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df[df['省份'] != 'hubei'].groupby('日期')[['疑似', '确诊', '治愈', '死亡']].sum().reset_index()\n none_hubei['省份'] = '非湖北'\n\n df = pd.concat([df, none_hubei], ignore_index=True)\n\n province_names = {\n 'beijing': '北京',\n 'shanghai': '上海',\n 'tianjin': '天津',\n 'chongqing': '重庆',\n 'xianggang': '香港',\n 'aomen': '澳门',\n 'anhui': '安徽',\n 'fujian': '福建',\n 'guangdong': '广东',\n 'guangxi': '广西',\n 'guizhou': '贵州',\n 'gansu': '甘肃',\n 'hainan': '海南',\n 'hebei': '河北',\n 'henan': '河南',\n 'heilongjiang': '黑龙江',\n 'hubei': '湖北',\n 'hunan': '湖南',\n 'jilin': '吉林',\n 'jiangsu': '江苏',\n 'jiangxi': '江西',\n 'liaoning': '辽宁',\n 'neimenggu': '内蒙古',\n 'ningxia': '宁夏',\n 'qinghai': '青海',\n 'shanxis': '陕西',\n 'shanxi': '山西',\n 'shandong': '山东',\n 'sichuan': '四川',\n 'taiwan': '台湾',\n 'xizang': '西藏',\n 'xinjiang': '新疆',\n 'yunnan': '云南',\n 'zhejiang': '浙江',\n '非湖北': '非湖北',\n }\n df['省份'] = df['省份'].apply(lambda x: province_names[x])\n\n province_df = df.pivot_table(values='确诊', index='日期', columns='省份')\n\n hubei_df = 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# 本代码由可视化策略环境自动生成 2020年2月7日 11:20
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


def m9_run_bigquant_run(input_1, input_2, input_3):
    df = input_1.read()[['日期', '省份', '疑似', '确诊', '治愈', '死亡']]

    none_hubei = df[df['省份'] != 'hubei'].groupby('日期')[['疑似', '确诊', '治愈', '死亡']].sum().reset_index()
    none_hubei['省份'] = '非湖北'

    df = pd.concat([df, none_hubei], ignore_index=True)

    province_names = {
        'beijing': '北京',
        'shanghai': '上海',
        'tianjin': '天津',
        'chongqing': '重庆',
        'xianggang': '香港',
        'aomen': '澳门',
        'anhui': '安徽',
        'fujian': '福建',
        'guangdong': '广东',
        'guangxi': '广西',
        'guizhou': '贵州',
        'gansu': '甘肃',
        'hainan': '海南',
        'hebei': '河北',
        'henan': '河南',
        'heilongjiang': '黑龙江',
        'hubei': '湖北',
        'hunan': '湖南',
        'jilin': '吉林',
        'jiangsu': '江苏',
        'jiangxi': '江西',
        'liaoning': '辽宁',
        'neimenggu': '内蒙古',
        'ningxia': '宁夏',
        'qinghai': '青海',
        'shanxis': '陕西',
        'shanxi': '山西',
        'shandong': '山东',
        'sichuan': '四川',
        'taiwan': '台湾',
        'xizang': '西藏',
        'xinjiang': '新疆',
        'yunnan': '云南',
        'zhejiang': '浙江',
        '非湖北': '非湖北',
    }
    df['省份'] = df['省份'].apply(lambda x: province_names[x])

    province_df = df.pivot_table(values='确诊', index='日期', columns='省份')

    hubei_df = province_df[province_df.columns[province_df.columns.isin({'湖北', '非湖北'})]]
    # hubei_df['湖北增长率'] = hubei_df['湖北'] / hubei_df['湖北'].shift(1) - 1
    # hubei_df['非湖北增长率'] = hubei_df['非湖北'] / hubei_df['非湖北'].shift(1) - 1
    others_df = province_df[province_df.columns[~province_df.columns.isin({'湖北', '非湖北'})]]
    
    data_1 = DataSource.write_df(hubei_df)
    data_2 = DataSource.write_df(others_df)
    return Outputs(data_1=data_1, data_2=data_2)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m9_post_run_bigquant_run(outputs):
    return outputs


m1 = M.use_datasource.v1(
    datasource_id='bigquant-ncov2019',
    start_date='',
    end_date='',
    m_cached=False
)

m5 = M.input_features.v1(
    features="""instrument=province

日期=date
国家=country
省份=province
疑似=sus_num
确诊=con_num
重症=crit_num
死亡=death_num
治愈=cure_num
密切接触=cont_num
医学观察=obse_num
疑似增长=sus_num - shift(sus_num, 1)
确诊增长=con_num - shift(con_num, 1)
疑似增长率=sus_num/shift(sus_num, 1) - 1
确诊增长率=con_num/shift(con_num, 1) - 1
死亡率=死亡/确诊
重症率=重症/确诊
治愈率=治愈/确诊
"""
)

m4 = M.derived_feature_extractor.v3(
    input_data=m1.data,
    features=m5.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={},
    m_cached=False
)

m2 = M.filter.v3(
    input_data=m4.data,
    expr='province == country',
    output_left_data=True,
    m_cached=False
)

m3 = M.plot_dataframe.v1(
    input_data=m2.data,
    title='全国疫情趋势',
    chart_type='line',
    x='日期',
    y='确诊,重症,死亡,治愈,疑似',
    options={
    'chart': {
        'height': 500
    }
},
    candlestick=False,
    pane_1='',
    pane_2='',
    pane_3='',
    pane_4=''
)

m6 = M.plot_dataframe.v1(
    input_data=m2.data,
    title='全国疫情趋势-密切接触者跟踪',
    chart_type='line',
    x='日期',
    y='密切接触,医学观察',
    options={
    'chart': {
        'height': 500
    }
},
    candlestick=False,
    pane_1='',
    pane_2='',
    pane_3='',
    pane_4=''
)

m7 = M.plot_dataframe.v1(
    input_data=m2.data,
    title='全国疫情趋势-增长趋势',
    chart_type='line',
    x='日期',
    y='疑似增长,确诊增长',
    options={
    'chart': {
        'height': 500
    }
},
    candlestick=False,
    pane_1='',
    pane_2='',
    pane_3='',
    pane_4=''
)

m10 = M.plot_dataframe.v1(
    input_data=m2.data,
    title='全国疫情趋势-增长率趋势',
    chart_type='line',
    x='日期',
    y='疑似增长率,确诊增长率',
    options={
    'chart': {
        'height': 500
    }
},
    candlestick=False,
    pane_1='',
    pane_2='',
    pane_3='',
    pane_4=''
)

m8 = M.plot_dataframe.v1(
    input_data=m2.data,
    title='全国疫情趋势-治愈/重症/死亡率',
    chart_type='line',
    x='日期',
    y='重症率,治愈率,死亡率',
    options={
    'chart': {
        'height': 500
    }
},
    candlestick=False,
    pane_1='',
    pane_2='',
    pane_3='',
    pane_4=''
)

m9 = M.cached.v3(
    input_1=m2.left_data,
    run=m9_run_bigquant_run,
    post_run=m9_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports='',
    m_cached=False
)

m11 = M.plot_dataframe.v1(
    input_data=m9.data_1,
    title='湖北/非湖北疫情趋势-确诊',
    chart_type='line',
    x='',
    y='',
    options={
    'chart': {
        'height': 500
    }
},
    candlestick=False,
    pane_1='',
    pane_2='',
    pane_3='',
    pane_4=''
)

m12 = M.plot_dataframe.v1(
    input_data=m9.data_2,
    title='各省(除湖北外)疫情趋势-确诊',
    chart_type='line',
    x='',
    y='',
    options={
    'chart': {
        'height': 500
    }
},
    candlestick=False,
    pane_1='',
    pane_2='',
    pane_3='',
    pane_4=''
)

[2020-02-07 11:20:10.280530] INFO: bigquant: use_datasource.v1 开始运行..

[2020-02-07 11:20:10.288502] INFO: bigquant: use_datasource.v1 运行完成[0.007994s].

[2020-02-07 11:20:10.292666] INFO: bigquant: input_features.v1 开始运行..

[2020-02-07 11:20:10.335567] INFO: bigquant: 命中缓存

[2020-02-07 11:20:10.337933] INFO: bigquant: input_features.v1 运行完成[0.045245s].

[2020-02-07 11:20:10.344300] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2020-02-07 11:20:10.420760] INFO: derived_feature_extractor: 提取完成 instrument=province, 0.001s

[2020-02-07 11:20:10.428530] INFO: derived_feature_extractor: 提取完成 日期=date, 0.002s

[2020-02-07 11:20:10.432308] INFO: derived_feature_extractor: 提取完成 国家=country, 0.002s

[2020-02-07 11:20:10.435565] INFO: derived_feature_extractor: 提取完成 省份=province, 0.001s

[2020-02-07 11:20:10.438231] INFO: derived_feature_extractor: 提取完成 疑似=sus_num, 0.001s

[2020-02-07 11:20:10.441455] INFO: derived_feature_extractor: 提取完成 确诊=con_num, 0.001s

[2020-02-07 11:20:10.443901] INFO: derived_feature_extractor: 提取完成 重症=crit_num, 0.001s

[2020-02-07 11:20:10.446022] INFO: derived_feature_extractor: 提取完成 死亡=death_num, 0.001s

[2020-02-07 11:20:10.448465] INFO: derived_feature_extractor: 提取完成 治愈=cure_num, 0.001s

[2020-02-07 11:20:10.450418] INFO: derived_feature_extractor: 提取完成 密切接触=cont_num, 0.001s

[2020-02-07 11:20:10.452021] INFO: derived_feature_extractor: 提取完成 医学观察=obse_num, 0.001s

[2020-02-07 11:20:10.456655] INFO: derived_feature_extractor: 提取完成 疑似增长=sus_num - shift(sus_num, 1), 0.004s

[2020-02-07 11:20:10.460511] INFO: derived_feature_extractor: 提取完成 确诊增长=con_num - shift(con_num, 1), 0.003s

[2020-02-07 11:20:10.464240] INFO: derived_feature_extractor: 提取完成 疑似增长率=sus_num/shift(sus_num, 1) - 1, 0.003s

[2020-02-07 11:20:10.467946] INFO: derived_feature_extractor: 提取完成 确诊增长率=con_num/shift(con_num, 1) - 1, 0.003s

[2020-02-07 11:20:10.469933] INFO: derived_feature_extractor: 提取完成 死亡率=死亡/确诊, 0.001s

[2020-02-07 11:20:10.472064] INFO: derived_feature_extractor: 提取完成 重症率=重症/确诊, 0.001s

[2020-02-07 11:20:10.474125] INFO: derived_feature_extractor: 提取完成 治愈率=治愈/确诊, 0.001s

[2020-02-07 11:20:10.534048] INFO: derived_feature_extractor: /data, 565

[2020-02-07 11:20:10.611928] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.267641s].

[2020-02-07 11:20:10.618147] INFO: bigquant: filter.v3 开始运行..

[2020-02-07 11:20:10.622661] INFO: filter: 使用表达式 province == country 过滤

[2020-02-07 11:20:10.708826] INFO: filter: 过滤 /data, 27/538/565

[2020-02-07 11:20:10.944857] INFO: bigquant: filter.v3 运行完成[0.326689s].

[2020-02-07 11:20:11.086533] INFO: bigquant: plot_dataframe.v1 运行完成[0.083064s].

[2020-02-07 11:20:11.160027] INFO: bigquant: plot_dataframe.v1 运行完成[0.071309s].

[2020-02-07 11:20:11.227095] INFO: bigquant: plot_dataframe.v1 运行完成[0.065012s].

[2020-02-07 11:20:11.304149] INFO: bigquant: plot_dataframe.v1 运行完成[0.073699s].

[2020-02-07 11:20:11.392193] INFO: bigquant: plot_dataframe.v1 运行完成[0.085553s].

[2020-02-07 11:20:11.400561] INFO: bigquant: cached.v3 开始运行..

[2020-02-07 11:20:11.769207] INFO: bigquant: cached.v3 运行完成[0.368634s].

[2020-02-07 11:20:11.841216] INFO: bigquant: plot_dataframe.v1 运行完成[0.069522s].

[2020-02-07 11:20:11.911342] INFO: bigquant: plot_dataframe.v1 运行完成[0.06714s].