回答问题

我有一组数据框,代表我放入字典的需求场景。我需要遍历字典中的每个数据帧以重新索引和重新采样等并返回字典。当我遍历数据框列表时,以下代码可以完美运行,但我需要维护每个场景的身份,因此需要维护字典。

这是适用于数据框列表的代码:

demand_dfs_list = [low_demand_df, med_low_demand_df, bc_demand_df, med_high_demand_df, high_demand_df]
dates = pd.date_range(start='2020-10-01', end='2070-09-30', freq='D')

demand_dfs_datetime = []
for df in demand_dfs_list:
    df.index = pd.to_datetime(df.index, format='%Y')
    df = df.tshift(-92, 'D')
    df = df.resample('D').ffill()
    df = df.reindex(dates)
    demand_dfs_datetime.append(df)

这是我以字典形式尝试过的:

demand_scenarios = {'low': low_demand_df, 'medium_low': med_low_demand_df, 'medium': bc_demand_df, 'medium_high': med_high_demand_df, 'high': high_demand_df}
dates = pd.date_range(start='2020-10-01', end='2070-09-30', freq='D')

demand_dict = {}
    for df in demand_scenarios:
        [df].index = pd.to_datetime([df].index, format='%Y')
        [df] = [df].tshift(-92, 'D')
        [df] = [df].resample('D').ffill()
        [df] = [df].reindex(dates)
        demand_dict[df] = df

跟进问题 我使用以下命令将上述需求_dict 字典传递到 xarray 中:

demand_xarray = xr.Dataset(demand_dict, coords = {'customers': customers, 'time': dates})

但是我的数据集如下所示:

<xarray.Dataset>
Dimensions:      (customers: 28, dim_0: 18262, dim_1: 28, time: 18262)
Coordinates:
  * dim_0        (dim_0) datetime64[ns] 2020-10-01 2020-10-02 ... 2070-09-30
  * dim_1        (dim_1) object 'Customer_1' ... 'Customer_x'
  * customers    (customers) <U29 'Customer_1' ... 'Customer_x'
  * time         (time) datetime64[ns] 2020-10-01 2020-10-02 ... 2070-09-30
Data variables:
    low          (dim_0, dim_1) float64 0.52 0.528 3.704 ... 7.744 0.92 64.47
    medium_low   (dim_0, dim_1) float64 0.585 0.594 4.167 ... 8.712 1.035 72.53
    medium       (dim_0, dim_1) float64 0.65 0.66 4.63 12.6 ... 9.68 1.15 80.59
    medium_high  (dim_0, dim_1) float64 0.715 0.726 5.093 ... 10.65 1.265 88.65
    high         (dim_0, dim_1) float64 0.78 0.792 5.556 ... 11.62 1.38 96.71

当我尝试像这样使用 drop_dims 函数时:

demand_xarray = xr.Dataset(demand_dict, coords = {'customers': customers, 'time': dates}).drop_dims(dim_0, dim_1)

我得到错误:

AttributeError: 'Dataset' object has no attribute 'drop_dims'

知道为什么我会收到此错误吗?

Answers

demand_scenarios = {'low': low_demand_df, 'medium_low': med_low_demand_df, 'medium': bc_demand_df, 'medium_high': med_high_demand_df, 'high': high_demand_df}
dates = pd.date_range(start='2020-10-01', end='2070-09-30', freq='D')

demand_dict = {}
    for key, df in demand_scenarios.items():
        df.index = pd.to_datetime([df].index, format='%Y')
        df = df.tshift(-92, 'D')
        df = df.resample('D').ffill()
        df = df.reindex(dates)
        demand_dict[key] = df

items()返回字典的键和值