dict理解嵌套列表以过滤多个变量的值

Question

I have a working example of dict comprehension on a list I iterate over: This generates various indicators (selections), separating the rows of my data into cases (which are not exclusive, by the way).

For context: This is done to count cases for specific rows (criterion defined by a column) when I aggregate the table to some groups. The indicators are collected in separate dataframes now to export separately, though I am also happy to keep all in one dataframe for a single aggregation, concatenation and export, if possible.

Now I want to nest this into another loop. This loop would define which other variable I select/filter for the values. So item 0 would still be the condition itself (sum of the indicator being the count of the cases), but item 1 the selected cases of TKOST (to see a selective sum for separate criteria later), item 2 for another variable I'd now read in.

But it would make sense for this loop to effect the variable names too, eg to have a blank neuro variable for the count (or neuro_count ), a neuro_cost for the sum of TKOST for the neuro cases etc. How is this possible?

The sample code basically comes from Alexander's answer on another question. The file I/O and pandas parts are provided for context.

import pandas as pd

items = {'neuro': 'N', 
         'cardio': 'C', 
         'cancer': 'L', 
         'anesthetics': 'N01', 
         'analgesics': 'N02', 
         'antiepileptics': 'N03', 
         'anti-parkinson drugs': 'N04', 
         'psycholeptics': 'N05', 
         'psychoanaleptics': 'N06', 
         'addiction_and_other_neuro': 'N07', 
         'Adrugs': 'A', 
         'Mdrugs': 'M', 
         'Vdrugs': 'V', 
         'all_drugs': ''}

# Create data containers using dictionary comprehension.
dfs = {item: pd.DataFrame() for item in items.keys()}
monthly_summaries = {item: list() for item in items.keys()}

# Perform monthly groupby operations.
for year in xrange(2005, 2013):
    for month in xrange(1, 13):
        if year == 2005 and month < 7:
            continue
        filename = 'PATH/STUB_' + str(year) + '_mon'+ str(month) +'.txt'
        monthly = pd.read_table(filename,usecols=[0,3,32])
        monthly['year'] = year
        monthly['month'] = month
        dfs = {name: monthly[(monthly.ATC.str.startswith('{0}'.format(code))) 
                             & (~(monthly.TKOST.isnull()))]
                     for name, code in items.iteritems()}
        [monthly_summaries[name].append(dfs[name].groupby(['LopNr','year','month']).sum()
                                        .astype(int, copy=False)) 
         for name in items.keys()]

# Now concatenate all of the monthly summaries into separate DataFrames.
dfs = {name: pd.concat([monthly_summaries[name]], ignore_axis=True) 
       for name in items.keys()}

# Now regroup the aggregate monthly summaries.
monthly_summaries = {name: dfs[name].reset_index().groupby(['LopNr','year','month']).sum()
                    for name in items.keys()}

# Finally, save the aggregated results to files.
[monthly_summaries[name].to_csv('PATH/monthly_{0}_costs.csv'.format(name))
 for name in items()]

Answer 1

You should prefer an explicit for loop:

for name in items.keys():
    monthly_summaries[name].append(dfs[name].groupby(['LopNr','year','month']).sum()
                                            .astype(int, copy=False)

# rather than
[monthly_summaries[name].append(dfs[name].groupby(['LopNr','year','month']).sum()
                                         .astype(int, copy=False)) 
    for name in items.keys()]

The latter creates a dummy list of None s (as well as being less readable) so is less efficient.

The former allows you to nest easily...

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But it would make sense for this loop to effect the variable names too, eg to have a blank neuro variable for the count (or neuro_count), a neuro_cost for the sum of TKOST for the neuro cases etc. How is this possible?

I usually add columns to do these counts, that way it can be vectorized/split/other.
(Then don't write these columns out to csv.)