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https://github.com/weecology/retriever
Quickly download, clean up, and install public datasets into a database management system
https://github.com/weecology/retriever
data data-retrieval data-science dataset datasets hacktobefest python
Last synced: about 1 month ago
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Quickly download, clean up, and install public datasets into a database management system
- Host: GitHub
- URL: https://github.com/weecology/retriever
- Owner: weecology
- License: other
- Created: 2011-06-28T19:01:15.000Z (over 13 years ago)
- Default Branch: main
- Last Pushed: 2024-06-07T03:38:37.000Z (6 months ago)
- Last Synced: 2024-07-18T00:36:01.941Z (5 months ago)
- Topics: data, data-retrieval, data-science, dataset, datasets, hacktobefest, python
- Language: Python
- Homepage: http://data-retriever.org
- Size: 77.4 MB
- Stars: 306
- Watchers: 30
- Forks: 133
- Open Issues: 54
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: docs/code_of_conduct.rst
- Citation: CITATION
Awesome Lists containing this project
- awesome-starred - weecology/retriever - Quickly download, clean up, and install public datasets into a database management system (python)
README
[](https://github.com/weecology/retriever/actions/workflows/python-package.yml)
[](https://ci.appveyor.com/project/ethanwhite/retriever/branch/main)
[](http://depsy.org/package/python/retriever)
[](https://codecov.io/github/weecology/retriever?branch=main)
[](http://retriever.readthedocs.io/en/latest/?badge=latest)
[](https://raw.githubusercontent.com/weecology/retriever/main/LICENSE)
[](https://gitter.im/weecology/retriever?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://doi.org/10.5281/zenodo.1038272)
[](https://doi.org/10.21105/joss.00451)
[](https://anaconda.org/conda-forge/retriever)
[](https://anaconda.org/conda-forge/retriever)
[](https://pypi.python.org/pypi/retriever)
Finding data is one thing. Getting it ready for analysis is another. Acquiring,
cleaning, standardizing and importing publicly available data is time consuming
because many datasets lack machine readable metadata and do not conform to
established data structures and formats. The Data Retriever automates the first
steps in the data analysis pipeline by downloading, cleaning, and standardizing
datasets, and importing them into relational databases, flat files, or
programming languages. The automation of this process reduces the time for a
user to get most large datasets up and running by hours, and in some cases days.
## Installing the Current Release
If you have Python installed you can install the current release using either `pip`:
```bash
pip install retriever
```
or `conda` after adding the `conda-forge` channel (`conda config --add channels conda-forge`):
```bash
conda install retriever
```
Depending on your system configuration this may require `sudo` for `pip`:
```bash
sudo pip install retriever
```
Precompiled binary installers are also available for Windows, OS X, and
Ubuntu/Debian on
the [releases page](https://github.com/weecology/retriever/releases). These do
not require a Python installation.
[List of Available Datasets](https://retriever.readthedocs.io/en/latest/datasets_list.html)
----------------------------
Installing From Source
----------------------
To install the Data Retriever from source, you'll need Python 3.6.8+ with the following packages installed:
* xlrd
The following packages are optionally needed to interact with associated
database management systems:
* PyMySQL (for MySQL)
* sqlite3 (for SQLite)
* psycopg2-binary (for PostgreSQL), previously psycopg2.
* pyodbc (for MS Access - this option is only available on Windows)
* Microsoft Access Driver (ODBC for windows)
### To install from source
Either use `pip` to install directly from GitHub:
```shell
pip install git+https://[email protected]/weecology/retriever.git
```
or:
1. Clone the repository
2. From the directory containing setup.py, run the following command: `pip
install .`. You may need to include `sudo` at the beginning of the
command depending on your system (i.e., `sudo pip install .`).
More extensive documentation for those that are interested in developing can be found [here](http://retriever.readthedocs.io/en/latest/?badge=latest)
Using the Command Line
----------------------
After installing, run `retriever update` to download all of the available dataset scripts.
To see the full list of command line options and datasets run `retriever --help`.
The output will look like this:
```shell
usage: retriever [-h] [-v] [-q]
{download,install,defaults,update,new,new_json,edit_json,delete_json,ls,citation,reset,help}
...
positional arguments:
{download,install,defaults,update,new,new_json,edit_json,delete_json,ls,citation,reset,help}
sub-command help
download download raw data files for a dataset
install download and install dataset
defaults displays default options
update download updated versions of scripts
new create a new sample retriever script
new_json CLI to create retriever datapackage.json script
edit_json CLI to edit retriever datapackage.json script
delete_json CLI to remove retriever datapackage.json script
ls display a list all available dataset scripts
citation view citation
reset reset retriever: removes configuration settings,
scripts, and cached data
help
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
-q, --quiet suppress command-line output
```
To install datasets, use `retriever install`:
```shell
usage: retriever install [-h] [--compile] [--debug]
{mysql,postgres,sqlite,msaccess,csv,json,xml} ...
positional arguments:
{mysql,postgres,sqlite,msaccess,csv,json,xml}
engine-specific help
mysql MySQL
postgres PostgreSQL
sqlite SQLite
msaccess Microsoft Access
csv CSV
json JSON
xml XML
optional arguments:
-h, --help show this help message and exit
--compile force re-compile of script before downloading
--debug run in debug mode
```
### Examples
These examples are using the [*Iris* flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set).
More examples can be found in the Data Retriever documentation.
Using Install
```shell
retriever install -h (gives install options)
```
Using specific database engine, retriever install {Engine}
```shell
retriever install mysql -h (gives install mysql options)
retriever install mysql --user myuser --password ******** --host localhost --port 8888 --database_name testdbase iris
```
install data into an sqlite database named iris.db you would use:
```shell
retriever install sqlite iris -f iris.db
```
Using download
```shell
retriever download -h (gives you help options)
retriever download iris
retriever download iris --path C:\Users\Documents
```
Using citation
```shell
retriever citation (citation of the retriever engine)
retriever citation iris (citation for the iris data)
```
Spatial Dataset Installation
----------------------------
**Set up Spatial support**
To set up spatial support for Postgres using Postgis please
refer to the [spatial set-up docs](https://retriever.readthedocs.io/en/latest/spatial_dbms.html).
```shell
retriever install postgres harvard-forest # Vector data
retriever install postgres bioclim # Raster data
# Install only the data of USGS elevation in the given extent
retriever install postgres usgs-elevation -b -94.98704597353938 39.027001800158615 -94.3599408119917 40.69577051867074
```
Website
-------
For more information see the
[Data Retriever website](http://www.data-retriever.org/).
Acknowledgments
---------------
Development of this software was funded by the [Gordon and Betty Moore
Foundation's Data-Driven Discovery
Initiative](https://www.moore.org/initiative-strategy-detail?initiativeId=data-driven-discovery) through
[Grant GBMF4563](http://www.moore.org/grants/list/GBMF4563) to Ethan White and
the [National Science Foundation](http://nsf.gov/) as part of a [CAREER award to
Ethan White](http://nsf.gov/awardsearch/showAward.do?AwardNumber=0953694).