python-fmrest is a wrapper around the FileMaker Data API.
No need to worry about manually requesting access tokens, setting the right http headers, parsing responses, ...
Some things you may use the python-fmrest library for:
If you haven't worked with Python and Virtualenvs before:
brew install python3/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)")pip3 install virtualenvIf you have worked with Python and Virtualenvs before, or after executing the steps above:
virtualenv venv --python=`which python3`source venv/bin/activatepip install python-fmrestLink to this notebook: https://github.com/davidhamann/python-fmrest/tree/master/examples/conf_dotfmp_2018.ipynb
fmrest extended privilege
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import fmrest
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fmrest.__version__
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fms = fmrest.Server(
'https://dotfmp-demo.davidhamann.de',
user='admin',
password='admin',
database='planets',
layout='Planets',
# if you are testing without cert/domain you may need the parameter verify_ssl=False here.
)
This gives you a server instance which provides all further methods to interact with the Data API.
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fms
Obtain a token from FMS:
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fms.login()
List all records from the Planets table
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planets = fms.get_records()
for planet in planets:
print(f'{planet.id}, {planet.record_id}, {planet.name}')
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type(planets), planets
Look at (some of) the moons of Jupiter (list records of a portal)
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record = fms.get_record(5, portals=[{'name': 'moons', 'limit': 5}])
portal = record['portal_moons']
record, portal
Fetching a record always gives you a Record instance. The portal rows, however, are returned as a Foundset.
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for row in portal:
print(row['Moons::name'])
You can inspect what fields are available:
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record.keys()
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record.values()
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record.to_dict()
And access the value by attribute or key:
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record.name, record['atmosphere']
So far we have seen Server, Foundset, Record. These are the main classes you need to be aware of when working with the library.
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find_request = [{'name': 'Earth'}, {'name': 'Jupiter'}]
foundset = fms.find(query=find_request)
earth = foundset[0]
earth
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earth.name = 'Blue Dot'
earth
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fms.edit(earth)
Handle outdated record values:
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# change back
earth.name = 'Earth'
fms.edit(earth, validate_mod_id=False)
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pluto = fms.create_record({'name': 'Pluto', 'id': 9})
pluto
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fms.delete_record(pluto)
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fms.get_record(
1,
scripts={
'after': ['say_hello', 'dotfmp']
}
)
fms.last_script_result
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fms.last_script_result['after'][1]
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with open('../scratch/dotfmp_logo.png', 'rb') as image:
result = fms.upload_container(3, 'image', image) # upload dotfmp logo into field with name "image" of record 3
result
Now retrieve the image again:
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earth = fms.get_record(3)
earth.image
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name, type_, length, response = fms.fetch_file(earth.image)
name, type_, length
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from IPython.display import Image
Image(response.content)
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find_request = [{'name': 'something that doesn\'t exist'}]
foundset = fms.find(query=find_request)
Turn Foundset into a Pandas DataFrame to do statistical analyses on your dataset, work with missing data, reshape/pivot, perform joins/merges, plot with matplotlib, export, etc.
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foundset = fms.get_records()
df = foundset.to_df()
df.loc[:, df.columns != 'image']
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df[['name', 'atmosphere', 'rings', 'confirmed_moons', 'mass']].set_index('name').T
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df.describe()
... or plot some data with matplotlib
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%matplotlib notebook
df.plot(x='name', y='confirmed_moons')
... or export the data in a different format
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path = 'data.csv'
df.to_csv(path, sep=";", index=False)
from IPython.display import FileLink
FileLink(path)
Read about Pandas here: https://pandas.pydata.org