pythonplotly-dash
How to show size of dataset in Dash?
I have such a dataset. User can choose certain time interval via slider to adjust time interval in minutes. I would like display the size of dataset at the beginning of web page. If user changes the time interval, the size of dataset should change dynamically as well as showing how many datapoints are removed dynamically.
import random
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
from dash import Dash, html, dcc, Input, Output,dash_table
df = pd.DataFrame({'DATE_TIME':pd.date_range('2022-11-01', '2022-11-06 23:00:00',freq='20min'),
'ID':[random.randrange(1, 3) for n in range(430)]})
df['VALUE1'] = [random.uniform(110, 160) for n in range(430)]
df['VALUE2'] = [random.uniform(50, 80) for n in range(430)]
df['INSPECTION'] = df['DATE_TIME'].dt.day
df['MODE'] = np.select([df['INSPECTION']==1, df['INSPECTION'].isin([2,3])], ['A', 'B'], 'C')
df['TIME'] = df['DATE_TIME'].dt.time
df['TIME'] = df['TIME'].astype('str')
df['TIMEINTERVAL'] = df.DATE_TIME.diff().astype('timedelta64[m]')
df['TIMEINTERVAL'] = df['TIMEINTERVAL'].fillna(0)
def to_day_period(s):
bins = ['0', '06:00:00', '13:00:00', '18:00:00', '23:00:00', '24:00:00']
labels = ['Nighttime', 'Daytime', 'Daytime', 'Nighttime', 'Nighttime']
return pd.cut(
pd.to_timedelta(s),
bins=list(map(pd.Timedelta, bins)),
labels=labels, right=False, ordered=False
)
df['TIME_OF_DAY'] = to_day_period(df['TIME'])
app = Dash(__name__)
app.layout = html.Div([
html.H4('Interactive Scatter Plot'),
html.P("Select ID:"),
dcc.Dropdown(df.ID.unique(), id='pandas-dropdown-1'), # for choosing ID,
html.P("Filter by time interval during nighttime (18:00-6:00):"),
dcc.RangeSlider(
id='range-slider-night',
min=0, max=600, step=10,
marks={0: '0', 50: '50', 100: '100', 150: '150', 200: '200', 250: '250', 300: '300', 350: '350', 400: '400',
450: '450', 500: '500', 550: '550', 600: '600'},
value=[0, 600]
),
html.P("Filter by time interval during daytime (6:00-18:00):"),
dcc.RangeSlider(
id='range-slider-morning',
min=0, max=600, step=10,
marks={0: '0', 50: '50', 100: '100', 150: '150', 200: '200', 250: '250', 300: '300', 350: '350', 400: '400',
450: '450', 500: '500', 550: '550', 600: '600'},
value=[0, 600]
),
dcc.Graph(id="scatter-plot", style={'width': '130vh', 'height': '80vh'}),
html.Div(id='dd-output-container')
])
@app.callback(
Output("scatter-plot", "figure"),
Input("pandas-dropdown-1", "value"),
Input("range-slider-night", "value"),
Input("range-slider-morning", "value"),
prevent_initial_call=True)
def update_lineplot(value, slider_range_night, slider_range_morning):
low_night, high_night = slider_range_night
low_morning, high_morning = slider_range_morning
df_night = df.query("ID == @value & TIME_OF_DAY == 'Night' & TIMEINTERVAL >= @low_night & TIMEINTERVAL < @high_night").copy()
df_morning = df.query("ID == @value & TIME_OF_DAY == 'Morning' & TIMEINTERVAL >= @low_morning & TIMEINTERVAL < @high_morning").copy()
df1 = pd.concat([df_night, df_morning], axis=0).sort_values(['DATE_TIME'])
if df1.shape[0] != 0:
fig = px.line(df1, x="DATE_TIME", y=["VALUE1", "VALUE2"],
facet_col='INSPECTION',
facet_col_wrap=2,
symbol='MODE',hover_data=['TIMEINTERVAL'],
facet_row_spacing=0.1,
facet_col_spacing=0.09)
fig.update_xaxes(matches=None, showticklabels=True)
return fig
else:
return no_update
app.run_server(debug=True, use_reloader=False)
I know that I can do such a task in Flask like in this link. But how can I adapt this solution in Dash?
Solution
Please, try it out and tell me if that what you are trying to do:
import random
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
from dash import Dash, html, dcc, Input, Output,dash_table, no_update
df = pd.DataFrame({'DATE_TIME':pd.date_range('2022-11-01', '2022-11-06 23:00:00',freq='20min'),
'ID':[random.randrange(1, 3) for n in range(430)]})
df['VALUE1'] = [random.uniform(110, 160) for n in range(430)]
df['VALUE2'] = [random.uniform(50, 80) for n in range(430)]
df['INSPECTION'] = df['DATE_TIME'].dt.day
df['MODE'] = np.select([df['INSPECTION']==1, df['INSPECTION'].isin([2,3])], ['A', 'B'], 'C')
df['TIME'] = df['DATE_TIME'].dt.time
df['TIME'] = df['TIME'].astype('str')
df['TIMEINTERVAL'] = df.DATE_TIME.diff().astype('timedelta64[m]')
df['TIMEINTERVAL'] = df['TIMEINTERVAL'].fillna(0)
def to_day_period(s):
bins = ['0', '06:00:00', '13:00:00', '18:00:00', '23:00:00', '24:00:00']
labels = ['Nighttime', 'Daytime', 'Daytime', 'Nighttime', 'Nighttime']
return pd.cut(
pd.to_timedelta(s),
bins=list(map(pd.Timedelta, bins)),
labels=labels, right=False, ordered=False
)
df['TIME_OF_DAY'] = to_day_period(df['TIME'])
app = Dash(__name__)
app.layout = html.Div([
html.H4('Interactive Scatter Plot'),
html.Div('Size of dataset = '+str(len(df)),id='size',style={'whiteSpace': 'pre-wrap'}),
html.P("Select ID:"),
dcc.Dropdown(df.ID.unique(), id='pandas-dropdown-1'), # for choosing ID,
html.P("Filter by time interval during nighttime (18:00-6:00):"),
dcc.RangeSlider(
id='range-slider-night',
min=0, max=600, step=10,
marks={0: '0', 50: '50', 100: '100', 150: '150', 200: '200', 250: '250', 300: '300', 350: '350', 400: '400',
450: '450', 500: '500', 550: '550', 600: '600'},
value=[0, 600]
),
html.P("Filter by time interval during daytime (6:00-18:00):"),
dcc.RangeSlider(
id='range-slider-morning',
min=0, max=600, step=10,
marks={0: '0', 50: '50', 100: '100', 150: '150', 200: '200', 250: '250', 300: '300', 350: '350', 400: '400',
450: '450', 500: '500', 550: '550', 600: '600'},
value=[0, 600]
),
dcc.Graph(id="scatter-plot", style={'width': '130vh', 'height': '80vh'}),
html.Div(id='dd-output-container')
])
@app.callback(
Output("scatter-plot", "figure"),
Output("size", "children"),
Input("pandas-dropdown-1", "value"),
Input("range-slider-night", "value"),
Input("range-slider-morning", "value"),
prevent_initial_call=True)
def update_lineplot(value, slider_range_night, slider_range_morning):
low_night, high_night = slider_range_night
low_morning, high_morning = slider_range_morning
df_night = df.query("(ID == @value) & (TIME_OF_DAY == 'Nighttime') & (TIMEINTERVAL >= @low_night) & (TIMEINTERVAL < @high_night)").copy()
df_morning = df.query("(ID == @value) & (TIME_OF_DAY == 'Daytime') & (TIMEINTERVAL >= @low_morning) & (TIMEINTERVAL < @high_morning)").copy()
df1 = pd.concat([df_night, df_morning], axis=0).sort_values(['DATE_TIME'])
text = f"The size of dataset = {len(df)} \n Selected points = {len(df1)}, unselected points = {len(df) - len(df1)}, \n df_night = {len(df_night)}, df_morning = {len(df_morning)}"
if df1.shape[0] != 0:
fig = px.line(df1, x="DATE_TIME", y=["VALUE1", "VALUE2"],
facet_col='INSPECTION',
facet_col_wrap=2,
symbol='MODE',hover_data=['TIMEINTERVAL'],
facet_row_spacing=0.1,
facet_col_spacing=0.09)
fig.update_xaxes(matches=None, showticklabels=True)
return fig,text
else:
return no_update
app.run_server(debug=True, use_reloader=False)
Output:
You will find here how I calculate the sizes ;
text = f"The size of dataset = {len(df)} \n Selected points = {len(df1)}, unselected points = {len(df) - len(df1)}, \n df_night = {len(df_night)}, df_morning = {len(df_morning)}"