从 python 中的函数更新 ipywidget 下拉列表

Question

我是 Python 的新手，我想从 ipywidget 创建一个交互式下拉列表。主要目的是根据其他两个小部件更新下拉列表。在下面的代码中，小部件 plotType 将根据小部件 headers_x 和 [ 的输入进行更新=53=]（均指选择用于绘图的数据框列）。如果 headers_x 和 headers_y 都有 Select选项，那么 plotType 需要显示“Make selection”。但是如果 headers_x 和 headers_y 选择了其他选项（来自数据框的列），那么 plotType 需要相应地改变。如果 headers_x 和 headers_y 都是数字，那么 plotType需要显示：numericVsNumeric，但如果 headers_x 是分类的并且 headers_y 是数字，然后 plotType 需要显示 'catergoricalVsNumeric' 我尝试了如下解决方案，但 plotType 小部件中的选项不会更新.任何帮助深表感谢。谢谢。

from ipywidgets import *
import seaborn.apionly as sns
df = sns.load_dataset('iris')

#identifies the columns in the dataframe
df_cols = list(df.columns.values)
df_cols.insert(0, 'Select')
str_cols = list(df.select_dtypes(include=['object']).columns.values)
str_cols.insert(0, 'Select')

#plot function
def set_plot(headers_x, headers_y, plotType):
    data = df
    #plotting functions to be added

#function to specify the type of plot based on users input
def set_plotType():
    data = df
        #If no selection has been made
    if headers_x.value == 'Select' and headers_y.value == 'Select':
        init = list(['Make Selection'])
    else:
        #if x and y are both numeric
        if data[headers_x.value].dtype == np.float and data[headers_y.value].dtype == np.float:
            init = list(['NumericVsNumeric'])
            #if x is categorical and y is numeric
        elif data[headers_x.value].dtype == np.object and data[headers_y.value].dtype == np.float:
            init = list(['CategoricalVsNumeric'])

    return init


#define widgets
headers_x = widgets.Dropdown(
        options=df_cols,
        value=df_cols[0],
        description='X'
    )

headers_x.set_title  = 'headers_x'

headers_y = widgets.Dropdown(
        options=df_cols,
        value=df_cols[0],
        description='Y'
    )

headers_y.set_title  = 'headers_y'

plotType = widgets.Dropdown(
        options=set_plotType(),
        #value=df_cols[0],
        description='Plot Type'
    )

plotType.set_title  = 'plotType'


#interact function
interact(set_plot, headers_x = headers_x, headers_y = headers_y, plotType = plotType)

Answer 1

我是通过观察来实现的。这意味着只要您的前两个下拉选项发生变化，它们就会运行 set_Plottype 功能。

我将你的 headers.x AND headers.y 更改为 OR，因为你需要两者都定义。

当 x 是数字且 y 是分类时，我还给了你第三个选项。

from ipywidgets import *
import numpy as np
import seaborn.apionly as sns
df = sns.load_dataset('iris')

#identifies the columns in the dataframe
df_cols = list(df.columns.values)
df_cols.insert(0, 'Select')
str_cols = list(df.select_dtypes(include=['object']).columns.values)
str_cols.insert(0, 'Select')

#plot function
def set_plot(headers_x, headers_y, plotType):
    data = df
    #plotting functions to be added

#function to specify the type of plot based on users input
def set_plotType(_):
    data = df
        #If no selection has been made
    if headers_x.value == 'Select' or headers_y.value == 'Select':
        plotType.options = list(['Make Selection'])
    else:
        #if x and y are both numeric
        if data[headers_x.value].dtype == np.float and data[headers_y.value].dtype == np.float:
            plotType.options = list(['NumericVsNumeric'])
            #if x is categorical and y is numeric
        elif data[headers_x.value].dtype == np.object and data[headers_y.value].dtype == np.float:
            plotType.options = list(['CategoricalVsNumeric'])
        elif data[headers_x.value].dtype == np.float and data[headers_y.value].dtype == np.object:
            plotType.options = list(['NumericalVsCategoric'])



#define widgets
headers_x = widgets.Dropdown(
        options=df_cols,
        value=df_cols[0],
        description='X'
    )

headers_x.set_title  = 'headers_x'

headers_y = widgets.Dropdown(
        options=df_cols,
        value=df_cols[0],
        description='Y'
    )

headers_y.set_title  = 'headers_y'

plotType = widgets.Dropdown(
        options=[],
        description='Plot Type'
    )

headers_x.observe(set_plotType)
headers_y.observe(set_plotType)


#interact function
interact(set_plot, headers_x = headers_x, headers_y = headers_y, plotType = plotType)

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