HTTPie - a CLI, cURL-like tool for humans.
Project description
undatum (pronounced un-da-tum) is a command line data processing tool. Its goal is to make CLI interaction with huge datasets so easy as possible. It provides a simple undatum command that allows to convert, split, calculate frequency, statistics and to validate data in CSV, JSON lines, BSON files.
1 Main features
Common data operations against CSV, JSON lines and BSON files
Built-in data filtering
Conversion between CSV, JSONl, BSON, XML, XLS, XLSX file types
Low memory footprint
Support for compressed datasets
Advanced statistics calculations
Date/datetime fields automatic recognition
Data validation
Documentation
Test coverage
2 Installation
2.1 macOS
On macOS, undatum can be installed via Homebrew (recommended):
$ brew install undatum
A MacPorts port is also available:
$ port install undatum
2.2 Linux
Most Linux distributions provide a package that can be installed using the system package manager, for example:
# Debian, Ubuntu, etc.
$ apt install undatum
# Fedora
$ dnf install undatum
# CentOS, RHEL, ...
$ yum install undatum
# Arch Linux
$ pacman -S undatum
2.3 Windows, etc.
A universal installation method (that works on Windows, Mac OS X, Linux, …, and always provides the latest version) is to use pip:
# Make sure we have an up-to-date version of pip and setuptools:
$ pip install --upgrade pip setuptools
$ pip install --upgrade undatum
(If pip installation fails for some reason, you can try easy_install undatum as a fallback.)
2.4 Python version
Python version 3.6 or greater is required.
3 Usage
Synopsis:
$ undatum [flags] [command] inputfile
See also undatum --help.
3.1 Examples
Get headers from file as headers command, JSONl data:
$ undatum headers examples/ausgovdir.jsonl
Analyze file and generate statistics stats command:
$ undatum stats examples/ausgovdir.jsonl
Get frequency command of values for field GovSystem in the list of Russian federal government domains from govdomains repository
$ undatum frequency examples/feddomains.csv --fields GovSystem
Get all unique values using uniq command of the item.type field
$ undatum uniq --fields item.type examples/ausgovdir.jsonl
convert command from XML to JSON lines file on tag item:
$ undatum convert --tagname item examples/ausgovdir.xml examples/ausgovdir.jsonl
Validate data with validate command against validation rule ru.org.inn and field VendorINN in data file. Output is statistcs only :
$ undatum validate -r ru.org.inn --mode stats --fields VendorINN examples/roszdravvendors_final.jsonl > inn_stats.json
Validate data with validate command against validation rule ru.org.inn and field VendorINN in data file. Output all invalid records :
$ undatum validate -r ru.org.inn --mode invalid --fields VendorINN examples/roszdravvendors_final.jsonl > inn_invalid.json
4 Commands
4.1 Frequency command
Field value frequency calculator. Returns frequency table for certain field
Get frequencies of values for field GovSystem in the list of Russian federal government domains from govdomains repository
$ undatum frequency examples/feddomains.csv --fields GovSystem
4.2 Uniq command
Returns all unique files of certain field(s). Accepts parameter fields with comma separated fields to gets it unique values. Provide single field name to get unique values of this field or provide list of fields to get combined unique values.
Returns all unique values of field regions in selected JSONl file
$ undatum uniq --fields region examples/reestrgp_final.jsonl
Returns all unique combinations of fields status and regions in selected JSONl file
$ undatum uniq --fields status,region examples/reestrgp_final.jsonl
4.3 Convert command
Converts data from one format to another. Supports conversions:
XML to JSON lines
CSV to JSON lines
XLS to JSON lines
XLSX to JSON lines
XLS to CSV
CSV to BSON
XLS to BSON
Conversion between XML and JSON lines require flag tagname with name of tag which should be converted into single JSON record.
Converts XML ausgovdir.xml with tag named item to ausgovdir.jsonl
$ undatum convert --tagname item examples/ausgovdir.xml examples/ausgovdir.jsonl
4.4 Validate command
Validate command used to check every value of of field against validation rules like rule to validate email or url.
Current supported rules:
common.email - checks if value is email
common.url - checks if value is url
ru.org.inn - checks if value is russian organization INN identifier
ru.org.ogrn - checks if value if russian organization OGRN identifier
Validate data with validate command against validation rule ru.org.inn and field VendorINN in data file. Output all invalid records :
$ undatum validate -r ru.org.inn --mode invalid --fields VendorINN examples/roszdravvendors_final.jsonl > inn_invalid.json
4.5 Headers command
Returns fieldnames of the file. Supports CSV, JSON, BSON file types. For CSV file it takes first line of the file and for JSON lines and BSON files it processes number of records provided as limit parameter with default value 10000.
Returns headers of JSON lines file with top 10 000 records (default value)
$ undatum headers examples/ausgovdir.jsonl
Returns headers of JSON lines file using top 50 000 records
$ undatum headers --limit 50000 examples/ausgovdir.jsonl
4.6 Stats command
Collects statistics about data in dataset. Right now supports only JSON lines files
Returns table with following data:
key - name of the key
ftype - data type of the values with this key
is_dictkey - if True, than this key is identified as dictionary value
is_uniq - if True, identified as unique field
n_uniq - number of unique values
share_uniq - share of unique values among all values
minlen - minimal length of the field
maxlen - maximum length of the field
avglen - average length of the field
Returns stats for JSON lines file
$ undatum stats examples/ausgovdir.jsonl
Analysis of JSON lines file and verifies each field that it’s date field, detects date format:
$ undatum stats --checkdates examples/ausgovdir.jsonl
4.7 Split command
Splits dataset into number of datasets based on number of records or field value. Chunksize parameter -c used to set size of chunk if dataset should be splitted by chunk size rule. If dataset should be splitted by field value than –fields parameter used.
Split dataset as 10000 records chunks, procuces files like filename_1.jsonl, filename_2.jsonl where filename is name of original file except extension.
$ undatum split -c 10000 examples/ausgovdir.jsonl
Split dataset as number of files based of field item.type”, generates files [filename]_[value1].jsonl, [filename]_[value2].jsonl and e.t.c. There are *[filename] - ausgovdir and [value1] - certain unique value from item.type field
$ undatum split --fields item.type examples/ausgovdir.jsonl
4.8 Select command
Select or re-order columns from file. Supports CSV, JSON lines, BSON
Returns columns item.title and item.type from ausgovdir.jsonl
$ undatum select --fields item.title,item.type examples/ausgovdir.jsonl
Returns columns item.title and item.type from ausgovdir.jsonl and stores result as selected.jsonl
$ undatum select --fields item.title,item.type -o selected.jsonl examples/ausgovdir.jsonl
4.9 Flatten command
Flatten data records. Write them as one value per row
Returns all columns as flattened key,value
$ undatum flatten examples/ausgovdir.jsonl
5 Advanced
5.1 Filtering
You could filter values of any file record by using filter attr for any command where it’s suported.
Returns columns item.title and item.type filtered with item.type value as role. Note: keys should be surrounded by “`” and text values by “’”.
$ undatum select --fields item.title,item.type --filter "`item.type` == 'role'" examples/ausgovdir.jsonl
5.2 Data containers
Sometimes, to keep keep memory usage as low as possible to process huge data files. These files are inside compressed containers like .zip, .gz, .bz2 or .tar.gz files. undatum could process compressed files with little memory footprint, but it could slow down file processing.
Returns headers from subs_dump_1.jsonl file inside subs_dump_1.zip file. Require parameter -z to be set and –format-in force input file type.
$ undatum headers --format-in jsonl -z subs_dump_1.zip
5.3 Date detection
JSON, JSON lines and CSV files do not support date and datetime data types. If you manually prepare your data, than you could define datetime in JSON schema for example.B But if data is external, you need to identify these fields.
undatum supports date identification via qddate python library with automatic date detection abilities.
$ undatum stats --checkdates examples/ausgovdir.jsonl
6 Data types
JSONl
JSON lines is a replacement to CSV and JSON files, with JSON flexibility and ability to process data line by line, without loading everithing into memory.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.