# Data formats 2 - CSV files¶

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There can be various formats for tabular data, among which you surely know Excel (.xls or .xslx). Unfortunately, if you want to programmatically process data, you should better avoid them and prefer if possible the CSV format, literally ‘Comma Separated Value’. Why? Excel format is very complex and may hide several things which have nothing to do with the raw data:

• formatting (bold fonts, colors …)

• merged cells

• formulas

• multiple tabs

• macros

Correctly parsing complex files may become a nightmare. Instead, CSVs are far simpler, so much so you can even open them witha simple text editor.

We will try to open some CSV, taking into consideration the possible problems we might get. CSVs are not necessarily the perfect solution for everything, but they offer more control over reading and typically if there are conversion problems is because we made a mistake, and not because the reader module decided on its own to exchange days with months in dates.

### Why parsing a CSV ?¶

To load and process CSVs there exist many powerful and intuitive modules such as Pandas in Python or R dataframes. Yet, in this notebook we will load CSVs using the most simple method possible, that is reading row by row, mimicking the method already seen in the previous part of the tutorial. Don’t think this method is primitive or stupid, according to the situation it may save the day. How? Some files may potentially occupy huge amounts of memory, and in moder laptops as of 2019 we only have 4 gigabytes of RAM, the memory where Python stores variables. Given this, Python base functions to read files try their best to avoid loading everything in RAM. Tyipcally a file is read sequentially one piece at a time, putting in RAM only one row at a time.

QUESTION 2.1: if we want to know if a given file of 1000 terabytes contains only 3 million rows in which the word ‘ciao’ is present, are we obliged to put in RAM all of the rows ?

QUESTION 2.2: What if we wanted to take a 100 terabyte file and create another one by appending to each row of the first one the word ‘ciao’? Should we put in RAM at the same time all the rows of the first file ? What about the rows of second one?

We will start with artifical example CSV. Let’s look at example-1.csv which you can find in the same folder as this Jupyter notebook. It contains animals with their expected lifespan:

animal, lifespan
dog, 12
cat, 14
pelican, 30
squirrel, 6
eagle, 25


We notice right away that the CSV is more structured than files we’ve seen in the previous section

• in the first line there are column names, separated with commas: animal, lifespan

• fields in successive rows are also separated by commas ,: dog, 12

Let’s try now to import this file in Python:

[1]:

import csv
with open('example-1.csv', encoding='utf-8', newline='') as f:

# we create an object 'my_reader' which will take rows from the file

# 'my_reader' is an object considered 'iterable', that is,
# if used in a 'for' will produce a sequnce of rows from csv
# NOTE: here every file row is converted into a list of Python strings !

print('We just read a row !')
print(row)  # prints variable 'row', which is a list of strings
print('')   # prints an empty string, to separate in vertical

We just read a row !
['animal', ' lifespan']

We just read a row !
['dog', '12']

We just read a row !
['cat', '14']

We just read a row !
['pelican', '30']

We just read a row !
['squirrel', '6']

We just read a row !
['eagle', '25']



We immediatly notice from output that example file is being printed, but there are square parrenthesis ( [] ). What do they mean? Those we printed are lists of strings

Let’s analyze what we did:

import csv


Python natively has a module to deal with csv files, which has the intuitive csv name. With this instruction, we just loaded the module.

What happens next? As already did for files with lines before, we open the file in a with block:

with open('example-1.csv', encoding='utf-8', newline='') as f:
print(row)


For now ignore the newline='' and notice how first we specificed the encoding

Once the file is open, in the row

my_reader = csv.reader(f, delimiter=',')


we ask to csv module to create a reader object called my_reader for our file, telling Python that comma is the delimiter for fields.

NOTE: my_reader is the name of the variable we are creating, it could be any name.

This reader object can be exploited as a sort of generator of rows by using a for cycle:

for row in my_reader:
print(row)


In for cycle we employ lettore to iterate in the reading of the file, producing at each iteration a row we call row (but it could be any name we like). At each iteration, the variable row gets printed.

If you look closely the prints of first lists, you will see that each time to each row is assigned only one Python list. The list contains as many elements as the number of fields in the CSV.

✪ EXERCISE 2.3: Rewrite in the cell below the instructions to read and print the CSV, paying attention to indentation:

Show solution
[2]:

#jupman-ourge-output
# write here


We just read a row !
['animal', ' lifespan']

We just read a row !
['dog', '12']

We just read a row !
['cat', '14']

We just read a row !
['pelican', '30']

We just read a row !
['squirrel', '6']

We just read a row !
['eagle', '25']



✪✪ EXERCISE 2.4: try to put into big_list a list containing all the rows extracted from the file, which will be a list of lists like so:

[['eagle', 'lifespan'],
['dog', '12'],
['cat', '14'],
['pelican', '30'],
['squirrel', '6'],
['eagle', '25']]


HINT: Try creating an empty list and then adding elements with .append method

Show solution
[3]:

#jupman-ourge-output
# write here


[['animal', ' lifespan'], ['dog', '12'], ['cat', '14'], ['pelican', '30'], ['squirrel', '6'], ['eagle', '25']]


✪✪ EXERCISE 2.5: You may have noticed that numbers in lists are represented as strings like '12' (note apeces), instead that like Python integer numbers (represented without apeces), 12:

We just read a row!
['dog', '12']


So, by reading the file and using normal for cycles, try to create a new variable big_list like this, which

• has only data, the row with the header is not present

• numbers are represented as proper integers

[['dog', 12],
['cat', 14],
['pelican', 30],
['squirrel', 6],
['eagle', 25]]


HINT 1: to jump a row you can use the instruction next(my_reader)

HINT 2: to convert a string into an integer, you can use for example. int('25')

Show solution
[4]:

#jupman-ourge-output
# write here


[['dog', 12], ['cat', 14], ['pelican', 30], ['squirrel', 6], ['eagle', 25]]


We said that my_reader generates a sequence of rows, and it is iterable. In for cycle, at every cycle we ask to read a new line, which is put into variable row. We might then ask ourselves, what happens if we directly print my_reader, without any for? Will we see a nice list or something else? Let’s try:

[5]:

import csv
with open('example-1.csv', encoding='utf-8', newline='') as f:

<_csv.reader object at 0x7f3a542a0cd0>


This result is quite disappointing

✪ EXERCISE 2.6: you probably found yourself in the same situation when trying to print a sequence generated by a call to range(5): instead of the actual sequence you get a range object. If you want to convert the generator to a list, what should you do?

Show solution
[6]:

# write here


[['animal', ' lifespan'], ['dog', '12'], ['cat', '14'], ['pelican', '30'], ['squirrel', '6'], ['eagle', '25']]


### Consuming a file¶

Not all sequences are the same. From what you’ve seen so far, going through a file in Python looks a lot like iterating a list. Which is very handy, but you need to pay attention to some things. Given that files potentially might occupy terabytes, basic Python functions to load them avoid loading everything into memory and typically a file is read one piece at a time. But if the whole file is loaded into Python environment in one shot, what happens if we try to go through it twice inside the same with ? What happens if we try using it outside with? To find out look at next exercises.

✪ EXERCISE 2.7: taking the solution to previous exercise, try to call print(list(my_reader)) twice, in sequence. Do you get the same output in both occasions?

Show solution
[7]:


# write here



✪ EXERCISE 2.8: Taking the solution from previous exercise (using only one print), try down here to move the print to the left (removing any spaces). Does it still work ?

Show solution
[8]:

# write here



✪✪ EXERCISE 2.9: Now that we understood which kind of beast my_reader is, try to produce this result as done before, but using a list comprehension instead of the for:

[['dog', 12],
['cat', 14],
['pelican', 30],
['squirrel', 6],
['eagle', 25]]

• If you can, try also to write the whole transformation to create big_list in one row, usinf the function itertools.islice to jump the header (for example itertools.islice(['A', 'B', 'C', 'D', 'E'], 2, None) first two elements and produces the sequence C D E F G - in our case the elements produced by my_reader would be rows)

Show solution
[9]:

#jupman-ourge-output
import csv
import itertools
with open('example-1.csv', encoding='utf-8', newline='') as f:
# write here


[['dog', 12], ['cat', 14], ['pelican', 30], ['squirrel', 6], ['eagle', 25]]


✪ EXERCISE 2.10: Create a file my-example.csv in the same folder where this Jupyter notebook is, and copy inside the content of the file example-1.csv. Then add a column description, remembering to separate the column name from the preceding one with a comma. As column values, put into successive rows strings like dogs walk, pelicans fly, etc according to the animal, remembering to separate them from lifespan using a comma, like this:

dog,12,dogs walk

After this, copy and paste down here the Python code to load the file, putting the file name my-example.csv, and try to load everything, just to check everything is working:

[10]:

# write here



✪ EXERCISE 2.11: Not every CSV is structured in the same way, sometimes when we write csvs or import them some tweak is necessary. Let’s see which problems may arise:

• In the file, try to put one or two spaces before numbers, for example write down here and look what happens

dog, 12,dogs fly


QUESTION 2.11.1: Does the space get imported?

QUESTION 2.11.2: if we convert to integer, is the space a problem?

QUESTION 2.11.3 Modify only dogs description from dogs walk to dogs walk, but don't fly and try to riexecute the cell which opens the file. What happens?

QUESTION 2.11.4: To overcome previous problem, a solution you can adopt in CSVs is to round strings containing commas with double quotes, like this: "dogs walk, but don't fly". Does it work ?

To read a CSV, instead of getting lists, you may more conveniently get dictionaries in the form of OrderedDicts

NOTE: different Python versions give different dictionaries:

• $$<$$ 3.6: dict

• 3.6, 3.7: OrderedDict

• $$\geq$$ 3.8: dict

Python 3.8 returned to old dict because in the implementation of its dictionariesthe key order is guaranteed, so it will be consistent with the one of CSV headers

[11]:

import csv
with open('example-1.csv', encoding='utf-8', newline='') as f:
print(d)

OrderedDict([('animal', 'dog'), (' lifespan', '12')])
OrderedDict([('animal', 'cat'), (' lifespan', '14')])
OrderedDict([('animal', 'pelican'), (' lifespan', '30')])
OrderedDict([('animal', 'squirrel'), (' lifespan', '6')])
OrderedDict([('animal', 'eagle'), (' lifespan', '25')])


## Writing a CSV¶

You can easily create a CSV by instantiating a writer object:

ATTENTION: BE SURE TO WRITE IN THE CORRECT FILE!

If you don’t pay attention to file names, you risk deleting data !

[12]:

import csv

# To write, REMEMBER to specify the w option.
# WARNING: 'w' *completely* replaces existing files !!
with open('written-file.csv', 'w', newline='') as csvfile_out:

my_writer = csv.writer(csvfile_out, delimiter=',')

my_writer.writerow(['some', 'example', 'data'])
my_writer.writerow(['some', 'other', 'example data'])


## Reading and writing a CSV¶

To create a copy of an existing CSV, you may nest a with for writing inside another for reading:

ATTENTION: CAREFUL NOT TO SWAP FILE NAMES!

When we read and write it’s easy to make mistakes and accidentally overwrite our precious data.

To avoid issues:

• use explicit names both for output files (es: example-1-enriched.csv and handles (i.e. csvfile_out)

• always check before carelessly executing code you just wrote !

[13]:

import csv

# To write, REMEMBER to specify the w option.
# WARNING: 'w' *completely* replaces existing files !!
# WARNING: handle here  is called *csvfile_out*
with open('example-1-enriched.csv', 'w', encoding='utf-8', newline='') as csvfile_out:
my_writer = csv.writer(csvfile_out, delimiter=',')

# Notice how this 'with' is *inside* the outer one:
# WARNING: handle here is called *csvfile_in*
with open('example-1.csv', encoding='utf-8', newline='') as csvfile_in:

row.append('something else')
my_writer.writerow(row)
my_writer.writerow(row)
my_writer.writerow(row)


Let’s see the new file was actually created by reading it:

[14]:

with open('example-1-enriched.csv', encoding='utf-8', newline='') as csvfile_in:

print(row)

['animal', ' lifespan', 'something else']
['animal', ' lifespan', 'something else']
['animal', ' lifespan', 'something else']
['dog', '12', 'something else']
['dog', '12', 'something else']
['dog', '12', 'something else']
['cat', '14', 'something else']
['cat', '14', 'something else']
['cat', '14', 'something else']
['pelican', '30', 'something else']
['pelican', '30', 'something else']
['pelican', '30', 'something else']
['squirrel', '6', 'something else']
['squirrel', '6', 'something else']
['squirrel', '6', 'something else']
['eagle', '25', 'something else']
['eagle', '25', 'something else']
['eagle', '25', 'something else']


## CSV Botteghe storiche¶

Usually in open data catalogs like the popular CKAN platform (for example dati.trentino.it, data.gov.uk, European data portal run instances of CKAN) files are organized in datasets, which are collections of resources: each resource directly contains a file inside the catalog (typically CSV, JSON or XML) or a link to the real file located in a server belonging to the organizazion which created the data.

The first dataset we wil look at will be ‘Botteghe storiche del Trentino’:

https://dati.trentino.it/dataset/botteghe-storiche-del-trentino

Here you will find some generic information about the dataset, of importance note the data provider: Provincia Autonoma di Trento and the license Creative Commons Attribution v4.0, which basically allows any reuse provided you cite the author.

Inside the dataset page, there is a resource called ‘Botteghe storiche’

https://dati.trentino.it/dataset/botteghe-storiche-del-trentino/resource/43fc327e-99b4-4fb8-833c-1807b5ef1d90

At the resource page, we find a link to the CSV file (you can also find it by clicking on the blue button ‘Go to the resource’):

http://www.commercio.provincia.tn.it/binary/pat_commercio/valorizzazione_luoghi_storici/Albo_botteghe_storiche_in_ordine_iscrizione_9_5_2019.1557403385.csv

Accordingly to the browser and operating system you have, by clicking on the link above you might get different results. In our case, on browser Firefox and operating system Linux we get (here we only show first 10 rows):

Numero,Insegna,Indirizzo,Civico,Comune,Cap,Frazione/LocalitÃ ,Note
1,BAZZANELLA RENATA,Via del Lagorai,30,Sover,38068,Piscine di Sover,"generi misti, bar - ristorante"
2,CONFEZIONI MONTIBELLER S.R.L.,Corso Ausugum,48,Borgo Valsugana,38051,,esercizio commerciale
3,FOTOGRAFICA TRINTINAGLIA UMBERTO S.N.C.,Largo Dordi,8,Borgo Valsugana,38051,,"esercizio commerciale, attivitÃ  artigianale"
4,BAR SERAFINI DI MINATI RENZO,,24,Grigno,38055,Serafini,esercizio commerciale
6,SEMBENINI GINO & FIGLI S.R.L.,Via S. Francesco,35,Riva del Garda,38066,,
7,HOTEL RISTORANTE PIZZERIA â€œALLA NAVEâ€,Via Nazionale,29,Lavis,38015,Nave San Felice,
8,OBRELLI GIOIELLERIA DAL 1929 S.R.L.,Via Roma,33,Lavis,38015,,
9,MACELLERIE TROIER S.A.S. DI TROIER DARIO E C.,Via Roma,13,Lavis,38015,,
10,NARDELLI TIZIANO,Piazza Manci,5,Lavis,38015,,esercizio commerciale


As expected, values are separated with commas.

### Problem: wrong characters ??¶

You can suddenly discover a problem in the first row of headers, in the column Frazione/LocalitÃ. It seems last character is wrong, in italian it should show accented like à. Is it truly a problem of the file ? Not really. Probably, the server is not telling Firefox which encoding is the correct one for the file. Firefox is not magical, and tries its best to show the CSV on the base of the info it has, which may be limited and / or even wrong. World is never like we would like it to be …

✪ 2.12 EXERCISE: download the CSV, and try opening it in Excel and / or LibreOffice Calc. Do you see a correct accented character? If not, try to set the encoding to ‘Unicode (UTF-8)’ (in Calc is called ‘Character set’).

WARNING: CAREFUL IF YOU USE Excel!

By clicking directly on File->Open in Excel, probably Excel will try to guess on its own how to put the CSV in a table, and will make the mistake to place everything in a column. To avoid the problem, we have to tell Excel to show a panel to ask us how we want to open the CSV, by doing like so:

• In old Excels, find File-> Import

• In recent Excels, click on tab Data and then select From text. For further information, see copytrans guide

• NOTE: If the file is not available, in the folder where this notebook is you will find the same file renamed to botteghe-storiche.csv

We should get a table like this. Notice how the Frazione/Località header displays with the right accent because we selected Character set: Unicode (UTF-8) which is the appropriate one for this dataset:

### Botteghe storiche in Python¶

Now that we understood a couple of things about encoding, let’s try to import the file in Python.

If we load in Python the first 5 entries with a csv DictReader and print them we should see something like this:

OrderedDict([('Numero', '1'),
('Insegna', 'BAZZANELLA RENATA'),
('Indirizzo', 'Via del Lagorai'),
('Civico', '30'),
('Comune', 'Sover'),
('Cap', '38068'),
('Frazione/Località', 'Piscine di Sover'),
('Note', 'generi misti, bar - ristorante')]),
OrderedDict([('Numero', '2'),
('Insegna', 'CONFEZIONI MONTIBELLER S.R.L.'),
('Indirizzo', 'Corso Ausugum'),
('Civico', '48'),
('Comune', 'Borgo Valsugana'),
('Cap', '38051'),
('Frazione/Località', ''),
('Note', 'esercizio commerciale')]),
OrderedDict([('Numero', '3'),
('Insegna', 'FOTOGRAFICA TRINTINAGLIA UMBERTO S.N.C.'),
('Indirizzo', 'Largo Dordi'),
('Civico', '8'),
('Comune', 'Borgo Valsugana'),
('Cap', '38051'),
('Frazione/Località', ''),
('Note', 'esercizio commerciale, attività artigianale')]),
OrderedDict([('Numero', '4'),
('Insegna', 'BAR SERAFINI DI MINATI RENZO'),
('Indirizzo', ''),
('Civico', '24'),
('Comune', 'Grigno'),
('Cap', '38055'),
('Frazione/Località', 'Serafini'),
('Note', 'esercizio commerciale')]),
OrderedDict([('Numero', '6'),
('Insegna', 'SEMBENINI GINO & FIGLI S.R.L.'),
('Indirizzo', 'Via S. Francesco'),
('Civico', '35'),
('Comune', 'Riva del Garda'),
('Cap', '38066'),
('Frazione/Località', ''),
('Note', '')])


We would like to know which different categories of bottega there are, and count them. Unfortunately, there is no specific field for Categoria, so we will need to extract this information from other fields such as Insegna and Note. For example, this Insegna contains the category BAR, while the Note (commercial enterprise) is a bit too generic to be useful:

'Insegna': 'BAR SERAFINI DI MINATI RENZO',
'Note': 'esercizio commerciale',


while this other Insegna contains just the owner name and Note holds both the categories bar and ristorante:

'Insegna': 'BAZZANELLA RENATA',
'Note': 'generi misti, bar - ristorante',


As you see, data is non uniform:

• sometimes the category is in the Insegna

• sometimes is in the Note

• sometimes is in both

• sometimes is lowercase

• sometimes is uppercase

• sometimes is single

• sometimes is multiple (bar - ristorante)

First we want to extract all categories we can find, and rank them according their frequency, from most frequent to least frequent.

To do so, you need to

• count all words you can find in both Insegna and Note fields, and sort them. Note you need to normalize the uppercase.

• consider a category relevant if it is present at least 11 times in the dataset.

• filter non relevant words: some words like prepositions, type of company ('S.N.C', S.R.L., ..), etc will appear a lot, and will need to be ignored. To detect them, you are given a list called stopwords.

NOTE: the rules above do not actually extract all the categories, for the sake of the exercise we only keep the most frequent ones.

To know how to proceed, read the following.

### Botteghe storiche - rank_categories¶

Load the file with csv.DictReader and while you are loading it, calculate the words as described above. Afterwards, return a list of words with their frequencies.

Do not load the whole file into memory, just process one dictionary at a time and update statistics accordingly.

Expected output:

[('BAR', 191),
('RISTORANTE', 150),
('HOTEL', 67),
('ALBERGO', 64),
('MACELLERIA', 27),
('PANIFICIO', 22),
('CALZATURE', 21),
('FARMACIA', 21),
('ALIMENTARI', 20),
('PIZZERIA', 16),
('SPORT', 16),
('TABACCHI', 12),
('FERRAMENTA', 12),
('BAZAR', 11)]

Show solution
[15]:

def rank_categories(stopwords):
raise Exception('TODO IMPLEMENT ME !')

stopwords = ['',
'S.N.C.', 'SNC','S.A.S.', 'S.R.L.', 'S.C.A.R.L.', 'SCARL','S.A.S', 'COMMERCIALE','FAMIGLIA','COOPERATIVA',
'-', '&', 'C.', 'ESERCIZIO',
'IL', 'DE', 'DI','A', 'DA', 'E', 'LA', 'AL',  'DEL', 'ALLA', ]
categories = rank_categories(stopwords)

categories

[15]:

[('BAR', 191),
('RISTORANTE', 150),
('HOTEL', 67),
('ALBERGO', 64),
('MACELLERIA', 27),
('PANIFICIO', 22),
('CALZATURE', 21),
('FARMACIA', 21),
('ALIMENTARI', 20),
('PIZZERIA', 16),
('SPORT', 16),
('TABACCHI', 12),
('FERRAMENTA', 12),
('BAZAR', 11)]


### Botteghe storiche - enrich¶

Once you found the categories, implement function enrich, which takes the db and previously computed categories, and WRITES a NEW file botteghe-enriched.csv where the rows are enriched with a new field Categorie, which holds a list of the categories a particular bottega belongs to.

• Write the new file with a DictWriter, see documentation

The new file should contain rows like this (showing only first 5):

OrderedDict([   ('Numero', '1'),
('Insegna', 'BAZZANELLA RENATA'),
('Indirizzo', 'Via del Lagorai'),
('Civico', '30'),
('Comune', 'Sover'),
('Cap', '38068'),
('Frazione/Località', 'Piscine di Sover'),
('Note', 'generi misti, bar - ristorante'),
('Categorie', "['BAR', 'RISTORANTE']")])
OrderedDict([   ('Numero', '2'),
('Insegna', 'CONFEZIONI MONTIBELLER S.R.L.'),
('Indirizzo', 'Corso Ausugum'),
('Civico', '48'),
('Comune', 'Borgo Valsugana'),
('Cap', '38051'),
('Frazione/Località', ''),
('Note', 'esercizio commerciale'),
('Categorie', '[]')])
OrderedDict([   ('Numero', '3'),
('Insegna', 'FOTOGRAFICA TRINTINAGLIA UMBERTO S.N.C.'),
('Indirizzo', 'Largo Dordi'),
('Civico', '8'),
('Comune', 'Borgo Valsugana'),
('Cap', '38051'),
('Frazione/Località', ''),
('Note', 'esercizio commerciale, attività artigianale'),
('Categorie', '[]')])
OrderedDict([   ('Numero', '4'),
('Insegna', 'BAR SERAFINI DI MINATI RENZO'),
('Indirizzo', ''),
('Civico', '24'),
('Comune', 'Grigno'),
('Cap', '38055'),
('Frazione/Località', 'Serafini'),
('Note', 'esercizio commerciale'),
('Categorie', "['BAR']")])
OrderedDict([   ('Numero', '6'),
('Insegna', 'SEMBENINI GINO & FIGLI S.R.L.'),
('Indirizzo', 'Via S. Francesco'),
('Civico', '35'),
('Comune', 'Riva del Garda'),
('Cap', '38066'),
('Frazione/Località', ''),
('Note', ''),
('Categorie', '[]')])

Show solution
[16]:

def enrich(categories):
raise Exception('TODO IMPLEMENT ME !')

enrich(rank_categories(stopwords))


[17]:

# let's see if we created the file we wanted
# (using botteghe-enriched-solution.csv to avoid polluting your file)

with open('botteghe-enriched-solution.csv', newline='',  encoding='utf-8',) as csvfile_in:
# better to pretty print the OrderedDicts, otherwise we get unreadable output
# for documentation see https://docs.python.org/3/library/pprint.html
import pprint
pp = pprint.PrettyPrinter(indent=4)
for i in range(5):
pp.pprint(d)


OrderedDict([   ('Numero', '1'),
('Insegna', 'BAZZANELLA RENATA'),
('Indirizzo', 'Via del Lagorai'),
('Civico', '30'),
('Comune', 'Sover'),
('Cap', '38068'),
('Frazione/Località', 'Piscine di Sover'),
('Note', 'generi misti, bar - ristorante'),
('Categorie', "['BAR', 'RISTORANTE']")])
OrderedDict([   ('Numero', '2'),
('Insegna', 'CONFEZIONI MONTIBELLER S.R.L.'),
('Indirizzo', 'Corso Ausugum'),
('Civico', '48'),
('Comune', 'Borgo Valsugana'),
('Cap', '38051'),
('Frazione/Località', ''),
('Note', 'esercizio commerciale'),
('Categorie', '[]')])
OrderedDict([   ('Numero', '3'),
('Insegna', 'FOTOGRAFICA TRINTINAGLIA UMBERTO S.N.C.'),
('Indirizzo', 'Largo Dordi'),
('Civico', '8'),
('Comune', 'Borgo Valsugana'),
('Cap', '38051'),
('Frazione/Località', ''),
('Note', 'esercizio commerciale, attività artigianale'),
('Categorie', '[]')])
OrderedDict([   ('Numero', '4'),
('Insegna', 'BAR SERAFINI DI MINATI RENZO'),
('Indirizzo', ''),
('Civico', '24'),
('Comune', 'Grigno'),
('Cap', '38055'),
('Frazione/Località', 'Serafini'),
('Note', 'esercizio commerciale'),
('Categorie', "['BAR']")])
OrderedDict([   ('Numero', '6'),
('Insegna', 'SEMBENINI GINO & FIGLI S.R.L.'),
('Indirizzo', 'Via S. Francesco'),
('Civico', '35'),
('Comune', 'Riva del Garda'),
('Cap', '38066'),
('Frazione/Località', ''),
('Note', ''),
('Categorie', '[]')])


## Continue¶

Go on with JSON format