lipinet package

lipinet package#

Submodules#

lipinet.databases module#

lipinet.databases.clean(df, name_of_resource, verbose=False)#

Some of the data sources need specialised cleaning to make them nicer to work with.

lipinet.databases.clean_columns(df, cols, strip_chars=None, trim_substrings=None, verbose=False)#
For each col in cols:

  1. .str.strip(strip_chars) — if strip_chars is None defaults to whitespace

  2. remove any of the trim_substrings at start or end

Parameters:

  • df (DataFrame) – your DataFrame

  • cols (list[str]) – list of column names to clean (if empty, all columns)

  • strip_chars (str | None) – string of characters to strip from ends (None → whitespace)

  • trim_substrings (list[str] | None) – list of literal substrings to drop if they appear at start or end

  • verbose (bool) – print before/after samples

Return type:

DataFrame

lipinet.databases.download_and_load_data(filename, url, file_format='csv', compressed=False, sep=',', encoding='utf-8', verbose=False)#

Checks if the specified file exists locally. If not, downloads it from the provided URL. Supports loading compressed files and handling different formats.

Parameters: - filename (str): The name of the file to be saved within the data directory. - url (str): The URL to download the file from if it’s not found locally. - file_format (str): The format of the file (‘json’ or ‘csv’). Defaults to ‘csv’. - compressed (bool): If True, expects the downloaded file to be in gzip format. Defaults to False. - sep (str): Separator to use if loading CSV/TSV data. Defaults to ‘,’. - encoding (str): Encoding to use for reading files. Defaults to ‘utf-8’. - verbose (bool): If True, prints additional information during the process. Defaults to False.

Returns: - data (DataFrame, dict, or list): The loaded data from the file, in the format specified.

lipinet.databases.get_prior_knowledge(name_of_resource, verbose=False)#

lipinet.parse_swisslipids module#

A standalone module that loads and processes SwissLipids data into a df_nodes using lipinet.

This module provides a helper function parse_swisslipids_data that can be imported into notebooks or other scripts. A thin wrapper in the main() function allows command-line execution.

lipinet.parse_swisslipids.main()#

Thin wrapper for command-line execution.

lipinet.parse_swisslipids.parse_swisslipids_data(verbose=False)#

Core function to process SwissLipids data and return nodes and edges dataframes.

Parameters:

verbose (bool) – If True, prints detailed output. Defaults to False.

Returns:

A dictionary with keys ‘df_nodes’ and ‘df_edges’.

Return type:

dict

lipinet.parse_rhea module#

lipinet.parse_rhea

A standalone module that loads and processes Rhea data into node and edge DataFrames for LipiNet. Provides a helper function parse_rhea_data and a CLI entrypoint.

lipinet.parse_rhea.build_rhea_ec_edges_and_nodes(df_ec)#
Given a DataFrame with EC hierarchy columns:

Main_Class, Subclass, Subsubclass, EC_number,

this function creates:

  • A DataFrame of edges linking each hierarchical level.

  • A DataFrame of unique nodes with a ‘ec_level’ column indicating the node’s level in the hierarchy.

Parameters:

df_ec (DataFrame)

lipinet.parse_rhea.explode_columns(df, columns, delimiter=';')#

Split and explode the specified columns of a DataFrame.

Parameters:

  • df (pd.DataFrame) – Input DataFrame.

  • columns (list of str) – List of column names to split by the delimiter.

  • delimiter (str) – The delimiter to use when splitting the column values.

Returns:

A new DataFrame with the specified columns exploded.

Return type:

pd.DataFrame

Note

Each row in the specified columns must produce lists of the same length.

lipinet.parse_rhea.main()#
lipinet.parse_rhea.parse_rhea_data(verbose=False)#

Core function to load and process Rhea data.

Parameters:

verbose (bool) – If True, prints detailed status.

Returns:

{‘df_edges’: DataFrame, ‘df_nodes’: DataFrame}

Return type:

dict

lipinet.parse_rhea.process_ec_numbers(df)#

Process the ‘EC number’ column of the input DataFrame.

Parameters:

df (pd.DataFrame) – A DataFrame containing an ‘EC number’ column.

Returns:

A new DataFrame with the following columns:

  • ’EC_number’: The reassembled EC number in the format ‘EC:Main_Class.Subclass.Subsubclass.Serial_Number’

  • ’Main_Class’: The first part of the EC number.

  • ’Subclass’: The second part of the EC number.

  • ’Subsubclass’: The third part of the EC number.

  • ’Serial_Number’: The fourth part of the EC number.

Return type:

pd.DataFrame

lipinet.utils module#

lipinet.utils.check_for_split_characters(df, delimiter='|')#
lipinet.utils.create_nodedf_from_edgedf(edge_df, props=['layer', 'id'], cols=['layer', 'node_id'])#
lipinet.utils.split_and_expand_large(df, split_col, delimiter, expand_cols)#

Splits a column by a delimiter and expands specified columns for large DataFrames, handling None/NaN values.

Parameters: df (pd.DataFrame): The original DataFrame. split_col (str): The name of the column to split. delimiter (str): The delimiter to split the column by. expand_cols (list): List of column names to be expanded with the split column.

Returns: pd.DataFrame: A new DataFrame with the split and expanded rows.

Module contents#

Contents