如何在特定日期绘制带有类别计数的 Dash 图

问题描述

如何绘制任意时间点主要/次要"问题数量的堆积条形图?

How to plot a stacked bar graph of the count of the number of "Major /Minor" issues at any point of time?

我在 csv 文件中有如下数据:

I have data in csv file as follows:

Issue_Date   Severity

20.2.2020    Major
20.2.2020    Minor
31.3.2020    Major
31.3.2020    Major
31.3.2020    Minor
01.4.2020    Major

我正在使用 Pandas 数据框读取上述 CSV，并尝试使用 group by 方法计算给定日期特定严重性的发生率

I am reading the above CSV using pandas data frame and I tried to count the occurrence of particular Severity on a given date using group by method

data = df.groupby(["Issue_Date", "Severity"]).size()

这里的数据"是一个系列输出:

Here "data" is a SeriesOutput:

Issue_Date     Severity
20.2.2020       Major     1
                Minor     1
31.3.2020       Major     2
                Minor     1
01.4.2020       Major     1

在 xaxis 上显示 Issue_date，在 yaxis 上显示计数并根据严重性绘制堆叠类别.

On xaxis display Issue_date and on yaxis display counts and plot stacked categories on the basis of severity.

如何使用破折号来实现它?

How can I achieve it using dash plotly?

推荐答案

您需要取消堆叠 groupby 并将它们作为 2 条轨迹添加到绘图中.这是一个工作示例:

You need to unstack the groupby and add them as 2 traces to the plotly graph.Here is a working example:

import pandas as pd
import plotly.graph_objects as go
import dash
import dash_core_components as dcc
import dash_html_components as html

data = {'Issue_Date': ['20.2.2020','20.2.2020','31.3.2020','31.3.2020','31.3.2020','01.4.2020'],
                   'Severity': ['Major','Minor','Major' ,'Major' ,'Minor','Major']}

df = pd.DataFrame (data, columns = ['Issue_Date', 'Severity'])

temp = df.groupby(["Issue_Date", "Severity"]).size()

temp1 = temp.rename('size').reset_index()

major=temp1[temp1['Severity']=='Major']
minor=temp1[temp1['Severity']=='Minor']

fig = go.Figure(data=[
    go.Bar(name='Major', x=major['Issue_Date'], y=major['size']),
    go.Bar(name='Minor', x=minor['Issue_Date'], y=minor['size'])
])

fig.update_layout(barmode='stack')

app = dash.Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=False)

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