We Keep Coding

Keep the value of dcc.dropdown and dcc.radioitems into dcc.store

I would like to keep the dropdown value in dcc.Store, so it can repeatedly to be call from all the page within the app.py.
The dd2 is come after the dd1 and raditem, which mean the value of dd2 is based on the selected value of dd1 and raditem.
I had tried to store the dd2 in storedd2, however, it is not workable. It can't display the information in page2.py in the table.
This is the code that I currently using. It is not issue if the callback is come from the same page (mean dropdown and table in the same page). However, when it go to multiple page, the raditem in the page1.py will not able to call from page2.py, therefore, need to store the value in dcc.Store only able to recall from other page.
page1.py
dcc.Store(id = 'storedd2', storage_type='session'),
dcc.Dropdown(id='dd1', value= '',
options = [{'label':x, 'value':x}
for x in date_cat],
searchable = True, search_value='',
placeholder= 'DD/MM/YYYY',
clearable=True
),
dcc.Dropdown(id='dd2', value='',
searchable = True, search_value='',
placeholder= 'Please select ...',
clearable=True,
),
dcc.RadioItems(id= 'raditem',
options=[
{'label': ' A', 'value': 'a'},
{'label': ' B', 'value': 'b'},
{'label': ' C', 'value': 'c'},
],
value='a',
)
#callback(
Output('dd2','options'),
Input('dd1', 'value'),
Input('raditem', 'value')
)
def update_dd (dd1, raditem):
if raditem == 'a':
id= df.drop_duplicates(['a'], inplace= False)
relevant_id= id[ df['date'] == dd1]['a'].values.tolist()
id_option= [dict(label=x,value=x)for x in relevant_id]
elif raditem == 'b':
id= df.drop_duplicates(['b'], inplace= False)
relevant_id= id[ df['date'] == dd1]['b'].values.tolist()
id_option= [dict(label=x,value=x)for x in relevant_id]
elif raditem == 'c':
id= df.drop_duplicates(['c'], inplace= False)
relevant_id= id[ df['date'] == dd1]['c'].values.tolist()
id_option= [dict(label=x,value=x)for x in relevant_id]
return id_option
#callback(
Output('storedd2','data'),
Input('dd2', 'value'),
)
def get_data (dd2):
return dd2
page2.py
dash_table.DataTable(id='table',
columns=[
{'name': '', "id": 'header'},
{'name': 'Information', "id": 'info'},
],
)
#callback(
Output('table', 'data'),
Input('dd2', 'value'),
Input('raditem', 'value')
)
def update_table(selection, raditem):
if len (selection) == 0:
return dash.no_update
else:
if raditem == 'a':
dff = df[(df['a'] == selection)]
elif raditem == 'b':
dff = df[(df['b'] == selection)]
elif raditem == 'c':
dff = df[(df['c'] == selection)]
columns = dff[['header','info']]
data= columns.to_dict('records')
return data
dataframe
enter image description here
Expected output:
page1.py
enter image description here
page2.py
enter image description here
Anyone can assist on this?
Or if have any suggestion, please let me know.

Python dash return several values inside for loop

For my dash app, in order to update some graphs dynamically, I have to use a function that I named update_graphs inside a for loop. Some of the graphs contain several traces while some others only have one. The update_graphs function is called inside a callback and returns a dict and an int to update the extendData property of the graph object. However, since I am using a return statement inside a for loop, I only get the first trace.
I am not familiar with the generators and the yield keyword, maybe this is an option. But I haven't been able to make it work.
I have also tried to store the results of the update_graphs inside a list but it is not working.
Any help is appreciated!
Here is the code for the app:
import dash
from dash.dependencies import Output, Input, State, MATCH, ALL
from dash import dcc, html, ctx
import plotly
import plotly.express as px
import random
import plotly.graph_objs as go
import pandas as pd
# Initializing the data with the correct format
init_store = {}
n=3
init_df = pd.DataFrame({'a':pd.Series(dtype='int'), 'b':pd.Series(dtype='int'), 'c':pd.Series(dtype='int'), 'd':pd.Series(dtype='int')}, index=range(50))
init_df['a'] = init_df.index
init_store['0'] = init_df
for i in range(n):
init_df = pd.DataFrame({'a':pd.Series(dtype='int'), 'b':pd.Series(dtype='int')}, index=range(50))
init_df['a'] = init_df.index
init_store[f'{i+1}'] = init_df
# Function to update the dataframes with the new observations
def get_data(json_data):
df = pd.read_json(json_data)
compteur = df['a'][len(df['a'])-1]
if len(df.columns) > 2:
new_row = {'a':compteur + 1, 'b':random.randint(13,26), 'c':random.randint(13,26), 'd':random.randint(13,26)}
else:
new_row = {'a':compteur + 1, 'b':random.randint(13,26)}
df = df.shift(periods=-1)
df.iloc[len(df)-1] = new_row
return(df.to_json())
# Function to update the graphs based on the dataframes
def update_graphs(json_data, column, index=0):
df = pd.read_json(json_data)
nb_obs = df.shape[0]
x_new = df['a'][len(df)-1]
y_new = df[column][nb_obs-1]
return dict(x=[[x_new]], y=[[y_new]]), index
colors = px.colors.qualitative.G10
def generate_graph_containers(index, json_data):
dataframe = pd.read_json(json_data)
X = dataframe['a']
Y = dataframe.loc[:, dataframe.columns != 'a']
graph_id = {'type': 'graph-', 'index': index}
return(
html.Div(
html.Div(
dcc.Graph(
id=graph_id,
style={"height": "8rem"},
config={
"staticPlot": False,
"editable": False,
"displayModeBar": False,
},
figure=go.Figure(
{
"data": [
{
"x": list(X),
"y": list(Y[Y.columns[i]]),
"mode": "lines",
"name": Y.columns[i],
"line": {"color": colors[i+2]},
}
for i in range(len(Y.columns))
],
"layout": {
"uirevision": True,
"margin": dict(l=0, r=0, t=4, b=4, pad=0),
"xaxis": dict(
showline=False,
showgrid=False,
zeroline=False,
showticklabels=False,
),
"yaxis": dict(
showline=False,
showgrid=False,
zeroline=False,
showticklabels=False,
),
"paper_bgcolor": "rgba(0,0,0,0)",
"plot_bgcolor": "rgba(0,0,0,0)",
}
}
)
)
)
)
)
app = dash.Dash(__name__)
store = [dcc.Store(id={'type':'store-', 'index':i}, data=init_store[str(i)].to_json()) for i in range(n)]
def make_layout():
return(
html.Div(
[
html.Div(
store
),
dcc.Interval(
id = 'interval',
interval = 1000,
n_intervals = 0
),
html.Div(
[
generate_graph_containers(str(i), store[i].data) for i in range(n)
]
)
]
)
)
app.layout = make_layout
#app.callback(
Output(component_id={'type':'store-', 'index':MATCH}, component_property='data'),
[
Input('interval', 'n_intervals'),
State(component_id={'type':'store-', 'index':MATCH}, component_property='data')
]
)
def update_data(time, data):
return(get_data(data))
#app.callback(
Output(component_id={'type':'graph-', 'index':MATCH}, component_property='extendData'),
Input(component_id={'type':'store-', 'index':MATCH}, component_property="data")
)
def update_graphs_callback(data):
triggered_id = ctx.triggered_id
print(triggered_id['index'])
columns = ['b', 'c', 'd']
if triggered_id['index'] == 0:
for i in range(len(columns)):
return(update_graphs(data, columns[i], i))
else:
return(update_graphs(data, 'b'))
if __name__ == '__main__':
app.run_server(debug=True)

I figured it out. The trick is in the format expected to update the extendData property of a figure. When trying to update several traces, the format should be a dictionary with a key for the x values and one for the y values. The values associated should be an array for each key, containing an array per trace. Don't forget to add the trace indices after the dictionary. So for example, in the case of 3 distinct traces, the function should return something like:
dict(x=[[x_0], [x_1], [x_2]], y=[[y_0], [y_1], [y_2]]), [0, 1, 2]
Therefore the update_graphs function should be:
def update_graphs(json_data):
df = pd.read_json(json_data)
nb_obs = df.shape[0]
x_new = []
y_new = []
trace_index = []
for i in range(len(df.columns)-1):
x_new.append([df['a'][len(df)-1]])
y_new.append([df[df.columns[i+1]][nb_obs-1]])
trace_index.append(i)
return(dict(x=x_new, y=y_new), trace_index)
And the callback to update the graphs should be changed to:
#app.callback(
Output(component_id={'type':'graph-', 'index':MATCH}, component_property='extendData'),
Input(component_id={'type':'store-', 'index':MATCH}, component_property="data")
)
def update_graphs_callback(data):
return(update_graphs(data))

Upload dataframe, show content and allow editing in dash

I am coding a dataframe viewer and in the main page there is a upload input, that reads a file and store the content in a dcc.Store(id="store").
#app.callback(
Output("store", "data"),
Input("upload-data", "contents"),
Input("upload-data", "filename"),
)
def read_df(contents, filename):
if contents: # Only if there is any file updated
df = parse_data(contents, filename)
return df.to_dict('records')
Then the dataframe content is showed in a dash_table.DataTable. I would like that the stored data were updated, if you edit the table. Would be possible do that avoiding circular dependencies, duplicated outputs or non existing objects error?
#app.callback(
Output("table_frame", "children"),
[Input("store", "data"),
Input("upload-data", "filename")]
)
def update_table_frame(df, filename):
table = html.Div()
if df is not None and len(df) > 0:
df = pd.DataFrame(df)
table = html.Div(
[
html.H5(filename),
dash_table.DataTable(
id="table",
data=df.to_dict('records'),
columns=[{"name": i, "id": i, 'deletable': True,
'renamable': True} for i in df.columns],
page_current=0,
page_size=PAGE_SIZE,
page_action='custom',
page_count=np.ceil(len(df) / PAGE_SIZE),
sort_action='custom',
sort_mode='multi',
sort_by=[],
filter_action='custom',
filter_query='',
# row_deletable=True,
editable=True,
style_table={'overflowX': 'auto'}
),
html.Button('Descargar dataframe', id='download', n_clicks=0, className="btn btn-primary"),
dcc.Download(id="download-df")
]
)
return table
I tried to update data stored in the same callback that reads dataframe. However it throws all kinds of errors.
#app.callback(
Output("store", "data"),
Input("upload-data", "contents"),
Input("upload-data", "filename"),
Input("store", "data"),
Input('table', "data"),
Input('table', "columns"),
Input('table', "page_current"),
Input('table', "page_size")
)
def read_df(contents, filename, df, rows, columns, page_current, page_size):
ctx = dash.callback_context
if not ctx.triggered:
input_id = ''
else:
input_id = ctx.triggered[0]['prop_id'].split('.')[0]
if input_id == 'table':
df = pd.DataFrame(df)
if len(rows) <= page_size:
step = abs(page_current - previous_page)
if previous_page < page_current:
return pd.DataFrame(pd.concat([df[0:((page_current - step) * page_size)], pd.DataFrame(rows),
df[((page_current - step + 1) * page_size):len(df)]], axis=0),
columns=[c['name'] for c in columns]).to_dict('records')
else:
return pd.DataFrame(pd.concat([df[0:((page_current + step) * page_size)], pd.DataFrame(rows),
df[((page_current + step + 1) * page_size):len(df)]], axis=0),
columns=[c['name'] for c in columns]).to_dict('records')
elif input_id == 'upload-data':
if contents: # Only if there is any file updated
df = parse_data(contents, filename)
return df.to_dict('records')

Create a Chart in dash-plot based on dropdown and date picker for a certain unique value

I have a simple database as in the below picture:
and the query looks like:
SELECT
[Date]
,[eNodeBName]
,[Downlinkbandwidth]
,[DownlinkEARFCN]
,[CellName]
,[LocalCellId]
,[PhysicalcellID]
,[LRRCConnReqAtt]
,[RRCSetupSuccessRate]
,[InterFreqSuccessRate4G]
,[IntraRATHOSucccessRate]
,[IntraFreqSuccessRate4G]
,[CellDLMaxThroughputMbps]
,[CellDownlinkAverageThroughputMbps]
FROM [myDB].[dbo].[input]
Now I need to create an interactive chart which depends on inputs like a date picker and drop down list.
As this is the sample GUI I created to figure chart throw this inputs as shown in the below picture:
Now I am creating chart based on the below columns name as in the below picture:
as this is the related query as the below:
SELECT
[Date]
,[CellName]
,[LRRCConnReqAtt]
,[RRCSetupSuccessRate]
,[InterFreqSuccessRate4G]
,[IntraRATHOSucccessRate]
,[IntraFreqSuccessRate4G]
,[CellDLMaxThroughputMbps]
,[CellDownlinkAverageThroughputMbps]
FROM [myDB].[dbo].[input]
So now the X-axis should be the Date column and the y-axis related to those below columns is the KPI columns:
SELECT
[LRRCConnReqAtt]
,[RRCSetupSuccessRate]
,[InterFreqSuccessRate4G]
,[IntraRATHOSucccessRate]
,[IntraFreqSuccessRate4G]
,[CellDLMaxThroughputMbps]
,[CellDownlinkAverageThroughputMbps]
FROM [myDB].[dbo].[input]
So now we have a unique column contains unique values is called CellName, this cell name I want create a simple chart for this unique value based on date columnn and KPI column.
So for example I want to show a line chart for a certain CellName = 2002334 for KPI LRRCConnReqAtt based on data from 27 of December to 9 of January. So I need a chart as the below picture and this is an example chart created in Excel.
and this is my code:
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
from sqlalchemy import create_engine
import datetime
from datetime import datetime as dt
from dash.dependencies import Input, Output
# connect db
engine = create_engine('mssql+pyodbc://xxxxxx\SMxxxxxARTRNO_EXPRESS/myDB?driver=SQL+Server+Native+Client+11.0')
cursor = engine.raw_connection().cursor()
start = datetime.datetime(2019, 12, 2)
end = datetime.datetime(2019, 12, 15)
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
lte_kpis = pd.read_sql('SELECT * FROM [myDB].[dbo].[input]',
engine)
lte_kpis_raw = pd.read_sql('SELECT LRRCConnReqAtt, RRCSetupSuccessRate, InterFreqSuccessRate4G, IntraRATHOSucccessRate, IntraFreqSuccessRate4G,CellDLMaxThroughputMbps, CellDownlinkAverageThroughputMbps FROM [myDB].[dbo].[input]',
engine)
scale_1 = ['LRRCConnReqAtt']
scale_2 = ['RRCSetupSuccessRate', 'InterFreqSuccessRate4G', 'IntraRATHOSucccessRate', 'IntraFreqSuccessRate4G']
scale_3 = ['CellDLMaxThroughputMbps', 'CellDownlinkAverageThroughputMbps']
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
availble_cell = lte_kpis['CellName'].unique()
# availble_cell = lte_kpis.unique(lte_kpis[['Date', 'Site Name', 'Cell CI', 'Cell LAC']].values.ravel('K'))
app.layout = html.Div([
dcc.Dropdown(
id='cell-name-xaxis-column',
options=[{'label': i, 'value': i} for i in availble_cell],
value='2205516'
),
dcc.Dropdown(
id='myColumns',
options=[{'label': col, 'value': col} for col in lte_kpis_raw.columns],
multi=True,
value='LRRCConnReqAtt'
),
dcc.DatePickerRange(
id='my-date-picker-range',
min_date_allowed=dt(1995, 8, 5),
max_date_allowed=dt(2030, 9, 19),
initial_visible_month=dt(2019, 10, 5),
start_date=dt(2019, 10, 1),
end_date=dt(2020, 1, 1)
),
html.Div(id='output-container-date-picker-range'),
dcc.Graph(
style={'height': 300},
id='my-graph'
)
])
#app.callback(
Output('my-graph', 'figure'),
[Input('cell-name-xaxis-column', 'value'),
Input('myColumns', 'value')])
def update_graph(xaxis_column_name, yaxis_column_name, date_value):
dff = lte_kpis[lte_kpis['Date'] == date_value]
return {
'data': [dict(
x=dff[dff['Date'] == xaxis_column_name]['Value'],
y=dff[dff['Date'] == yaxis_column_name]['Value'],
text=dff[dff['Date'] == yaxis_column_name]['CellName'],
mode='line',
line={
'size': 15,
'opacity': 0.5
}
)],
}
if __name__ == '__main__':
app.run_server(debug=True)
Note that I want to put more than one KPI in one chart with different plots....
As the scale values in those KPI are little bit different so I tried to create a three types of objects with columns name scale values as the below code
scale_1 = ['LRRCConnReqAtt']
scale_2 = ['RRCSetupSuccessRate', 'InterFreqSuccessRate4G', 'IntraRATHOSucccessRate', 'IntraFreqSuccessRate4G']
scale_3 = ['CellDLMaxThroughputMbps', 'CellDownlinkAverageThroughputMbps']
and this is the error I found:
TypeError: update_graph() missing 1 required positional argument: 'date_value'
Traceback (most recent call last)
File "C:\Users\mwx825326\PycharmProjects\MyReference\venv\Lib\site-packages\dash\dash.py", line 1337, in add_context
output_value = func(*args, **kwargs) # %% callback invoked %%
TypeError: update_graph() missing 1 required positional argument: 'date_value'
Traceback (most recent call last):
File "C:\Users\mwx825326\PycharmProjects\MyReference\venv\Lib\site-packages\dash\dash.py", line 1337, in add_context
output_value = func(*args, **kwargs) # %% callback invoked %%
TypeError: update_graph() missing 1 required positional argument: 'date_value'
Any assistance appreciated.!!!!

Now this question is solved..
But I need it in more static way ,and the way I have to remove one Drop-down and create a chart based on one Drop-down and Date-Picker instead of 2 Drop-down as the removable drop down is based on Columns in DB in y-axis...
First I created Arrays for every Column should be the y-axis column, as I want to make it static charts for every multi or single Column in one Chart based on another Column or multiple Columns value in database....
as the below code:
SHOW_COLUMNS1 = [
'lrrc_re_est_succ',
'cell_dl_max_throughput'
]
SHOW_COLUMNS2 = [
'interfreq_success_rate_4g',
'intrarat_ho_success_rate'
]
SHOW_COLUMNS3 = [
'rcc_setup_success_rate',
'interfreq_success_rate_4g'
]
SHOW_COLUMNS4 = [
'cell_downlink_average_throughput'
]
after that the connection to the Date-Base
# connect db
engine = create_engine('mssql+pyodbc://xxxxxx\zzzzzz/myDB?driver=SQL+Server+Native+Client+11.0')
cursor = engine.raw_connection().cursor()
lte_kpis = pd.read_sql('SELECT * FROM [myDB].[dbo].[lte_details]', engine)
after that I created a GUI for the interactive Charts and pandas read as the below code:
lte_kpis = pd.read_sql('SELECT * FROM [myDB].[dbo].[lte_details]', engine)
pd.set_option('display.max_columns', 10000)
print(lte_kpis)
lte_kpis.set_index('date', inplace=True)
availble_cell = lte_kpis['cell_name'].unique()
plots = []
app.layout = html.Div([
html.H5(
'Huawei KPI Dashbord'),
html.Label('Choose the "Cell-Name"'),
dcc.Dropdown(
id='cell-name-xaxis-column',
options=[{'label': i, 'value': i} for i in availble_cell],
value= availble_cell[0]
),
html.Label('Choose Date Destination'),
dcc.DatePickerRange(
id='date-picker-range',
min_date_allowed=dt(1995, 8, 5),
max_date_allowed=dt(2030, 9, 19),
initial_visible_month=dt(2019, 10, 5),
start_date=dt(2019, 10, 1),
end_date=dt(2020, 1, 1)
),
html.Div(id='output-container-date-picker-range-%s'),
dcc.Dropdown(
id='yaxis-columns',
options=[{'label': col, 'value': col} for col in SHOW_COLUMNS1],
multi=True,
disabled=True,
value=[SHOW_COLUMNS1[0], SHOW_COLUMNS1[1]]
),
dcc.Graph(
style={'height': 300},
id='my-graph'
),
dcc.Dropdown(
id='yaxis-columns2',
options=[{'label': col, 'value': col} for col in SHOW_COLUMNS2],
multi=True,
disabled=True,
value=[SHOW_COLUMNS2[0], SHOW_COLUMNS2[1]]
),
dcc.Graph(
style={'height': 300},
id='my-graph2'
),
dcc.Dropdown(
id='yaxis-columns3',
options=[{'label': col, 'value': col} for col in SHOW_COLUMNS3],
multi=True,
disabled=True,
value=[SHOW_COLUMNS3[0], SHOW_COLUMNS3[1]]
),
dcc.Graph(
style={'height': 300},
id='my-graph3'
),
dcc.Dropdown(
id='yaxis-columns4',
options=[{'label': col, 'value': col} for col in SHOW_COLUMNS4],
multi=True,
disabled=True,
value=[SHOW_COLUMNS4[0]]
),
dcc.Graph(
style={'height': 300},
id='my-graph4'
),
])
and this is Call-back
#app.callback(
Output(component_id='my-graph2', component_property='figure'),
[Input(component_id='cell-name-xaxis-column', component_property='value'),
Input(component_id='yaxis-columns2', component_property='value'),
Input(component_id='date-picker-range', component_property='start_date'),
Input(component_id='date-picker-range', component_property='end_date')])
def update_graph(cell_name, yaxis_cols, start_date, end_date):
if not isinstance(yaxis_cols, list):
yaxis_cols = [yaxis_cols]
print(yaxis_cols)
print((start_date, end_date))
sql_statement = "SELECT date, %s, %s FROM [myDB].[dbo].[lte_details] WHERE ([cell_name]='%s' AND [date]>='%s' AND [date]<='%s')" \
% (SHOW_COLUMNS2[0], SHOW_COLUMNS2[1], cell_name, start_date, end_date)
df = pd.read_sql(sql_statement, engine)
scatters = []
for col in yaxis_cols:
if col == 'lrrc_conn_req_att':
scatters.append(go.Bar(
x=df['date'],
y=df[col],
mode='lines',
name=col
))
else:
scatters.append(go.Scatter(
x=df['date'],
y=df[col],
name=col
))
figure = {
'data': scatters,
}
return figure
Note that as the previous error is related to the missing of data type in data base when importing the excel file to the data base related to the below Code:
col_dict = {
'date': 'Date',
'enodeb_name': 'eNodeB Name',
'downlink_bandwidth': 'Downlink bandwidth',
...........................
}
LTE_DETAILS_TABLE = 'lte_details'
cols = list(col_dict.keys())
# connect db
engine = create_engine('mssql+pyodbc://xxxxx\xxxxxx/myDB?driver=SQL+Server+Native+Client+11.0')
connection = engine.connect()
meta = MetaData()
ltedetails = Table(
LTE_DETAILS_TABLE, meta,
Column('id', Integer, primary_key=True),
Column(cols[0], Date),
Column(cols[1], String),
Column(cols[2], String),
.................................
)
if engine.dialect.has_table(engine, LTE_DETAILS_TABLE):
ltedetails.drop(engine)
meta.create_all(engine)
lte_df = pd.read_excel(os.path.join(os.path.dirname(__file__), 'input.xlsx'), sheet_name='LTE Details', dtype={
col_dict[cols[0]]: str,
col_dict[cols[1]]: str,
col_dict[cols[2]]: str,
.....................................
})
lte_df['Date'] = pd.to_datetime(lte_df['Date'], errors='coerce')
query = db.insert(ltedetails)
values_list = []
row_count = 1
for i in lte_df.index:
row = lte_df.loc[i]
record = {'id': row_count}
for col in col_dict.keys():
if col == cols[3] or col == cols[4] or col == cols[5] or col == cols[6] or col == cols[7] or col == cols[8]:
record[col] = int(row[col_dict[col]])
elif col == cols[9] or col == cols[10] or col == cols[11] or col == cols[12] or col == cols[13] or col == cols[14]:
record[col] = float(row[col_dict[col]])
else:
record[col] = row[col_dict[col]]
values_list.append(record)
row_count += 1
ResultProxy = engine.execute(query, values_list)
connection.close()
engine.dispose()
So I stuck now the I want to remove the Dropp-down with id ``which called id='yaxis-columns', ...
So If there's any comment about how to improve this code performance and Complete to solve my issue..
I am glad for that.

How to update dataframes each time a new date is passed

I am building a dashboard that generates a report based on the input dates. 5 csv's are generated as part of the report, which are displayed on drop down approach and made downloadable. The problem is the dataframes which are are reading the csv's are not getting updated once a new report is generated. It still holds data of the last run. How do i fix the same?
from kontekst_report_server_datewise import kontekst
def generate_table(dataframe,max_rows=10):
return html.Table(
# Header
[html.Tr([html.Th(col) for col in dataframe.columns])] +
# Body
[html.Tr([
html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
]) for i in range(min(len(dataframe), max_rows))]
)
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
# external_stylesheets = ['https://maxcdn.bootstrapcdn.com/bootstrap/3.3.0/css/bootstrap.min.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div([
dcc.DatePickerRange(id='date-picker-range',
start_date=datetime(2019, 4, 3), end_date=datetime(2019, 4, 4)),
dcc.Dropdown(
id='my-dropdown',
options=[
{'label' : 'Returns Summary' , 'value' : 'df1'},
{'label' : 'Return Guide Doc Details' , 'value' : 'df2'},
{'label' : 'Return Guide id Counts' , 'value' : 'df3'},
{'label' : 'Non Return Guide id Counts', 'value' : 'df4'},
{'label' : 'Order Creation Details' , 'value' : 'df5'}
],
# value='df1'
# multi = True
),
html.Div(id='output-container'),
html.A('Download CSV', id = 'my-link'),
html.Div(id='kontekst')
])
#app.callback(
dash.dependencies.Output('output-container', 'children'),
[dash.dependencies.Input('my-dropdown', 'value')])
def update_output(value):
df1 = pd.read_csv('returns_summary.csv')
df2 = pd.read_csv('ReturnGuideDocDetails.csv')
df3 = pd.read_csv('Return_guideid_counts.csv')
df4 = pd.read_csv('NonReturn_guideid_counts.csv')
df5 = pd.read_csv('OrderCreationdetails.csv')
if value == 'df1':
return generate_table(df1)
elif value == 'df2':
return generate_table(df2)
elif value == 'df3' :
return generate_table(df3)
elif value == 'df4' :
return generate_table(df4)
elif value == 'df5' :
return generate_table(df5)
#app.callback(Output('my-link', 'href'), [Input('my-dropdown', 'value')])
def update_link(value):
return '/dash/urlToDownload?value={}'.format(value)
if __name__ == '__main__':
app.run_server(debug=True)```