Groupby df by column and create plotly contour plots

Question

我有以下格式的 df：

df = pd.DataFrame({'col1':[11,12,23,21,23,43,23,12,21,34],
                    'col2':[111,121,213,211,123,143,213,122,210,171],
                    'col3':[2111,2121,2213,2121,3123,4143,3213,2122,1210,989],
                    'name':['aa','bb','cc','cc','aa','aa','cc','bb','aa','bb']})

这是我使用 Plotly 创建简单等高线图的代码：

x = np.array(df['col1'])
y = np.array(df['col2'])
z = np.array(df['col3'])
xi = np.linspace(x.min(), x.max(), 100)
yi = np.linspace(y.min(), y.max(), 100)
grid_x, grid_y = np.meshgrid(xi,yi)
Z = griddata((x,y), z, (grid_x, grid_y), method='linear')   
fig = go.Figure(go.Contour(x=xi, y=yi, z=Z, colorscale='Jet'))
fig.show()

此等高线图是所有

name

值的单个图。我想创建按

name

列分组的类似等高线图。这意味着，在这个例子中，它应该为每个

'aa'

、

'bb'

和

'cc'

值绘制 3 个等高线图。

Answer 1

由于您只想获取“名称”中具有特定值的行，因此对于该列中的每个唯一值，请执行以下操作：

for name in np.unique(df['name']):
    subdf = df[df['name'] == name]
    x = np.array(subdf['col1'])
    y = np.array(subdf['col2'])
    z = np.array(subdf['col3'])
    # ...

此外，作为旁注，您可以包含导入以使您的代码更具可重现性。

import pandas as pd
import numpy as np
from scipy.interpolate import griddata
import plotly.graph_objects as go

Answer 2

你可以试试：

groups = df.groupby('name')

# loop over each group
for name, group in groups:
    x = np.array(group['col1'])
    y = np.array(group['col2'])
    z = np.array(group['col3'])
    xi = np.linspace(x.min(), x.max(), 100)
    yi = np.linspace(y.min(), y.max(), 100)
    grid_x, grid_y = np.meshgrid(xi,yi)
    Z = griddata((x,y), z, (grid_x, grid_y), method='linear')
    fig = go.Figure(go.Contour(x=xi, y=yi, z=Z, colorscale='Jet'))
    fig.update_layout(title=name)
    fig.show()