Hungarian Elections¶

A dataset of 7 tables. 7 tables are many, so here, the first thing to look at is probably the ERD

How This is Created¶

This dataset was done without any request from any researchers. Sometimes we just make these if we find an interesting source or have some ideas we would like to test.

Note

This is a larger dataset, so the table profiles are more minimal here.

Entity Relationship Diagram (ERD)¶

What is an Entity Relationship Diagram (ERD)?

An entity relationship diagram (ERD) shows the relationships of entity sets stored in a database. An entity in this context is an object, a component of data, for example a variable. An entity set is a collection of similar entities. These entities can have attributes that define their properties.

By defining the entities, and their attributes, and showing the relationships between them, an ER diagram illustrates the logical structure of databases.

Each table demonstrates an entity set, where the rows represent the entities. Each entity set contains a primary key (PK) which is unique in the table. A primary key can be one entity, for example, a tax number or a set of entities like longitude and latitude in spatial data.

An entity set may have got a foreign key (FK). It means that the table is connected to another table’s primary key. This relationship can be represented by a line with a sign on its every ending. The sign specifies how many instances of an entity relate to one instance of another entity:

double lines (one)
branching line (more)

erDiagram affiliation { VARCHAR organization FK VARCHAR party } nominating_organization { VARCHAR nid PK } district_hierarchy { VARCHAR child FK VARCHAR parent FK } geographical_unit { VARCHAR level_info VARCHAR name VARCHAR unit_id PK } election { BOOLEAN is_individual DATETIME start_date VARCHAR election_id PK } election_precinct { VARCHAR election_id FK FLOAT eligible_voters BOOLEAN external_votes VARCHAR geo_info VARCHAR geo_unit_id FK VARCHAR name VARCHAR precinct_id PK } vote_record { VARCHAR organization FK VARCHAR precinct_id FK INTEGER vote_count VARCHAR vid PK } affiliation ||--|{ nominating_organization : "organization -> nid" district_hierarchy ||--|{ geographical_unit : "child -> unit_id; parent -> unit_id" election_precinct ||--|{ geographical_unit : "geo_unit_id -> unit_id" election_precinct ||--|{ election : "election_id -> election_id" vote_record ||--|{ election_precinct : "precinct_id -> precinct_id" vote_record ||--|{ nominating_organization : "organization -> nid"

Exploratory Data Analysis (EDA)¶

What does exploratory data analysis contain?

These are some summaries of the initial analyses we conducted on the tables of the dataset. They are based on our ideas that we think can be further explored in a research project.

The exploration notebooks can be checked by clicking on the Open Notebook button. If you just want to see the output figures of the analysis, then open them with the Figures button.

Hungarian elections analysis

Open Notebook

Number of votes gained by political parties in Hungarian elections
party	DK	FIDESZ	FKGP	JOBBIK	KDNP	LMP	MDF	MSZP	PM	SZDSZ
election_id
2018-04-08	306853	2607899	0	1090331	2607899	399507	0	680680	680680	0
2010-04-11	0	2706282	0	855263	2706282	383173	135357	990253	0	0
2006-04-09	0	2272962	822	116463	2272962	0	271881	2336684	0	350196
1998-05-10	0	1263504	617713	0	113710	0	136180	1445794	0	350871
2014-04-06	1287384	2142112	7956	1015570	2142112	265220	0	1287384	1287384	0
1994-05-08	0	378951	476262	0	378948	0	633112	1781707	0	1065316
2022-04-03	1930740	2803778	0	1930740	2803778	1930740	0	1930740	1930740	0
2002-04-07	0	2306761	38567	0	0	0	2306761	2361962	0	310899
1990-03-25	0	439257	576116	0	316402	0	1213501	533832	0	1049524

Percentage of votes gained by political parties in Hungarian elections
party	DK	FIDESZ	FKGP	JOBBIK	KDNP	LMP	MDF	MSZP	PM	SZDSZ
election_id
2018-04-08	0.056236	0.477945	0.000000	0.199823	0.477945	0.073217	0.000000	0.124747	0.124747	0.000000
2010-04-11	0.000000	0.527795	0.000000	0.166798	0.527795	0.074729	0.026398	0.193125	0.000000	0.000000
2006-04-09	0.000000	0.421047	0.000152	0.021574	0.421047	0.000000	0.050364	0.432851	0.000000	0.064871
1998-05-10	0.000000	0.282987	0.138349	0.000000	0.025468	0.000000	0.030500	0.323814	0.000000	0.078584
2014-04-06	0.263551	0.438529	0.001629	0.207905	0.438529	0.054295	0.000000	0.263551	0.263551	0.000000
1994-05-08	0.000000	0.070447	0.088537	0.000000	0.070446	0.000000	0.117695	0.331218	0.000000	0.198041
2022-04-03	0.361846	0.525465	0.000000	0.361846	0.525465	0.361846	0.000000	0.361846	0.361846	0.000000
2002-04-07	0.000000	0.411576	0.006881	0.000000	0.000000	0.000000	0.411576	0.421425	0.000000	0.055471
1990-03-25	0.000000	0.089824	0.117810	0.000000	0.064701	0.000000	0.248149	0.109164	0.000000	0.214618

Tables¶

How should I use this?

Table information

Each table has a Table Profile, generated by pandas-profiling. This includes descriptive statistics based on the table data and metadata, which can save significant time during the pre-analysis. Each profiling contains descriptives of the variables and additionally may show interactions, correlations, missing data information, and samples of the data. To see the profiling click on Open Table Profile.

Furthermore, you can check the first 5 rows of the tables by clicking on the First 5 rows button.

Download links

The table data can be downloaded in CSV format with Download CSV button. Before this step, we recommend checking the table profile!

Updating information

Some datasets are updated periodically. In this case, you can check the updating period at the top of this section.

There are 7 tables

Affiliation Table

Download CSV

Open Table Profile

Size: 276 × 2 (7.66 kB)