python破折号表条件格式色标

如何解决python破折号表条件格式色标

我想按色标按值从高到小对列着色 像这样

当前，我在函数中创建破折号表，并通过循环将其发送给每一列；

def make_table_in_div(df,column_name):
    pv = pd.pivot_table(df,index=[column_name],values=['val1'],aggfunc=['mean','count']).reset_index()
    pv.columns = [column_name,'val1','count']
    print(column_name)
    div = html.Div([html.H1(column_name),dash_table.DataTable(
        columns=[{"name": i,"id": i} for i in pv.columns],data=pv.to_dict('records'),)],style={'height': 30,'margin-right': 'auto','margin-left': 'auto','width': '800px'})  # 'width': '50%',return div

div = [make_table_in_div(df,column_name) for column_name in ['column_name']]
return div

破折号表看起来像是流动的图片，我想给value列上色

解决方法

这是可能的。您应该查看以下链接：https://dash.plotly.com/datatable/conditional-formatting 特别是“在单个列上用色标突出显示”部分

我为您写了一个简单的例子：

import dash
import dash_table
import pandas as pd
import dash_html_components as html
import colorlover
from jupyter_dash import JupyterDash

# Dash Application
df = pd.DataFrame(list(zip(
    [5,6,7,8,9,10,11,12,13,14],[0.328,0.323,0.193,0.231,0.216,0.284,0.250,0.258,0.394,0.455],[67,99,109,104,88,74,32,31,33,22]
)),columns=['column_name','value','count'])

app = JupyterDash(__name__)

# Function for styling table,defined below
cols = ['value']
(styles,legend) = discrete_background_color_bins(df,columns = cols)

app.layout = html.Div([
    legend,dash_table.DataTable(
        id = 'table',columns = [{"name": i,"id": i} for i in df.columns],data = df.to_dict('records'),style_data_conditional = styles
    )
])

app.run_server(mode='inline')

此函数使用给定的色标为指定列的每一行返回样式列表。

要获得色标，您需要使用pip install colorlover

安装colorlover

其他色阶可以在这里找到：https://github.com/plotly/colorlover

# Function for styling the table
def discrete_background_color_bins(df,n_bins=7,columns='all'):

    bounds = [i * (1.0 / n_bins) for i in range(n_bins+1)]
    if columns == 'all':
        if 'id' in df:
            df_numeric_columns = df.select_dtypes('number').drop(['id'],axis=1)
        else:
            df_numeric_columns = df.select_dtypes('number')
    else:
        df_numeric_columns = df[columns]
    df_max = df_numeric_columns.max().max()
    df_min = df_numeric_columns.min().min()
    ranges = [
        ((df_max - df_min) * i) + df_min
        for i in bounds
    ]
    styles = []
    legend = []
    for i in range(1,len(bounds)):
        min_bound = ranges[i - 1]
        max_bound = ranges[i]
        backgroundColor = colorlover.scales[str(n_bins+4)]['div']['RdYlGn'][2:-2][i - 1]
        color = 'black'

        for column in df_numeric_columns:
            styles.append({
                'if': {
                    'filter_query': (
                        '{{{column}}} >= {min_bound}' +
                        (' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
                    ).format(column=column,min_bound=min_bound,max_bound=max_bound),'column_id': column
                },'backgroundColor': backgroundColor,'color': color
            })
        legend.append(
            html.Div(style={'display': 'inline-block','width': '60px'},children=[
                html.Div(
                    style={
                        'backgroundColor': backgroundColor,'borderLeft': '1px rgb(50,50,50) solid','height': '10px'
                    }
                ),html.Small(round(min_bound,2),style={'paddingLeft': '2px'})
            ])
        )

    return (styles,html.Div(legend,style={'padding': '5px 0 5px 0'}))

,

感谢The answer of Kristian Haga。 -效果很好。

我想为与我有相同问题的未来用户总结选项。当我们想在多列上运行它时，有两个选择：

1. 原始函数将对所有具有相同比例（最小和最大）的列进行着色，因此，如果我运行多个列（来自示例：值和计数），则它将返回基于min范围着色的表格样式所有列中的最大值和最大值（来自示例：0.193,109）。
   discrete_background_color_bins(df,columns=['value','count'])

   def discrete_background_color_bins(df,columns='all'):
   
    bounds = [i * (1.0 / n_bins) for i in range(n_bins+1)]
    if columns == 'all':
        if 'id' in df:
            df_numeric_columns = df.select_dtypes('number').drop(['id'],axis=1)
        else:
            df_numeric_columns = df.select_dtypes('number')
    else:
        df_numeric_columns = df[columns]
    df_max = df_numeric_columns.max().max()
    df_min = df_numeric_columns.min().min()
    ranges = [
        ((df_max - df_min) * i) + df_min
        for i in bounds
    ]
    styles = []
    legend = []
    for i in range(1,len(bounds)):
        min_bound = ranges[i - 1]
        max_bound = ranges[i]
        backgroundColor = colorlover.scales[str(n_bins+4)]['div']['RdYlGn'][2:-2][i - 1]
        color = 'black'
   
        for column in df_numeric_columns:
            styles.append({
                'if': {
                    'filter_query': (
                        '{{{column}}} >= {min_bound}' +
                        (' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
                    ).format(column=column,'column_id': column
                },'color': color
            })
        legend.append(
            html.Div(style={'display': 'inline-block',children=[
                html.Div(
                    style={
                        'backgroundColor': backgroundColor,'height': '10px'
                    }
                ),style={'paddingLeft': '2px'})
            ])
        )
   
    return (styles,style={'padding': '5px 0 5px 0'}))
   

2. 如果我们要根据每列的最小值和最大值分别给它上色， 我们将使用以下功能：
   （非常相似，但首先在列上运行）

    def discrete_background_color_bins(df,columns='all'):
   
        bounds = [i * (1.0 / n_bins) for i in range(n_bins+1)]
        if columns == 'all':
            if 'id' in df:
                df_numeric_columns = df.select_dtypes('number').drop(['id'],axis=1)
            else:
                df_numeric_columns = df.select_dtypes('number')
        else:
            df_numeric_columns = df[columns]
        df_max = df_numeric_columns.max().max()
        df_min = df_numeric_columns.min().min()
        ranges = [
            ((df_max - df_min) * i) + df_min
            for i in bounds
        ]
        styles = []
        legend = []
        for i in range(1,len(bounds)):
            min_bound = ranges[i - 1]
            max_bound = ranges[i]
            backgroundColor = colorlover.scales[str(n_bins+4)]['div']['RdYlGn'][2:-2][i - 1]
            color = 'black'
   
            for column in df_numeric_columns:
                styles.append({
                    'if': {
                        'filter_query': (
                            '{{{column}}} >= {min_bound}' +
                            (' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
                        ).format(column=column,'column_id': column
                    },'color': color
                })
            legend.append(
                html.Div(style={'display': 'inline-block',children=[
                    html.Div(
                        style={
                            'backgroundColor': backgroundColor,'height': '10px'
                        }
                    ),style={'paddingLeft': '2px'})
                ])
            )
   
        return (styles,style={'padding': '5px 0 5px 0'}))