Python/Dash by Plotly: Group cases by date

Multi tool use
Multi tool use


Python/Dash by Plotly: Group cases by date



I got to plot barchart only for total cases of occurences and not group cases by date of occurences with this code:


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



How to do groupby date?
Dataset as below


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



Thanks




3 Answers
3



I used the following content in my data.csv:


data.csv


date enrolled
6/29/2018 1
6/29/2018 1
6/29/2018 0
6/29/2018 1
6/20/2018 1
6/22/2018 1
6/19/2018 1
6/27/2018 1
6/28/2018 0
6/27/2018 1
6/19/2018 1
6/20/2018 1
6/27/2018 1
6/27/2018 0
6/26/2018 1
6/27/2018 0
6/27/2018 1



This is one possible solution for your problem with plotly:


import plotly
import plotly.graph_objs as go

import pandas as pd

# Read in data (might be already given in your code)
df = pd.read_csv('data.csv', delimiter=' ')
groups = df.groupby(['date'])

xData =
yData =

# Group data by keys and get sum
for group, data in df.groupby(['date']):
xData.append(group)
yData.append(data['enrolled'].sum())

data = [go.Bar(
x=xData,
y=yData
)]

plotly.offline.plot(data, filename='a-simple-plot')



This example uses dash to publish the graphics:


import pandas as pd

# Read in data (might be already given in your code)
df = pd.read_csv('data.csv', delimiter=' ')
groups = df.groupby(['date'])

xData =
yData =

# Group data by keys and get sum
for group, data in df.groupby(['date']):
xData.append(group)
yData.append(data['enrolled'].sum())

# Dash specific stuff
import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()

app.layout = html.Div(children=[
html.H1(
children='Hello Dash',
),

html.Div(children='Dash: A web application framework for Python.', style={
'textAlign': 'center'
}),

dcc.Graph(
id='example-graph-2',
figure={
'data': [
{'x':xData, 'y': yData, 'type': 'bar'},
],
'layout': {}
}

)
])

if __name__ == '__main__':
app.run_server(debug=True)





can I have your email? I want to share my code. Thanks
– Miguel Bambo
Jul 1 at 14:00





Please add you example to your stackoverflow post
– Nils
Jul 1 at 14:02





it doesnt work. For instance, in my Dash code I dont need this code: plotly.offline.plot(data, filename='a-simple-plot')
– Miguel Bambo
Jul 1 at 14:03





Yes sure, because this example includes no dash code (basicaly because you have no dash code posted). It is only here to show you how to proceed. The assinment of the data to your bar chart should be the same in dash
– Nils
Jul 1 at 14:05






Please find a second example using dash in my post
– Nils
Jul 1 at 14:15


#### Importing DASH COMPONENTS ##############################################################################
#coding: utf-8

import dash
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html

from plotly import graph_objs as go # or
#import plotly.graph_objs as go
import ipywidgets as widgets
from scipy import special

import datetime #To allow displaying today's Date in upper right corner

import json
import pandas as pd
import os
from flask import Flask
import numpy as np



#### Preparing FLASK App ####################################################################################
server = Flask('my app')

#### SCATTER PLOT ##########################################################################################

dfb=pd.read_csv('final_test.csv', encoding="latin-1", infer_datetime_format=True, parse_dates=['date'], skipinitialspace=True)

trace1=go.Bar( #Trace Enrollment

x=pd.to_datetime(dfb['date']),
y=dfb.set_index('date').resample('M')["enrolled"].sum(),
#y=dfb.groupby('date').enrolled.sum(),
#mode='lines + markers',
name='Enrollment',
)

trace2=go.Bar( #Trace empty enrollment
x=pd.to_datetime(dfb['date']),
y=dfb[dfb['enrolled'].isnull()].sum(),
name='Not Answered',
#xaxis='Performance'
)

trace3=go.Bar( #Trace Rejection to Enrollment
x=pd.to_datetime(dfb['date']),
y=dfb[dfb['enrolled'] == 2].sum(),
name='Rejected Participation',
#xaxis='Performance'
)

#### PERFORMANCE % ##########################################################################################



#############################################################################################################
#### CREATE STANDARD TABLES OF OUTPUT #########################################################################
def make_dash_table(df):
''' Return a dash definitio of an HTML table for a Pandas dataframe '''
table =
for index, row in df.iterrows():
html_row =
for i in range(len(row)):
html_row.append(html.Td([row[i]]))
table.append(html.Tr(html_row))
return table

#############################################################################################################

app = dash.Dash()

# Describe the layout, or the UI, of the app
app.layout = html.Div([

html.Div([ # page 1

html.A(['Print PDF'],
className="button no-print",
style={'position': "absolute", 'top': '-40', 'right': '0'}),

html.Div([ # subpage 1

# Row 1 (Header)

html.Div([

html.Div([
html.H5(
'An Example of DashBoard in Dash from Plotly'),
html.H6('Summary',
style={'color': '#7F90AC'}),
], className="nine columns padded"),

html.Div([
html.H1(
#[html.Span('03', style={'opacity': '0.5'}), html.Span('17')]),
datetime.datetime.now().strftime('%Y-%m-%d'), style={'opacity': '1','color': 'white', 'fontSize': 12}),
html.H1(datetime.datetime.now().strftime('%H:%M:%S'), style={'font-family': 'Times New Roman','opacity': '0.5','color': 'white', 'fontSize': 12}),
html.H6('Daily Updates')
], className="three columns gs-header gs-accent-header padded", style={'float': 'right'}),

], className="row gs-header gs-text-header"),

html.Br(),

# Row 2

html.Div([

html.Div([
html.H6('Resume',
className="gs-header gs-text-header padded"),



], className="four columns"),



html.Div([

html.Div(children=[

html.H6(["Performance"],
className="gs-header gs-table-header padded"),
dcc.Graph(
id='example-graph',
figure={
'data': [trace1, trace2, trace3],
'layout':
go.Layout(
title='', width="508", height="300", legend=dict(x=0, y=7),
margin={'l': 20, 'b': 40, 't': 10, 'r': 65},

font=dict(
family='sans-serif',
size=8,
color='#000'
),

plot_bgcolor='#D9E0EC',



xaxis=dict(


title='',
tickangle=45,
ticklen=5,
#zeroline=False,
gridwidth=2,
showticklabels=True,
nticks=6,
),
yaxis=dict(
title='',
ticklen=5,
gridwidth=4,
),

)#, barmode='stack')
})
]),


], className="eight columns"),
], className="row "),



], className="subpage"),

], className="page"),



])

if 'DYNO' in os.environ:
app.scripts.append_script({
'external_url': 'https://cdn.rawgit.com/chriddyp/ca0d8f02a1659981a0ea7f013a378bbd/raw/e79f3f789517deec58f41251f7dbb6bee72c44ab/plotly_ga.js'
})

external_css = ["https://cdnjs.cloudflare.com/ajax/libs/normalize/7.0.0/normalize.min.css",
"https://cdnjs.cloudflare.com/ajax/libs/skeleton/2.0.4/skeleton.min.css",
"//fonts.googleapis.com/css?family=Raleway:400,300,600",
"https://cdn.rawgit.com/plotly/dash-app-stylesheets/5047eb29e4afe01b45b27b1d2f7deda2a942311a/goldman-sachs-report.css",
"https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css"]

for css in external_css:
app.css.append_css({"external_url": css})

external_js = ["https://code.jquery.com/jquery-3.2.1.min.js",
"https://cdn.rawgit.com/plotly/dash-app-stylesheets/a3401de132a6d0b652ba11548736b1d1e80aa10d/dash-goldman-sachs-report-js.js"]

for js in external_js:
app.scripts.append_script({"external_url": js})


if __name__ == '__main__':
app.server.run()


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





@jezrael, any thoughts?
– Miguel Bambo
2 days ago






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