AirMap

AirMap is a data mapper powered by Airtable. Easily keep track of data dictionaries, mappings, sources, columns, and validations.

Benefits

• Update master data mappings in Airtable, AirMap will automatically update your pipelines to match what you have in the cloud!
• Airtable centralizes all of your data's information, everything you need in one place.
• No need to send static documents back and forth, easily collaborate with others via Airtable's easy-to-use web/desktop GUIs.
• Quickly manage, control, and share your data mappings, validations, and data dictionaries.
  
  

Check out the Airtable base here to get an idea of how the data information is organized.

Examples:

Map data using a data-map from Airtable

(e.g. 'Project A - Client Summary', view the base here)

Demo Input Data Sources

Internal_Import_Format2	Internal_Table_Format1	Internal_Import_Format1
ServicerID DocumentID 0 1938472929 AJ3fLN32jS1SK2 1 1293018083 J2sjldw3nSk4S2l 2 9420410832 2Pn28dh1lsdD0Q	ServiceLineID ProviderID 0 20394822 1938472929 1 20428284 1293018083 2 19428292 9420410832	ServiceDate ClientDOB PurchaseAmount ProviderID PurchaseCount ClientReview 0 10/09/2021 7/4/1776 $103.20 1938472929 4 4.5 1 10/07/2021 12/30/1899 $120.12 1293018083 5 4.1 2 10/08/2021 1/26/1980 $84.23 9420410832 2 3.7

# Pass the data sources to AirMap and specify the output format you want to create.
# AirMap will automatically organize and map the data sources into your output format designed in Airtable.

Internal_Import_Format1 = pd.read_csv("Internal_Import_Format1.csv",dtype=str)
Internal_Import_Format2 = pd.read_csv("Internal_Import_Format2.csv",dtype=str)
Internal_Table_Format1 = pd.read_csv("Internal_Table_Format1.csv",dtype=str)

data = {'Internal_Import_Format1' : Internal_Import_Format1,
        'Internal_Import_Format2' : Internal_Import_Format2,
        'Internal_Table_Format1' : Internal_Table_Format1 }

airMap = airtableDataMapper(base_key,airAPI_key)
airMap.findMappingView("Project A - Client Summary").mapData(data)

Resulting output table

	Line Service ID	ServiceProviderID	Date of Service	PurchaserDOB	PurchaseAmount	Document
0	20394822	1938472929	10/09/2021	7/4/1776	$103.20	AJ3fLN32jS1SK2
1	20428284	1293018083	10/07/2021	12/30/1899	$120.12	J2sjldw3nSk4S2l
2	19428292	9420410832	10/08/2021	1/26/1980	$84.23	2Pn28dh1lsdD0Q

You can use AirMap to check a mapping's requirements directly in Python:

# Check input data sources used for a chosen mapping
airMap.findMappingView("Project A - Client Summary").viewInputSources()

['Internal_Import_Format2', 'Internal_Table_Format1', 'Internal_Import_Format1']

# View input data sources used for a chosen mapping
airMap.findMappingView("Project A - Client Summary").viewInputColumns()

	InputSource	InputColumn	OutputColumn
0	Internal_Import_Format1	ServiceDate	Date of Service
1	Internal_Import_Format1	ClientDOB	PurchaserDOB
2	Internal_Import_Format1	ProviderID	ServiceProviderID
3	Internal_Import_Format1	PurchaseAmount	PurchaseAmount
5	Internal_Table_Format1	ServiceLineID	Line Service ID
4	Internal_Import_Format2	DocumentID	Document

# View all mapping details for a chosen mapping
airMap.findMappingView("Project A - Client Summary").viewMap()

	OutputColumn	InputColumn	InputSource	MaxLength	ColumnPosition	DataType	Format	Active	Description	Required
0	Date of Service	ServiceDate	Internal_Import_Format1	10	1	Date	mm-dd-yyyy	Yes	Date of services rendered	Y
1	PurchaserDOB	ClientDOB	Internal_Import_Format1	10	2	Date	mm-dd-yyyy	Yes	Client's date of birth	Y
2	ServiceProviderID	ProviderID	Internal_Import_Format1	10	3	String	##########	Yes	Service providers ID	Y
3	PurchaseAmount	PurchaseAmount	Internal_Import_Format1	20	4	String	$dd.cc	Yes	Client's purchase	Y
5	Line Service ID	ServiceLineID	Internal_Table_Format1	8	5	String	########	Yes	Service providers ID	Y
4	Document	DocumentID	Internal_Import_Format2	10	6	String	NaN	Yes	DocumentID	N