Use insanely fast regex engine RIPGREP as a python module! Search results are captured and converted to dict/numpy/pandas/generator

These details have not been verified by PyPI

Project links

Homepage

Project description

PYRipGREP

Use the insanely fast regex engine RIPGREP https://github.com/BurntSushi/ripgrep as a python module! Search results are converted directly to dict/numpy/pandas/generator

You can use the class PyRipGREP as you would use ripgrep, but you will get a string as result. Because of that, I created the class RePatterns where your results are directly converted to dict/numpy/pandas/generator

#Install

pip install PyRipGrep

Check it out:

You'll find the files xaa.txt / xab.txt here: https://github.com/hansalemaos/PYRipGREP/tree/main/textfilesfortests

Make sure to have rg.exe in your path or pass the path when you create the instance:

RePatterns(executeable=r"c:\path\rg.exe")

    outputtype = "np"



    suchennach = ["weniger", "mehr"]



    filetosearch = [

        r"F:\woerterbuecher\wtxt\xaa.txt", # download here: https://github.com/hansalemaos/PYRipGREP/blob/main/textfilesfortests/xaa.txt

        r"F:\woerterbuecher\wtxt\xab.txt", #download here: https://github.com/hansalemaos/PYRipGREP/blob/main/textfilesfortests/xab.txt

    ]

    np_or_df = "np"

    binary = True

    dfa_size = "1G"  # Yes, I have a lot of RAM hahaha

    ignore_case = True



    df = RePatterns(executeable=r"rg.exe").find_all_in_files(

        re_expression=suchennach,

        path_to_search=filetosearch,

        outputtype=outputtype,

        binary=binary,

        dfa_size=dfa_size,

        ignore_case=ignore_case,

    )

    print(f"{df=}")



    suchennach = ["sein"]



    df2 = RePatterns().find_all_in_files(

        re_expression=suchennach,

        path_to_search=filetosearch,

        outputtype=outputtype,

        binary=binary,

        dfa_size=dfa_size,

        ignore_case=ignore_case,

    )

    print(f"{df2=}")



    df3 = RePatterns().find_all_in_files(

        re_expression=[r"Buch"],

        path_to_search=filetosearch,

        outputtype=outputtype,

        binary=False,

        dfa_size=dfa_size,

        ignore_case=ignore_case,

    )

    print(f"{df3=}")



    dateistrings = [

        "Das ist ein neues\nHaus Maus Buch",

        "Was kostet das neue Buch?\nBuch Haus Maus",

    ]

    df4 = RePatterns().find_all_in_var_json(

        re_expression=[r"Buch", "Haus"],

        variable=dateistrings[0],

        outputtype=outputtype,

        binary=True,

        ignore_case=True,

    )

    print(f"{df4=}")



    df5 = RePatterns().find_all_in_var(

        re_expression=["mein", r"Buch"],

        variable="Das ist mein Buch. Wo hast du das Buch gekauft?",

        outputtype=outputtype,

        binary=False,

        dfa_size=dfa_size,

        ignore_case=ignore_case,

    )

    print(f"{df5=}")



    df6 = RePatterns().sub_in_files(

        re_expression=[r"Buch", "Haus"],

        repl="Auto",

        path_to_search=filetosearch,

        outputtype=outputtype,

        binary=False,

        dfa_size=dfa_size,

        ignore_case=ignore_case,

    )

    print(f"{df6=}")



    df7 = RePatterns().find_all_in_files_json(

        re_expression=[r"Buch", "Haus"],

        search_in=filetosearch,

        outputtype=outputtype,

        binary=True,

        ignore_case=True,

    )

    print(f"{df7=}")



    df8 = RePatterns().find_all_in_files_json(

        re_expression=[r"Buch", "Haus"],

        search_in=r"F:\nur_df",

        outputtype=outputtype,

        binary=True,

        ignore_case=True,

    )

    print(f"{df8=}")



    text = r"""Guy Reffitt, der am 6. Januar am Sturm aufs US-Kapitol teilnahm, muss für sieben Jahre ins Gefängnis. Der stern hat seine Familie anderthalb Jahre lang begleitet – bis zum Urteil gestern in Washington. Über einen Tag vor Gericht, der Amerikas ganze Verlorenheit offenbart.

    Am Ende ist es eine 18 Jahre junge Frau aus Texas, gerade mit der High School fertig, die den Satz des Tages sagt: "Wenn mein Vater so lange ins Gefängnis muss", sagt sie, "dann verdient Trump lebenslang."



    Es ist Peyton Reffitt, die Tochter eines Mannes, der am 6. Januar 2021 am Sturm aufs Kapitol teilnahm. Der stern hat die ganze Familie, die nicht mehr ganz ist, seitdem begleitet. Gestern wurde Peytons Vater, Guy Reffitt, in Washington zu über sieben Jahren Haft verurteilt. Bei niemandem sonst, der am 6. Januar dabei war, fiel das Urteil bisher so hoch aus."""



    df9 = RePatterns().find_all_in_files(

        re_expression=r"\d+\s+\w{5}",

        path_to_search=filetosearch[0],

        outputtype=outputtype,

    )

    print(f"{df9=}")

    df10 = RePatterns().find_all_in_files(

        re_expression=r"\d+\s+\w{5}",

        path_to_search=r"F:\nur_df",

        outputtype=outputtype,

    )

    print(f"{df10=}")

    df11 = RePatterns().sub_in_files(

        re_expression=r"\d+\s+(\w{5})",

        repl="$1",

        path_to_search=r"F:\nur_df",

        outputtype=outputtype,

    )

    print(f"{df11=}")

    df12 = RePatterns().find_all_in_var(

        re_expression=r"\d+\.?\s+\w{5}", variable=text, outputtype=outputtype

    )

    print(f"{df12=}")

    df13 = RePatterns().sub_all_in_var(

        re_expression=r"\d+\.?\s+(\w{5})",

        repl="dudu $1",

        variable=text,

        outputtype=outputtype,

    )

    print(f"{df13=}")

    df14 = RePatterns().find_all_in_var_json(

        re_expression=r"\d+\.?\s+(\w{5})[.?!]", variable=text, outputtype=outputtype

    )

    print(f"{df14=}")



    suchennach = ["Sein"]



    dfxx = RePatterns().find_all_in_files(

        re_expression=r"\w\w[ener]\b",

        path_to_search=filetosearch[1],

        outputtype="df",

        binary=True,

        dfa_size="1G",

        ignore_case=True,

    )

    print(f"{dfxx=}")

Output:

    df=array([['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '15243', '15242',

        'Mehr'],

       ['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '22162', '22161',

        'mehr'],

       ['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '26981', '26980',

        'mehr'],

       ...,

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52397917', '52397916',

        'mehr'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52403287', '52403286',

        'mehr'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52404523', '52404522',

        'mehr']], dtype='<U30')

df2=array([['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '4966', '4965', 'sein'],

       ['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '5021', '5020', 'sein'],

       ['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '7164', '7163', 'Sein'],

       ...,

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52415836', '52415835',

        'sein'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52420887', '52420886',

        'sein'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52422346', '52422345',

        'Sein']], dtype='<U30')

df3=array([['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '1051', '1050', 'buch'],

       ['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '28055', '28054',

        'buch'],

       ['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '31815', '31814',

        'Buch'],

       ...,

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52302767', '52302766',

        'buch'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52322927', '52322926',

        'Buch'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52323198', '52323197',

        'Buch']], dtype='<U30')

df4=<generator object RePatterns._generator_json.<locals>.<genexpr> at 0x00000000129C8820>

df5=array([['<stdin>', '1', '9', '8', 'mein'],

       ['<stdin>', '1', '14', '13', 'Buch'],

       ['<stdin>', '1', '35', '34', 'Buch']], dtype='<U7')

df6=array([['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '515', '514', 'Auto'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '543', '542', 'Auto'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '3358', '3357', 'Auto'],

       ...,

       ['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '52423818', '52423817',

        'Auto'],

       ['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '52426297', '52426296',

        'Auto'],

       ['F:\\woerterbuecher\\wtxt\\xab.txt', '1', '52426444', '52426443',

        'Auto']], dtype='<U30')

df7=<generator object RePatterns._generator_json.<locals>.<genexpr> at 0x00000000129B4DD0>

df8=<generator object RePatterns._generator_json.<locals>.<genexpr> at 0x00000000129E8890>

df9=array([['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '493', '492',

        '1904 verfa'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '840', '839',

        '1925 übern'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '890', '889',

        '1935 schuf'],

       ...,

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52428295', '52428294',

        '2001 Bürge'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52428359', '52428358',

        '1991 Bürge'],

       ['F:\\woerterbuecher\\wtxt\\xaa.txt', '1', '52428418', '52428417',

        '1979 Bürge']], dtype='<U30')

df10=array([['F:\\nur_df', '1', '205', '204', '30 Kilom'],

       ['F:\\nur_df', '1', '245', '244', '30 Kilom'],

       ['F:\\nur_df', '1', '292', '291', '60 Kilom'],

       ...,

       ['F:\\nur_df', '1', '2016132', '2016131', '75 Jahre'],

       ['F:\\nur_df', '1', '2016203', '2016202', '2005 emeri'],

       ['F:\\nur_df', '1', '2017110', '2017109', '85 Jahre']],

      dtype='<U14')

df11=array([['F:\\nur_df', '1', '205', '204', 'Kilom'],

       ['F:\\nur_df', '1', '242', '241', 'Kilom'],

       ['F:\\nur_df', '1', '286', '285', 'Kilom'],

       ...,

       ['F:\\nur_df', '1', '2111612', '2111611', 'Carlo'],

       ['F:\\nur_df', '1', '2111911', '2111910', 'gelan'],

       ['F:\\nur_df', '1', '2113124', '2113123', 'verfü']], dtype='<U9')

df12=array([['<stdin>', '1', '21', '20', '6. Janua'],

       ['<stdin>', '1', '303', '302', '18 Jahre'],

       ['<stdin>', '1', '551', '550', '6. Janua'],

       ['<stdin>', '1', '799', '798', '6. Janua']], dtype='<U8')

df13=array([['<stdin>', '1', '21', '20', 'dudu Janua'],

       ['<stdin>', '1', '305', '304', 'dudu Jahre'],

       ['<stdin>', '1', '555', '554', 'dudu Janua'],

       ['<stdin>', '1', '805', '804', 'dudu Janua']], dtype='<U10')

df14=<generator object RePatterns._generator_json.<locals>.<genexpr> at 0x00000000129E8E40>

dfxx=                            aa_filename  aa_line  ...  aa_byte_offset_o  aa_string

0        F:\woerterbuecher\wtxt\xab.txt        1  ...                10        von

1        F:\woerterbuecher\wtxt\xab.txt        1  ...                33        tin

2        F:\woerterbuecher\wtxt\xab.txt        1  ...                46        ber

3        F:\woerterbuecher\wtxt\xab.txt        1  ...                78        ber

4        F:\woerterbuecher\wtxt\xab.txt        1  ...                85        ton

                                 ...      ...  ...               ...        ...

3035300  F:\woerterbuecher\wtxt\xab.txt        1  ...          52428744        che

3035301  F:\woerterbuecher\wtxt\xab.txt        1  ...          52428756        che

3035302  F:\woerterbuecher\wtxt\xab.txt        1  ...          52428775        rde

3035303  F:\woerterbuecher\wtxt\xab.txt        1  ...          52428782        der

3035304  F:\woerterbuecher\wtxt\xab.txt        1  ...          52428790        ten

[3035305 rows x 5 columns]

This is how you can use the class PyRipGREP directly (output as string!):

        dfa_size: str = "1G",

        regexstart = PyRipGREP()

        search_for = _to_list(re_expression)

        for suche in search_for:

            regexstart.regexp(option=suche, activated=True, multi_allowed=True)



        (

            regexstart

            .binary(activated=True)

            .byte_offset(activated=True) 

            .context_separator(option=" ")

            .dfa_size_limit(option=dfa_size)

            .field_match_separator(option= "ÇÇ") 

            .ignore_case(activated=True)

            .null_data(activated=True)

            .line_number(activated=True)

            .no_ignore(activated=True)

            .multiline(activated=True)

            .multiline_dotall(activated=True)

            .block_buffered(activated=True)

            .crlf(activated=True)

            .no_config(activated=True)

            .only_matching(activated=True)

            .trim(activated=True)

            .vimgrep(activated=True)

            .with_filename(activated=True)

            .add_target_file_or_folder('c:\\whatever.txt')

        )

Project details

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

This version

0.21

Oct 2, 2022

0.2

Sep 29, 2022

0.1

Aug 30, 2022

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PYRipGREP-0.21.tar.gz (75.5 kB view details)

Uploaded Oct 2, 2022 Source

Built Distribution

PYRipGREP-0.21-py3-none-any.whl (74.9 kB view details)

Uploaded Oct 2, 2022 Python 3

File details

Details for the file PYRipGREP-0.21.tar.gz.

File metadata

Download URL: PYRipGREP-0.21.tar.gz
Upload date: Oct 2, 2022
Size: 75.5 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for PYRipGREP-0.21.tar.gz
Algorithm	Hash digest
SHA256	`0830cc1c5a22330c8f24070c8e561d5450e3211059fbed9c9504de0729cac606`
MD5	`970bfc3608e6580eae80d028717714b9`
BLAKE2b-256	`d11c22f9a58b1fac2f5a00f2175348ae0a9b8d7b9536b17f35ccefa54c1ec924`

See more details on using hashes here.

File details

Details for the file PYRipGREP-0.21-py3-none-any.whl.

File metadata

Download URL: PYRipGREP-0.21-py3-none-any.whl
Upload date: Oct 2, 2022
Size: 74.9 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for PYRipGREP-0.21-py3-none-any.whl
Algorithm	Hash digest
SHA256	`b24f066f6dd57a9a0caf80b29708ad29d6b1ec765b7096ebf74eaef03fe207f8`
MD5	`7118a72e3c3327e5c4f801d6595e18bd`
BLAKE2b-256	`e902c61c919bcf292dff251ba296b63e9d5a36fa7e81d36e4735bf29d8d712d5`