dashboard

dashboard 

최적의 대시보드는 중요한 비지니스 질문에 대한 해답을 제시한다.

 

web-based dashboarding

ploty, panel, voila, streamlit, bokeh, matplotlib, Flask

 

1. ploty 

python library이다. 

40가지 unique한 chart가 있다. 

financial, maps, scientific, statistical, 3-dimensional style

# Import required packages

import pandas as pd

import dash

import dash_core_components as dcc

import dash_html_components as html

import plotly.express as px

from dash.dependencies import Input, Output

# Add Dataframe

# Add a bar graph figure

# Add Dataframe

df = pd.DataFrame({

    "Fruit": ["Apples", "Oranges", "Bananas", "Apples", "Oranges", "Bananas"],

    "Amount": [4, 1, 2, 2, 4, 5],

    "City": ["SF", "SF", "SF", "NYC", "MTL", "NYC"]

})

fig = px.bar(df, x="Fruit", y="Amount", color="City", barmode="group")

app = dash.Dash()

app.layout = html.Div(children=[

    html.H1(

        children='US Domestic Airline Flights Performance',

        style={

            'textAlign': 'center'

        }

    ),

   

    # Create dropdown

    html.Label('Report type'),

    dcc.Dropdown(options=[

            {'label': 'Yearly airline performance report', 'value':'A'},

            {'label': 'Yearly average flight delay statistics', 'value':'B'}

        ],

        value='A' # Providing a vallue to dropdown

    ),

    html.Label('Choose Year'),

        dcc.Dropdown(

        options=[

            {'label': str(year), 'value': year} for year in range(2005, 2020)

        ],

        placeholder="year"

    ),

    # Bar graph

    dcc.Graph(id='example-graph-2',figure=fig)

])

# Run Application

if __name__ == '__main__':

    app.run_server()

airline_data =  pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/airline_data.csv',

                            encoding = "ISO-8859-1",

                            dtype={'Div1Airport': str, 'Div1TailNum': str,

                                   'Div2Airport': str, 'Div2TailNum': str})

# Randomly sample 500 data points. Setting the random state to be 42 so that we get same result.

data = airline_data.sample(n=500, random_state=42)