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Datasets:
stefan-it
/
d-info-2005-names

Modalities:
Tabular
Text
Formats:
csv
Languages:
German
Size:
100K - 1M
Tags:
onomastics
surnames
forenames
germany
demographics
historical
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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metadata 
license: other
license_name: research-use-only
license_link: LICENSE
language:
  - de
pretty_name: German Name Frequencies by State & District (D-Info 2005)
size_categories:
  - 100K<n<1M
tags:
  - onomastics
  - surnames
  - forenames
  - germany
  - demographics
  - historical
configs:
  - config_name: surnames_district
    data_files: surnames_district.csv
  - config_name: forenames_district
    data_files: forenames_district.csv
  - config_name: surnames_state
    data_files: surnames_state.csv
  - config_name: forenames_state
    data_files: forenames_state.csv
  - config_name: distinctive_surnames_state
    data_files: distinctive_surnames_state.csv
  - config_name: distinctive_forenames_state
    data_files: distinctive_forenames_state.csv

German Name Frequencies by State & District (D-Info 2005)

Regional frequency of surnames and forenames in Germany, from the D-Info 2005 telephone-directory CD-ROM (klickTel, data status 02.06.2005), at two administrative levels aligned with census-2022 geography:

  • State = Bundesland — the 16 federal states.
  • District = Landkreis / kreisfreie Stadt — the 377 districts, keyed by their 5-digit Kreisschlüssel (AGS).

For every name each table gives its number of 2005 telephone listings in a region and the conditional probability P(name | region). Aggregate statistics only — no individuals, addresses or phone numbers.

Subsets (configs)

There is no default/combined config — load a subset by name:

from datasets import load_dataset
ds = load_dataset("stefan-it/d-info-2005-names", "surnames_district", split="train")
Config Level Threshold Rows Content
surnames_district District N ≥ 10 170,642 P(surname | Landkreis)
forenames_district District N ≥ 10 89,497 P(forename | Landkreis)
surnames_state State N ≥ 20 124,109 P(surname | Bundesland)
forenames_state State N ≥ 20 19,453 P(forename | Bundesland)
distinctive_surnames_state State N ≥ 20 124,109 surnames ranked by regional lift
distinctive_forenames_state State N ≥ 20 19,453 forenames ranked by regional lift

Schema

District subsets (*_district):

Column Type Meaning
district string 5-digit AGS Kreisschlüssel (e.g. 01001 = Flensburg)
district_name string district name
state string ISO 3166-2:DE code of the district's Bundesland
name string surname / forename (UTF-8; umlauts & ß preserved)
count int listings carrying the name in that district
prob float count / observations-of-that-kind in that district

State subsets (*_state): columns state, name, count, prob (same meaning, region = Bundesland).

Distinctive subsets add two columns and are sorted by lift descending within each state:

Column Type Meaning
p_national float P(name) — the name's share nationwide
lift float prob / p_national — regional over-representation

State ISO codes: SH Schleswig-Holstein, HH Hamburg, NI Niedersachsen, HB Bremen, NW Nordrhein-Westfalen, HE Hessen, RP Rheinland-Pfalz, BW Baden-Württemberg, BY Bayern, SL Saarland, BE Berlin, BB Brandenburg, MV Mecklenburg-Vorpommern, SN Sachsen, ST Sachsen-Anhalt, TH Thüringen.

The numbers

P(name∣region)=count(name, region)observations of that kind in region P(\text{name} \mid \text{region}) = \frac{\text{count}(\text{name},\ \text{region})}{\text{observations of that kind in region}}

  • Surnames are conditioned on all listings in the region (≈ every listing has a surname).
  • Forenames are conditioned on listings whose forename is known (2005 lists many people by initial only), and a forename is its first given name — compound names are aggregated to word 1, so no row contains a space and there is no separate compound table.

Distinctive names measure regional concentration rather than raw frequency (Müller is #1 in every state and so tells you nothing regional):

lift(name)=P(name∣state)P(name)=share of the name in the stateshare of the name nationwide \text{lift}(\text{name}) = \frac{P(\text{name} \mid \text{state})}{P(\text{name})} = \frac{\text{share of the name in the state}}{\text{share of the name nationwide}}

lift = 1 → as common regionally as nationally; lift ≫ 1 → concentrated in the state → typical of it. Rows are ranked by lift. Because the only floor is the k-threshold (N ≥ 20), the very top of each state list is rare-but- highly-localised names; for common-and-typical names (e.g. München-region Huber, Bavarian Aigner) filter on a higher count (say ≥ 200) before reading off the ranking. Example top-lift forenames: BY → Emmeran, Kunigunda, Kreszenz (Bavarian-Catholic); SN → Rico, Anett, Sylke (East-German).

Privacy

Per-cell k-anonymity. A (name, region) row is published only if its own count meets the threshold — N ≥ 10 for districts, N ≥ 20 for states — so every published cell represents at least that many people sharing the name in that region and no individual can be singled out (GDPR Recital 26). A national-total floor alone is not sufficient at district level: a name common nationwide can still be count = 1 in one district. Only aggregate counts are released.

Provenance & licence

Source: D-Info 2005 CD-ROM, klickTel (data status 02.06.2005). Only aggregate, k-anonymized frequency statistics are published here. Released under a custom research-use-only term (see LICENSE): use for non-commercial research, with attribution, and no attempt at re-identification.