Plotly：如何自定義 3D 散點圖的符號？

Question

在下面的例子中：

import plotly.express as px
df = px.data.iris()
fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
                    color='petal_length', symbol='species')
fig.show()

symbol由圓形、菱形和正方形代表的'species'決定。 當樣本量變大並且樣本點塗抹圖時，這可能不是很清楚。 我們如何自定義符號，比如使用圓形、方形和十字形（或其他對比形狀的組合）？

---

更新：

我將代碼應用於另一個數據集和代碼：

fig = px.scatter_3d(df8, x='X', y='Y', z='Z',
                    color='P', symbol='C')

# specify trace names and symbols in a dict
symbols = {'True': 'cross',
           'False':'circle-open'}

# set all symbols in fig
for i, d in enumerate(fig.data):
    fig.data[i].marker.symbol = symbols[fig.data[i].name]

fig.show()

其中C有兩個值： True或False 。 但是它捕獲了錯誤：

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-183-ea1e2ec7dd8e> in <module>
     36 # set all symbols in fig
     37 for i, d in enumerate(fig.data):
---> 38     fig.data[i].marker.symbol = symbols[fig.data[i].name]
     39 
     40 fig.show()

KeyError: 'P, True'

---

更新：

可重現的例子：

{'ID': {0: '672590',
  1: '672120',
  2: '672090',
  3: '672349',
  4: '672453',
  5: '672560',
  6: '672051',
  7: '880505',
  8: '672593',
  9: '880097',
  10: '891458',
  11: '672091',
  12: '672569',
  13: '672603',
  14: '790030',
  15: '672350',
  16: '673480',
  17: 'I00042',
  18: '880297',
  19: '894620'},
 'X': {0: 0.20111215435497176,
  1: 0.21248998904335528,
  2: 0.2086689759935364,
  3: 0.22337836085443835,
  4: 0.17847099434376115,
  5: 0.24827331723865761,
  6: 0.14411891907440183,
  7: 0.20863940038267367,
  8: 0.166299824101773,
  9: 0.20548401328860527,
  10: 0.18007828100726822,
  11: 0.21887731187605308,
  12: 0.1971207940494219,
  13: 0.19247420041228508,
  14: 0.21605657330040987,
  15: 0.15779241902165092,
  16: 0.22536060645732897,
  17: 0.19268784843224268,
  18: 0.2400112771421119,
  19: 0.22548124117213691},
 'Y': {0: 2473.923076923077,
  1: 2031.1538461538462,
  2: 2383.1923076923076,
  3: 1830.7692307692307,
  4: 1780.2307692307693,
  5: 1194.8461538461538,
  6: 1641.0,
  7: 1563.3076923076924,
  8: 1246.2307692307693,
  9: 931.6153846153846,
  10: 1207.076923076923,
  11: 799.6538461538462,
  12: 560.8461538461538,
  13: 1158.076923076923,
  14: 1221.6923076923076,
  15: 3030.076923076923,
  16: 1178.076923076923,
  17: 552.3846153846154,
  18: 1380.3076923076924,
  19: 1027.5384615384614},
 'Z': {0: 385.84615384615387,
  1: 288.46153846153845,
  2: 281.9230769230769,
  3: 273.61538461538464,
  4: 252.0,
  5: 231.69230769230768,
  6: 213.30769230769232,
  7: 203.3846153846154,
  8: 191.07692307692307,
  9: 189.46153846153845,
  10: 181.07692307692307,
  11: 176.76923076923077,
  12: 173.30769230769232,
  13: 169.6153846153846,
  14: 166.15384615384616,
  15: 165.30769230769232,
  16: 160.53846153846155,
  17: 159.84615384615384,
  18: 159.0,
  19: 145.3846153846154},
 'C': {0: True,
  1: True,
  2: True,
  3: True,
  4: True,
  5: True,
  6: True,
  7: True,
  8: True,
  9: True,
  10: True,
  11: False,
  12: False,
  13: True,
  14: True,
  15: True,
  16: True,
  17: False,
  18: True,
  19: True},
 'P': {0: 'P',
  1: 'P',
  2: 'P',
  3: 'P',
  4: 'P',
  5: 'X',
  6: 'P',
  7: 'P',
  8: 'P',
  9: 'P',
  10: 'P',
  11: 'P',
  12: 'P',
  13: 'P',
  14: 'P',
  15: 'P',
  16: 'P',
  17: 'X',
  18: 'P',
  19: 'P'}}

Answer 1

答案：

# specify trace names and symbols in a dict
symbols = {'setosa': 'cross',
           'versicolor':'circle-open',
           'virginica':'diamond-open'}

# set all symbols in fig
for i, d in enumerate(fig.data):
    fig.data[i].marker.symbol = symbols[fig.data[i].name]

細節：

在這種情況下，您可以手動設置任何標記符號，例如：

fig.data[<i>].marker.symbol = <symbol>

其中<i>是一個整數索引，指定您要更改的跟蹤，而 3D 分散對象的<symbol>屬性是一個枚舉，可以指定為以下枚舉值之一：

['circle', 'circle-open', 'square', 'square-open',
'diamond', 'diamond-open', 'cross', 'x']

示例 1 - 單個跟蹤：

fig.data[2].marker.symbol = 'circle-open'
fig.show()

情節 1：

示例 2 - 多個跟蹤：

為了使事情更加動態，您可以使用以下方法檢索所有跟蹤名稱：

['setosa', 'versicolor', 'virginica']

然后您可以指定自己的名稱和符號字典，並使用以下方法設置所有跟蹤的所有符號：

# specify trace names and symbols in a dict
symbols = {'setosa': 'cross',
           'versicolor':'circle-open',
           'virginica':'diamond-open'}

# set all symbols in fig
for i, d in enumerate(fig.data):
    fig.data[i].marker.symbol = symbols[fig.data[i].name]

fig.show()

情節 2：

完整代碼：

import plotly.express as px
df = px.data.iris()
fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
                    color='petal_length', symbol='species')
#fig.show()

# specify trace names and symbols in a dict
symbols = {'setosa': 'cross',
           'versicolor':'circle-open',
           'virginica':'diamond-open'}

# set all symbols in fig
for i, d in enumerate(fig.data):
    fig.data[i].marker.symbol = symbols[fig.data[i].name]

fig.show()

編輯：OP 對附錄的回答

這里發生的情況是您在px.scatter3d使用color='P', symbol='C'分配顏色和符號。 這會對現在的跟蹤名稱產生影響，例如'name': 'P, True' 。 這導致以下內容中斷：

for i, d in enumerate(fig.data):
    fig.data[i].marker.symbol = symbols[fig.data[i].name]

根據您定義的符號字典判斷：

# specify trace names and symbols in a dict
symbols = {'True': 'cross',
           'False':'circle-open'}

看起來您只想通過'name': 'P, True'的最后一部分來區分您的符號'name': 'P, True'是True還是False 。 您可以通過在symbols[fig.data[i].name.split(', ')[1]]指定來對您的符號字典進行子集化，從而得到：

帶有數據示例的完整代碼：

import pandas as pd
import plotly.express as px

df8 = pd.DataFrame({'ID': {0: '672590',
  1: '672120',
  2: '672090',
  3: '672349',
  4: '672453',
  5: '672560',
  6: '672051',
  7: '880505',
  8: '672593',
  9: '880097',
  10: '891458',
  11: '672091',
  12: '672569',
  13: '672603',
  14: '790030',
  15: '672350',
  16: '673480',
  17: 'I00042',
  18: '880297',
  19: '894620'},
 'X': {0: 0.20111215435497176,
  1: 0.21248998904335528,
  2: 0.2086689759935364,
  3: 0.22337836085443835,
  4: 0.17847099434376115,
  5: 0.24827331723865761,
  6: 0.14411891907440183,
  7: 0.20863940038267367,
  8: 0.166299824101773,
  9: 0.20548401328860527,
  10: 0.18007828100726822,
  11: 0.21887731187605308,
  12: 0.1971207940494219,
  13: 0.19247420041228508,
  14: 0.21605657330040987,
  15: 0.15779241902165092,
  16: 0.22536060645732897,
  17: 0.19268784843224268,
  18: 0.2400112771421119,
  19: 0.22548124117213691},
 'Y': {0: 2473.923076923077,
  1: 2031.1538461538462,
  2: 2383.1923076923076,
  3: 1830.7692307692307,
  4: 1780.2307692307693,
  5: 1194.8461538461538,
  6: 1641.0,
  7: 1563.3076923076924,
  8: 1246.2307692307693,
  9: 931.6153846153846,
  10: 1207.076923076923,
  11: 799.6538461538462,
  12: 560.8461538461538,
  13: 1158.076923076923,
  14: 1221.6923076923076,
  15: 3030.076923076923,
  16: 1178.076923076923,
  17: 552.3846153846154,
  18: 1380.3076923076924,
  19: 1027.5384615384614},
 'Z': {0: 385.84615384615387,
  1: 288.46153846153845,
  2: 281.9230769230769,
  3: 273.61538461538464,
  4: 252.0,
  5: 231.69230769230768,
  6: 213.30769230769232,
  7: 203.3846153846154,
  8: 191.07692307692307,
  9: 189.46153846153845,
  10: 181.07692307692307,
  11: 176.76923076923077,
  12: 173.30769230769232,
  13: 169.6153846153846,
  14: 166.15384615384616,
  15: 165.30769230769232,
  16: 160.53846153846155,
  17: 159.84615384615384,
  18: 159.0,
  19: 145.3846153846154},
 'C': {0: True,
  1: True,
  2: True,
  3: True,
  4: True,
  5: True,
  6: True,
  7: True,
  8: True,
  9: True,
  10: True,
  11: False,
  12: False,
  13: True,
  14: True,
  15: True,
  16: True,
  17: False,
  18: True,
  19: True},
 'P': {0: 'P',
  1: 'P',
  2: 'P',
  3: 'P',
  4: 'P',
  5: 'X',
  6: 'P',
  7: 'P',
  8: 'P',
  9: 'P',
  10: 'P',
  11: 'P',
  12: 'P',
  13: 'P',
  14: 'P',
  15: 'P',
  16: 'P',
  17: 'X',
  18: 'P',
  19: 'P'}})

fig = px.scatter_3d(df8, x='X', y='Y', z='Z',
                    color='P', symbol='C')

# specify trace names and symbols in a dict
symbols = {'True': 'cross',
           'False':'circle-open'}

# set all symbols in fig
for i, d in enumerate(fig.data):
    fig.data[i].marker.symbol = symbols[fig.data[i].name.split(', ')[1]]

fig.show()