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Python pipe command line tool
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pypipe Demo Quick links Installation Basic usage and Examples | ppp
line | ppp rec | ppp csv | ppp text | ppp file | ppp custom -N NAME
-c, --counter pypipe is a code generator. Print generated code. -p,
--print Save generated code to a file. -o PATH, --output PATH Main
codes Default main code Code wrappping Disable code wrappping. -n,
--no-wrapping Pre and Post codes. -b CODE, --pre CODE, -a CODE,
--post CODE Inner loop. -e CODE, --loop-head CODE, -f CODE, --filter
CODE Import modules. -i MODULE, --import MODULE
README.md
pypipe
$ echo "pypipe" | ppp "line[::2]"
ppp
pypipe is a Python command-line tool for pipeline processing.
Demo
Alt text
Quick links
* Installation
* Basic usage and Examples
* pypipe is a code generator.
Installation
pypipe is a single Python file and uses only the standard library.
You can use it by placing pypipe.py in a directory included in your
PATH (e.g., ~/.local/bin). If execute permission is not already
present, please add it.
chmod +x pypipe.py
To make it easier to type, it's recommended to create a symbolic
link.
ln -s pypipe.py ppp
Note
pypipe requires Python 3.6 or later.
Basic usage and Examples
| ppp line
Processing line by line. You can get the line string as line or l and
the line number as i.
$ cat staff.txt |ppp 'i, line.upper()'
1 NAME WEIGHT BIRTH AGE SPECIES CLASS
2 SIMBA 250 1994-06-15 29 LION MAMMAL
3 DUMBO 4000 1941-10-23 81 ELEPHANT MAMMAL
4 GEORGE 20 1939-01-01 84 MONKEY MAMMAL
5 POOH 1 1921-08-21 102 TEDDY BEAR ARTIFACT
6 BOB 0 1999-05-01 24 SPONGE DEMOSPONGE
| ppp rec
Split each line by TAB. You can get the list includes splitted
strings as rec or r and the record number as i..
cat staff.txt |ppp rec 'r[:3]'
Name Weight Birth
Simba 250 1994-06-15
Dumbo 4000 1941-10-23
George 20 1939-01-01
Pooh 1 1921-08-21
Bob 0 1999-05-01
Using the -l LENGTH, --length LENGTH option allows you to get the
values of each field as f1, f2, f3, ....
$ tail -n +2 staff.txt |ppp rec -l5 'f"{f1} is {f4} years old"'
Simba is 29 years old
Dumbo is 81 years old
George is 84 years old
Pooh is 102 years old
Bob is 24 years old
When using the -H, --header option, it treats the first line as a
header line and skips it. The header values can be obtained from a
list named header, and you can access the values of each field using
the format dic["FIELD_NAME"].
$ cat staff.txt |ppp rec -H 'rec[0], dic["Birth"]'
Simba 1994-06-15
Dumbo 1941-10-23
George 1939-01-01
Pooh 1921-08-21
Bob 1999-05-01
You can change the delimiter by using the -d DELIMITER, --delimiter
DELIMITER option.
$ cat staff.csv |ppp rec -d , -l6 f1
Name
Simba
Dumbo
George
Pooh
Bob
| ppp csv
csv is similar to rec, but the difference is that while rec simply
splits the line using the specified DELIMITER like this, 'line.split
(DELIMITER))', csv uses the csv library for parsing. Furthermore, rec
is tab-separated by default, whereas csv is comma-separated.
You can specify options to pass to csv.reader and csv.writer using
the -O NAME=VALUE, --csv-opt NAME=VALUE option.
$ cat staff.csv |ppp csv -O 'quoting=csv.QUOTE_ALL'
"Name","Weight","Birth","Age","Species","Class"
"Simba","250","1994-06-15","29","Lion","Mammal"
"Dumbo","4000","1941-10-23","81","Elephant","Mammal"
"George","20","1939-01-01","84","Monkey","Mammal"
"Pooh","1","1921-08-21","102","Teddy bear","Artifact"
"Bob","0","1999-05-01","24","Sponge","Demosponge"
| ppp text
In ppp text, the entire standard input is read as a single piece of
text. You can access the read text as text.
$ cat staff.txt | ppp text 'len(text)'
231
For example, ppp text is particularly useful when working with a
indented JSON file. Using the -j, --json option allows you to decode
the text into JSON. The decoded data can be obtained as a dic.
$ cat staff.json |ppp text -j 'dic["data"][0]'
{'Name': 'Simba', 'Weight': 250, 'Birth': '1994-06-15', 'Age': 29, 'Species': 'Lion', 'Class': 'Mammal'}
Note
You can also use -j, --json option in line and file.
| ppp file
In ppp file, it receives a list of file paths from standard input. It
then opens each received file path, reads the contents of the file
into text, and repeats this process for each received file path in a
loop. The received paths can be obtained as path.
$ ls staff.txt staff.csv staff.json staff.xml |ppp file 'path, len(text)'
staff.csv 231
staff.json 1046
staff.txt 231
staff.xml 1042
For example, ppp file is usuful, especially when processing a large
number of JSON files.
find . -name '*.json'| ppp file --json ...
| ppp custom -N NAME
You can easily create custom commands using pypipe. First, you define
custom commands. The definition file is, by default, located at ~
/.config/pypipe/pypipe_custom.py. You can change the path of this
file using the PYPIPE_CUSTOM environment variable.
The following is an example of defining custom commands xpath and
sum.
~/.config/pypipe/pypipe_custom.py
TEMPLATE_XPATH = r"""
from lxml import etree
{imp}
def output(e):
if isinstance(e, etree._Element):
print(etree.tostring(e).decode().rstrip())
else:
_print(e)
{pre}
tree = etree.parse(sys.stdin)
for e in tree.xpath('{path}'):
{loop_head}
{loop_filter}
{main}
{post}
"""
TEMPLATE_SUM = r"""
import re
import sys
{imp}
ptn = re.compile(r'{pattern}')
s = 0
def add_or_print(*args):
global s
rec = args[0]
if len(args) == 2:
if isinstance(args[1], int):
i = args[1]
if len(rec) >= i:
s += rec[i-1]
else:
print(args[1])
else:
print(*args[1:])
for line in sys.stdin:
line = line.rstrip('\r\n')
rec = [{type}(e) for e in ptn.findall(line)]
if not rec:
continue
{loop_head}
{loop_filter}
{main}
print(s)
"""
custom_command = {
"xpath": {
"template": TEMPLATE_XPATH,
"code_indent": 1,
"default_code": "e",
"wrapper": 'output({})',
"options": {
"path": {"default": '/'}
}
},
"sum": {
"template": TEMPLATE_SUM,
"code_indent": 1,
"default_code": "1",
"wrapper": 'add_or_print(rec, {})',
"options": {
"pattern": {"default": r'\d+'},
"type": {"default": 'int'}
}
},
}
You can use them as follows:
$ cat staff.xml |ppp custom -N xpath -O path='./Animal/Age'
29
81
84
102
24
$ seq 10000| ppp c -Nsum -f 'rec[0] % 3 == 0'
16668333
-c, --counter
Using the -c, --counter option allows for easy data aggregation. When
you specify the -c, --counter option, it creates an instance of
collections.Counter, which can be accessed as either counter or c.
The -c, --counter option is available for use in all commands.
An example of aggregating data by the 'Gender' and 'Hobby' fields.
$ cat people.csv |ppp csv -H --counter 'dic["Gender"], dic["Hobby"]'| head -n10
Female Cooking 4
Male Hiking 3
Female Reading 3
Male Gardening 3
Female Traveling 3
Male Playing Music 3
Female Dancing 3
Female Hiking 3
Female Painting 2
Male Photography 2
This is an example to aggregate data based on whether female
individuals are 30 years or older.
cat people.csv |ppp csv -H -c -f 'dic["Gender"] == "Female"' 'int(dic["Age"]) >= 30'
False 16
True 10
When using the -c, --counter option, it uses counter[{}] += 1 as the
wrapper. If you want to count in a different way, you can disable the
wrapping by using the -n, --no-wrapping option and add your own
counting code.
$ cat population.csv |ppp csv -H -c -n 'counter[dic["State"]] += int(dic["Population"])'
New York 8398748
Texas 7751480
California 7327731
Illinois 2705994
Arizona 1680992
Pennsylvania 1584138
Florida 903889
Ohio 892533
Indiana 876862
North Carolina 792862
Washington 753675
Michigan 673104
Information about Code wrapping.
pypipe is a code generator.
pypipe is a command-line tool for pipeline processing, but it can
also be thought of as a code generator. It generates code internally
using the given arguments and then executes the generated code using
the exec function. Therefore, instead of executing the generated
code, you have the option to print it to the standard output or save
it to a file.
Print generated code. -p, --print
To check the generated code, you can use the -p, --print option.
ppp file -m rb -i hashlib -b 'total = 0' -b '_p("PATH", "SIZE", "MD5")' -e 'size = len(text)' -f 'path.stem == "staff"' 'total += size' 'path, size, hashlib.md5(text).hexdigest()' -a 'print(f"Total size: {total}", file=sys.stderr)' -p
The generated code is output as follows.
# IMPORT
import sys
from functools import partial
import gzip
from pathlib import Path
import hashlib
def _open(path):
if path.suffix == '.gz':
return gzip.open(path, 'rb')
else:
return open(path, 'rb')
# PRE
_p = partial(print, sep="\t") # ABBREV
I, S, B, L, D, SET = 0, "", False, [], {}, set() # ABBREV
def _print(*args, delimiter='\t'):
if len(args) == 1 and isinstance(args[0], (list, tuple)):
print(*args[0], sep=delimiter)
else:
print(*args, sep=delimiter)
total = 0
_p("PATH", "SIZE", "MD5")
for i, line in enumerate(sys.stdin, 1):
path = Path(line.rstrip('\r\n'))
with _open(path) as file:
text = file.read()
# LOOP HEAD
size = len(text)
# LOOP FILTER
if not (path.stem == "staff"): continue
# MAIN
total += size
_print(path, size, hashlib.md5(text).hexdigest())
# POST
print(f"Total size: {total}", file=sys.stderr)
Check that there are no issues with the generated code and execute
it.
$ find . -type f |ppp file -m rb -i hashlib -b 'total = 0' -b '_p("PATH", "SIZE", "MD5")' -e 'size = len(text)' -f 'path.stem == "staff"' 'total += size' 'path, size, hashlib.md5(text).hexdigest()' -a 'print(f"Total size: {total}", file=sys.stderr)'
PATH SIZE MD5
my_zoo.csv 186 e091408cc9174f1da86b50ee8e2fba96
my_zoo.xml 888 9edd78d97e45eccbac2b80747bd9c70b
my_zoo.json 887 7f15b3b8a23b91b60184113a38fa3e19
my_zoo.txt 186 4581c312d81815c3662f785ba9e7bd50
Total size: 2147
Save generated code to a file. -o PATH, --output PATH
For writing more complex code, it's a good practice to create a
template code with pypipe and edit the templated code manually.
Here's the process you can follow:
1. Create a template code with pypipe and save it to a file, for
example:
ppp line --output /tmp/pipe.py ...
2. Edit the code in /tmp/pipe.py to suit your needs.
3. Execute the modified code by piping input to it, for example:
cat sample.txt | /tmp/pipe.py
Main codes
The main code is specified as positional arguments. You can specify
multiple main codes. The placement of the main code varies depending
on the command. In commands like line, rec, csv, and file, the main
code is added within the loop processing with proper indentation.
However, in the text command, where there is no loop processing, the
main code is added without indentation. In the custom command, the
main code is added according to the definitions provided in the
pypipe_custom.py file.
$ ppp text -pqrn "for word in text.split():" " print(word)"
import sys
from functools import partial
text = sys.stdin.read()
for word in text.split(): # <- HERE
print(word) # <- HERE
You can also write it with line breaks in the terminal as follows:
$ ppp text -pqrn '
> for word in text.split():
> print(word)
> '
Default main code
If no main code is specified in the arguments, pypipe adds a
predefined default code. For example, the default code in Line mode
is 'line'.
ppp -pqr
import sys
from functools import partial
def _print(*args, delimiter='\t'):
if len(args) == 1 and isinstance(args[0], (list, tuple)):
print(*args[0], sep=delimiter)
else:
print(*args, sep=delimiter)
for i, line in enumerate(sys.stdin, 1):
line = line.rstrip("\r\n")
_print(line) # Default code with wrappping.
Code wrappping
By default, pypipe wraps the last code specified in the arguments
with a predefined wrapper. For example, in ppp line, it uses '_print
({})' as the wrapper. However, if the -c, --counter option is
specified, it uses 'counter[{}] += 1' as the wrapper instead.
$ ppp line 'year = int(line)' year -pqr
import sys
from functools import partial
def _print(*args, delimiter='\t'):
if len(args) == 1 and isinstance(args[0], (list, tuple)):
print(*args[0], sep=delimiter)
else:
print(*args, sep=delimiter)
for i, line in enumerate(sys.stdin, 1):
line = line.rstrip("\r\n")
year = int(line)
_print(year) # Wrapping
Disable code wrappping. -n, --no-wrapping
If you want to disable the wrapping of the last code specified in the
arguments by a predefined wrapper, you can use the -n, --no-wrapping
option.
ppp line -n 'n = max(len(line), n)' -a 'print(n)' -pqr
import sys
from functools import partial
for i, line in enumerate(sys.stdin, 1):
line = line.rstrip("\r\n")
n = max(len(line), n) # No wrapping
print(n)
Pre and Post codes. -b CODE, --pre CODE, -a CODE, --post CODE
The code specified with -b CODE, --pre CODE will be added before the
loop processing or the main code. This can be useful for declaring
variables or performing any necessary setup before entering a loop or
executing the main code. The code specified with -a CODE, --post CODE
will be added after the loop processing or the main code. This can be
useful for displaying aggregated results or performing any additional
actions after the loop or main code execution.
$ ppp rec --pqrn -b 'TOTAL = 0' -b 'MAX = 0' 'TOTAL += int(rec[0])' 'MAX = max(MAX, int(rec[0]))' -a 'print(f"TOTAL: {TOTAL}")' -a 'print(f"MAX: {MAX}")'
import sys
from functools import partial
TOTAL = 0 # PRE
MAX = 0 # PRE
for i, line in enumerate(sys.stdin, 1):
line = line.rstrip("\r\n")
rec = line.split('\t')
TOTAL += int(rec[0])
MAX = max(MAX, int(rec[0]))
print(f"TOTAL: {TOTAL}") # POST
print(f"MAX: {MAX}") # POST
Inner loop. -e CODE, --loop-head CODE, -f CODE, --filter CODE
In the loop processing of line, rec, csv, and file commands, the code
is added in the following positions:
for ... :
{loop_head} # Added with the -e CODE, --loop-head CODE option.
{filter} # Added with the -f CODE, --filter CODE option.
{main} # The main code is added here.
"loop_head" is added using the -e CODE, --loop-head CODE option,
while "filter" is added using the -f CODE, --filter CODE option.
Please note that the "loop_head" code is added as-is, while the
"loop_filter" is wrapped with if not ({}): continue.
$ ppp line -pqrn -e 'line = line.replace("foo", "bar")' -e 'line = line.upper()' -f '"BAR" in line' 'print(line)'
import sys
from functools import partial
for i, line in enumerate(sys.stdin, 1):
line = line.rstrip("\r\n")
line = line.replace("foo", "bar") # LOOP_HEAD
line = line.upper() # LOOP_HEAD
if not ("BAR" in line): continue # FILTER
print(line) # MAIN
Import modules. -i MODULE, --import MODULE
By using the -i MODULE, --import MODULE option, you can import any
modules. If the value specified with --import is in the form of a
sentence, like import math or from math import sqrt, it will be added
as an import statement just as it is. If only the module name is
provided, like math, it will automatically be given an import
statement, such as import math.
ppp text -i zlib -i 'from base64 import b64encode' 'b64encode
(zlib.compress(text.encode()))'
$ ppp text -pqrn -i zlib -i 'from base64 import b64encode' 'print(b64encode(zlib.compress(text.encode())))'
import sys
from functools import partial
import zlib # <- HERE
from base64 import b64encode # <- HERE
text = sys.stdin.read()
print(b64encode(zlib.compress(text.encode())))
Usage example.
$ seq 5 |ppp -i math 'line, math.sqrt(int(line))'
1 1.0
2 1.4142135623730951
3 1.7320508075688772
4 2.0
5 2.23606797749979
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Python pipe command line tool
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