Significant Figures Lost to Float Parsing (.csv)
Eight written measurements where the trailing zeros carry the precision of the measurement, alongside what a float parse leaves behind. Parsing 1.200 to the double 1.2 is not a rounding error, it is the loss of the claim that the value is known to four figures.
as_written,as_float,significant_figures,trailing_zeros_meaningful,implied_uncertainty,what_naive_parsing_loses
1.200,1.2,4,true,±0.0005,the two trailing zeros
1.2,1.2,2,false,±0.05,nothing
0.00340,0.0034,3,true,±0.000005,the trailing zero
100,100.0,1 to 3 (ambiguous),unknown,unstated,the ambiguity itself
1.00e2,100.0,3,true,±0.5,the explicit precision
1e2,100.0,1,false,±50,nothing
12.340,12.34,5,true,±0.0005,the trailing zero
0.10,0.1,2,true,±0.005,the trailing zero
Specifications
- Rows
- 8
- Columns
- 6
- Issue
- trailing zeros carry precision that a float cannot store
- Ambiguous Case
- 100
- Recommendation
- keep the written form as text alongside the float
- Exponential Cases
- 2
Testing contract
Reference control- Scenario
- Parse each as_written string to a float, then attempt to recover the significant_figures column from the float alone.
- Expected result
- The float values are correct in every row, but the significant figure count cannot be recovered for any row with trailing zeros, which is exactly the four rows flagged trailing_zeros_meaningful.
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
“Significant Figures Lost to Float Parsing (.csv)” is a deterministic Novus Examples fixture for Scientific data, CSV parsing, Editor testing. Citation catalogs (BibTeX, RIS), chemistry structures (MDL Molfile, PDB), and gridded binary data (NetCDF, FITS) — for testing reference managers, molecule viewers, and scientific-data loaders.
Documented properties for this file: 8 rows · 6 columns. 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.
Scientific fixtures are small, valid, and fully synthetic — no real organism, patient, sample, or observation. Point your parser or loader at the file and check it reads the documented records, variables, or headers; binary formats ship a readable twin or metadata listing for comparison.
Code examples
import pandas as pd
df = pd.read_csv("significant-figures.csv")
print(df.head())
print(df.dtypes)Related files
- csvCalibration Certificate — Error and Expanded Uncertainty (.csv)Five calibration points with the nominal value, the measured value, the signed error and the expanded uncertainty at k=2 with its confidence level stated. It is a synthetic illustration of the layout, not an accredited certificate, and nothing in it refers to a real instrument or laboratory.
- csvCensored Values and Detection Limits — Non-Numeric Results (.csv)Nine laboratory results where only four are plain numbers: two are below the detection limit, one is above range, two are missing in different spellings, and one is a legitimate small negative near the blank. Coercing the censored strings to numbers or to NaN both bias the summary, and the file distinguishes every case explicitly.
- csvGUM-Style Uncertainty Budget — Sources Combined in Quadrature (.csv)A five-source uncertainty budget laid out the way the GUM prescribes: each source with its distribution, its divisor, its sensitivity coefficient and its contribution, then the combined and expanded values. Rectangular sources are divided by the square root of three, which is the step budget spreadsheets most often get wrong.
- csvInterlaboratory Comparison — z-Scores and En Numbers (.csv)Six fictional laboratories reporting the same measurand, scored both by z-score against a fixed sigma and by En number against each lab's own claimed uncertainty. The two statistics disagree about which labs are acceptable, which is the point: En rewards an honest uncertainty claim and z does not.
- csvSpectrometer Absorbance With Expanded Uncertainty (.csv)A synthetic absorbance scan from 400 to 630 nm with a Gaussian band at 520 nm, carrying its expanded uncertainty at k=2 in a dedicated column and a per-row quality flag. The unit and the coverage factor live in the column names, which is the convention most instrument exports actually use.
- csvThermocouple Time Series With Standard Uncertainty (.csv)A full day of thermocouple readings at 30-minute cadence with a constant type-B standard uncertainty and two rows flagged as recovering. The timestamps are explicit UTC with a Z suffix so a reader has no excuse to guess a timezone.
Generated by generation/scientific.py. Free for any use, no attribution required — license.