Convert (and merge) positive numeric metadata (in)to feature table.

Convert numeric sample metadata from TSV file into a feature table. Optionally merge with an existing feature table. Only numeric metadata will be converted; categorical columns will be silently dropped. By default, if a table is used as input only samples found in both the table and metadata (intersection) are merged, and others are silently dropped. Set missing_samples="error" to raise an error if samples found in the table are missing from the metadata file. The metadata file can always contain a superset of samples. Note that columns will be dropped if they are non- numeric, contain only unique values, contain no unique values (zero variance), contain only empty cells, or contain negative values. This method currently only converts postive numeric metadata into feature data. Tip: convert categorical columns to dummy variables to include them in the output feature table.

Parameters

metadata : Metadata: Metadata file to convert to feature table.
table : FeatureTable[Frequency], optional: Feature table containing all features that should be used for target prediction.
missing_samples : Str % Choices('error', 'ignore'), optional: How to handle missing samples in metadata. "error" will fail if missing samples are detected. "ignore" will cause the feature table and metadata to be filtered, so that only samples found in both files are retained.
missing_values : Str % Choices('drop_samples', 'drop_features', 'error', 'fill'), optional: How to handle missing values (nans) in metadata. Either "drop_samples" with missing values, "drop_features" with missing values, "fill" missing values with zeros, or "error" if any missing values are found.

Returns

converted_table : FeatureTable[Frequency]: Converted feature table