Get your CRM export in

One call, read_gifts(path_or_rows, source=...), turns a gift export from any of the five CRMs below into the donor-level feature table every model in this library expects. Pick your CRM's tab for the exact column mapping and a runnable example; export steps vary by CRM configuration and version, so treat them as a starting point, not exact click-by-click instructions.

Raiser's Edge / RE NXTSalesforce NPSPBloomerangDonorPerfectCiviCRM

Run a gift export or query (Raiser's Edge NXT: Reports > Gift reports, or Query for a custom pull) and include at minimum the constituent ID, gift date, gift amount, and gift type columns, saved as CSV.

Your column	Maps to	Notes
`Constituent ID` (or `constit_id`, `donor_id`)	`contact_id`	required
`Gift Date` (or `Pledged on` for a pledge row)	`receive_date`	required
`Gift Amount`	`total_amount`	required
`Gift Type`	excluded via `exclude_gift_types`	pledges are excluded by default so a promise isn't double-counted with its payments

from philanthropy.ingest import read_gifts

rows = [
    {"Constituent ID": "88", "Gift Date": "2025-01-10",
     "Gift Amount": "1200.00", "Gift Type": "Pledge"},
    {"Constituent ID": "88", "Gift Date": "2025-02-10",
     "Gift Amount": "100.00", "Gift Type": "Pay-Cash"},
]
features = read_gifts(rows, source="raisers_edge")
print(features.loc["88", "total_gift_amount"])  # 100.0: the pledge is excluded

A real export is a CSV file with these same column headers; pass its path instead of a list of rows: read_gifts("gifts.csv", source="raisers_edge").

Export your Opportunity report (Reports > New Report, an Opportunity report type) with the account/contact key, close date, amount, and stage columns, saved as CSV.

Your column	Maps to	Notes
`Account ID` (or `AccountId`, `Primary Contact`)	`contact_id`	required
`Close Date`	`receive_date`	required
`Amount`	`total_amount`	required
`Stage`	filtered via `include_stages`	only `Closed Won` counts as money received by default; `Pledged` is excluded

from philanthropy.ingest import read_gifts

rows = [
    {"Account ID": "88", "Close Date": "2025-01-10",
     "Amount": "100.00", "Stage": "Pledged"},
    {"Account ID": "88", "Close Date": "2025-02-10",
     "Amount": "100.00", "Stage": "Closed Won"},
]
features = read_gifts(rows, source="npsp")
print(features.loc["88", "total_gift_amount"])  # 100.0: the pledge is excluded

A real export is a CSV file with these same column headers; pass its path instead of a list of rows: read_gifts("gifts.csv", source="npsp").

Export your transaction list (Transactions > Export) with the account number, date, amount, and transaction type columns, saved as CSV.

Your column	Maps to	Notes
`Account Number` (or `AccountId`)	`contact_id`	required
`Date`	`receive_date`	required
`Amount`	`total_amount`	required
`Transaction Type`	filtered via `exclude_entry_types`	`Pledge` rows are excluded by default so the promise isn't double-counted with its `PledgePayment` rows

from philanthropy.ingest import read_gifts

rows = [
    {"Account Number": "88", "Date": "2025-01-10",
     "Amount": "1200.00", "Transaction Type": "Pledge"},
    {"Account Number": "88", "Date": "2025-02-10",
     "Amount": "100.00", "Transaction Type": "PledgePayment"},
]
features = read_gifts(rows, source="bloomerang")
print(features.loc["88", "total_gift_amount"])  # 100.0: the pledge is excluded

A real export is a CSV file with these same column headers; pass its path instead of a list of rows: read_gifts("gifts.csv", source="bloomerang").

Export a gift report (Reports > Report Writer, or the "Gift/Pledge detail" canned report) with the donor ID, gift date, gift amount, and record type columns, saved as CSV.

Your column	Maps to	Notes
`Donor ID` (or `DonorID`)	`contact_id`	required
`Gift Date`	`receive_date`	required
`Gift Amount`	`total_amount`	required
`Record Type`	filtered via `exclude_record_types`	`P` (Pledge) and `M` (a split gift's Main total) are excluded by default; `G` (a regular gift or pledge payment) counts

from philanthropy.ingest import read_gifts

rows = [
    {"Donor ID": "88", "Gift Date": "2025-01-10",
     "Gift Amount": "1200.00", "Record Type": "P"},
    {"Donor ID": "88", "Gift Date": "2025-02-10",
     "Gift Amount": "100.00", "Record Type": "G"},
]
features = read_gifts(rows, source="donorperfect")
print(features.loc["88", "total_gift_amount"])  # 100.0: the pledge is excluded

A real export is a CSV file with these same column headers; pass its path instead of a list of rows: read_gifts("gifts.csv", source="donorperfect").

Export a contribution search result (Search > Find Contributions, or the equivalent APIv4 Contribution.get call) with the contact ID, contribution date, total amount, and contribution status columns, saved as CSV.

Your column	Maps to	Notes
`Contact ID`	`contact_id`	required
`Contribution Date` (or `receive_date` from an APIv4 pull)	`receive_date`	required
`Total Amount`	`total_amount`	required
`Contribution Status`	filtered via `statuses`	only `Completed` counts by default; `Failed`, `Cancelled`, and pending statuses are excluded

from philanthropy.ingest import read_gifts

rows = [
    {"Contact ID": "101", "Contribution Date": "2025-01-15",
     "Total Amount": "250.00", "Contribution Status": "Completed"},
    {"Contact ID": "101", "Contribution Date": "2025-06-02",
     "Total Amount": "99.00", "Contribution Status": "Failed"},
]
features = read_gifts(rows, source="civicrm")
print(features.loc["101", "total_gift_amount"])  # 250.0: the failed contribution is excluded

A real export is a CSV file with these same column headers; pass its path instead of a list of rows: read_gifts("gifts.csv", source="civicrm").

Next step

read_gifts returns one row per donor, indexed by contact_id, with columns like total_gift_amount, gift_count, and recency_days. Feed that straight into a model, e.g. DonorPropensityModel or LapsePredictor, the same way the Results pages do, or see Use the CLI to run this from the command line without writing any code.