pimdb 0.1.0

maintain a local copy of essential information from IMDb

pimdb

Pimdb is a python package and command line utility to maintain a local copy of the essential parts of the Internet Movie Database (IMDb) based in the TSV files available from IMDb datasets.

License

The IMDb datasets are only available for personal and non-commercial use. For details refer to the previous link.

Pimdb is open source and distributed under the BSD license. The source code is available from https://github.com/roskakori/pimdb.

Installation

Pimdb is available from PyPI and can be installed using:

$ pip install pimdb

Quick start

Downloading datasets

To download the current IMDb datsets to the current folder, run:

pimdb download all

(This downloads about 1 GB of data and might take a couple of minutes).

Transferring datasets into tables

To import them in a local SQLite database pimdb.db located in the current folder, run:

pimdb transfer all

(This will take a while. On a reasonably modern laptop with a local database you can expect about 2 hours).

The resulting database contains one tables for each dataset. The table names are PascalCase variants of the dataset name. For example, the date from the dataset title.basics are stored in the table TitleBasics. The column names in the table match the names from the datasets, for example TitleBasics.primaryTitle. A short description of all the datasets and columns can be found at the download page for the IMDb datasets.

Querying tables

To query the tables, you can use any database tool that supports SQLite, for example the freely available and platform independent community edition of DBeaver or the command line shell for SQLite.

Databases other than SQLite

Optionally you can specify a different database using the --database option with an SQLAlchemy engine configuration, which generally uses the template "dialect+driver://username:password@host:port/database". SQLAlchemy supports several SQL dialects out of the box, and there are external dialects available for other SQL databases and other forms of tabular data such as pydruid (for Pandas), PyHive (for Presto and Hive) or Solr (for the Solr search platform).

Here's an example for using a PostgreSQL database:

pimdb transfer --database "postgresql://user:password@localhost:5432/mydatabase" all

Building normalized tables

The tables so far are almost verbatim copies of the IMDb datasets with the exception that possible duplicate rows have been removed. This means that NameBasics.nconst and TitleBasics.tconst are unique, which sadly is not always (but still sometimes) the case for the datasets in the .tsv.gz files.

This data model already allows to perform several kinds of queries quite easily and efficiently.

However, the IMDb datasets do not offer a simple way to query N:M relations. For example, the column NameBasics.knownForTitles contains a comma separated list of tconsts like "tt2076794,tt0116514,tt0118577,tt0086491".

To perform such queries efficiently you can build strictly normalized tables derived from the dataset tables by running:

pimdb build

If you did specify a --database for the transfer command before, you have to specify the same value for build in order to find the source data. These tables generally use snake_case names for both tables and columns, for example title_allias.is_original.

Querying normalized tables

N:M relations are stored in tables using the naming template some_to_other, for example name_to_known_for_title. These relation tables contain only the numeric ID's to the respective actual data and a numeric column ordering to remember the sort order of the comma separated list in the IMDb dataset column.

For example, here is an SQL query to list the titles Alan Smithee is known for:

select
    title.primary_title,
    title.start_year
from
    name_to_known_for_title
    join name on
        name.id = name_to_known_for_title.name_id
    join title on
        title.id = name_to_known_for_title.title_id
where
    name.primary_name = 'Alan Smithee'

Reference

To get an overview of general command line option and available commands run:

pimdb --help

To learn the available command line options for a specific command run for example:

pimdb transfer --help

Changes

Version 0.1.0, 2020-04-11

• Initial public release.