Styjun

I have a set of dataframes that represent scenarios of demand that I have put into a dictionary. I need to loop through each dataframe in the dictionary to reindex and resample etc. and the return to the dictionary. The below code works perfectly when I loop through a list of dataframes but I need to maintain the identity of each scenario, hence the dictionary.

This is the code that works with a list of dataframes:

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)

This is what I have tried in dictionary form:

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

FOLLOW UP QUESTION
I passed the above demand_dict dictionary into an xarray using the below:

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

However my dataset looks like the following:

<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

When I try and use the drop_dims function like so:

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

I get the error:

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

Any idea why I am getting this error?

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() return the key of the dictionary and the value