빅데이터 분석기사 실기 시험을 준비하며 연습한 코드들입니다.

주로 다음의 kaggle data를 사용하여 학습했습니다. https://www.kaggle.com/agileteam/bigdatacertificationkr

import numpy as np import pandas as pd import random from random import randint

import plotly.express as px import plotly.graph_objects as go from dash import Dash, dcc, html, Input, Output

row = ['취급건수', '취급액', '약정금리', '기준금리', '조달원가', 'Duration', '업무원가', '공통원가', '채널별가산금리', '모집수수료', '신용원가', '목표이익률', '조정금리', '우대금리', '프로모션금리', '상품운영금리', '조달조정금리', '적용이익율']

col = pd.date_range('2022-07-01', '2022-07-31')

channel = ['전체', '제휴연계외', '제휴연계', '상담사', 'direct', '카카오', '엘포인트', '토스', '핀다', '카카오페이', '핀크', '시럽', '알다', '신한카드', '케이뱅크', '뱅크샐러드', '하나카드', '페이코', '키움증권', 'NICE평가정보']

data = pd.DataFrame()

for i in range(len(channel)): data_temp = pd.DataFrame({'취급건수':np.random.binomial(400, 0.5, size=len(col)), '취급액':np.random.binomial(10000, 0.4, size=len(col)), '약정금리':np.random.normal(12, 1, size=len(col)), '기준금리':np.random.normal(14, 1, size=len(col)), '조달원가':np.random.normal(4, 0.5, size=len(col)), 'Duration':np.random.normal(43, 2, size=len(col)), '업무원가':np.random.normal(2.5, 0.3, size=len(col)), '공통원가':np.random.normal(1.4, 0.1, size=len(col)), '채널별가산금리':abs(np.random.normal(0.1, 0.1, size=len(col))), '모집수수료':abs(np.random.normal(1, 0.1, len(col))), '신용원가':np.random.normal(5, 0.5, len(col)), '목표이익률':np.random.normal(3.2, 0.1, len(col)), '조정금리':abs(np.random.normal(2.3, 0.2, len(col)))(-1), '우대금리':abs(np.random.normal(0, 0.05, len(col)))(-1), '프로모션금리':abs(np.random.normal(1.8, 0.3, len(col)))(-1), '상품운영금리':abs(np.random.normal(0.5, 0.2, len(col)))(-1), '조달조정금리':abs(np.random.normal(0.01, 0.01, len(col)))*(-1), '적용이익율':abs(np.random.normal(0.8, 0.1, len(col)))}).T data_temp.columns=col data_temp['channel'] = channel[i] data=pd.concat([data,data_temp],axis=0)

df = data.reset_index().melt(id_vars=['index','channel'], var_name = ['date']).rename(columns = {'index':'type'})

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = Dash(name, external_stylesheets=external_stylesheets)

def create_time_series(dff, axis_type, title): fig = px.scatter(dff, x='date', y='value') fig.update_traces(mode='lines+markers') fig.update_xaxes(showgrid=False) fig.update_yaxes() fig.add_annotation(x=0, y=0.85, xanchor='left', yanchor='bottom', xref='paper', yref='paper', showarrow=False, align='left', text=title) fig.update_layout(height=225, margin={'l': 20, 'b': 30, 'r': 10, 't': 10}) return fig

markdown_text = '''

• 대상기준 : 개인신용대출 신규상품 당일 실행 및 당일 출금 고객 (실행일 이후 수시출입금, 당일취소 제외)
• 집계기준 : 원가별 출금액 가준평균금리
• 고객별 예상 마진율 : 약정금리 - sum(조달원가, 업무원가, 신용원가) '''

dcc.Markdown(children=markdown_text)

app.layout = html.Div([ html.Div([ dcc.Markdown(children=markdown_text) ]),

html.Div([

    html.Div([
        dcc.Dropdown(
            df['channel'].unique(),
            'first channel',
            id='crossfilter-xaxis-column',
        )
    ],
    style={'width': '49%', 'display': 'inline-block'}),

    html.Div([
        dcc.Dropdown(
            df['channel'].unique(),
            'second channel',
            id='crossfilter-yaxis-column'
        )
    ], style={'width': '49%', 'float': 'right', 'display': 'inline-block'}),
    
    html.Div([
        dcc.RadioItems(
            df['type'].unique(),
            '취급건수',
            id='crossfilter-yaxis-type',
            labelStyle={'display': 'inline-block', 'marginTop': '5px'}
        )])
], style={
    'padding': '10px 5px'
}),

# 우측상단 그래프
html.Div([
    dcc.Graph(
        id='crossfilter-indicator-scatter',
        hoverData={'points': [{'customdata': 'type'}]}
    )
], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),
html.Div([
    dcc.Graph(id='x-time-series'),
    dcc.Graph(id='y-time-series'),
], style={'display': 'inline-block', 'width': '49%'}),


# 우측하단 그래프
html.Div(dcc.Slider(
    df['channel'].min(),
    df['date'].max(),
    step=None,
    id='crossfilter-date--slider',
    value=df['date'].max(),
    marks={str(date): str(date) for date in df['date'].unique()}
), style={'width': '49%', 'padding': '0px 20px 20px 20px'})  

])

@app.callback( Output('crossfilter-indicator-scatter', 'figure'), Input('crossfilter-xaxis-column', 'value'), Input('crossfilter-yaxis-column', 'value'), Input('crossfilter-yaxis-type', 'value'), Input('crossfilter-date--slider', 'value')) def update_graph(xaxis_column_name, yaxis_column_name, yaxis_type, date_value): dff = df[df['date'] == date_value] dff1 = dff.groupby(['type','channel']).mean().reset_index() fig = px.histogram(dff1[(dff1['channel'] == xaxis_column_name)|(dff1['channel'] == yaxis_column_name)], x='type', y='value', color = 'channel', barnorm = "percent")
fig.update_traces() fig.update_xaxes() fig.update_yaxes() fig.update_layout(margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest') return fig

@app.callback( Output('x-time-series', 'figure'), Input('crossfilter-indicator-scatter', 'hoverData'), Input('crossfilter-xaxis-column', 'value'), Input('crossfilter-yaxis-type', 'value')) def update_y_timeseries(hoverData, xaxis_column_name, axis_type): country_name = hoverData['points'][0]['customdata'] dff = df[df['type'] == axis_type] dff = dff[dff['channel'] == xaxis_column_name] title = '{}
{}'.format(country_name, xaxis_column_name) return create_time_series(dff, axis_type, title)

@app.callback( Output('y-time-series', 'figure'), Input('crossfilter-indicator-scatter', 'hoverData'), Input('crossfilter-yaxis-column', 'value'), Input('crossfilter-yaxis-type', 'value')) def update_x_timeseries(hoverData, yaxis_column_name, axis_type): dff = df[df['type'] == axis_type] dff = dff[dff['channel'] == yaxis_column_name] return create_time_series(dff, axis_type, yaxis_column_name)

if name == 'main': app.run_server(host='localhost',port=8007)

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = Dash(name, external_stylesheets=external_stylesheets)

def create_time_series(dff, axis_type, title): fig = px.scatter(dff, x='date', y='value') fig.update_traces(mode='lines+markers') fig.update_xaxes(showgrid=False) fig.update_yaxes(type='linear' if axis_type == 'Linear' else 'log') fig.add_annotation(x=0, y=0.85, xanchor='left', yanchor='bottom', xref='paper', yref='paper', showarrow=False, align='left', text=title) fig.update_layout(height=225, margin={'l': 20, 'b': 30, 'r': 10, 't': 10}) return fig

markdown_text = '''

• 대상기준 : 개인신용대출 신규상품 당일 실행 및 당일 출금 고객 (실행일 이후 수시출입금, 당일취소 제외)
• 집계기준 : 원가별 출금액 가준평균금리
• 고객별 예상 마진율 : 약정금리 - sum(조달원가, 업무원가, 신용원가) '''

dcc.Markdown(children=markdown_text)

app.layout = html.Div([ html.Div([ dcc.Markdown(children=markdown_text) ]),

html.Div([

    html.Div([
        dcc.Dropdown(
            df['channel'].unique(),
            'Fertility rate, total (births per woman)',
            id='crossfilter-xaxis-column',
        ),
        dcc.RadioItems(
            ['Linear', 'Log'],
            'Linear',
            id='crossfilter-xaxis-type',
            labelStyle={'display': 'inline-block', 'marginTop': '5px'}
        )
    ],
    style={'width': '49%', 'display': 'inline-block'}),

    html.Div([
        dcc.Dropdown(
            df['channel'].unique(),
            'Life expectancy at birth, total (dates)',
            id='crossfilter-yaxis-column'
        ),
        dcc.RadioItems(
            ['Linear', 'Log'],
            'Linear',
            id='crossfilter-yaxis-type',
            labelStyle={'display': 'inline-block', 'marginTop': '5px'}
        )
    ], style={'width': '49%', 'float': 'right', 'display': 'inline-block'})
], style={
    'padding': '10px 5px'
}),

# 우측상단 그래프
html.Div([
    dcc.Graph(
        id='crossfilter-indicator-scatter',
        hoverData={'points': [{'customdata': '적용이익율'}]}
    )
], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),
html.Div([
    dcc.Graph(id='x-time-series'),
    dcc.Graph(id='y-time-series'),
], style={'display': 'inline-block', 'width': '49%'}),


# 우측하단 그래프
html.Div(dcc.Slider(
    df['channel'].min(),
    df['date'].max(),
    step=None,
    id='crossfilter-date--slider',
    value=df['date'].max(),
    marks={str(date): str(date) for date in df['date'].unique()}
), style={'width': '49%', 'padding': '0px 20px 20px 20px'})

])

@app.callback( Output('crossfilter-indicator-scatter', 'figure'), Input('crossfilter-xaxis-column', 'value'), Input('crossfilter-yaxis-column', 'value'), Input('crossfilter-xaxis-type', 'value'), Input('crossfilter-yaxis-type', 'value'), Input('crossfilter-date--slider', 'value')) def update_graph(xaxis_column_name, yaxis_column_name, xaxis_type, yaxis_type, date_value): dff = df[df['date'] == date_value]

fig = px.scatter(x=dff[dff['channel'] == xaxis_column_name]['value'],
                 y=dff[dff['channel'] == yaxis_column_name]['value'],
                 hover_name=dff[dff['channel'] == yaxis_column_name]['type'])

fig.update_traces(customdata=dff[dff['channel'] == yaxis_column_name]['type'])
fig.update_xaxes(title=xaxis_column_name, type='linear' if xaxis_type == 'Linear' else 'log')
fig.update_yaxes(title=yaxis_column_name, type='linear' if yaxis_type == 'Linear' else 'log')
fig.update_layout(margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest')
return fig

@app.callback( Output('x-time-series', 'figure'), Input('crossfilter-indicator-scatter', 'hoverData'), Input('crossfilter-xaxis-column', 'value'), Input('crossfilter-xaxis-type', 'value')) def update_y_timeseries(hoverData, xaxis_column_name, axis_type): country_name = hoverData['points'][0]['customdata'] dff = df[df['type'] == country_name] dff = dff[dff['channel'] == xaxis_column_name] title = '{}
{}'.format(country_name, xaxis_column_name) return create_time_series(dff, axis_type, title)

@app.callback( Output('y-time-series', 'figure'), Input('crossfilter-indicator-scatter', 'hoverData'), Input('crossfilter-yaxis-column', 'value'), Input('crossfilter-yaxis-type', 'value')) def update_x_timeseries(hoverData, yaxis_column_name, axis_type): dff = df[df['type'] == hoverData['points'][0]['customdata']] dff = dff[dff['channel'] == yaxis_column_name] return create_time_series(dff, axis_type, yaxis_column_name)

if name == 'main': app.run_server(host='localhost',port=8007)

#!/usr/bin/env python

import numpy as np import pandas as pd import random from random import randint

row = ['취급건수', '취급액', '약정금리', '기준금리', '조달원가', 'Duration', '업무원가', '공통원가', '채널별가산금리', '모집수수료', '신용원가', '목표이익률', '조정금리', '우대금리', '프로모션금리', '상품운영금리', '조달조정금리', '적용이익율']

col = pd.date_range('2022-07-01', '2022-07-31')

channel = ['전체', '제휴연계외', '제휴연계', '상담사', 'direct', '카카오', '엘포인트', '토스', '핀다', '카카오페이', '핀크', '시럽', '알다', '신한카드', '케이뱅크', '뱅크샐러드', '하나카드', '페이코', '키움증권', 'NICE평가정보']

data = pd.DataFrame()

for i in range(len(channel)): data_temp = pd.DataFrame({'취급건수':np.random.binomial(400, 0.5, size=len(col)), '취급액':np.random.binomial(10000, 0.4, size=len(col)), '약정금리':np.random.normal(12, 1, size=len(col)), '기준금리':np.random.normal(14, 1, size=len(col)), '조달원가':np.random.normal(4, 0.5, size=len(col)), 'Duration':np.random.normal(43, 2, size=len(col)), '업무원가':np.random.normal(2.5, 0.3, size=len(col)), '공통원가':np.random.normal(1.4, 0.1, size=len(col)), '채널별가산금리':abs(np.random.normal(0.1, 0.1, size=len(col))), '모집수수료':abs(np.random.normal(1, 0.1, len(col))), '신용원가':np.random.normal(5, 0.5, len(col)), '목표이익률':np.random.normal(3.2, 0.1, len(col)), '조정금리':abs(np.random.normal(2.3, 0.2, len(col)))(-1), '우대금리':abs(np.random.normal(0, 0.05, len(col)))(-1), '프로모션금리':abs(np.random.normal(1.8, 0.3, len(col)))(-1), '상품운영금리':abs(np.random.normal(0.5, 0.2, len(col)))(-1), '조달조정금리':abs(np.random.normal(0.01, 0.01, len(col)))*(-1), '적용이익율':abs(np.random.normal(0.8, 0.1, len(col)))}).T data_temp.columns=col data_temp['channel'] = channel[i] data=pd.concat([data,data_temp],axis=0)

data = data.reset_index().rename(columns={'index': 'type'})

data

df = pd.DataFrame() for i in range(len(col)): temp = data.iloc[:,[0,i+1,-1]] temp['date'] = temp.columns[1] temp.columns = ['type', 'value', 'channel', 'date'] df = pd.concat([df,temp],axis=0)

df

import pandas as pd import plotly.express as px # (version 4.7.0 or higher) import plotly.graph_objects as go from dash import Dash, dcc, html, Input, Output # pip install dash (version 2.0.0 or higher)

app = Dash(name)

app.layout = html.Div([

html.H1("개인신용대출 금리원가별 Monitoring", style={'text-align': 'center'}),

dcc.Dropdown(id="my-dropdown",
             options=[
                 {"label": "취급건수", "value": '취급건수'},
                 {"label": "취급액", "value": "취급액"},
                 {"label": "약정금리", "value": '약정금리'},
                 {"label": "기준금리", "value": '기준금리'}],
             multi=False,
             value='취급건수',
             ),

html.Div(id='output_container', children=[]),
html.Br(),

dcc.Graph(id='my-graph', figure={})

])

a=pd.read_csv('https://plotly.github.io/datasets/country_indicators.csv')

a[a['Country Name'] =='Japan']

df

df

from dash import Dash, html, dcc, Input, Output import pandas as pd import plotly.express as px

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = Dash(name, external_stylesheets=external_stylesheets)

markdown_text = '''

• 대상기준 : 개인신용대출 신규상품 당일 실행 및 당일 출금 고객 (실행일 이후 수시출입금, 당일취소 제외)
• 집계기준 : 원가별 출금액 가준평균금리
• 고객별 예상 마진율 : 약정금리 - sum(조달원가, 업무원가, 신용원가) '''

dcc.Markdown(children=markdown_text)

app.layout = html.Div([ html.Div([ dcc.Markdown(children=markdown_text) ]),

html.Div([

    html.Div([
        dcc.Dropdown(
            df['channel'].unique(),
            'Fertility rate, total (births per woman)',
            id='crossfilter-xaxis-column',
        ),
        dcc.RadioItems(
            ['Linear', 'Log'],
            'Linear',
            id='crossfilter-xaxis-type',
            labelStyle={'display': 'inline-block', 'marginTop': '5px'}
        )
    ],
    style={'width': '49%', 'display': 'inline-block'}),

    html.Div([
        dcc.Dropdown(
            df['channel'].unique(),
            'Life expectancy at birth, total (dates)',
            id='crossfilter-yaxis-column'
        ),
        dcc.RadioItems(
            ['Linear', 'Log'],
            'Linear',
            id='crossfilter-yaxis-type',
            labelStyle={'display': 'inline-block', 'marginTop': '5px'}
        )
    ], style={'width': '49%', 'float': 'right', 'display': 'inline-block'})
], style={
    'padding': '10px 5px'
}),

# 우측상단 그래프
html.Div([
    dcc.Graph(
        id='crossfilter-indicator-scatter',
        hoverData={'points': [{'customdata': '적용이익율'}]}
    )
], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),
html.Div([
    dcc.Graph(id='x-time-series'),
    dcc.Graph(id='y-time-series'),
], style={'display': 'inline-block', 'width': '49%'}),


# 우측하단 그래프
html.Div(dcc.Slider(
    df['channel'].min(),
    df['date'].max(),
    step=None,
    id='crossfilter-date--slider',
    value=df['date'].max(),
    marks={str(date): str(date) for date in df['date'].unique()}
), style={'width': '49%', 'padding': '0px 20px 20px 20px'})

])

@app.callback( Output('crossfilter-indicator-scatter', 'figure'), Input('crossfilter-xaxis-column', 'value'), Input('crossfilter-yaxis-column', 'value'), Input('crossfilter-xaxis-type', 'value'), Input('crossfilter-yaxis-type', 'value'), Input('crossfilter-date--slider', 'value')) def update_graph(xaxis_column_name, yaxis_column_name, xaxis_type, yaxis_type, date_value): dff = df[df['date'] == date_value]

fig = px.scatter(x=dff[dff['channel'] == xaxis_column_name]['value'],
        y=dff[dff['channel'] == yaxis_column_name]['value'],
        hover_name=dff[dff['channel'] == yaxis_column_name]['type']
        )

fig.update_traces(customdata=dff[dff['channel'] == yaxis_column_name]['type'])

fig.update_xaxes(title=xaxis_column_name, type='linear' if xaxis_type == 'Linear' else 'log')

fig.update_yaxes(title=yaxis_column_name, type='linear' if yaxis_type == 'Linear' else 'log')

fig.update_layout(margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest')

return fig

def create_time_series(dff, axis_type, title):

fig = px.scatter(dff, x='date', y='value')

fig.update_traces(mode='lines+markers')

fig.update_xaxes(showgrid=False)

fig.update_yaxes(type='linear' if axis_type == 'Linear' else 'log')

fig.add_annotation(x=0, y=0.85, xanchor='left', yanchor='bottom',
                   xref='paper', yref='paper', showarrow=False, align='left',
                   text=title)

fig.update_layout(height=225, margin={'l': 20, 'b': 30, 'r': 10, 't': 10})

return fig

@app.callback( Output('x-time-series', 'figure'), Input('crossfilter-indicator-scatter', 'hoverData'), Input('crossfilter-xaxis-column', 'value'), Input('crossfilter-xaxis-type', 'value')) def update_y_timeseries(hoverData, xaxis_column_name, axis_type): country_name = hoverData['points'][0]['customdata'] dff = df[df['type'] == country_name] dff = dff[dff['channel'] == xaxis_column_name] title = '{}
{}'.format(country_name, xaxis_column_name) return create_time_series(dff, axis_type, title)

@app.callback( Output('y-time-series', 'figure'), Input('crossfilter-indicator-scatter', 'hoverData'), Input('crossfilter-yaxis-column', 'value'), Input('crossfilter-yaxis-type', 'value')) def update_x_timeseries(hoverData, yaxis_column_name, axis_type): dff = df[df['type'] == hoverData['points'][0]['customdata']] dff = dff[dff['channel'] == yaxis_column_name] return create_time_series(dff, axis_type, yaxis_column_name)

if name == 'main': app.run_server(host='localhost',port=8007)

import pandas as pd import plotly.express as px # (version 4.7.0 or higher) import plotly.graph_objects as go from dash import Dash, dcc, html, Input, Output # pip install dash (version 2.0.0 or higher)

app = Dash(name)

app.layout = html.Div([

html.H1("개인신용대출 금리원가별 Monitoring", style={'text-align': 'center'}),

dcc.Dropdown(id="slct_year",
             options=[
                 {"label": "2015", "value": 2015},
                 {"label": "2016", "value": 2016},
                 {"label": "2017", "value": 2017},
                 {"label": "2018", "value": 2018}],
             multi=False,
             value=2015,
             style={'width': "40%"}
             ),

html.Div(id='output_container', children=[]),
html.Br(),

dcc.Graph(id='my_bee_map', figure={})

])

@app.callback( [Output(component_id='output_container', component_property='children'), Output(component_id='my_bee_map', component_property='figure')], [Input(component_id='slct_year', component_property='value')] ) def update_graph(option_slctd): print(option_slctd) print(type(option_slctd))

container = "The year chosen by user was: {}".format(option_slctd)

dff = df.copy()
dff = dff[dff["Year"] == option_slctd]
dff = dff[dff["Affected by"] == "Varroa_mites"]

# Plotly Express
fig = px.choropleth(
    data_frame=dff,
    locationmode='USA-states',
    locations='state_code',
    scope="usa",
    color='Pct of Colonies Impacted',
    hover_data=['State', 'Pct of Colonies Impacted'],
    color_continuous_scale=px.colors.sequential.YlOrRd,
    labels={'Pct of Colonies Impacted': '% of Bee Colonies'},
    template='plotly_dark'
)

fig = go.Figure(
    data=[go.Choropleth(
        locationmode='USA-states',
        locations=dff['state_code'],
        z=dff["Pct of Colonies Impacted"].astype(float),
        colorscale='Reds',
    )]
)

fig.update_layout(
    title_text="Bees Affected by Mites in the USA",
    title_xanchor="center",
    title_font=dict(size=24),
    title_x=0.5,
    geo=dict(scope='usa'),
)

return container, fig 

if name == 'main': app.run_server(host='localhost',port=8005)

import dash from dash import dcc from dash import html import plotly.express as px import pandas as pd

app = dash.Dash(name)

df = pd.DataFrame( { "Fruit": [ "Apples", "Oranges", "Bananas", "Apples", "Oranges", "Bananas", ], "Amount": [4, 1, 2, 2, 4, 5], "City": ["SF", "SF", "SF", "Montreal", "Montreal", "Montreal"], } )

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

app.layout = html.Div( children=[ html.H1(children='Hello Dash'), html.Div( children=''' Dash: A web application framework for your data. ''' ), dcc.Graph(id='example-graph', figure=fig), ] )

if name == 'main': app.run_server(host='localhost',port=8005)

import dash from dash import dcc from dash import html

app = dash.Dash(name)

app.layout = html.Div( [ html.Div( children=[ html.Label('Dropdown'), dcc.Dropdown( options=[ {'label': 'New York City', 'value': 'NYC'}, {'label': u'Montréal', 'value': 'MTL'}, {'label': 'San Francisco', 'value': 'SF'}, ], value='MTL', ), html.Br(), html.Label('Multi-Select Dropdown'), dcc.Dropdown( options=[z {'label': 'New York City', 'value': 'NYC'}, {'label': u'Montréal', 'value': 'MTL'}, {'label': 'San Francisco', 'value': 'SF'}, ], value=['MTL', 'SF'], multi=True, ), html.Br(), html.Label('Radio Items'), dcc.RadioItems( options=[ {'label': 'New York City', 'value': 'NYC'}, {'label': u'Montréal', 'value': 'MTL'}, {'label': 'San Francisco', 'value': 'SF'}, ], value='MTL', ), ], style={'padding': 10, 'flex': 1}, ), html.Div( children=[ html.Label('Checkboxes'), dcc.Checklist( options=[ {'label': 'New York City', 'value': 'NYC'}, {'label': u'Montréal', 'value': 'MTL'}, {'label': 'San Francisco', 'value': 'SF'}, ], value=['MTL', 'SF'], ), html.Br(), html.Label('Text Input'), dcc.Input(value='MTL', type='text'), html.Br(), html.Label('Slider'), dcc.Slider( min=0, max=9, marks={ i: 'Label {}'.format(i) if i == 1 else str(i) for i in range(1, 6) }, value=5, ), ], style={'padding': 10, 'flex': 1}, ), ], style={'display': 'flex', 'flex-direction': 'row'}, )

if name == 'main': app.run_server(host='localhost',port=8005)

df

@app.callback( Output('intermediate-value', 'data'), Input('dropdown', 'value')) def clean_data(value): cleaned_df = slow_processing_step(value)

 # a few filter steps that compute the data
 # as it's needed in the future callbacks
 df_1 = cleaned_df[cleaned_df['fruit'] == 'apples']
 df_2 = cleaned_df[cleaned_df['fruit'] == 'oranges']
 df_3 = cleaned_df[cleaned_df['fruit'] == 'figs']

 datasets = {
     'df_1': df_1.to_json(orient='split', date_format='iso'),
     'df_2': df_2.to_json(orient='split', date_format='iso'),
     'df_3': df_3.to_json(orient='split', date_format='iso'),
 }

 return json.dumps(datasets)

@app.callback( Output('graph1', 'figure'), Input('intermediate-value', 'data')) def update_graph_1(jsonified_cleaned_data): datasets = json.loads(jsonified_cleaned_data) dff = pd.read_json(datasets['df_1'], orient='split') figure = create_figure_1(dff) return figure

@app.callback( Output('graph2', 'figure'), Input('intermediate-value', 'data')) def update_graph_2(jsonified_cleaned_data): datasets = json.loads(jsonified_cleaned_data) dff = pd.read_json(datasets['df_2'], orient='split') figure = create_figure_2(dff) return figure

@app.callback( Output('graph3', 'figure'), Input('intermediate-value', 'data')) def update_graph_3(jsonified_cleaned_data): datasets = json.loads(jsonified_cleaned_data) dff = pd.read_json(datasets['df_3'], orient='split') figure = create_figure_3(dff) return figure

absl-py==1.2.0 alembic==1.8.1 altair==4.2.0 astor==0.8.1 asttokens==2.0.8 astunparse==1.6.3 attrs==22.1.0 autograd==1.4 autograd-gamma==0.5.0 autopage==0.5.1 backcall==0.2.0 bayesian-optimization==1.2.0 beautifulsoup4==4.11.1 bob==11.1.1 Brotli==1.0.9 cachetools==5.2.0 catboost==1.0.6 category-encoders==2.5.0 certifi==2022.6.15 charset-normalizer==2.1.1 click==8.1.3 cliff==4.0.0 cmaes==0.8.2 cmd2==2.4.2 colorama==0.4.5 colorlog==6.6.0 cycler==0.11.0 Cython==0.29.28 dash==2.6.1 dash-core-components==2.0.0 dash-html-components==2.0.0 dash-table==5.0.0 decorator==5.1.1 distlib==0.3.5 eli5==0.13.0 entrypoints==0.4 ephem==4.1.3 executing==0.10.0 filelock==3.8.0 Flask==2.2.2 Flask-Compress==1.12 flatbuffers==1.12 fonttools==4.36.0 formulaic==0.4.0 future==0.18.2 gast==0.4.0 gensim==4.2.0 google==3.0.0 google-api-core==2.8.2 google-auth==2.11.0 google-auth-oauthlib==0.4.6 google-cloud==0.34.0 google-cloud-automl==2.8.1 google-pasta==0.2.0 googleapis-common-protos==1.56.4 graphviz==0.20.1 greenlet==1.1.2 grpcio==1.47.0 grpcio-status==1.47.0 h5py==3.7.0 huggingface-hub==0.8.1 idna==3.3 importlib-metadata==4.12.0 interface-meta==1.3.0 ipython==8.4.0 itsdangerous==2.1.2 jedi==0.18.1 Jinja2==3.1.2 joblib==1.1.0 jsonschema==4.14.0 keras==2.9.0 Keras-Preprocessing==1.1.2 kiwisolver==1.4.4 korean-lunar-calendar==0.2.1 libclang==14.0.6 lifelines==0.27.1 lightgbm==3.3.2 llvmlite==0.39.0 Mako==1.2.1 Markdown==3.4.1 MarkupSafe==2.1.1 matplotlib==3.5.3 matplotlib-inline==0.1.6 missingno==0.5.1 missingpy==0.2.0 mpmath==1.2.1 networkx==2.8.6 ngboost==0.3.12 numba==0.56.0 numpy==1.22.4 oauthlib==3.2.0 opt-einsum==3.3.0 optuna==2.10.1 packaging==21.3 pandas==1.4.3 parso==0.8.3 patsy==0.5.2 pbr==5.10.0 pickleshare==0.7.5 Pillow==9.2.0 pipeline==0.1.0 pipenv==2022.8.19 platformdirs==2.5.2 plotly @ file:///C:/Users/Administrator/Downloads/plotly-5.10.0-py2.py3-none-any.whl prettytable==3.3.0 prompt-toolkit==3.0.30 proto-plus==1.22.0 protobuf==3.19.4 pure-eval==0.2.2 pyasn1==0.4.8 pyasn1-modules==0.2.8 PyBrain==0.3 pydot==1.4.2 Pygments==2.13.0 PyMeeus==0.5.11 PyMySQL==1.0.2 pyod==1.0.4 pyparsing==3.0.9 pyperclip==1.8.2 pyreadline3==3.4.1 pyrsistent==0.18.1 python-dateutil==2.8.2 python-math==0.0.1 pytz==2022.2.1 PyYAML==6.0 ramp==0.1.4 regex==2022.8.17 requests==2.28.1 requests-oauthlib==1.3.1 rsa==4.9 scikit-learn==1.1.2 scipy==1.9.0 seaborn==0.11.2 setuptools-git==1.2 six==1.16.0 slicer==0.0.7 smart-open==6.1.0 soupsieve==2.3.2.post1 SQLAlchemy==1.4.40 stack-data==0.4.0 stats==0.1.2a0 statsmodels==0.13.2 stevedore==4.0.0 sympy==1.10.1 tabulate==0.8.10 tenacity==8.0.1 tensorboard==2.9.1 tensorboard-data-server==0.6.1 tensorboard-plugin-wit==1.8.1 tensorflow==2.9.1 tensorflow-estimator==2.9.0 tensorflow-io-gcs-filesystem==0.26.0 termcolor==1.1.0 Theano==1.0.5 threadpoolctl==3.1.0 tokenizers==0.12.1 toolz==0.12.0 torch==1.12.1 tqdm==4.64.0 traitlets==5.3.0 transformers==4.21.1 typing_extensions==4.3.0 urllib3==1.26.12 virtualenv==20.16.3 virtualenv-clone==0.5.7 wcwidth==0.2.5 Werkzeug==2.2.2 wrapt==1.14.1 xgboost==1.6.2 zipp==3.8.1