Display whole previous month data with the month-to-date 's data in python chart

Dash Python
1

How to display all the previous month data with the selected date’s data in plotly python?

Let says selected date, 15/03/2022, so, the bar chart should show all the previous whole month data (Jan, Feb) and for Mar, data should be show month-to-date data (01/03/2022 to 15/03/2022).

This is what I can get now, but it is not what I want. For Jan and Feb, the data is correct as it taking the whole month data, but for the Mar, it also taking the whole month data instead of month-to-date.

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image1530×461 24.1 KB

Code as per below:
(1) this code will get month-to-date data (01/03/2022 to 15/03/2022)

month_category = list(df['Month'].unique())
date_category = list(df['Settlement_Date'].unique())

bar_groupby = df.groupby(['Settlement_Date','Date','Month'])['MTD'].agg(['sum']).reset_index().rename(columns={'sum':'Total_Tx_Amount'})


app.layout = dbc.Container([  
dbc.Row([
    dbc.Col([
        html.H3('Month'),
        html.Br(),
        
        dcc.Dropdown(id='month_dd', value= 'Jan',
                      options = [{'label':x, 'value':x} 
                                for x in month_category],
                      
                      searchable = True, search_value='',
                      placeholder= 'Please select ...'
                      
                      ),
        html.Br(),
        
        html.H3('Date'),
        html.Br(),
        
        dcc.Dropdown(id='date_dd')
        
            ])
        ]),


dbc.Row([
    dbc.Col([
        html.P("Bar Chart:",
                style={"textDecoration":"underline"}),
        
                                
        dcc.Graph(id='bar-fig', figure={})
        
            ])
        ])
])

@app.callback(
    Output('date_dd','options'),
    Input('month_dd', 'value')
)


def update_dd (month_dd):
    month_date= df.drop_duplicates(['Month','Settlement_Date'], inplace= False)
    relevant_date= month_date[month_date['Month']== month_dd]['Settlement_Date'].values.tolist()
    date_option= [dict(label=x,value=x)for x in relevant_date]


    return date_option

@app.callback(
    Output('bar-fig', 'figure'),
    Input('date_dd', 'value')
)


def update_graph(selection):
    if len (selection) ==0:
        return dash.no_update

    else:
        dff = bar_groupby[bar_groupby['Settlement_Date'] == selection]    
        fig = px.bar(dff, x='Month', y='Total_Tx_Amount', title='Bar_chart', color='Month')
        
        return fig

(2) this code will get data for all the month (Jan, Feb, Mar)

month_category = list(df['Month'].unique())
date_category = list(df['Settlement_Date'].unique())



bar_groupby = df.groupby(['Settlement_Date','Date','Month'])['MTD'].agg(['sum']).reset_index().rename(columns={'sum':'Total_Tx_Amount'})



app.layout = dbc.Container([  
    dbc.Row([
        dbc.Col([
            html.H3('Month'),
            html.Br(),
        
            dcc.Dropdown(id='month_dd', value= 'Jan',
                      options = [{'label':x, 'value':x} 
                                for x in month_category],
                      
                      searchable = True, search_value='',
                      placeholder= 'Please select ...'
                      
                      ),
        html.Br(),
        
        html.H3('Date'),
        html.Br(),
        
        dcc.Dropdown(id='date_dd')
        
            ])
        ]),


dbc.Row([
    dbc.Col([
        html.P("Bar Chart:",
                style={"textDecoration":"underline"}),
        
                                
        dcc.Graph(id='bar-fig', figure={})
        
            ])
        ])

  ])

@app.callback(
    Output('date_dd','options'),
    Input('month_dd', 'value')
    )


def update_dd (month_dd):
    month_date= df.drop_duplicates(['Month','Settlement_Date'], inplace= False)
    relevant_date= month_date[month_date['Month']== month_dd]['Settlement_Date'].values.tolist()
    date_option= [dict(label=x,value=x)for x in relevant_date]


    return date_option

@app.callback(
    Output('bar-fig', 'figure'),
    Input('date_dd', 'value')
)


def update_graph(selection):
    if len (selection) ==0:
        return dash.no_update

    else:
        fig = px.bar( bar_groupby, x='Month', y='Total_Tx_Amount', title='Bar_chart', color='Month')
        return fig

both code getting the correct data, but I m not able to join both data into a same chart.

Can anyone assist or advise on this?

2

Hi beginof,

is there a reason your groupby is at the top of your code?
I would suggest to group by your dataframe in the update_graph function. If you’ve picked a specific day, retrieve all data until you hit your date.

Something like:
bar_groupby = df[df[‘Date’] <= selection ]].groupby([‘Settlement_Date’,‘Date’,‘Month’])[‘MTD’].agg([‘sum’]).reset_index().rename(columns={‘sum’:‘Total_Tx_Amount’})

If you want the full month:
bar_groupby = df.groupby([‘Settlement_Date’,‘Date’,‘Month’])[‘MTD’].agg([‘sum’]).reset_index().rename(columns={‘sum’:‘Total_Tx_Amount’})

A second option might be to concatenate your dataframes.
concatenated = pandas.concat([df1, df2]).

Best Regards

3

Hi @steviesblog,

The code suggested I can get the MTD data but it will either all three months showing the MTD data based on the date selected (let say today is 15/03/2022, chart data showing 1st-15th of the month; mean 01/01/2022 - 15/01/2022; 01/02/2022 - 15/02/2022; 01/03/2022 - 15/03/2022 ) or all three month showing the whole month data.

If let say,
today is 15/03/2022,

how can the chart display the Jan and Feb (both are whole month data) plus Mar MTD’data (from 01/03/2022 - 15/03/2022)?