Python Plotly：在create_annotated_heatmap（）函数中定义Z参数的最佳方法

我对Plotly和Dash是全新的。我正在尝试创建一个热图，以显示欠旋转的数值

位于的文档说明可以使用

ff.create\u annotated\u heatmap（）

函数，如下所示：

import plotly.figure_factory as ff

z = [[.1, .3, .5],
     [1.0, .8, .6],
     [.6, .4, .2]]

x = ['Team A', 'Team B', 'Team C']
y = ['Game Three', 'Game Two', 'Game One']

z_text = [['Win', 'Lose', 'Win'],
          ['Lose', 'Lose', 'Win'],
          ['Win', 'Win', 'Lose']]

fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_text, colorscale='Viridis')
fig.show()

df = pd.DataFrame({'Make':['Ford', 'Ford', 'Ford', 'Buick', 'Buick', 'Buick', 'Mercedes', 'Mercedes', 'Mercedes'],
                          'Score':['88.6', '76.6', '86.2', '79.1', '86.8', '96.4', '97.3', '98.7', '98.5'],
                          'Dimension':['Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling'],
                          'Month':['Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19']})

ford_scores = df[(df['Make'].isin(['Ford']))]['Score'].astype(float).tolist()
buick_scores = df[(df['Make'].isin(['Buick']))]['Score'].astype(float).tolist()
mercedes_scores = df[(df['Make'].isin(['Mercedes']))]['Score'].astype(float).tolist()

import plotly.figure_factory as ff
fig = ff.create_annotated_heatmap(
            z=[ford_scores, buick_scores, mercedes_scores],
            x=df['Dimension'].unique().tolist(),
            y=df['Make'].unique().tolist(),
            colorscale=['red', 'orange', 'yellow', 'green'],
            hoverongaps=False
            )

fig.show()

df = pd.DataFrame({'Make':['Ford', 'Ford', 'Ford', 'Buick', 'Buick', 'Buick', 'Mercedes', 'Mercedes', 'Mercedes'],
                          'Score':['88.6', '76.6', '86.2', '79.1', '86.8', '96.4', '97.3', '98.7', '98.5'],
                          'Dimension':['Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling'],
                          'Month':['Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19']})

df['Score'] = pd.to_numeric(df['Score'])
df = pd.pivot_table(df, values='Score', index='Make', columns=['Dimension'])

import plotly.figure_factory as ff

fig = ff.create_annotated_heatmap(
            z=df.to_numpy(),
            x=df.columns.tolist(),
            y=df.index.tolist(),
            colorscale=['red', 'orange', 'yellow', 'green'],
            hoverongaps=False
            )

fig.show()

第一个参数，

data

，似乎是一个列表列表

我的数据如下：

import plotly.figure_factory as ff

z = [[.1, .3, .5],
     [1.0, .8, .6],
     [.6, .4, .2]]

x = ['Team A', 'Team B', 'Team C']
y = ['Game Three', 'Game Two', 'Game One']

z_text = [['Win', 'Lose', 'Win'],
          ['Lose', 'Lose', 'Win'],
          ['Win', 'Win', 'Lose']]

fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_text, colorscale='Viridis')
fig.show()

df = pd.DataFrame({'Make':['Ford', 'Ford', 'Ford', 'Buick', 'Buick', 'Buick', 'Mercedes', 'Mercedes', 'Mercedes'],
                          'Score':['88.6', '76.6', '86.2', '79.1', '86.8', '96.4', '97.3', '98.7', '98.5'],
                          'Dimension':['Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling'],
                          'Month':['Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19']})

ford_scores = df[(df['Make'].isin(['Ford']))]['Score'].astype(float).tolist()
buick_scores = df[(df['Make'].isin(['Buick']))]['Score'].astype(float).tolist()
mercedes_scores = df[(df['Make'].isin(['Mercedes']))]['Score'].astype(float).tolist()

import plotly.figure_factory as ff
fig = ff.create_annotated_heatmap(
            z=[ford_scores, buick_scores, mercedes_scores],
            x=df['Dimension'].unique().tolist(),
            y=df['Make'].unique().tolist(),
            colorscale=['red', 'orange', 'yellow', 'green'],
            hoverongaps=False
            )

fig.show()

df = pd.DataFrame({'Make':['Ford', 'Ford', 'Ford', 'Buick', 'Buick', 'Buick', 'Mercedes', 'Mercedes', 'Mercedes'],
                          'Score':['88.6', '76.6', '86.2', '79.1', '86.8', '96.4', '97.3', '98.7', '98.5'],
                          'Dimension':['Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling'],
                          'Month':['Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19']})

df['Score'] = pd.to_numeric(df['Score'])
df = pd.pivot_table(df, values='Score', index='Make', columns=['Dimension'])

import plotly.figure_factory as ff

fig = ff.create_annotated_heatmap(
            z=df.to_numpy(),
            x=df.columns.tolist(),
            y=df.index.tolist(),
            colorscale=['red', 'orange', 'yellow', 'green'],
            hoverongaps=False
            )

fig.show()

我的代码如下：

import plotly.figure_factory as ff

z = [[.1, .3, .5],
     [1.0, .8, .6],
     [.6, .4, .2]]

x = ['Team A', 'Team B', 'Team C']
y = ['Game Three', 'Game Two', 'Game One']

z_text = [['Win', 'Lose', 'Win'],
          ['Lose', 'Lose', 'Win'],
          ['Win', 'Win', 'Lose']]

fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_text, colorscale='Viridis')
fig.show()

df = pd.DataFrame({'Make':['Ford', 'Ford', 'Ford', 'Buick', 'Buick', 'Buick', 'Mercedes', 'Mercedes', 'Mercedes'],
                          'Score':['88.6', '76.6', '86.2', '79.1', '86.8', '96.4', '97.3', '98.7', '98.5'],
                          'Dimension':['Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling'],
                          'Month':['Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19']})

ford_scores = df[(df['Make'].isin(['Ford']))]['Score'].astype(float).tolist()
buick_scores = df[(df['Make'].isin(['Buick']))]['Score'].astype(float).tolist()
mercedes_scores = df[(df['Make'].isin(['Mercedes']))]['Score'].astype(float).tolist()

import plotly.figure_factory as ff
fig = ff.create_annotated_heatmap(
            z=[ford_scores, buick_scores, mercedes_scores],
            x=df['Dimension'].unique().tolist(),
            y=df['Make'].unique().tolist(),
            colorscale=['red', 'orange', 'yellow', 'green'],
            hoverongaps=False
            )

fig.show()

df = pd.DataFrame({'Make':['Ford', 'Ford', 'Ford', 'Buick', 'Buick', 'Buick', 'Mercedes', 'Mercedes', 'Mercedes'],
                          'Score':['88.6', '76.6', '86.2', '79.1', '86.8', '96.4', '97.3', '98.7', '98.5'],
                          'Dimension':['Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling'],
                          'Month':['Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19']})

df['Score'] = pd.to_numeric(df['Score'])
df = pd.pivot_table(df, values='Score', index='Make', columns=['Dimension'])

import plotly.figure_factory as ff

fig = ff.create_annotated_heatmap(
            z=df.to_numpy(),
            x=df.columns.tolist(),
            y=df.index.tolist(),
            colorscale=['red', 'orange', 'yellow', 'green'],
            hoverongaps=False
            )

fig.show()

此代码有效，但当

Make

列中的值不是“Ford”、“Buick”或“Mercedes”（或者如果元素数量增加或减少）时，它会发生惊人的故障

如您所见，我正在手动定义

ford_scores

、

buick_scores

和

mercedes_scores

，然后将它们传递给create_annotated_heatmap（）函数中的Z参数

这是哈奇。一定有更好的办法

是否有方法将“

df

”数据框传递给Z参数，以便函数“理解”Z参数由“Score”列中的值组成？如果没有，是否有其他方法传递Z参数，这样做不需要预先了解数据和预处理列表？（也就是说，它对于传递的信息是不可知的和灵活的）

---

谢谢

事实证明，有一种更好（且不太老套）的方法！承蒙Plotly的朋友介绍，解决方案如下：

import plotly.figure_factory as ff

z = [[.1, .3, .5],
     [1.0, .8, .6],
     [.6, .4, .2]]

x = ['Team A', 'Team B', 'Team C']
y = ['Game Three', 'Game Two', 'Game One']

z_text = [['Win', 'Lose', 'Win'],
          ['Lose', 'Lose', 'Win'],
          ['Win', 'Win', 'Lose']]

fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_text, colorscale='Viridis')
fig.show()

df = pd.DataFrame({'Make':['Ford', 'Ford', 'Ford', 'Buick', 'Buick', 'Buick', 'Mercedes', 'Mercedes', 'Mercedes'],
                          'Score':['88.6', '76.6', '86.2', '79.1', '86.8', '96.4', '97.3', '98.7', '98.5'],
                          'Dimension':['Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling'],
                          'Month':['Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19']})

ford_scores = df[(df['Make'].isin(['Ford']))]['Score'].astype(float).tolist()
buick_scores = df[(df['Make'].isin(['Buick']))]['Score'].astype(float).tolist()
mercedes_scores = df[(df['Make'].isin(['Mercedes']))]['Score'].astype(float).tolist()

import plotly.figure_factory as ff
fig = ff.create_annotated_heatmap(
            z=[ford_scores, buick_scores, mercedes_scores],
            x=df['Dimension'].unique().tolist(),
            y=df['Make'].unique().tolist(),
            colorscale=['red', 'orange', 'yellow', 'green'],
            hoverongaps=False
            )

fig.show()

df = pd.DataFrame({'Make':['Ford', 'Ford', 'Ford', 'Buick', 'Buick', 'Buick', 'Mercedes', 'Mercedes', 'Mercedes'],
                          'Score':['88.6', '76.6', '86.2', '79.1', '86.8', '96.4', '97.3', '98.7', '98.5'],
                          'Dimension':['Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling', 'Speed', 'MPG', 'Styling'],
                          'Month':['Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19', 'Apr-19']})

df['Score'] = pd.to_numeric(df['Score'])
df = pd.pivot_table(df, values='Score', index='Make', columns=['Dimension'])

import plotly.figure_factory as ff

fig = ff.create_annotated_heatmap(
            z=df.to_numpy(),
            x=df.columns.tolist(),
            y=df.index.tolist(),
            colorscale=['red', 'orange', 'yellow', 'green'],
            hoverongaps=False
            )

fig.show()

希望这对将来的人有帮助