python破折号表条件格式色标

Question

我想根据这样的值从高到小用色标按值对列进行着色

目前，我在一个函数中创建了破折号表，并为每一列循环发送它；

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

破折号表看起来像流动的图片，我想给值列着色

Answer 1

感谢克里斯蒂安·哈加的回答。- 效果很好。

我想为未来和我有同样问题的用户总结一下选项。当我们想要在多列上运行它时，有两种选择：

1. 原始函数将以相同的比例（最小值和最大值）对所有列进行着色，因此，如果我运行多列（来自示例：值和计数），它会返回根据所有列中的最小值和最大值范围进行着色的表格样式（来自示例：0.193,109）。
   discrete_background_color_bins(df, columns=['value','count'])

   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'}))
   

2. 如果我们想根据每列的最小值和最大值分别对其进行着色， 我们将使用下面的函数：（
   非常相似，但首先在列上运行）

    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'}))
   

Answer 2

这个有可能。您应该查看此链接：https://dash.plotly.com/datatable/conditional-formatting 特别是“在单列上使用色阶突出显示”部分

我为你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阶返回指定列的每行的样式列表。

要获得色阶，您需要安装 colorloverpip install 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'}))