malariagen_data.as1.As1.sample_metadata

As1.sample_metadata(sample_sets: Sequence[str] | str | None = None, sample_query: str | None = None, sample_query_options: dict | None = None, sample_indices: List[int] | None = None) → DataFrame

Access sample-level metadata for one or more sample sets. This method returns a pandas DataFrame where each row corresponds to a single sample. The metadata is assembled by merging multiple sources including general metadata, sequence quality control (QC) metadata, surveillance flags, and—when available—AIM and cohort metadata.

Parameters

sample_setssequence of str or str or None, optional

List of sample sets and/or releases. Can also be a single sample set or release.

sample_querystr or None, optional

A pandas query string to be evaluated against the sample metadata, to select samples to be included in the returned data. E.g., “country == ‘Uganda’”. If the query returns zero results, a warning will be emitted with fuzzy-match suggestions for possible typos or case mismatches.

sample_query_optionsdict or None, optional

A dictionary of arguments that will be passed through to pandas query() or eval(), e.g. parser, engine, local_dict, global_dict, resolvers.

sample_indiceslist of int or None, optional

Advanced usage parameter. A list of indices of samples to select, corresponding to the order in which the samples are found within the sample metadata. Either provide this parameter or sample_query, not both.

Returns

DataFrame

A pandas DataFrame with one row per sample. Columns are grouped by metadata source: General metadata (present for all sample sets): - sample_id - Unique identifier for the sample. - partner_sample_id - Sample ID used by the contributing partner. - contributor - Name of the contributing institution or individual. - country - Country where the sample was collected. - location - Specific collection site (e.g., village or site name). - year - Year of collection. - month - Month of collection. - quarter - Quarter of the year derived from month (1–4). - latitude - GPS latitude of the collection site. - longitude - GPS longitude of the collection site. - sex_call - Sex determination call; 'F' for female, 'M' for male. - sample_set - Sample set containing the sample. - release - Data release containing the sample. - study_id - Identifier of the study the sample set belongs to. - study_url - URL of the study the sample set belongs to. - terms_of_use_expiry_date - Expiry date of terms of use for the sample. - terms_of_use_url - URL of the terms of use for the sample. - unrestricted_use - Whether the sample can be used without restrictions. - is_surveillance - Whether the sample can be used for surveillance. Sequence QC metadata (present for all sample sets, values may be missing if QC data is unavailable for a given sample set): - mean_cov - Mean sequencing coverage across the genome. - median_cov - Median sequencing coverage across the genome. - modal_cov - Modal (most frequent) sequencing coverage. - mean_cov_2L - Mean coverage on chromosome arm 2L. - median_cov_2L - Median coverage on chromosome arm 2L. - mode_cov_2L - Modal coverage on chromosome arm 2L. - mean_cov_2R - Mean coverage on chromosome arm 2R. - median_cov_2R - Median coverage on chromosome arm 2R. - mode_cov_2R - Modal coverage on chromosome arm 2R. - mean_cov_3L - Mean coverage on chromosome arm 3L. - median_cov_3L - Median coverage on chromosome arm 3L. - mode_cov_3L - Modal coverage on chromosome arm 3L. - mean_cov_3R - Mean coverage on chromosome arm 3R. - median_cov_3R - Median coverage on chromosome arm 3R. - mode_cov_3R - Modal coverage on chromosome arm 3R. - mean_cov_X - Mean coverage on chromosome X. - median_cov_X - Median coverage on chromosome X. - mode_cov_X - Modal coverage on chromosome X. - frac_gen_cov - Fraction of the genome covered. - divergence - Sequence divergence from the reference. - contam_pct - Estimated contamination percentage. - contam_LLR - Log-likelihood ratio for contamination estimate. AIM (Ancestry-Informative Marker) metadata (only present when an AIM analysis is available for the data resource, e.g., Ag3): - aim_species_fraction_arab - Fraction of gambcolu vs. arabiensis AIMs indicating arabiensis. - aim_species_fraction_colu - Fraction of gambiae vs. coluzzii AIMs indicating coluzzii. - aim_species_fraction_colu_no2l - Fraction of gambiae vs. coluzzii AIMs indicating coluzzii, excluding chromosome arm 2L. - aim_species_gambcolu_arabiensis - Taxon assigned by gambcolu vs. arabiensis AIMs. - aim_species_gambiae_coluzzii - Taxon assigned by gambiae vs. coluzzii AIMs. - aim_species - Final species assignment combining both AIM analyses. Cohort metadata (only present when a cohorts analysis is available for the data resource; quarter columns are only present for cohorts analyses from 20230223 onwards): - country_iso - ISO code of the country of collection. - admin1_name - Name of the first-level administrative region. - admin1_iso - ISO code of the first- level administrative region. - admin2_name - Name of the second- level administrative region. - taxon - Taxon assigned by combining AIM and cohort analyses. - cohort_admin1_year - Cohort grouping by admin level 1 and year. - cohort_admin1_month - Cohort grouping by admin level 1 and month. - cohort_admin1_quarter - Cohort grouping by admin level 1 and quarter (cohorts analysis >= 20230223 only). - cohort_admin2_year - Cohort grouping by admin level 2 and year. - cohort_admin2_month - Cohort grouping by admin level 2 and month. - cohort_admin2_quarter - Cohort grouping by admin level 2 and quarter (cohorts analysis >= 20230223 only). The exact columns present depend on the data resource and sample sets requested. The returned DataFrame is a copy and can be safely modified without affecting internal caches.

Notes

Some samples in the dataset are lab crosses — mosquitoes bred in the laboratory that have no real collection date. These samples use year=-1 and month=-1 as sentinel values. They may cause unexpected results in date-based analyses (e.g., pd.to_datetime will fail on negative year values).

To exclude lab cross samples, use:

df = api.sample_metadata(sample_query="year >= 0")