Python 对于df列中的i ], data=df.to_dict（“记录”）， 可编辑=假， 过滤器\u action=“native”， sort_action=“native”， sort_mode=“multi”， 列_可选=“单个”， 行_可选=“多”， 行

对于df列中的i ], data=df.to_dict（“记录”）， 可编辑=假， 过滤器\u action=“native”， sort_action=“native”， sort_mode=“multi”， 列_可选=“单个”， 行_可选=“多”， 行_deletable=True， 选定的_列=[]， 选定的_行=[]， page_action=“native”， 页面_当前=0， 页面大小=10， 花式细胞={ “空白”：“正常”， “高度”：“自动”， }, ) #初始化应用程序 app=dash.dash（_名称__） app.title='点图演示-所有实体' app.layout=html.Div([ #可修改表 容器([ dbc.行( [ dbc.Col（locs，宽度=4）， dbc.Col（猫，宽度=4）， dbc.Col（日期拾取，宽度=4） ] ), dbc.行( 桌子 ), ]，style={'backgroundColor'：'#faf9f9'}）， #由修改表生成的图 Div（dcc.Graph（id='key\u Graph\u l'）） ], 样式={'backgroundColor'：'#faf9f9'}） #添加回调函数 @app.callback( 输出（'datatable'，'data'）， [ 输入（'choose_cat'，'value'）， 输入（'选择位置'，'值'）， 输入（'选择日期'，'开始日期'）， 输入（'选择日期'，'结束日期'） ] ) def更新表格（选择目录、位置、开始日期、结束日期）： df=ti_data.copy（） #df['date']=pd.to_datetime（df['date']） #df['date']=df['date'].dt.date 如果choose_cat不是无： df=df[df['category']==选择\u cat] 如果choose_loc不是无： df=df[df['locale']==选择位置] 如果开始日期不是无，结束日期不是无： #开始日期=datetime.datetime.strtime（开始日期，“%Y-%m-%dT00:00:00”）.date（） #end_date=datetime.datetime.strtime（end_date，“%Y-%m-%dT00:00:00”）.date（） df=df.loc[（df.date>=开始日期）和（df.date=开始日期）和（df.date

# Importing needed libraries
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
import dash_table
from dash.dependencies import Input, Output
import plotly.express as px
import pandas as pd
import random
import datetime
import plotly.graph_objects as go

# Reading the dataframe
df = pd.read_csv('./beanstalk_app/ti_data_ti_dash_v1.csv')

categories = df['category']
locales = df['locale']

# clean dataframe
ti_data = df.copy()
ti_data = ti_data.sample(1000)

# Contents of the app
# 1.Controls
# 1.1 Dropdown list

locs = dcc.Dropdown(id='choose_loc',
                    options=[
                        {
                            'label': locale, 'value': locale
                        } for locale in locales.unique()
                    ],
                    placeholder='Select Locale')

cats = dcc.Dropdown(id='choose_cat',
                    options=[
                        {
                            'label': c, 'value': c
                        } for c in categories.unique()
                    ],
                    placeholder='Select Category')


# 1.2 DatePicker
datepick = dcc.DatePickerRange(
    id='choose_date',
    min_date_allowed=df.date.min(),
    max_date_allowed=df.date.max(),
    initial_visible_month=df.date.min(),
    clearable=True,
    start_date=df.date.min(),
    end_date=df.date.max()
)


# 2.Interactive table
table = dash_table.DataTable(
    id='datatable',
    columns=[
            {"name": i, "id": i, "selectable": True, "presentation": "markdown"}
            if i == "google_query"
            else {"name": i, "id": i, "selectable": True}
            for i in df.columns
        ],
    data=df.to_dict('records'),
    editable=False,
    filter_action="native",
    sort_action="native",
    sort_mode="multi",
    column_selectable="single",
    row_selectable="multi",
    row_deletable=True,
    selected_columns=[],
    selected_rows=[],
    page_action="native",
    page_current=0,
    page_size=10,
    style_cell={
        'whiteSpace': 'normal',
        'height': 'auto',
    },

)

# Initialize the app

app = dash.Dash(__name__)
app.title = 'Dot Graph Demo - All Entities'
app.layout = html.Div([
    # modifiable table
    dbc.Container([
        dbc.Row(
            [
                dbc.Col(locs, width=4),
                dbc.Col(cats, width=4),
                dbc.Col(datepick, width=4)
            ]
        ),
        dbc.Row(
            table
        ),
    ], style={'backgroundColor': '#faf9f9'}),
    # graphs resulting from modified table
    html.Div(dcc.Graph(id='key_graph_l'))
],
style={'backgroundColor': '#faf9f9'})


# add callback functions
@app.callback(
    Output('datatable', 'data'),
    [
        Input('choose_cat', 'value'),
        Input('choose_loc', 'value'),
        Input('choose_date', 'start_date'),
        Input('choose_date', 'end_date')
    ]
)
def update_table(choose_cat, choose_loc, start_date, end_date):
    df = ti_data.copy()
    df['date'] = pd.to_datetime(df['date'])
    df['date'] = df['date'].dt.date
    if choose_cat is not None:
        df = df[df['category'] == choose_cat]
    if choose_loc is not None:
        df = df[df['locale'] == choose_loc]
    if start_date is not None and end_date is not None:
        start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date()
        end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date()
        df = df.loc[(df.date >= start_date) & (df.date <= end_date)]
    data = df.to_dict('records')
    return data


@app.callback(
    Output('datatable', 'style_data_conditional'),
    Input('datatable', 'selected_columns')
)
def update_styles(selected_columns):
    return [{
        'if': {'column_id': column},
        'background_color': '#D2F3FF'
    } for column in selected_columns]

@app.callback(
    Output('key_graph_l', 'figure'),
    [
        Input('choose_date', 'start_date'),
        Input('choose_date', 'end_date'),
        Input('choose_cat', 'value'),
        Input('choose_loc', 'value')
    ]
)

def update_key_l(choose_loc, choose_cat, start_date, end_date):
    df = ti_data.copy()
    if choose_cat is not None:
        df = df[df['category'] == choose_cat]
    if choose_loc is not None:
        df = df[df['locale'] == choose_loc]
    if start_date is not None and end_date is not None:
        df = df.loc[(df.date >= start_date) & (df.date <= end_date)]
        df = df.sort_values('total_count', ascending=False)[:25]
        figure = px.scatter(df, x='keyword', y='date', color='locale', size='total_count',labels={'keyword': 'Keyword', 'total_count': 'Total Count', 'locale': 'Locale', 'date': 'Date'},
                            title='Weighing top words accross locales')

    return figure


# requirements for beanstalk (aws)
if __name__ == '__main__':
    app.run_server(debug=True)

# Importing needed libraries
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
import dash_table
from dash.dependencies import Input, Output
import plotly.express as px
import pandas as pd
import random
import datetime
import plotly.graph_objects as go
from jupyter_dash import JupyterDash

# Reading the dataframe
df = pd.read_csv('./beanstalk_app/ti_data_ti_dash_v1.csv')

categories = df['category']
locales = df['locale']

# clean dataframe
ti_data = df.copy()
ti_data = ti_data.sample(4) # sample data

# Contents of the app
# 1.Controls
# 1.1 Dropdown list

locs = dcc.Dropdown(id='choose_loc',
                    options=[
                        {
                            'label': locale, 'value': locale
                        } for locale in locales.unique()
                    ],
                    placeholder='Select Locale')

cats = dcc.Dropdown(id='choose_cat',
                    options=[
                        {
                            'label': c, 'value': c
                        } for c in categories.unique()
                    ],
                    placeholder='Select Category')


# 1.2 DatePicker
datepick = dcc.DatePickerRange(
    id='choose_date',
    min_date_allowed=df.date.min(),
    max_date_allowed=df.date.max(),
    initial_visible_month=df.date.min(),
    clearable=True,
    start_date=df.date.min(),
    end_date=df.date.max()
)


# 2.Interactive table
table = dash_table.DataTable(
    id='datatable',
    columns=[
            {"name": i, "id": i, "selectable": True, "presentation": "markdown"}
            if i == "google_query"
            else {"name": i, "id": i, "selectable": True}
            for i in df.columns
        ],
    data=df.to_dict('records'),
    editable=False,
    filter_action="native",
    sort_action="native",
    sort_mode="multi",
    column_selectable="single",
    row_selectable="multi",
    row_deletable=True,
    selected_columns=[],
    selected_rows=[],
    page_action="native",
    page_current=0,
    page_size=10,
    style_cell={
        'whiteSpace': 'normal',
        'height': 'auto',
    },

)

# Initialize the app
app = dash.Dash(__name__)

app.title = 'Dot Graph Demo - All Entities'
app.layout = html.Div([
    # modifiable table
    dbc.Container([
        dbc.Row(
            [
                dbc.Col(locs, width=4),
                dbc.Col(cats, width=4),
                dbc.Col(datepick, width=4)
            ]
        ),
        dbc.Row(
            table
        ),
    ], style={'backgroundColor': '#faf9f9'}),
    # graphs resulting from modified table
    html.Div(dcc.Graph(id='key_graph_l'))
],
style={'backgroundColor': '#faf9f9'})


# add callback functions
@app.callback(
    Output('datatable', 'data'),
    [
        Input('choose_cat', 'value'),
        Input('choose_loc', 'value'),
        Input('choose_date', 'start_date'),
        Input('choose_date', 'end_date')
    ]
)
def update_table(choose_cat, choose_loc, start_date, end_date):
    df = ti_data.copy()
    # df['date'] = pd.to_datetime(df['date'])
    # df['date'] = df['date'].dt.date
    if choose_cat is not None:
        df = df[df['category'] == choose_cat]
    if choose_loc is not None:
        df = df[df['locale'] == choose_loc]
    if start_date is not None and end_date is not None:
        # start_date = datetime.datetime.strptime(start_date, "%Y-%m-%dT00:00:00").date()
        # end_date = datetime.datetime.strptime(end_date, "%Y-%m-%dT00:00:00").date()
        df = df.loc[(df.date >= start_date) & (df.date <= end_date)]
    data = df.to_dict('records')
    return data


@app.callback(
    Output('datatable', 'style_data_conditional'),
    Input('datatable', 'selected_columns')
)
def update_styles(selected_columns):
    return [{
        'if': {'column_id': column},
        'background_color': '#D2F3FF'
    } for column in selected_columns]

@app.callback(
    Output('key_graph_l', 'figure'),
    [
        Input('choose_cat', 'value'),
        Input('choose_loc', 'value'),
        Input('choose_date', 'start_date'),
        Input('choose_date', 'end_date')
    ]
)

def update_key_l(choose_cat, choose_loc, start_date, end_date):
    df = ti_data.copy()
    if choose_cat is not None:
        df = df[df['category'] == choose_cat]
    if choose_loc is not None:
        df = df[df['locale'] == choose_loc]
    if start_date is not None and end_date is not None:
        df = df.loc[(df.date >= start_date) & (df.date <= end_date)]
        df = df.sort_values('total_count', ascending=False)[:25]
        figure = px.scatter(df, x='keyword', y='date', color='locale', size='total_count',labels={'keyword': 'Keyword', 'total_count': 'Total Count', 'locale': 'Locale', 'date': 'Date'},
                            title='Weighing top words accross locales')

    return figure


# requirements for beanstalk (aws)
if __name__ == '__main__':
    app.run_server(debug=True)