Pickling & CSVPreservation through Serialization and Tabulation

Pickle

Module for (de)serialization: Storing complete Python objects into files and later

loading them back.

● Supports almost all data types – good.

● Works only with Python – bad.

import pickle

pickle.dump(object, openBinaryFile) # Save object to an open file

object = pickle.load(openBinaryFile) # Restore an object from an open file

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What Is CSV?

● “Comma Separated Values”

● Tabular file with rows and columns.

● All rows have the same number of fields.

● Fields separated by commas.

○ “Commas” do not have to be commas. Any other character can be used, such as TAB (TSV,

“tab separated values”), vertical bar, space...

● The first row often serves as headers.

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CSV Example

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Student, ID, E-mail Address, Phone Number, Class, Academic Level

“Almarar, Hassan A”, 16897**, halmarar2@suffolk.edu, Junior, UG

“Arakelyan, Artur”, 17577**, aarakelyan@suffolk.edu, Sophomore, UG

“Batista, Christopher A”, 16357**, cbatista@suffolk.edu, Senior, UG

Complete file...

Reading CSV

import csv

with open("path-words.csv") as csvfileIn:

reader = csv.reader(csvfileIn, delimiter=',', quotechar='"')

# Returns the next row parsed as a list, if necessary

headers = next(reader)

# Process the rest of the file

for row in reader:

do_something(row)

# Or, since reader is a generator:

all_rows = list(reader)

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Writing CSV

import csv

with open("path-words.csv", "w") as csvfileOut:

writer = csv.writer(csvfileOut, delimiter=',', quotechar='"')

writer.writerow([..., ..., ...]) # Write headers

# Write the rest of the file; each row is a list of strings or numbers

writer.writerows([row1, row2, row3 ...])

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Example: Who Are the Students? (students.py)

import csv, collections

with open("class-2017.csv") as mystudents:

reader = csv.reader(mystudents)

headers = next(reader)

class_position = headers.index("Class") # Where is the Class column?

class_levels = [row[class_position] for row in reader]

who_s_who = collections.Counter(class_levels) # Summary

with open("class-summary.csv", "w") as levels:

writer = csv.writer(levels)

writer.writerow(['Class', 'count']) # New headers

writer.writerows(who_s_who.items()) # New content

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Whenever

possible, use

Pandas

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