Avoid too many return statements within this function.

Open

            return PreprocessedColumn.apply(list_format)

Avoid too many return statements within this function.

Open

        return PreprocessedColumn.apply(list_format)

Avoid too many return statements within this function.

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            return result

Avoid too many return statements within this function.

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        return False

Avoid too many return statements within this function.

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        return result

Avoid too many return statements within this function.

Open

            return not self._right(metric or x)  # type: ignore

Avoid too many return statements within this function.

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            return x >= value

Avoid too many return statements within this function.

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        return Predicate(Relation.leq, value, component=component), i

Avoid too many return statements within this function.

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        return Predicate(Relation._and, left=left, right=right, component=left._component), i

Avoid too many return statements within this function.

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        return Predicate(Relation._not, right=right, component=right._component), i

Avoid too many return statements within this function.

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            return result

Function _process_dicts has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def _process_dicts(
        self,
        dicts: List[DictType],
        reducer: Callable[[DictType, DictType], DictType],
        pre_processor: Callable[[DictType], Tuple[TrackData, int]],

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Function _histogram_from_sketch has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

def _histogram_from_sketch(
    sketch: kll_doubles_sketch,
    max_buckets: Optional[int] = None,
    avg_per_bucket: Optional[float] = None,
    min_n_buckets: Optional[int] = None,

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Function _validate_timestamp has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

def _validate_timestamp(timestamp: Union[date, datetime, pd.Timestamp, str]) -> datetime:
    if isinstance(timestamp, pd.Timestamp):
        return timestamp.to_pydatetime()
    if isinstance(timestamp, str):
        try:

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Function truncate_time_ms has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

def truncate_time_ms(t: int, granularity: TimeGranularity) -> int:
    dt = datetime.fromtimestamp(t / 1000, tz=tz.tzutc()).replace(second=0, microsecond=0)

    if granularity == TimeGranularity.Minute:
        trunc = dt

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Method toSummaryDict has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

  public HashMap<String, Object> toSummaryDict(
      Optional<String> columnMetric, Optional<SummaryConfig> config) throws UnsupportedError {
    SummaryConfig summaryConfig = config.orElse(new SummaryConfig());
    HashMap<String, Object> summary = new HashMap<>();

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Method getMetricComponentPaths has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

  public List<String> getMetricComponentPaths() {
    ArrayList<String> paths = new ArrayList<>();
    for (String metricName : this.getMetricNames()) {
      Optional<Metric<?>> metric = this.getMetric(metricName);
      if (metric.isPresent()) {

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Function feature_statistics has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def feature_statistics(
        self, feature_name: Union[str, List[str]], profile: str = "reference", cell_height: Optional[str] = None
    ) -> HTML:
        """
        Generate a report for the main statistics of specified feature, for a given profile (target or reference).

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Function zero_padding_frequent_items has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

def zero_padding_frequent_items(
    target_feature_items: List[FrequentItemEstimate], reference_feature_items: List[FrequentItemEstimate]
) -> Tuple[List[FrequentItemEstimate], List[FrequentItemEstimate]]:
    """Fills estimate value of item with 0 when such item is present in the other profile but absent in the current profile.
    This is done for both profiles passed.

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Function is_k_item_relevant has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def is_k_item_relevant(self, row: pd.core.series.Series, k: int) -> int:
        if self.convert_non_numeric:
            return 1 if row[self.prediction_column][k - 1] in row[self.target_column] else 0
        else:
            index_ki = row[self.prediction_column].index(k)

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