CSV files

CSV files#

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, as they are simple text files.

Why parsing a CSV ?#

To load and process CSVs there exist many powerful and intuitive modules such as Polars in Python or R dataframes. 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 (RAM), potentially more than what your computer may have! An advanteg of Python base functions is that they allow to read files sequentially, one line at a time, thus requiring a very limited amount of RAM.

Question

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 ?

Answer

No, it is sufficient to keep in memory one row at a time, and hold the count in another variable

Question

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?

Answer

No, it is enough to keep in RAM one row at a time, which is first read from the first file and then written right away in the second file.

Reading a CSV#

We will start with a simple example CSV. Let’s look at example-1.csv which you can find in the data folder inside the Jupyter notebook’s directory. It contains animals with their expected lifespan:

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

Python natively has a module to deal with csv files, which has the intuitive csv name. Let’s first load the module:

import csv

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:

with open("data/example-1.csv", newline="") as f:
    # we create an object 'my_reader' which will take rows from the file
    my_reader = csv.reader(f, delimiter=",")

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

    for row in my_reader:
        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 immediately 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:

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

with open('data/example-1.csv', newline='') as f:

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 my_reader 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:

What’s a reader ?#

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:

with open("data/example-1.csv", newline="") as f:
    my_reader = csv.reader(f, delimiter=",")
    print(my_reader)

<_csv.reader object at 0x7f092c0d26c0>

This result is quite disappointing

✪ EXERCISE 2.4: 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?

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

✪✪ EXERCISE 2.5: You may have noticed that the numbers in the lists are represented as strings like '12', while Python integer numbers are printed simply as 12:

['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')

print(big_list)

[['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 looking at next exercises.

✪ EXERCISE 2.6: after getting my_reader, try to call print(list(my_reader)) twice, in sequence. Do you get the same output in both occasions?

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

✪ EXERCISE 2.8: try down here to move the print to the left (removing its indentation). Does it still work ?

Exercise 2.9 - Create a new file in the data folder where your notebook is, name it as you will, and add these contents:

animal,lifespan,description
dog,12,dogs walk
cat,14,cats walk
pelican,30,pelicans fly
squirrel,6,squirrels fly
eagle,25,eagles fly

Write a script to read the file.

Question

Then, modify only dogs description from dogs walk to dogs walk, but don't fly and try to rerun the cell which opens the file. What happens?

Answer

Python reads one element more in the list

To overcome this 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".

Reading as dictionaries#

To read a CSV, instead of getting lists, you may more conveniently get dictionaries.

with open('data/example-1.csv', newline='') as f:    
    my_reader = csv.DictReader(f)   # Notice we now used DictReader
    for d in my_reader:
        print(d)

{'animal': 'dog', 'lifespan': '12'}
{'animal': 'cat', 'lifespan': '14'}
{'animal': 'pelican', 'lifespan': '30'}
{'animal': 'squirrel', 'lifespan': '6'}
{'animal': 'eagle', 'lifespan': '25'}

Writing a CSV#

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

Warning

ATTENTION: BE SURE TO WRITE IN THE CORRECT FILE!

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

# To write, REMEMBER to specify the `w` option.
# WARNING: 'w' *completely* replaces existing files !!
with open('data/written-file.csv', 'w', newline='') as csvfile_out: 
    my_writer = csv.writer(csvfile_out, delimiter=',')    
    my_writer.writerow(['This', 'is', 'a header'])
    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:

Warning

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)
backup data to read
always check before carelessly executing code you just wrote !

# WARNING: handle here is called *csvfile_in*
with open('data/example-1.csv', newline='') as csvfile_in:    
    my_reader = csv.reader(csvfile_in, delimiter=',')
    # Notice this 'with' is *inside* the outer one
    # To write, REMEMBER to specify the `w` option.    
    # WARNING 1: handle here  is called *csvfile_out*
    # WARNING 2: 'w' *completely* replaces existing files !!
    with open('data/example-1-enriched.csv', 'w', newline='') as csvfile_out: 
        my_writer = csv.writer(csvfile_out, delimiter=',')              
        
        for row in my_reader:
            row.append('something else')
            my_writer.writerow(row)   

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

with open('data/example-1-enriched.csv', newline='') as csvfile_in:    
    my_reader = csv.reader(csvfile_in, delimiter=',')      

    for row in my_reader:
        print(row)

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

Exercise - air quality#

You will now analyze data about air quality in Trentino. You are given a dataset which records various pollutants (‘Inquinante’) at various stations ('Stazione') in Trentino. Pollutants values can be 'PM10', 'Biossido Zolfo', and a few others. Each station records some set of pollutants. For each pollutant values are recorded ('Valore') 24 times per day.

Data provider: dati.trentino.it

load_air_quality#

Implement a function to load the dataset air-quality.csv

USE encoding latin-1

Tip

Look at the dataset by yourself!

Here we show only first rows, but to get a clear picture of the dataset you need to study it a bit by yourself

Tip

Every field is a string, including 'Valore' !

air_quality = load_air_quality('data/air-quality.csv')
air_quality[:5]

[{'Stazione': 'Parco S. Chiara',
  'Inquinante': 'PM10',
  'Data': '2019-05-04',
  'Ora': '1',
  'Valore': '17',
  'Unità di misura': 'µg/mc'},
 {'Stazione': 'Parco S. Chiara',
  'Inquinante': 'PM10',
  'Data': '2019-05-04',
  'Ora': '2',
  'Valore': '19',
  'Unità di misura': 'µg/mc'},
 {'Stazione': 'Parco S. Chiara',
  'Inquinante': 'PM10',
  'Data': '2019-05-04',
  'Ora': '3',
  'Valore': '17',
  'Unità di misura': 'µg/mc'},
 {'Stazione': 'Parco S. Chiara',
  'Inquinante': 'PM10',
  'Data': '2019-05-04',
  'Ora': '4',
  'Valore': '15',
  'Unità di misura': 'µg/mc'},
 {'Stazione': 'Parco S. Chiara',
  'Inquinante': 'PM10',
  'Data': '2019-05-04',
  'Ora': '5',
  'Valore': '13',
  'Unità di misura': 'µg/mc'}]

calc_avg_pollution#

Implement a function to RETURN a dictionary containing two elements tuples as keys:

first tuple element is the station (‘Stazione’),
second tuple element is the name of a pollutant (‘Inquinante’)

To each tuple key, you must associate as value the average for that station and pollutant over all days.

calc_avg_pollution(air_quality)

{('Parco S. Chiara', 'PM10'): 11.385752688172044,
 ('Parco S. Chiara', 'PM2.5'): 7.9471544715447155,
 ('Parco S. Chiara', 'Biossido di Azoto'): 20.828146143437078,
 ('Parco S. Chiara', 'Ozono'): 66.69541778975741,
 ('Parco S. Chiara', 'Biossido Zolfo'): 1.2918918918918918,
 ('Via Bolzano', 'PM10'): 12.526881720430108,
 ('Via Bolzano', 'Biossido di Azoto'): 29.28493894165536,
 ('Via Bolzano', 'Ossido di Carbonio'): 0.5964769647696474,
 ('Piana Rotaliana', 'PM10'): 9.728744939271255,
 ('Piana Rotaliana', 'Biossido di Azoto'): 15.170068027210885,
 ('Piana Rotaliana', 'Ozono'): 67.03633916554509,
 ('Rovereto', 'PM10'): 9.475806451612904,
 ('Rovereto', 'PM2.5'): 7.764784946236559,
 ('Rovereto', 'Biossido di Azoto'): 16.284167794316645,
 ('Rovereto', 'Ozono'): 70.54655870445345,
 ('Borgo Valsugana', 'PM10'): 11.819407008086253,
 ('Borgo Valsugana', 'PM2.5'): 7.413746630727763,
 ('Borgo Valsugana', 'Biossido di Azoto'): 15.73806275579809,
 ('Borgo Valsugana', 'Ozono'): 58.599730458221025,
 ('Riva del Garda', 'PM10'): 9.912398921832883,
 ('Riva del Garda', 'Biossido di Azoto'): 17.125845737483086,
 ('Riva del Garda', 'Ozono'): 68.38159675236807,
 ('A22 (Avio)', 'PM10'): 9.651821862348179,
 ('A22 (Avio)', 'Biossido di Azoto'): 33.0650406504065,
 ('A22 (Avio)', 'Ossido di Carbonio'): 0.4228848821081822,
 ('Monte Gaza', 'PM10'): 7.794520547945205,
 ('Monte Gaza', 'Biossido di Azoto'): 4.34412955465587,
 ('Monte Gaza', 'Ozono'): 99.0858310626703}

Exercise - botteghe storiche#

Usually in open data catalogs (like 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 organization which created the data.

The dataset we will look at here 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

import example in LibreOffice Calc

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 table

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.

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  

[('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

enrich(rank_categories(stopwords))                

from pprint import pprint

# let's see if we created the file we wanted 
# (using botteghe-enriched-solution.csv to avoid polluting your file)
with open('data/botteghe-enriched-solution.csv', newline='',  encoding='utf-8',) as csvfile_in:
    reader = csv.DictReader(csvfile_in,  delimiter=',')
    # better to pretty print the OrderedDicts, otherwise we get unreadable output   
    # for documentation see https://docs.python.org/3/library/pprint.html
    for i in range(5):
        d = next(reader)
        pprint(d)

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

CSV files

Contents

CSV files#

Why parsing a CSV ?#

Reading a CSV#

What’s a reader ?#

Consuming a file#

Reading as dictionaries#

Writing a CSV#

Reading and writing a CSV#

Exercise - air quality#

load_air_quality#

calc_avg_pollution#

Exercise - botteghe storiche#

Problem: wrong characters ??#

Botteghe storiche in Python#

Botteghe storiche - rank_categories#

Botteghe storiche - enrich#