This is a package to automatically detect column content in tabular files. The script reads either the whole file or the first few rows and performs various checks to see for each column if it matches with various content types. This is currently done through regex and string comparison.
Currently supported file types: csv, xls, xlsx, ods.
You can also directly feed the URL of a remote file (from data.gouv.fr for instance).
You need to have python >= 3.7 installed. We recommend using a virtual environement.
pip install csv-detective
Say you have a tabular file located at file_path. This is how you could use csv_detective:
# Import the csv_detective package
from csv_detective import routine
import os # for this example only
# Replace by your file path
file_path = os.path.join('.', 'tests', 'code_postaux_v201410.csv')
# Open your file and run csv_detective
inspection_results = routine(
file_path, # or file URL
num_rows=-1, # Value -1 will analyze all lines of your file, you can change with the number of lines you wish to analyze
save_results=False, # Default False. If True, it will save result output into the same directory as the analyzed file, using the same name as your file and .json extension
output_profile=True, # Default False. If True, returned dict will contain a property "profile" indicating profile (min, max, mean, tops...) of every column of you csv
output_schema=True, # Default False. If True, returned dict will contain a property "schema" containing basic [tableschema](https://specs.frictionlessdata.io/table-schema/) of your file. This can be use to validate structure of other csv which should match same structure.
)
The program creates a Python dictionnary with the following information :
{
"encoding": "windows-1252", # Encoding detected
"separator": ";", # Detected CSV separator
"header_row_idx": 0 # Index of the header (aka how many lines to skip to get it)
"headers": ['code commune INSEE', 'nom de la commune', 'code postal', "libellé d'acheminement"], # Header row
"total_lines": 42, # Number of rows (excluding header)
"nb_duplicates": 0, # Number of exact duplicates in rows
"heading_columns": 0, # Number of heading columns
"trailing_columns": 0, # Number of trailing columns
"categorical": ['Code commune'] # Columns that contain less than 25 different values (arbitrary threshold)
"columns": { # Property that conciliate detection from labels and content of a column
"Code commune": {
"python_type": "string",
"format": "code_commune_insee",
"score": 1.0
},
},
"columns_labels": { # Property that return detection from header columns
"Code commune": {
"python_type": "string",
"format": "code_commune_insee",
"score": 0.5
},
},
"columns_fields": { # Property that return detection from content columns
"Code commune": {
"python_type": "string",
"format": "code_commune_insee",
"score": 1.25
},
},
"profile": {
"column_name" : {
"min": 1, # only int and float
"max: 12, # only int and float
"mean": 5, # only int and float
"std": 5, # only int and float
"tops": [ # 10 most frequent values in the column
"xxx",
"yyy",
"..."
],
"nb_distinct": 67, # number of distinct values
"nb_missing_values": 102 # number of empty cells in the column
}
},
"schema": { # TableSchema of the file if `output_schema` was set to `True`
"$schema": "https://frictionlessdata.io/schemas/table-schema.json",
"name": "",
"title": "",
"description": "",
"countryCode": "FR",
"homepage": "",
"path": "https://github.com/datagouv/csv-detective",
"resources": [],
"sources": [
{"title": "Spécification Tableschema", "path": "https://specs.frictionlessdata.io/table-schema"},
{"title": "schema.data.gouv.fr", "path": "https://schema.data.gouv.fr"}
],
"created": "2023-02-10",
"lastModified": "2023-02-10",
"version": "0.0.1",
"contributors": [
{"title": "Table schema bot", "email": "[email protected]", "organisation": "data.gouv.fr", "role": "author"}
],
"fields": [
{
"name": "Code commune",
"description": "Le code INSEE de la commune",
"example": "23150",
"type": "string",
"formatFR": "code_commune_insee",
"constraints": {
"required": False,
"pattern": "^([013-9]\\d|2[AB1-9])\\d{3}$",
}
}
]
}
}
The output slightly differs depending on the file format:
- csv files have
encodingandseparator - xls, xls, ods files have
engineandsheet_name
Includes :
- Communes, Départements, Régions, Pays
- Codes Communes, Codes Postaux, Codes Departement, ISO Pays
- Codes CSP, Description CSP, SIREN
- E-Mails, URLs, Téléphones FR
- Years, Dates, Jours de la Semaine FR
- UUIDs, Mongo ObjectIds
For each column, 3 scores are computed for each format, the higher the score, the more likely the format:
- the field score based on the values contained in the column (0.0 to 1.0).
- the label score based on the header of the column (0.0 to 1.0).
- the overall score, computed as
field_score * (1 + label_score/2)(0.0 to 1.5).
The overall score computation aims to give more weight to the column contents while still leveraging the column header.
This option allows you to select the output mode you want to pass. To do so, you have to pass a limited_output argument to the routine function. This variable has two possible values:
limited_outputdefaults toTruewhich means report will contain only detected column formats based on a pre-selected threshold proportion in data. Report result is the standard output (an example can be found above in 'Output' section). Only the format with highest score is present in the output.limited_output=Falsemeans report will contain a full list of all column format possibilities for each input data columns with a value associated which match to the proportion of found column type in data. With this report, user can adjust its rules of detection based on a specific threshold and has a better vision of quality detection for each columns. Results could also be easily transformed into a dataframe (columns types in column / column names in rows) for analysis and test.
- Smarter refactors
- Improve performances
- Test other ways to load and process data (
pandasalternatives) - Add more and more detection modules...
Related ideas:
- store column names to make a learning model based on column names for (possible pre-screen)
- normalising data based on column prediction
- entity resolution (good luck...)
Organisations such as data.gouv.fr aggregate huge amounts of un-normalised data. Performing cross-examination across datasets can be difficult. This tool could help enrich the datasets metadata and facilitate linking them together.
udata-hydra is a crawler that checks, analyzes (using csv-detective) and APIfies all tabular files from data.gouv.fr.
An early version of this analysis of all resources on data.gouv.fr can be found here.
The release process uses bumpr.
pip install -r requirements-build.txtbumprwill handle bumping the version according to your command (patch, minor, major)- It will update the CHANGELOG according to the new version being published
- It will push a tag with the given version to github
- CircleCI will pickup this tag, build the package and publish it to pypi
bumprwill have everything ready for the next version (version, changelog...)
bumpr -d -vThis will release a patch version:
bumpr -vSee bumpr options for minor and major:
$ bumpr -h
usage: bumpr [-h] [--version] [-v] [-c CONFIG] [-d] [-st] [-b | -pr] [-M] [-m] [-p]
[-s SUFFIX] [-u] [-pM] [-pm] [-pp] [-ps PREPARE_SUFFIX] [-pu]
[--vcs {git,hg}] [-nc] [-P] [-nP]
[file] [files ...]
[...]
optional arguments:
-h, --help show this help message and exit
--version show program's version number and exit
-v, --verbose Verbose output
-c CONFIG, --config CONFIG
Specify a configuration file
-d, --dryrun Do not write anything and display a diff
-st, --skip-tests Skip tests
-b, --bump Only perform the bump
-pr, --prepare Only perform the prepare
bump:
-M, --major Bump major version
-m, --minor Bump minor version
-p, --patch Bump patch version
-s SUFFIX, --suffix SUFFIX
Set suffix
-u, --unsuffix Unset suffix
[...]