python 破折号 table 条件格式色标

Question

我想根据值从高到小用色标按值给列着色像这样

目前，我在一个函数中创建破折号 table 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

短划线 table 看起来像流动的图片，我想给值列上色

Answer 1

这是可能的。你应该看看这个 link：https://dash.plotly.com/datatable/conditional-formatting 特别是 'Highlighting with a colorscale on a single column'

部分

我已经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returns使用给定色标为指定列的每一行创建样式列表。

要获得色标，您需要使用 pip install colorlover

安装 colorlover

可在此处找到其他色标：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'}))

Answer 2

感谢。 - 效果很好。

我想为将来和我有相同问题的用户总结一下选项。当我们想运行它在多个列上时，有两种选择：

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，因此如果我运行具有多个列（来自示例：值和计数），其 returns table 样式根据所有列的最小值和最大值范围着色（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'}))

如果我们想根据最小值和最大值分别为每一列着色，我们将使用下面的函数：
（非常相似，但运行ning 在第一列）

 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'}))

python 破折号 table 条件格式色标

python dash table conditional formatting color scale

python-3.x

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