从python中的函数更新ipywidget下拉列表

Question

我是 Python 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headers_y的输入进行更新（两者均指为绘图选择的数据框列）。如果headers_x和headers_y都有Select选项，则plotType需要显示“ Make selection ”。但是，如果headers_x和headers_y选择了其他选项（数据框中的列），则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，那么plotType需要显示' catgoricalVsNumeric ' 我已经尝试了我的解决方案如下，但 plotType 中的选项小部件不更新。任何帮助深表感谢。谢谢你。

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

我通过使用观察实现了这一点。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 函数。

I changed your headers.x AND headers.y to an OR, as you need both defined.

I also gave you a third option for when x is numeric and y is categorical.

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)