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datagristle

Tough and flexible tools for data analysis, transformation, validation and movement.

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Introduction

Datagristle is a toolbox of tough and flexible command line tools for working with data. It's kind of an interactive mix between ETL and data analysis optimized for rapid analysis and manipulation of a wide variety of data at the command line.

More info is on the DataGristle wiki here: wiki

And examples of all csv utilities can be found here: examples

Installation

  • Using pip:

    $ pip install datagristle
    

Dependencies

  • Python 3.8
  • or Python 3.9

CSV Utilities provided in this release:

  • gristle_differ
    • Allows two identically-structured files to be compared by key columns and split into same, inserts, deletes, chgold and chgnew files.
    • The user can configure which columns are included in the comparison.
    • Post delta transformations can include assign sequence numbers, copying field values, etc.
  • gristle_converter (was: gristle_file_converter)
    • Converts an input file with one csv dialect into an output file with another.
  • gristle_freaker
    • Produces a frequency distribution of multiple columns from input file.
  • gristle_profiler (was: gristle_determinator)
    • Identifies file formats, generates metadata, prints file analysis report
    • This is the most mature - and also used by the other utilities so that you generally do not need to enter file structure info.
  • gristle_slicer
    • Used to extract a subset of columns and rows out of an input file.
  • gristle_sorter
    • CSV-aware sort utility that handles data that breaks unix sorts.
  • gristle_validator
    • Validates csv files by confirming that all records have the right number of fields, and by applying a json schema to each record.
  • gristle_viewer
    • Shows one record from a file at a time - formatted based on metadata.

File and Directory Utilities provided in this release:

  • gristle_dir_merger
    • Used to consolidate large directories with options to control matching criteria as well as matching actions.

gristle_slicer

Extracts subsets of input files based on user-specified columns and rows.
The input csv file can be piped into the program through stdin or identified
via a command line option.  The output will default to stdout, or redirected
to a filename via a command line option.

The columns and rows are specified using python list slicing syntax -
so individual columns or rows can be listed as can ranges.   Inclusion
or exclusion logic can be used - and even combined.

Examples:
   $ gristle_slicer -i sample.csv
                Prints all rows and columns
   $ gristle_slicer -i sample.csv -c":5, 10:15, dept" -C 13
                Prints columns 0-4 and 10,11,12,14, and the col associated 
                with the header field 'dept' for all records
   $ gristle_slicer -i sample.csv -C:-1
                Prints all columns except for the last for all records
   $ gristle_slicer -i sample.csv -c:5 -r-100
                Prints columns 0-4 for the last 100 records
   $ gristle_slicer -i sample.csv -c:5 -r-100 -d'|' --quoting=quote_all
                Prints columns 0-4 for the last 100 records, csv
                dialect info (delimiter, quoting) provided manually)
   $ cat sample.csv | gristle_slicer -c:5 -r-100 -d'|' --quoting=quote_all
                Prints columns 0-4 for the last 100 records, csv
                dialect info (delimiter, quoting) provided manually)
Many more examples can be found here:
   https://github.com/kenfar/DataGristle/tree/master/examples/gristle_slicer

gristle_freaker

Creates a frequency distribution of values from columns of the input file
and prints it out in columns - the first being the unique key and the last
being the count of occurances.

Examples:
   $ gristle_freaker -i sample.csv -c 0
                Creates two columns from the input - the first with
                unique keys from column 0, the second with a count of
                how many times each exists.
   $ gristle_freaker -i sample.csv -c home_state
                This is the same as the previous example - but in this case
                the column reference uses the name of the field from the
                file header.
   $ gristle_freaker -i sample.csv -d '|'  -c 0 --sortcol 1 --sortorder forward --writelimit 25
                In addition to what was described in the first example,
                this example adds sorting of the output by count ascending
                and just prints the first 25 entries.
   $ gristle_freaker -i sample.csv -d '|'  -c 0,1
                Creates three columns from the input - the first two
                with unique key combinations from columns 0 & 1, the
                third with the number of times each combination exists.
Many more examples can be found here:
   https://github.com/kenfar/DataGristle/tree/master/examples/gristle_freaker

gristle_sorter

Provides a csv dialect-aware sort that can safely handle delimiters, quotes, and newlines
within fields.

Examples:
   $ gristle_sorter -i sample.csv -k 0sf -D
                Sort file by the 0-position string column in forward (ascending) direction,
                dedupes the results and writes them to stdout.  The csv dialect is auto-
                detected.
   $ gristle_sorter -i sample.csv -k 0~s~f dept-s-r -D
                This example uses the optional tildes to separate the parts of the key,
                and uses a fieldname reference from the file header (dept) rather than a
                numeric field position.
   $ gristle_sorter -i sample.csv --keys 0sf 3ir --outfile sample_out.csv
                Sorts file by the 0-position column string in forward direction followed
                by the position 3 column integer in reverse direction.  The output is not
                deduped, but is written to a file.  The csv dialect is auto-detected.
   $ gristle_sorter -i sample.csv -k 0sf -d '|' -q quote_all --doublequote --has-header
                Sort file by the 0-position string column in forward (ascending) direction,
                specifies the csv dialect explicitly, including that the file has a header
                that will be written to the top of the output file.
Many more examples can be found here:
   https://github.com/kenfar/DataGristle/tree/master/examples/gristle_sorter

gristle_profiler

Analyzes the structures and contents of csv files in the end producing a
report of its findings.  It is intended to speed analysis of csv files by
automating the most common and frequently-performed analysis tasks.  It's
useful in both understanding the format and data and quickly spotting issues.

Examples:
   $ gristle_profiler --infiles japan_station_radiation.csv
                This command will analyze a file with radiation measurements
                from various Japanese radiation stations.

    File Structure:
    format type:       csv
    field cnt:         4
    record cnt:        100
    has header:        True
    delimiter:
    csv quoting:       False
    skipinitialspace:  False
    quoting:           QUOTE_NONE
    doublequote:       False
    quotechar:         "
    lineterminator:    '\n'
    escapechar:        None

    Field Analysis Progress:
    Analyzing field: 0
    Analyzing field: 1
    Analyzing field: 2
    Analyzing field: 3

    Fields Analysis Results:

        ------------------------------------------------------
        Name:             station_id
        Field Number:     0
        Wrong Field Cnt:  0
        Type:             timestamp
        Min:              1010000001
        Max:              1140000006
        Unique Values:    99
        Known Values:     99
        Top Values not shown - all values are unique

        ------------------------------------------------------
        Name:             datetime_utc
        Field Number:     1
        Wrong Field Cnt:  0
        Type:             timestamp
        Min:              2011-02-28 15:00:00
        Max:              2011-02-28 15:00:00
        Unique Values:    1
        Known Values:     1
        Top Values:
            2011-02-28 15:00:00                      x 99 occurrences

        ------------------------------------------------------
        Name:             sa
        Field Number:     2
        Wrong Field Cnt:  0
        Type:             integer
        Min:              -999
        Max:              52
        Unique Values:    35
        Known Values:     35
        Mean:             2.45454545455
        Median:           38.0
        Variance:         31470.2681359
        Std Dev:          177.398613681
        Top Values:
            41                                       x 7 occurrences
            42                                       x 7 occurrences
            39                                       x 6 occurrences
            37                                       x 5 occurrences
            46                                       x 5 occurrences
            17                                       x 4 occurrences
            38                                       x 4 occurrences
            40                                       x 4 occurrences
            45                                       x 4 occurrences
            44                                       x 4 occurrences

        ------------------------------------------------------
        Name:             ra
        Field Number:     3
        Wrong Field Cnt:  0
        Type:             integer
        Min:              -888
        Max:              0
        Unique Values:    2
        Known Values:     2
        Mean:             -556.121212121
        Median:           -888.0
        Variance:         184564.833792
        Std Dev:          429.610095077
        Top Values:
            -888                                     x 62 occurrences
            0                                        x 37 occurrences

Many more examples can be found here:
   https://github.com/kenfar/DataGristle/tree/master/examples/gristle_profiler

gristle_converter

Converts a file from one csv dialect to another

Examples:
   $ gristle_converter -i foo.csv -o bar.csv \
     --delimiter=',' --has-header --quoting=quote-all doublequote \
     --out-delimiter='|'  --out-has-no-header --out-quoting quote_none --out-escapechar='\'
         Copies input file to output while completely changing every aspect
         of the csv dialect.
Many more examples can be found here:
   https://github.com/kenfar/DataGristle/tree/master/examples/gristle_converter

gristle_validator

Splits a csv file into two separate files based on how records pass or fail
validation checks:
   - Field count - checks the number of fields in each record against the
     number required.  The correct number of fields can be provided in an
     argument or will default to using the number from the first record.
   - Schema - uses csv file requirements defined in a json-schema file for
     quality checking.  These requirements include the number of fields,
     and for each field - the type, min & max length, min & max value,
     whether or not it can be blank, existance within a list of valid
     values, and finally compliance with a regex pattern.

The output can just be the return code (0 for success, 1+ for errors), can
be some high level statistics, or can be the csv input records split between
good and erroneous files.  Output can also be limited to a random subset.

Examples:
   $ gristle_validator  -i sample.csv -f 3
         Prints all valid input rows to stdout, prints all records with
         other than 3 fields to stderr along with an extra final field that
         describes the error.
   $ gristle_validator  -i sample.csv
         Prints all valid input rows to stdout, prints all records with
         other than the same number of fields found on the first record to
         stderr along with an extra final field that describes the error.
   $ gristle_validator  -i sample.csv -o sample_good.csv --errfile sample_err.csv
         Same comparison as above, but explicitly splits good and bad data
         into separate files.
   $ gristle_validator  -i sample.csv --randomout 1
         Same comparison as above, but only writes a random 1% of data out.
   $ gristle_validator  -i sample.csv --verbosity quiet
         Same comparison as above, but writes nothing out.  Exit code can be
         used to determine if any bad records were found.
   $ gristle_validator  -i sample.csv --validschema sample_schema.csv
         The above command checks both field count as well as validations
         described in the sample_schema.csv file.  Here's an example of what
         that file might look like:
            items:
                - title:            rowid
                  blank:            False
                  required:         True
                  dg_type:          integer
                  dg_minimum:       1
                  dg_maximum:       60
                - title:            start_date
                  blank:            False
                  minLength:        8
                  maxLength:        10
                  pattern:          '[0-9]*/[0-9]*/[1-2][0-9][0-9][0-9]'
                - title:            location
                  blank:            False
                  minLength:        2
                  maxLength:        2
                  enum:             ['ny','tx','ca','fl','wa','ga','al','mo']
   $ gristle_validator  -i sample.csv -o good.csv -e -
     --validschema schema.csv --err-out-fields --err-out-text
         The above command writes error records to stderr.  Err-out-fields 
         adds error descriptions to the end of the error records, while
         err-out-text added even more detailed error descriptions as records
         following invalid records.

gristle_viewer

Displays a single record of a file, one field per line, with field names
displayed as labels to the left of the field values.  Also allows simple
navigation between records.

Examples:
   $ gristle_viewer -i sample.csv -r 3
                Presents the third record in the file with one field per line
                and field names from the header record as labels in the left
                column.
   $ gristle_viewer -i sample.csv -r 3  -d '|' -q quote_none
                In addition to what was described in the first example this
                adds explicit csv dialect overrides.

Many more examples can be found here:
   https://github.com/kenfar/DataGristle/tree/master/examples/gristle_viewer

gristle_differ

gristle_differ compares two files, typically an old and a new file, based
on explicit keys in a way that is far more accurate than diff.  It can also
compare just subsets of columns, and perform post-delta transforms to
populate fields with static values, values from other fields, variables
from the command line, or incrementing sequence numbers.

More info on the wiki here:  https://github.com/kenfar/DataGristle/wiki/gristle_differ

Examples:

   $ gristle_differ --infiles file0.dat file1.dat --key-cols 0 2 --ignore_cols  19 22 33

        - Sorts both files on columns 0 & 2
        - Dedupes both files on column 0
        - Compares all fields except fields 19,22, and 23
        - Automatically determines the csv dialect
        - Produces the following files:
           - file1.dat.insert
           - file1.dat.delete
           - file1.dat.same
           - file1.dat.chgnew
           - file1.dat.chgold

   $ gristle_differ --infiles file0.dat file1.dat --key-cols 0 --compare-cols 1 2 3 4 5 6 7  -d '|'

        - Sorts both files on columns 0
        - Dedupes both files on column 0
        - Compares fields 1,2,3,4,5,6,7
        - Uses '|' as the field delimiter
        - Produces the same output file names as example 1.


   $ gristle_differ --infiles file0.dat file1.dat --config-fn ./foo.yml  \
               --variables batchid:919 --variables pkid:82304

        - Produces the same output file names as example 1.
        - But in this case it gets the majority of its configuration items from
          the config file ('foo.yml').  This could include key columns, comparison
          columns, ignore columns, post-delta transformations, and other information.
        - The two variables options are used to pass in user-defined variables that
          can be referenced by the post-delta transformations.  The batchid will get
          copied into a batch_id column for every file, and the pkid is a sequence
          that will get incremented and used for new rows in the insert, delete and
          chgnew files.

Many more examples can be found here:
    https://github.com/kenfar/DataGristle/tree/master/examples/gristle_differ

gristle_metadata

Gristle_metadata provides a command-line interface to the metadata database.
It's mostly useful for scripts, but also useful for occasional direct
command-line access to the metadata.

Examples:
   $ gristle_metadata --table schema --action list
                Prints a list of all rows for the schema table.
   $ gristle_metadata --table element --action put --prompt
                Allows the user to input a row into the element table and
                prompts the user for all fields necessary.

gristle_md_reporter

Gristle_md_reporter allows the user to create data dictionary reports that
combine information about the collection and fields along with field value
descriptions and frequencies.

Examples:
   $ gristle_md_reporter --report datadictionary --collection_id 2
                Prints a data dictionary report of collection_id 2.
   $ gristle_md_reporter --report datadictionary --collection_name presidents
                Prints a data dictionary report of the president collection.
   $ gristle_md_reporter --report datadictionary --collection_id 2 --field_id 3
                Prints a data dictionary report of the president collection,
                only shows field-level information for field_id 3.

gristle_dir_merger

Gristle_dir_merger consolidates directory structures of files.  Is both fast
and flexible with a variety of options for choosing which file to use based
on full (name and md5) and partial matches (name only) .

Examples
   $ gristle_dir_merger --source-dir /tmp/foo --dest-dir /data/foo
         - Compares source of /tmp/foo to dest of /data/foo.
         - Files will be consolidated into /data/foo, and deleted from /tmp/foo.
         - Comparison will be: match-on-name-and-md5 (default)
         - Full matches will use: keep_dest (default)
         - Partial matches will use: keep_newest (default)
         - Bottom line: this is what you normally want.
   $ gristle_dir_merger --source-dir /tmp/foo --dest-dir /data/foo --dry-run
         - Same as the first example - except it only prints what it would do
           without actually doing it.
         - Bottom line: this is a good step to take prior to running it for real.
   $ gristle_dir_merger --source-dir /tmp/foo --dest-dir /data/foo -r
         - Same as the first example - except it runs recursively through
           the directories.
   $ gristle_dir_merger --source-dir /tmp/foo --dest-dir /data/foo 
     --on-partial-match keep-biggest
         - Comparison will be: match-on-name-and-md5 (default)
         - Full matches will use: keep_dest (default)
         - Partial matches will use: keep_biggest (override)
         - Bottom line: this is a good combo if you know that some files
           have been modified on both source & dest, and newest isn't the best.
   $ gristle_dir_merger --source-dir /tmp/foo --dest-dir /data/foo 
     --match-on name_only --on-full-match keep-source
         - Comparison will be: match-on-name-only (override)
         - Full matches will use: keep_source (override)
         - Bottom line: this is a good way to go if you have
           files that have changed in both directories, but always want to
           use the source files.

Licensing

  • Gristle uses the BSD license - see the separate LICENSE file for further information

Copyright

  • Copyright 2011-2021 Ken Farmer

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