![READ CSV EXAMPLE
import csv
exFile = open(‘example.csv’ , ‘r’)
exReader = csv.reader(exFile)
for row in exReader:
print row
exFile.close()
import csv
exFile = open(‘example.csv’ , ‘r’)
exReader = csv.reader(exFile)
exReader = list(exReader)
for i in (0, 10, 1):
print exReader[i]
exFile.close()](https://image.slidesharecdn.com/csvfilemanipulation-160407224855/85/CSV-File-Manipulation-5-320.jpg)
![WRITE CSV EXAMPLE
import csv
outFile = open(‘outputFile.csv’, ‘w’)
outWriter = csv.writer(outFile)
outWriter.writerow([‘Date’, ‘ID’, ‘GPA’])
outWriter.writerow([’01/12/2015’, ‘700514323’, ‘3.0’])
outWriter.writerow([’01/12/2015’, ‘700645798’, ‘2.64’])
outFile.close()](https://image.slidesharecdn.com/csvfilemanipulation-160407224855/85/CSV-File-Manipulation-6-320.jpg)
CSV File Manipulation
- 2. STRUCTURED TEXT FILES • Simple text files are a collection of lines with an escape sequence at the end of each line. • There is no definitive way to identify specific pieces of information unless there is a specified format to the file. • Ex. /etc/passwd username:*:UID:GID: name: home Path: shell • However there are several structured files • Tab Delimited – values separated with a tab • CSV – values separated with a ‘,’ • HTML/XML – tags , ‘< >’
- 3. COMMA SEPARATED VALUES • Delimited files are a common format often used as an exchange format for spreadsheets and databases. • Each line in a CSV file represents a row in the spreadhseet • Usually there is a header that denoted each of the column names. • Since CSV’s are a formatted text file they can still have end of line escape sequencesID Term Course Grade 800412564 201652 ISY150 A 800798465 201652 CIS120 A 800125498 201652 CIS120 C 800174658 201652 CIS150 F
- 4. MANIPULATING CSV FILES VS. PLAIN TEXT FILES • Since CSV files are just formatted text files the process to read them is similar to processing text files. • Create a file stream, create reader/writer object, process the reader/writer, close stream • When files are read in they need to be processed as lists(arrays) and each element is a unique element in the array that does not need to be split. • There is a unique module for processing csv files • Code: import csv
- 5. READ CSV EXAMPLE import csv exFile = open(‘example.csv’ , ‘r’) exReader = csv.reader(exFile) for row in exReader: print row exFile.close() import csv exFile = open(‘example.csv’ , ‘r’) exReader = csv.reader(exFile) exReader = list(exReader) for i in (0, 10, 1): print exReader[i] exFile.close()
- 6. WRITE CSV EXAMPLE import csv outFile = open(‘outputFile.csv’, ‘w’) outWriter = csv.writer(outFile) outWriter.writerow([‘Date’, ‘ID’, ‘GPA’]) outWriter.writerow([’01/12/2015’, ‘700514323’, ‘3.0’]) outWriter.writerow([’01/12/2015’, ‘700645798’, ‘2.64’]) outFile.close()
- 7. PROCESS CSV FILES IN A DIRECTORY EXAMPLE import csv, os for currFile in os.listdir(‘~/Documents’) if (not currFile.endswith(‘.csv’)): continue else: # process csv file
Editor's Notes
- #4: CSV vs escel and otherspreadsheets No types – all strings No fonts, sizes or colors No multiple worsheets No cell widths or heights No merged cells No images or charts