 ', 125:
> 'SJCtest6 ', 126: 'SJCtest6 ', 127: 'SJCtest6 ', 128: 'SJCtest6
> ', 129: 'SJCtest6 ', 130: 'SJCtest6 ', 131: 'SJCtest6 ', 132:
> 'SJCa ', 133: 'SJCa ', 134: 'SJCa ', 135: 'SJCa ', 136: 'SJCa
> ', 137: 'SJCa ', 138: 'SJCa ', 139: 'SJCa ', 140: 'SJCa ',
> 141: 'SJCa ', 142: 'SJCa ', 143: 'SJCa ', 144: 'SJCa ', 145:
> 'SJCa ', 146: 'SJCb ', 147: 'SJCb ', 148: 'SJCb ', 149: 'SJCb
> ', 150: 'SJCb ', 151: 'SJCb ', 152: 'SJCb ', 153: 'SJCb ',
> 154: 'SJCb ', 155: 'SJCb ', 156: 'SJCb ', 157: 'SJCb ', 158:
> 'SJCb ', 159: 'SJCb ', 160: 'SJCb ', 161: 'SJCb ', 162: 'SJCb
> ', 163: 'SJCb ', 164: 'SJCb '}, 'Start_Date': {0: '4/1/2019',
> 1: '5/1/2019', 2: '6/1/2019', 3: '7/1/2019', 4: '8/1/2019', 5:
> '9/1/2019', 6: '10/1/2019', 7: '11/1/2019', 8: '12/1/2019', 9:
> '1/1/2020', 10: '2/1/2020', 11: '3/1/2020', 12: '4/1/2020',
> 13: '5/1/2020', 14: '6/1/2020', 15: '7/1/2020', 16: '8/1/2020',
> 17: '9/1/2020', 18: '10/1/2020', 19: '4/1/2019', 20: '5/1/2019',
> 21: '6/1/2019', 22: '7/1/2019', 23: '8/1/2019', 24: '9/1/2019',
> 25: '10/1/2019', 26: '11/1/2019', 27: '12/1/2019', 28:
> '1/1/2020', 29: '2/1/2020', 30: '3/1/2020', 31: '4/1/2020',
> 32: '5/1/2020', 33: '6/1/2020', 34: '7/1/2020', 35: '8/1/2020',
> 36: '9/1/2020', 37: '4/1/2019', 38: '5/1/2019', 39: '6/1/2019',
> 40: '7/1/2019', 41: '8/1/2019', 42: '9/1/2019', 43: '10/1/2019',
> 44: '11/1/2019', 45: '12/1/2019', 46: '1/1/2020', 47:
> '2/1/2020', 48: '3/1/2020', 49: '4/1/2020', 50: '5/1/2020',
> 51: '6/1/2020', 52: '7/1/2020', 53: '8/1/2020', 54: '9/1/2020',
> 55: '10/1/2020', 56: '4/1/2019', 57: '5/1/2019', 58: '6/1/2019',
> 59: '7/1/2019', 60: '8/1/2019', 61: '9/1/2019', 62: '10/1/2019',
> 63: '11/1/2019', 64: '12/1/2019', 65: '1/1/2020', 66:
> '2/1/2020', 67: '3/1/2020', 68: '4/1/2020', 69: '5/1/2020',
> 70: '6/1/2020', 71: '7/1/2020', 72: '8/1/2020', 73: '9/1/2020',
> 74: '10/1/2020', 75: '4/1/2019', 76: '5/1/2019', 77: '6/1/2019',
> 78: '7/1/2019', 79: '8/1/2019', 80: '9/1/2019', 81: '10/1/2019',
> 82: '11/1/2019', 83: '12/1/2019', 84: '1/1/2020', 85:
> '2/1/2020', 86: '3/1/2020', 87: '4/1/2020', 88: '5/1/2020',
> 89: '6/1/2020', 90: '7/1/2020', 91: '8/1/2020', 92: '9/1/2020',
> 93: '10/1/2020', 94: '4/1/2019', 95: '5/1/2019', 96: '6/1/2019',
> 97: '7/1/2019', 98: '8/1/2019', 99: '9/1/2019', 100:
> '10/1/2019', 101: '11/1/2019', 102: '12/1/2019', 103:
> '1/1/2020', 104: '2/1/2020', 105: '3/1/2020', 106: '4/1/2020',
> 107: '5/1/2020', 108: '6/1/2020', 109: '7/1/2020', 110:
> '8/1/2020', 111: '9/1/2020', 112: '10/1/2020', 113: '4/1/2019',
> 114: '5/1/2019', 115: '6/1/2019', 116: '7/1/2019', 117:
> '8/1/2019', 118: '9/1/2019', 119: '10/1/2019', 120: '11/1/2019',
> 121: '12/1/2019', 122: '1/1/2020', 123: '2/1/2020', 124:
> '3/1/2020', 125: '4/1/2020', 126: '5/1/2020', 127: '6/1/2020',
> 128: '7/1/2020', 129: '8/1/2020', 130: '9/1/2020', 131:
> '10/1/2020', 132: '4/1/2019', 133: '5/1/2019', 134: '6/1/2019',
> 135: '7/1/2019', 136: '8/1/2019', 137: '9/1/2019', 138:
> '10/1/2019', 139: '11/1/2019', 140: '12/1/2019', 141:
> '1/1/2020', 142: '2/1/2020', 143: '3/1/2020', 144: '4/1/2020',
> 145: '5/1/2020', 146: '4/1/2019', 147: '5/1/2019', 148:
> '6/1/2019', 149: '7/1/2019', 150: '8/1/2019', 151: '9/1/2019',
> 152: '10/1/2019', 153: '11/1/2019', 154: '12/1/2019', 155:
> '1/1/2020', 156: '2/1/2020', 157: '3/1/2020', 158: '4/1/2020',
> 159: '5/1/2020', 160: '6/1/2020', 161: '7/1/2020', 162:
> '8/1/2020', 163: '9/1/2020', 164: '10/1/2020'}, 'End_Date': {0:
> '5/1/2019', 1: '6/1/2019', 2: '7/1/2019', 3: '8/1/2019', 4:
> '9/1/2019', 5: '10/1/2019', 6: '11/1/2019', 7: '12/1/2019', 8:
> '1/1/2020', 9: '2/1/2020', 10: '3/1/2020', 11: '4/1/2020', 12:
> '5/1/2020', 13: '6/1/2020', 14: '7/1/2020', 15: '8/1/2020',
> 16: '9/1/2020', 17: '10/1/2020', 18: '11/1/2020', 19:
> '5/1/2019', 20: '6/1/2019', 21: '7/1/2019', 22: '8/1/2019',
> 23: '9/1/2019', 24: '10/1/2019', 25: '11/1/2019', 26:
> '12/1/2019', 27: '1/1/2020', 28: '2/1/2020', 29: '3/1/2020',
> 30: '4/1/2020', 31: '5/1/2020', 32: '6/1/2020', 33: '7/1/2020',
> 34: '8/1/2020', 35: '9/1/2020', 36: '10/1/2020', 37: '5/1/2019',
> 38: '6/1/2019', 39: '7/1/2019', 40: '8/1/2019', 41: '9/1/2019',
> 42: '10/1/2019', 43: '11/1/2019', 44: '12/1/2019', 45:
> '1/1/2020', 46: '2/1/2020', 47: '3/1/2020', 48: '4/1/2020',
> 49: '5/1/2020', 50: '6/1/2020', 51: '7/1/2020', 52: '8/1/2020',
> 53: '9/1/2020', 54: '10/1/2020', 55: '11/1/2020', 56:
> '5/1/2019', 57: '6/1/2019', 58: '7/1/2019', 59: '8/1/2019',
> 60: '9/1/2019', 61: '10/1/2019', 62: '11/1/2019', 63:
> '12/1/2019', 64: '1/1/2020', 65: '2/1/2020', 66: '3/1/2020',
> 67: '4/1/2020', 68: '5/1/2020', 69: '6/1/2020', 70: '7/1/2020',
> 71: '8/1/2020', 72: '9/1/2020', 73: '10/1/2020', 74:
> '11/1/2020', 75: '5/1/2019', 76: '6/1/2019', 77: '7/1/2019',
> 78: '8/1/2019', 79: '9/1/2019', 80: '10/1/2019', 81:
> '11/1/2019', 82: '12/1/2019', 83: '1/1/2020', 84: '2/1/2020',
> 85: '3/1/2020', 86: '4/1/2020', 87: '5/1/2020', 88: '6/1/2020',
> 89: '7/1/2020', 90: '8/1/2020', 91: '9/1/2020', 92: '10/1/2020',
> 93: '11/1/2020', 94: '5/1/2019', 95: '6/1/2019', 96: '7/1/2019',
> 97: '8/1/2019', 98: '9/1/2019', 99: '10/1/2019', 100:
> '11/1/2019', 101: '12/1/2019', 102: '1/1/2020', 103: '2/1/2020',
> 104: '3/1/2020', 105: '4/1/2020', 106: '5/1/2020', 107:
> '6/1/2020', 108: '7/1/2020', 109: '8/1/2020', 110: '9/1/2020',
> 111: '10/1/2020', 112: '11/1/2020', 113: '5/1/2019', 114:
> '6/1/2019', 115: '7/1/2019', 116: '8/1/2019', 117: '9/1/2019',
> 118: '10/1/2019', 119: '11/1/2019', 120: '12/1/2019', 121:
> '1/1/2020', 122: '2/1/2020', 123: '3/1/2020', 124: '4/1/2020',
> 125: '5/1/2020', 126: '6/1/2020', 127: '7/1/2020', 128:
> '8/1/2020', 129: '9/1/2020', 130: '10/1/2020', 131: '11/1/2020',
> 132: '5/1/2019', 133: '6/1/2019', 134: '7/1/2019', 135:
> '8/1/2019', 136: '9/1/2019', 137: '10/1/2019', 138: '11/1/2019',
> 139: '12/1/2019', 140: '1/1/2020', 141: '2/1/2020', 142:
> '3/1/2020', 143: '4/1/2020', 144: '5/1/2020', 145: '6/1/2020',
> 146: '5/1/2019', 147: '6/1/2019', 148: '7/1/2019', 149:
> '8/1/2019', 150: '9/1/2019', 151: '10/1/2019', 152: '11/1/2019',
> 153: '12/1/2019', 154: '1/1/2020', 155: '2/1/2020', 156:
> '3/1/2020', 157: '4/1/2020', 158: '5/1/2020', 159: '6/1/2020',
> 160: '7/1/2020', 161: '8/1/2020', 162: '9/1/2020', 163:
> '10/1/2020', 164: '11/1/2020'}, 'Diff': {0: 160.427898, 1:
> 136.02479, 2: 174.05132, 3: 112.042449, 4: 141.848796, 5: 103.55216800000001, 6: 125.608671, 7: 145.259097, 8: 115.512121, 9: 185.719089, 10: 126.738628, 11: 231.346125, 12: 97.025865,
> 13: 42.85235, 14: 124.66596000000001, 15: 357.99735400000003,
> 16: 490.958654, 17: 204.54779299999998, 18: 287.602512, 19:
> 12.384862, 20: -2.6173490000000004, 21: -5.618695, 22: 3.204252, 23: -25.378199, 24: -10.971694, 25: 4.3095989999999995, 26:
> -18.804298, 27: 2.172899, 28: -13.781198000000002, 29: -41.914787, 30: -54.478880000000004, 31: -12.569756, 32: 5.212716, 33: -8.354158, 34: -24.797067000000002, 35: -15.030110999999998, 36: -6.733579, 37: 20.515503, 38: 16.911068, 39: -3.20907, 40: -6.310559, 41: -28.37903, 42: -2.468109, 43: 17.733227, 44: -7.246225999999999, 45: 9.816127, 46: -27.043909000000003, 47: -14.444707000000001, 48: -16.315007,
> 49: -13.173909, 50: 3.065297, 51: -38.023148, 52:
> -17.331557999999998, 53: -79.566677, 54: -9.81724, 55: -14.70161, 56: 0.7713479999999999, 57: 1.438835, 58: 2.093475, 59: 15.108785999999998, 60: -30.160431, 61: -4.704619999999999,
> 62: -11.864922, 63: 8.38772, 64: -9.329917, 65:
> -13.411660999999999, 66: -23.234234, 67: -30.517737, 68: -4.082799, 69: -0.431963, 70: -17.669752, 71: -29.476111, 72: -39.368627000000004, 73: -10.156292, 74: -5.152712999999999, 75: 14.799759, 76: 24.846001, 77: 1.522557, 78: 29.085378000000002, 79: 17.490861, 80: 9.210548, 81: 5.272687, 82: -34.114118, 83:
> -14.522017000000002, 84: 0.977517, 85: 13.740992000000002, 86: -46.837142, 87: -35.071144, 88: 31.125302, 89: -6.35655, 90: -76.485238, 91: -30.657497999999997, 92: -3.9887309999999996, 93: 10.158892999999999, 94: 13.419127, 95: 5.169942, 96:
> -1.2159360000000001, 97: -14.750085, 98: 2.939289, 99: 7.890761, 100: -17.168810999999998, 101: -4.825164, 102: -23.056132, 103:
> -21.905035, 104: -11.41048, 105: -14.285372, 106: -8.342461, 107: 12.303987, 108: 10.871853999999999, 109: 28.386903000000004,
> 110: -44.330884999999995, 111: 16.979916, 112: 19.051773999999998,
> 113: 10.106931, 114: -5.156007, 115: 4.943011, 116: -3.461768,
> 117: 4.1415239999999995, 118: 6.816288, 119: 2.135951, 120:
> -13.823120000000001, 121: -10.989626, 122: 0.418397, 123: -18.981256, 124: 11.320344, 125: 10.697455999999999, 126: -10.048842, 127: -6.781886999999999, 128: 1.679831, 129: -14.090216, 130: 9.935841, 131: -8.609016, 132: 0.633534, 133: -0.998959, 134: -11.890945, 135: -4.731419, 136: -16.508375, 137: -6.705882000000001, 138: -4.5014970000000005, 139: 2.958923,
> 140: -2.216122, 141: 0.555845, 142: 7.907547999999999, 143:
> -19.098307000000002, 144: 0.5591109999999999, 145: 1.6575990000000003, 146: 141.94400100000001, 147: 120.173635, 148: 142.15808700000002, 149: 185.25196, 150: 145.080391, 151:
> 153.529132, 152: 105.33828700000001, 153: 114.990931, 154: 110.346369, 155: 313.465552, 156: 183.225067, 157: 97.446786, 158: 147.22139199999998, 159: 67.11041800000001, 160: 202.904259,
> 161: 283.446608, 162: 435.956408, 163: 138.866722, 164:
> 265.60073700000004}, 'Percent': {0: 11.10808895, 1: 8.476798147, 2: 9.998946416, 3: 5.8515509020000005, 4: 6.998691053, 5:
> 4.774983768999999, 6: 5.528085128, 7: 6.058015553, 8: 4.542250752, 9: 6.985672892999999, 10: 4.455896349, 11: 7.786734158, 12: 3.0298095369999998, 13: 1.29879184, 14: 3.7299973910000004, 15: 10.32609443, 16: 12.83580027, 17: 4.7394278430000005, 18: 6.362291972, 19: 5.780550638999999, 20: -1.154871997, 21: -2.50814364, 22: 1.4671525440000002, 23: -11.45206837, 24: -5.591372156, 25: 2.3263223280000003, 26: -9.919797042999999, 27: 1.2724943000000002, 28: -7.96914578, 29: -26.336533399999997, 30: -46.46943194, 31: -20.02922699, 32: 10.38652118, 33: -15.07969942, 34: -52.70827375, 35: -67.55468889, 36: -93.27974007, 37: 9.402813387, 38: 7.084646625, 39: -1.255449187, 40: -2.500199374, 41: -11.53189369, 42: -1.133654425, 43: 8.238642233, 44: -3.110265117, 45: 4.348584722, 46: -11.48128956, 47: -6.927792359, 48: -8.407238632, 49: -7.411730457000001, 50: 1.862608372, 51: -22.68204813, 52: -13.37184892, 53: -70.86406019, 54: -30.00925321, 55: -64.20813421, 56: 0.34885011899999996, 57: 0.6484658520000001, 58: 0.937425444, 59: 6.702646341, 60: -12.53946732, 61: -2.236423138, 62:
> -5.769221736, 63: 4.328161823, 64: -4.614618367, 65: -6.954385416, 66: -12.94817733, 67: -19.53685876, 68: -3.248354565, 69: -0.35521689700000003, 70: -14.58219683, 71: -28.47831486, 72: -53.18101653, 73: -29.30350463, 74: -21.02918559, 75: 4.4530310239999995, 76: 7.157090832000001, 77: 0.409291454, 78: 7.786816239, 79: 4.344409532, 80: 2.192481078, 81: 1.2281839890000001, 82: -7.849900319, 83: -3.626278258, 84:
> 0.253279403, 85: 3.5513629, 86: -11.68991986, 87: -9.911988763, 88: 9.764667122999999, 89: -1.8167818040000001, 90: -22.26494689,
> 91: -11.48058055, 92: -1.6874205469999999, 93: 4.371453667, 94:
> 3.693729404, 95: 1.3723787669999998, 96: -0.318404648, 97: -3.8747904839999996, 98: 0.803264717, 99: 2.139245828, 100: -4.557108588999999, 101: -1.3418922629999999, 102: -6.499190071, 103: -6.60391336, 104: -3.68326232, 105: -4.787607361, 106:
> -2.936484265, 107: 4.4619360210000005, 108: 3.7741835389999996, 109: 9.496161526, 110: -13.54370813, 111: 6.000260663, 112:
> 6.351306448, 113: 6.044575831, 114: -2.907847111, 115: 2.87121369, 116: -1.954690645, 117: 2.385137588, 118: 3.834107345, 119: 1.157091192, 120: -7.402630086, 121: -6.355712315, 122: 0.258397668, 123: -11.69241483, 124: 7.896613534, 125: 6.915983594, 126: -6.076406612, 127: -4.366230503, 128: 1.1308641259999999, 129: -9.379480147999999, 130: 7.298593929, 131: -5.89378179, 132: 0.68192986, 133:
> -1.067986868, 134: -12.84984155, 135: -5.86684522, 136: -21.74577714, 137: -11.28804499, 138: -8.54156872, 139: 6.138900437999999, 140: -4.331875908, 141: 1.1357134979999999, 142: 15.97542343, 143: -33.26897683, 144: 1.459536053, 145:
> 4.264846401000001, 146: 9.352928661, 147: 7.241180253, 148: 7.987486665, 149: 9.638910633, 150: 6.885082487999999, 151: 6.816699167, 152: 4.378551195, 153: 4.57927269, 154: 4.201896452, 155: 11.4551689, 156: 6.007535563999999, 157: 3.013993024, 158: 4.4202763439999995, 159: 1.9296724969999999, 160: 5.723796667, 161: 7.562955548, 162: 10.81435501, 163:
> 3.108563316, 164: 5.766284604}, 'Date': {0: '04-01-2019 to 05-01-2019', 1: '05-01-2019 to 06-01-2019', 2: '06-01-2019 to
> 07-01-2019', 3: '07-01-2019 to 08-01-2019', 4: '08-01-2019 to
> 09-01-2019', 5: '09-01-2019 to 10-01-2019', 6: '10-01-2019 to
> 11-01-2019', 7: '11-01-2019 to 12-01-2019', 8: '12-01-2019 to
> 01-01-2020', 9: '01-01-2020 to 02-01-2020', 10: '02-01-2020 to
> 03-01-2020', 11: '03-01-2020 to 04-01-2020', 12: '04-01-2020 to
> 05-01-2020', 13: '05-01-2020 to 06-01-2020', 14: '06-01-2020 to
> 07-01-2020', 15: '07-01-2020 to 08-01-2020', 16: '08-01-2020 to
> 09-01-2020', 17: '09-01-2020 to 10-01-2020', 18: '10-01-2020 to
> 11-01-2020', 19: '04-01-2019 to 05-01-2019', 20: '05-01-2019 to
> 06-01-2019', 21: '06-01-2019 to 07-01-2019', 22: '07-01-2019 to
> 08-01-2019', 23: '08-01-2019 to 09-01-2019', 24: '09-01-2019 to
> 10-01-2019', 25: '10-01-2019 to 11-01-2019', 26: '11-01-2019 to
> 12-01-2019', 27: '12-01-2019 to 01-01-2020', 28: '01-01-2020 to
> 02-01-2020', 29: '02-01-2020 to 03-01-2020', 30: '03-01-2020 to
> 04-01-2020', 31: '04-01-2020 to 05-01-2020', 32: '05-01-2020 to
> 06-01-2020', 33: '06-01-2020 to 07-01-2020', 34: '07-01-2020 to
> 08-01-2020', 35: '08-01-2020 to 09-01-2020', 36: '09-01-2020 to
> 10-01-2020', 37: '04-01-2019 to 05-01-2019', 38: '05-01-2019 to
> 06-01-2019', 39: '06-01-2019 to 07-01-2019', 40: '07-01-2019 to
> 08-01-2019', 41: '08-01-2019 to 09-01-2019', 42: '09-01-2019 to
> 10-01-2019', 43: '10-01-2019 to 11-01-2019', 44: '11-01-2019 to
> 12-01-2019', 45: '12-01-2019 to 01-01-2020', 46: '01-01-2020 to
> 02-01-2020', 47: '02-01-2020 to 03-01-2020', 48: '03-01-2020 to
> 04-01-2020', 49: '04-01-2020 to 05-01-2020', 50: '05-01-2020 to
> 06-01-2020', 51: '06-01-2020 to 07-01-2020', 52: '07-01-2020 to
> 08-01-2020', 53: '08-01-2020 to 09-01-2020', 54: '09-01-2020 to
> 10-01-2020', 55: '10-01-2020 to 11-01-2020', 56: '04-01-2019 to
> 05-01-2019', 57: '05-01-2019 to 06-01-2019', 58: '06-01-2019 to
> 07-01-2019', 59: '07-01-2019 to 08-01-2019', 60: '08-01-2019 to
> 09-01-2019', 61: '09-01-2019 to 10-01-2019', 62: '10-01-2019 to
> 11-01-2019', 63: '11-01-2019 to 12-01-2019', 64: '12-01-2019 to
> 01-01-2020', 65: '01-01-2020 to 02-01-2020', 66: '02-01-2020 to
> 03-01-2020', 67: '03-01-2020 to 04-01-2020', 68: '04-01-2020 to
> 05-01-2020', 69: '05-01-2020 to 06-01-2020', 70: '06-01-2020 to
> 07-01-2020', 71: '07-01-2020 to 08-01-2020', 72: '08-01-2020 to
> 09-01-2020', 73: '09-01-2020 to 10-01-2020', 74: '10-01-2020 to
> 11-01-2020', 75: '04-01-2019 to 05-01-2019', 76: '05-01-2019 to
> 06-01-2019', 77: '06-01-2019 to 07-01-2019', 78: '07-01-2019 to
> 08-01-2019', 79: '08-01-2019 to 09-01-2019', 80: '09-01-2019 to
> 10-01-2019', 81: '10-01-2019 to 11-01-2019', 82: '11-01-2019 to
> 12-01-2019', 83: '12-01-2019 to 01-01-2020', 84: '01-01-2020 to
> 02-01-2020', 85: '02-01-2020 to 03-01-2020', 86: '03-01-2020 to
}} }}
Any suggestions is appreciated任何建议表示赞赏
Update更新
Is there a way to make the full line display the marker dots on them?有没有办法让整行在它们上面显示标记点？ I have tried this: mode = 'markers+lines' but did not get the desired result:我试过这个： mode = 'markers+lines' 但没有得到想要的结果：
**Update, I have figured this out:**
fig.update_traces(mode='markers+lines')
Updated Question:更新的问题：
Also,is there a way to add the Date and Percent titles on the hover annotation here?.另外，有没有办法在此处的悬停注释上添加日期和百分比标题？。 I am researching the Plotly docs.我正在研究 Plotly 文档。
1 个解决方案
解决方案1
1 已采纳 2020-11-10 09:41:31
I've put together a suggestion that should do exactly what you're asking for.我已经提出了一个建议，该建议应该完全符合您的要求。 The following figure is produced by the code sample below.下图是由下面的代码示例生成的。 The code uses a dictionary that contains the different upper and lower limits for your different PODs along with a possibility to set different colors for different pods:该代码使用一个字典，其中包含不同 POD 的不同上限和下限，以及为不同 Pod 设置不同颜色的可能性：
lim = {'IAD': {'lower': 90,'upper': 350, 'color':'yellow'},
'SJCtest': {'lower': 10,'upper': 12, 'color':'green'}}
Plot阴谋
I was tempted to filter your dataframe first, and then add new traces to the figure based on that.我很想先过滤你的数据框，然后根据它向图中添加新的痕迹。 But my solution actually goes through each datapoint for each trace, and colors the points based on the dictionary above.但我的解决方案实际上遍历每个跟踪的每个数据点，并根据上面的字典为点着色。 So no need for any datamunging.所以不需要任何数据处理。 Take a look, run a few tests, and let me know how it works out for you.看一看，运行一些测试，然后让我知道它对您的效果如何。
Complete code完整代码
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
df = pd.DataFrame({'POD': {0: 'IAD',
1: 'IAD',
2: 'IAD',
3: 'IAD',
4: 'IAD',
5: 'IAD',
6: 'IAD',
7: 'IAD',
8: 'IAD',
9: 'IAD',
10: 'IAD',
11: 'IAD',
12: 'IAD',
13: 'IAD',
14: 'IAD',
15: 'IAD',
16: 'IAD',
17: 'IAD',
18: 'IAD',
19: 'SJCtest',
20: 'SJCtest',
21: 'SJCtest',
22: 'SJCtest',
23: 'SJCtest',
24: 'SJCtest'},
'Start': {0: '4/1/2019',
1: '5/1/2019',
2: '6/1/2019',
3: '7/1/2019',
4: '8/1/2019',
5: '9/1/2019',
6: '10/1/2019',
7: '11/1/2019',
8: '12/1/2019',
9: '1/1/2020',
10: '2/1/2020',
11: '3/1/2020',
12: '4/1/2020',
13: '5/1/2020',
14: '6/1/2020',
15: '7/1/2020',
16: '8/1/2020',
17: '9/1/2020',
18: '10/1/2020',
19: '4/1/2019',
20: '5/1/2019',
21: '6/1/2019',
22: '7/1/2019',
23: '8/1/2019',
24: '9/1/2019'},
'End': {0: '5/1/2019',
1: '6/1/2019',
2: '7/1/2019',
3: '8/1/2019',
4: '9/1/2019',
5: '10/1/2019',
6: '11/1/2019',
7: '12/1/2019',
8: '1/1/2020',
9: '2/1/2020',
10: '3/1/2020',
11: '4/1/2020',
12: '5/1/2020',
13: '6/1/2020',
14: '7/1/2020',
15: '8/1/2020',
16: '9/1/2020',
17: '10/1/2020',
18: '11/1/2020',
19: '5/1/2019',
20: '6/1/2019',
21: '7/1/2019',
22: '8/1/2019',
23: '9/1/2019',
24: '10/1/2019'},
'Diff': {0: 160.4279,
1: 136.0248,
2: 174.0513,
3: 112.0424,
4: 141.8488,
5: 103.5522,
6: 125.6087,
7: 145.2591,
8: 115.5121,
9: 185.7191,
10: 126.7386,
11: 231.3461,
12: 97.02587,
13: 42.85235,
14: 124.666,
15: 357.9974,
16: 490.9587,
17: 204.5478,
18: 287.6025,
19: 12.38486,
20: -2.61735,
21: -5.6187,
22: 3.204252,
23: -25.3782,
24: -10.9717},
'Percent': {0: 11.108089999999999,
1: 8.476797999999999,
2: 9.998946,
3: 5.851551000000001,
4: 6.998691,
5: 4.774984,
6: 5.528085,
7: 6.058016,
8: 4.542251,
9: 6.985672999999999,
10: 4.455896,
11: 7.786734,
12: 3.02981,
13: 1.298792,
14: 3.729997,
15: 10.326089999999999,
16: 12.8358,
17: 4.739428,
18: 6.362292,
19: 5.780551,
20: -1.15487,
21: -2.50814,
22: 1.4671530000000002,
23: -11.4521,
24: -5.5913699999999995},
'Date': {0: '04-01-2019 to 05-01-2019',
1: '05-01-2019 to 06-01-2019',
2: '06-01-2019 to 07-01-2019',
3: '07-01-2019 to 08-01-2019',
4: '08-01-2019 to 09-01-2019',
5: '09-01-2019 to 10-01-2019',
6: '10-01-2019 to 11-01-2019',
7: '11-01-2019 to 12-01-2019',
8: '12-01-2019 to 01-01-2020',
9: '01-01-2020 to 02-01-2020',
10: '02-01-2020 to 03-01-2020',
11: '03-01-2020 to 04-01-2020',
12: '04-01-2020 to 05-01-2020',
13: '05-01-2020 to 06-01-2020',
14: '06-01-2020 to 07-01-2020',
15: '07-01-2020 to 08-01-2020',
16: '08-01-2020 to 09-01-2020',
17: '09-01-2020 to 10-01-2020',
18: '10-01-2020 to 11-01-2020',
19: '04-01-2019 to 05-01-2019',
20: '05-01-2019 to 06-01-2019',
21: '06-01-2019 to 07-01-2019',
22: '07-01-2019 to 08-01-2019',
23: '08-01-2019 to 09-01-2019',
24: '09-01-2019 to 10-01-2019'}})
fig = px.line(df, x="Date", y="Diff", color = 'POD')
import plotly.graph_objects as go
included = 0
lim = {'IAD': {'lower': 90,'upper': 350, 'color':'yellow'},
'SJCtest': {'lower': 10,'upper': 12, 'color':'green'}}
for i, d in enumerate(fig.data):
for j, y in enumerate(d.y):
if y < lim[d.name]['lower'] or y > lim[d.name]['upper']:
if j == 0:
fig.add_traces(go.Scatter(x=[fig.data[i]['x'][j]],
y=[fig.data[i]['y'][j]],
mode = 'markers',
marker = dict(color=lim[d.name]['color']),
name = d.name + ' threshold',
legendgroup = d.name + ' threshold'))
included = included + 1
else:
fig.add_traces(go.Scatter(x=[fig.data[i]['x'][j-1], fig.data[i]['x'][j]],
y=[fig.data[i]['y'][j-1], fig.data[i]['y'][j]],
mode = 'lines',
# marker = dict(color='yellow'),
line = dict(width = 6, color = lim[d.name]['color']),
name = d.name + ' threshold',
legendgroup = d.name + ' threshold',
showlegend = False if included > 0 else True,
))
included = included + 1
fig.show()
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