Python/Dash by Plotly:按d分组案例

Question

我只能绘制总发生案例的条形图，而不是按发生日期分组，代码如下：

 trace1=go.Bar(
    x=pd.to_datetime(dfb['date']),
    y=dfb.set_index('date').resample('M')["enrolled"].sum(),
    )

如何按日期分组？ 数据集如下

^{pr2}$

谢谢

Answer 1

Save this data as final_test.csv to have same results as mine. Thanks

date    enrolled
6/29/2018   1
6/29/2018   1
6/29/2018   
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/20/2018   1
6/20/2018   1
6/22/2018   1
6/19/2018   1
6/19/2018   1
6/27/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   
6/28/2018   1
6/28/2018   1
6/20/2018   1
6/20/2018   1
6/19/2018   1
6/19/2018   1
6/26/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/19/2018   1
6/19/2018   1
6/19/2018   1
6/22/2018   1
6/20/2018   1
6/20/2018   1
6/20/2018   1
6/20/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/19/2018   1
6/19/2018   1
6/19/2018   1
6/19/2018   1
6/19/2018   1
6/20/2018   1
6/20/2018   1
6/20/2018   1
6/20/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/20/2018   1
6/20/2018   1
6/20/2018   1
6/20/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/19/2018   1
6/26/2018   1
6/26/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   
6/26/2018   1
6/27/2018   
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/29/2018   1
6/26/2018   
6/27/2018   1
6/28/2018   
6/28/2018   1
6/19/2018   1
6/19/2018   1
6/19/2018   1
6/20/2018   1
6/20/2018   1
6/20/2018   1
6/20/2018   1
6/20/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/20/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/20/2018   1
6/21/2018   1
6/21/2018   1
6/21/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/26/2018   1
6/26/2018   1
6/22/2018   1
6/22/2018   1
6/22/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/27/2018   1
6/27/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/19/2018   1
6/19/2018   1
6/19/2018   1
6/20/2018   1
6/20/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/27/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/26/2018   1
6/27/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/27/2018   1
6/27/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/28/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1
6/29/2018   1