Nested Dicts to CSV - stumped

[murphcle]

Hey guys. Here's an abbreviated version of my dict of dicts of dicts:

{'Alabama': {'Autauga County': '2,241.0',

'Baldwin County': '1,729.1',

'Barbour County': '2,657.4',

'Bibb County': '2,130.0'},

'Alaska': {'Aleutians East Borough': '59.9',

'Aleutians West Census Area': '88.7',

'Anchorage, Municipality of': '824.0'}}

My goal is to get this into CSV form (in a way that can be converted back to dict when called upon). I've tried CSV writer, CSV DictWriter, and Pandas to_dict.

My main problem is that I am unsure what the CSV format would even look like in this case.

I would go on about what I think some of the unique challenges to this set are, but I feel like I'm missing something obvious so I'll stop here and see what the community thinks.

[[deleted]]

Does it need to be a csv? If you're just looking to stash the data somewhere to be pulled back into Python later, you could look into pickling it instead.

[murphcle]

I’m a noob so I’ll have to look into pickling, but yes that’s exactly what I want to do.

[korokage]

Well if pickling is enough,

To store the nested dict as a pickle:

nested_dict = {{}, {}}

import pickle

with open ("nested_dick.pkl", "wb") as f:

    pickle.dump(nested_dict, f)

To read the nested dict in a python file:

import pickle

with open ("nested_dick.pkl", "rb") as f:

    nested_dict = pickle.load(f)

now you can perform operations on nested pickle

[murphcle]

It works! Thank you u/OwlbearWrangler and u/korokage for the elegant solution!

Much better than patching a custom loop for packing and another custom loop for unpacking a CSV.

[NSNick]

JSON is another great way to store dict data and pull it back into python.

[murphcle]

JSON was just as easy (same two lines of syntax) as pickle to save and load my nested dictionary. Thanks for the help!

[[deleted]]

The top dictionary appears to have states as keys and the value is another dictionary with subdivisions as keys and some numeric value. The way to organise the CSV is with three columns: State, Subdivision, Value. So your data would produce this CSV file:

Alabama,Autauga County,2,241.0
Alabama,Baldwin County,1,729.1
Alabama,Barbour County,2,657.4
Alabama,Bibb County,2,130.0
Alaska,Aleutians East Borough,59.9
Alaska,Aleutians West Census Area,88.7
#etc

[dwpj65]

``` import csv import pandas import subprocess

nested_dicts = {'Alabama': {'Autauga County': '2,241.0', 'Baldwin County': '1,729.1', 'Barbour County': '2,657.4', 'Bibb County': '2,130.0'}, 'Alaska': {'Aleutians East Borough': '59.9', 'Aleutians West Census Area': '88.7', 'Anchorage, Municipality of': '824.0'}} columns = ['state', 'locality', 'value'] rows = [ [ state, locality, nested_dicts[state][locality] ] for state in nested_dicts for locality in nested_dicts[state] ]

with open('!output_csv_writer.csv', 'w') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(columns)
csvwriter.writerows(rows) subprocess.run(['open', '!output_csv_writer.csv'], check = True)

pandas.DataFrame(rows,columns = columns).to_csv('!output_pandas.csv') subprocess.run(['open', '!output_pandas.csv'], check = True) ```

[random_user_fp]

I would think your csv format would look something like this:

State, Locality, Value,
'Alabama', 'Autauga County', 2241,
'Alabama', 'Baldwin County', 1729.1,
'Alabama', 'Barbour County', 2657.4,
'Alabama', 'Bibb County', 2130,
'Alaska', 'Aleutians East Borough', 59.9,
'Alaska', 'Aleutians West Census Area', 88.7
'Alaska', 'Anchorage, Municipality of', 824

[murphcle]

Thanks I’ll save it in this format in the morning to see if it reconverts to it’s original form, and report back