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Tern Client Services: Sales pipeline export

Sales pipeline export for Tern Client Services. 12 deals across 4 companies and four stages. weighted_amount equals amount times probability on every row, amount totals 34500.00 and weighted_amount totals 15825.00. The 3 won deals total 9000.00, which is the sum of the project budgets on project-profitability.csv, and the lost deals carry probability 0.00 and a weighted amount of 0.00.

csv

text/csv

1 KB
Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
12

Binary csv: no in-browser preview. Download it above to open in a compatible application.

Specifications

Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
12
Stages
4
Pipeline Usd
34500.00
Weighted Usd
15825.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Import the pipeline and recompute the weighted value by stage and in total.
Expected result
All 12 rows recompute: weighted_amount = amount * probability to the cent. Amounts sum to 34500.00 and weighted amounts to 15825.00. Filtering to stage won gives 9000.00, matching the budgets of the three projects that were opened from those deals.

What is a .csv file?

CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.

How to use this file

Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.

How to use this file for testing

“Tern Client Services: Sales pipeline export” is a deterministic Novus Examples fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: 12 rows · UTF-8. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Code examples

import pandas as pd

df = pd.read_csv("pipeline-export.csv")
print(df.head())
print(df.dtypes)

Generated by generation/industry_documents.py. Free for any use, no attribution required, license.