根据 pandas 数据框向散点图添加过滤器

Question

我假装使用以下数据框的过滤器制作散点图（代表整个赛季的球员、球队和赛季，并计算篮球队球员所做的辅助和非辅助点）：

player          team_name       season          assisted    notassisted

A. DANRIDGE     NACIONAL        Season_17_18    130         445

A. DANRIDGE     NACIONAL        Season_18_19    132         382

D. ROBINSON     TROUVILLE       Season_18_19    89          286

D. DAVIS        AGUADA          Season_18_19    101         281

E. BATISTA      WELCOME         Season_17_18    148         278

F. MARTINEZ     GOES            Season_18_19    52          259

D. ALVAREZ      AGUADA          Season_17_18    114         246

M. HICKS        H. MACABI       Season_17_18    140         245

在 x 轴上我想放置辅助点，在 y 轴上放置非辅助点。但我也想按赛季、球队和球员进行过滤，所以当我选择球队的一名确定球员时，我可以看到他们的分数用一种颜色显示，而其他分数显示为灰色，或者例如，如果我想选择两个或更多我可以在他们之间进行比较（用不同的颜色），并且其他点是可见的，但是是灰色的。我还想比较两个不同球队的球员和过滤器的组合。

我正在学习数据科学，使用图书馆 plotly express 我可以制作散点图并按团队过滤，我可以比较两个不同的团队（或赛季或球员）。

但是我无法以一种奇特的方式添加多个过滤器，而且我也不知道如何显示选定的过滤器并将其他过滤器置于灰色（不会使它们消失）。

代码如下：

import plotly.express as px

fig = px.scatter(pointsperplayer, x='assisted', y='notassisted', hover_name='player', 

                 hover_data=['team_name','season'], color='season')

fig.show()

图形结果如下：

总而言之，我想要三个过滤器，一个是赛季，另一个是球队，另一个是球员，能够在每个过滤器中有多个选择，并获得不同的颜色，其余的点变灰，这样我就可以将结果与其他结果进行比较，我不确定是否可以使用 plotly express 或者我是否应该使用不同的库。

Answer 1

所以我无法操纵图例，但我可以通过在此处找到的下拉小部件添加过滤器。根据您的 IDE，您可能需要使用 Jupyter 来使小部件工作。我遇到了 VSCode 无法显示小部件的问题。我下面的功能是按球队名称、赛季或球员进行过滤，并在该过滤器中比较两个选项。我希望这可以扩展以满足您的需求。import pandas as pdimport plotly.express as pximport plotly.graph_objects as goimport ipywidgets as ipyfrom ipywidgets import Output, VBox, widgets# First gather the data I need and choose the display colorsplayerData = pd.read_csv("playerData.csv")teamNames = list(playerData['team_name'].unique().tolist());seasons = list(playerData['season'].unique().tolist());players = list(playerData['player'].unique().tolist());color1 = 'red'color2 = 'blue'color3 = 'gray'# This creates the initial figure.# Note that px.scatter generates multiple scatter plot 'traces'. Each trace contains # the data points associated with 1 team/season/player depending on what the property# of 'color' is set to.trace1 = px.scatter(playerData, x='assisted', y='notassisted', color='team_name')fig = go.FigureWidget(trace1)# Create all our drop down widgetsfilterDrop = widgets.Dropdown(    description='Filter:',    value='team_name',    options=['team_name', 'season','player']  )teamDrop1 = widgets.Dropdown(    description='Team Name:',    value='NACIONAL',    options=list(playerData['team_name'].unique().tolist())  )teamDrop2 = widgets.Dropdown(    description='Team Name:',    value='NACIONAL',    options=list(playerData['team_name'].unique().tolist())  )playerDrop1 = widgets.Dropdown(    description='Player:',    value='A. DANRIDGE',    options=list(playerData['player'].unique().tolist())  )playerDrop2 = widgets.Dropdown(    description='Player:',    value='A. DANRIDGE',    options=list(playerData['player'].unique().tolist())  )seasonDrop1 = widgets.Dropdown(    description='Season:',    value='Season_17_18',    options=list(playerData['season'].unique().tolist())  )seasonDrop2 = widgets.Dropdown(    description='Season:',    value='Season_17_18',    options=list(playerData['season'].unique().tolist())  )# This will be called when the filter dropdown changes. def filterResponse(change):    # generate the new traces that are filtered by teamname, season, or player    tempTrace = px.scatter(playerData, x='assisted', y='notassisted', color=filterDrop.value)    with fig.batch_update():        # Delete the old traces and add the new traces in one at a time        fig.data = []        for tr in tempTrace.data:            fig.add_scatter(x = tr.x, y = tr.y, hoverlabel = tr.hoverlabel, hovertemplate = tr.hovertemplate, \                           legendgroup = tr.legendgroup, marker = tr.marker, mode = tr.mode, name = tr.name)    # Call response so that it will color the markers appropriately    response(change)# This is called by all the other drop downsdef response(change):    # colorList is a list of strings the length of the # of traces     if filterDrop.value == 'team_name':        colorList = [color1 if x == teamDrop1.value else color2 if x == teamDrop2.value else color3 for x in teamNames]    elif filterDrop.value == 'season':        colorList = [color1 if x == seasonDrop1.value else color2 if x == seasonDrop2.value else color3 for x in seasons]    else:        colorList = [color1 if x == playerDrop1.value else color2 if x == playerDrop2.value else color3 for x in players]    with fig.batch_update():        # Color each trace according to our chosen comparison traces        for i in range(len(colorList)):            fig.data[i].marker.color = colorList[i]# These determine what function should be called when a drop down changesteamDrop1.observe(response, names="value")seasonDrop1.observe(response, names="value")playerDrop1.observe(response, names="value")teamDrop2.observe(response, names="value")seasonDrop2.observe(response, names="value")playerDrop2.observe(response, names="value")filterDrop.observe(filterResponse, names="value")# HBox and VBox are used to organize the other widgets and figurescontainer1 = widgets.HBox([filterDrop]) container2 = widgets.HBox([teamDrop1, seasonDrop1, playerDrop1])container3 = widgets.HBox([teamDrop2, seasonDrop2, playerDrop2])widgets.VBox([container1, container2, container3, fig])结果如下所示