Tough and flexible tools for data analysis, transformation, validation and movement.
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
Using pip:
$ pip install datagristle
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 100:1:-1
Prints records 1 to 100 in reverse order
$ gristle_slicer -i sample.csv -c:5 -r :100:3
Prints every third record from 0 to 99
$ gristle_slicer -i sample.csv -c:5 -r :100:0.25
Prints a random 25% of the records from 0 to 99
$ 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
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
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 0sf 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
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
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
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.
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 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 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 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 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.