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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 ...])
6](https://image.slidesharecdn.com/picklingandcsv-170420070738/75/Pickling-and-CSV-6-2048.jpg)
![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
7](https://image.slidesharecdn.com/picklingandcsv-170420070738/75/Pickling-and-CSV-7-2048.jpg)
Uploaded byDmitry Zinoviev
Pickling and CSV
Pickle allows for (de)serialization of Python objects to files for storage and later retrieval. CSV files store tabular data with rows and columns separated by commas (or other delimiters). CSV files can be read and written with the csv module, parsing each row into a list. An example script counts the number of students at each academic level from a CSV file and writes the summary to a new CSV file.
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Pickling and CSV
- 1. Pickling & CSV Preservationthrough Serialization and Tabulation
- 2. 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 2
- 3. 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. 3
- 4. CSV Example 4 Student, ID,E-mail Address, Phone Number, Class, Academic Level “Almarar, Hassan A”, 16897**, [email protected], Junior, UG “Arakelyan, Artur”, 17577**, [email protected], Sophomore, UG “Batista, Christopher A”, 16357**, [email protected], Senior, UG Complete file...
- 5. Reading CSV import csv withopen("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) 5
- 6. Writing CSV import csv withopen("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 ...]) 6
- 7. Example: Who Arethe 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 7
- 8.