kd_ext.contrib API

Contrib operators.

kd_ext.contrib.average_rank(x: DataSlice) -> DataSlice

Computes average rank natively in koladata.

kd_ext.contrib.flatten_cyclic_references(x, *, max_recursion_depth)

Creates a DataSlice with tree-like copy of the input DataSlice.

The entities themselves and all their attributes including both top-level and
non-top-level attributes are cloned (with new ItemIds) while creating the
tree-like copy. The max_recursion_depth argument controls the maximum number
of times the same entity can occur on the path from the root to a leaf.
Note: resulting DataBag might have an exponential size, compared to the input
DataBag.

Args:
  x: DataSlice to flatten.
  max_recursion_depth: Maximum recursion depth.

Returns:
  A DataSlice with tree-like attributes structure.

kd_ext.contrib.pearson_correlation(x: DataSlice, y: DataSlice) -> DataSlice

Computes Pearson correlation for koladata slices x and y.

Args:
  x: First DataSlice.
  y: Second DataSlice.

Returns:
  A DataSlice containing the correlation.

kd_ext.contrib.pearson_correlation_with_ci(x: DataSlice, y: DataSlice, alpha: float = 0.05) -> DataSlice

Computes Pearson correlation and confidence interval for koladata slices.

Args:
  x: First DataSlice.
  y: Second DataSlice.
  alpha: Significance level for CI.

Returns:
  A DataSlice of objects with 'correlation', 'lower_ci', and 'upper_ci'
  attributes.

kd_ext.contrib.sanitize_names(x)

Returns a DataSlice which recursivelly sanitizes all attribute names.

Invalid attribute names (those that are not valid Python identifiers) are
replaced with valid ones. Invalid characters are replaced with '_', and
modified names are all prefixed with "san_". Collisions are resolved by
appending suffixes like '_0', '_1', etc.

The sanitization is done per-schema: attributes on different schemas are
sanitized independently without interfering with each other.

The returned DataSlice preserves the original Object IDs; only attribute
names are changed.

Example:
  o = kd.obj(**{'#': 1, '?': 2})
  result = kd_ext.contrib.sanitize_names(o)
  # result has attributes 'san__' and 'san___0' (or similar)

Args:
  x: DataSlice to sanitize.

Returns:
  A new DataSlice with sanitized attribute names in a new immutable DataBag.

kd_ext.contrib.spearman_correlation(x: DataSlice, y: DataSlice) -> DataSlice

Computes Spearman correlation using average ranks.

Args:
  x: First DataSlice.
  y: Second DataSlice.

Returns:
  A DataSlice containing the correlation.

kd_ext.contrib.spearman_correlation_with_ci(x: DataSlice, y: DataSlice, alpha: float = 0.05) -> DataSlice

Computes Spearman correlation and confidence interval.

Args:
  x: First DataSlice.
  y: Second DataSlice.
  alpha: Significance level for CI.

Returns:
  A DataSlice of objects with 'correlation', 'lower_ci', and 'upper_ci'
  attributes.

kd_ext.contrib.value_counts(x)

Returns Dicts mapping entries in `x` to their count over the last dim.

Similar to Pandas' `value_counts`.

The output is a `x.get_ndim() - 1`-dimensional DataSlice containing one
Dict per aggregated row in `x`. Each Dict maps the values to the number of
occurrences (as an INT64) in the final dimension.

Example:
  x = kd.slice([[4, 3, 4], [None, 2], [2, 1, 4, 1], [None]])
  kd_ext.contrib.value_counts(x)
    # -> [Dict{4: 2, 3: 1}, Dict{2: 1}, Dict{2: 1, 1: 2, 4: 1}, Dict{}]

Args:
  x: the non-scalar DataSlice to compute occurrences for.