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Description:

csvsed 0.2.4

A stream-oriented CSV modification tool. Like a stripped-down “sed”
command, but for tabular data.

TL;DR
Install:
$ pip install csvsed
Use:
# given a sample CSV
$ cat sample.csv

Employee ID,Age,Wage,Status
8783,47,"104,343,873.83","All good, but nowhere to go."
2003,32,"98,878,784.00",A-OK

# modify that data with a series of `csvsed` pipes
$ cat sample.csv \
| csvsed -c Wage s/,//g \ # remove commas from the Wage column
| csvsed -c Status 'y/A-Z/a-z/' \ # convert Status to all lowercase
| csvsed -c Status 's/.*(ok|good).*/\1/' \ # restrict to keywords 'ok' & 'good'
| csvsed -c Age 'e/xargs -I {} echo "{}*2" | bc/' # double the Age column

Employee ID,Age,Wage,Status
8783,94,104343873.83,good
2003,64,98878784.00,ok

Installation
$ pip install csvsed

Usage and Examples
Installation of the csvsed python package also installs the
csvsed command-line tool. Use csvsed --help for all command
line options, but here are some examples to get you going. Given the
input file sample.csv:
Employee ID,Age,Wage,Status
8783,47,"104,343,873.83","All good, but nowhere to go."
2003,32,"98,878,784.00",A-OK
Removing thousands-separators from the “Wage” column using the “s”
(substitute) modifier:
$ cat sample.csv | csvsed -c Wage s/,//g
Employee ID,Age,Wage,Status
8783,47,104343873.83,"All good, but nowhere to go."
2003,32,98878784.00,A-OK
Convert/extract some text using the “s” (substitute) and “y”
(transliterate) modifiers:
$ cat sample.csv | csvsed -c Status 's/^All (.*),.*/\1/' \
| csvsed -c Status 's/^A-(.*)/\1/' \
| csvsed -c Status 'y/a-z/A-Z/'
Employee ID,Age,Wage,Status
8783,47,"104,343,873.83",GOOD
2003,32,"98,878,784.00",OK
Square the “Age” column using the “e” (execute) modifier:
$ cat sample.csv | csvsed -c Age 'e/xargs -I {} echo "{}^2" | bc/'
Employee ID,Age,Wage,Status
8783,2209,"104,343,873.83","All good, but nowhere to go."
2003,1024,"98,878,784.00",A-OK
That, however, called the external program for each column (quite
inefficient with large data sets)… so let’s do that more
efficiently, with a “continuous” mode program. Given the following
id2name.py program which takes a CSV on STDIN with a single column
(an employee ID) and writes a CSV to STDOUT with the IDs converted to
names:
#!/usr/bin/env python
import sys, csvkit
table = {'8783': 'ElfenKyng', '2003': 'Stradivarius'}
# NOTE: *not* using csvkit's reader because it reads-ahead
# causing problems since this must be stream-oriented...
writer = csvkit.CSVKitWriter(sys.stdout)
while True:
item = sys.stdin.readline()
if not item: break
item = item.strip()
writer.writerow([table[item] if item in table else item])
sys.stdout.flush()
Then the following will efficiently convert the ‘Employee ID’ column
to names:
$ cat sample.csv | csvsed -c 'Employee ID' 'e|./id2name.py|c'
Employee ID,Age,Wage,Status
ElfenKyng,47,"104,343,873.83","All good, but nowhere to go."
Stradivarius,32,"98,878,784.00",A-OK