feat: update spark description methods

src/ydata_profiling/model/spark/describe_boolean_spark.py

@@ -4,12 +4,13 @@
 
 from ydata_profiling.config import Settings
 from ydata_profiling.model.summary_algorithms import describe_boolean_1d
+from ydata_profiling.model.var_description.default import VarDescriptionHashable
 
 
 @describe_boolean_1d.register
 def describe_boolean_1d_spark(
-    config: Settings, df: DataFrame, summary: dict
-) -> Tuple[Settings, DataFrame, dict]:
+    config: Settings, df: DataFrame, summary: VarDescriptionHashable
+) -> Tuple[Settings, DataFrame, VarDescriptionHashable]:
     """Describe a boolean series.
 
     Args:

src/ydata_profiling/model/spark/describe_categorical_spark.py

@@ -4,12 +4,13 @@
 
 from ydata_profiling.config import Settings
 from ydata_profiling.model.summary_algorithms import describe_categorical_1d
+from ydata_profiling.model.var_description.default import VarDescriptionHashable
 
 
 @describe_categorical_1d.register
 def describe_categorical_1d_spark(
-    config: Settings, df: DataFrame, summary: dict
-) -> Tuple[Settings, DataFrame, dict]:
+    config: Settings, df: DataFrame, summary: VarDescriptionHashable
+) -> Tuple[Settings, DataFrame, VarDescriptionHashable]:
     """Describe a categorical series.
 
     Args:

src/ydata_profiling/model/spark/describe_date_spark.py

@@ -6,6 +6,7 @@
 
 from ydata_profiling.config import Settings
 from ydata_profiling.model.summary_algorithms import describe_date_1d
+from ydata_profiling.model.var_description.default import VarDescriptionHashable
 
 
 def date_stats_spark(df: DataFrame, summary: dict) -> dict:
@@ -21,8 +22,8 @@ def date_stats_spark(df: DataFrame, summary: dict) -> dict:
 
 @describe_date_1d.register
 def describe_date_1d_spark(
-    config: Settings, df: DataFrame, summary: dict
-) -> Tuple[Settings, DataFrame, dict]:
+    config: Settings, df: DataFrame, summary: VarDescriptionHashable
+) -> Tuple[Settings, DataFrame, VarDescriptionHashable]:
     """Describe a date series.
 
     Args:

src/ydata_profiling/model/spark/describe_numeric_spark.py

@@ -9,9 +9,10 @@
     describe_numeric_1d,
     histogram_compute,
 )
+from ydata_profiling.model.var_description.default import VarDescriptionHashable
 
 
-def numeric_stats_spark(df: DataFrame, summary: dict) -> dict:
+def numeric_stats_spark(df: DataFrame, summary: VarDescriptionHashable) -> dict:
     column = df.columns[0]
 
     expr = [
@@ -29,8 +30,8 @@ def numeric_stats_spark(df: DataFrame, summary: dict) -> dict:
 
 @describe_numeric_1d.register
 def describe_numeric_1d_spark(
-    config: Settings, df: DataFrame, summary: dict
-) -> Tuple[Settings, DataFrame, dict]:
+    config: Settings, df: DataFrame, summary: VarDescriptionHashable
+) -> Tuple[Settings, DataFrame, VarDescriptionHashable]:
     """Describe a boolean series.
 
     Args:
@@ -51,7 +52,7 @@ def describe_numeric_1d_spark(
     summary["kurtosis"] = stats["kurtosis"]
     summary["sum"] = stats["sum"]
 
-    value_counts = summary["value_counts"]
+    value_counts = summary.value_counts
 
     n_infinite = (
         value_counts.where(F.col(df.columns[0]).isin([np.inf, -np.inf]))
@@ -106,12 +107,12 @@ def describe_numeric_1d_spark(
     ).stat.approxQuantile("abs_dev", [0.5], quantile_threshold)[0]
 
     # FIXME: move to fmt
-    summary["p_negative"] = summary["n_negative"] / summary["n"]
+    summary["p_negative"] = summary["n_negative"] / summary.n
     summary["range"] = summary["max"] - summary["min"]
     summary["iqr"] = summary["75%"] - summary["25%"]
     summary["cv"] = summary["std"] / summary["mean"] if summary["mean"] else np.NaN
-    summary["p_zeros"] = summary["n_zeros"] / summary["n"]
-    summary["p_infinite"] = summary["n_infinite"] / summary["n"]
+    summary["p_zeros"] = summary["n_zeros"] / summary.n
+    summary["p_infinite"] = summary["n_infinite"] / summary.n
 
     # TODO - enable this feature
     # because spark doesn't have an indexing system, there isn't really the idea of monotonic increase/decrease
@@ -124,14 +125,14 @@ def describe_numeric_1d_spark(
     # display in pandas display
     # the alternative is to do this in spark natively, but it is not trivial
     infinity_values = [np.inf, -np.inf]
-    infinity_index = summary["value_counts_without_nan"].index.isin(infinity_values)
+    infinity_index = summary.value_counts_without_nan.index.isin(infinity_values)
 
     summary.update(
         histogram_compute(
             config,
-            summary["value_counts_without_nan"][~infinity_index].index.values,
-            summary["n_distinct"],
-            weights=summary["value_counts_without_nan"][~infinity_index].values,
+            summary.value_counts_without_nan[~infinity_index].index.values,
+            summary.n_distinct,
+            weights=summary.value_counts_without_nan[~infinity_index].values,
         )
     )
 

src/ydata_profiling/model/spark/describe_supported_spark.py

@@ -3,13 +3,17 @@
 from pyspark.sql import DataFrame
 
 from ydata_profiling.config import Settings
+from ydata_profiling.model.spark.var_description.default_spark import (
+    get_default_spark_description,
+)
 from ydata_profiling.model.summary_algorithms import describe_supported
+from ydata_profiling.model.var_description.default import VarDescription
 
 
 @describe_supported.register
 def describe_supported_spark(
     config: Settings, series: DataFrame, summary: dict
-) -> Tuple[Settings, DataFrame, dict]:
+) -> Tuple[Settings, DataFrame, VarDescription]:
     """Describe a supported series.
     Args:
         series: The Series to describe.
@@ -18,16 +22,6 @@ def describe_supported_spark(
         A dict containing calculated series description values.
     """
 
-    # number of non-NaN observations in the Series
-    count = summary["count"]
-    n_distinct = summary["value_counts"].count()
+    series_description = get_default_spark_description(config, series, summary)
 
-    summary["n_distinct"] = n_distinct
-    summary["p_distinct"] = n_distinct / count if count > 0 else 0
-
-    n_unique = summary["value_counts"].where("count == 1").count()
-    summary["is_unique"] = n_unique == count
-    summary["n_unique"] = n_unique
-    summary["p_unique"] = n_unique / count
-
-    return config, series, summary
+    return config, series, series_description

src/ydata_profiling/model/spark/describe_text_spark.py

@@ -4,12 +4,13 @@
 
 from ydata_profiling.config import Settings
 from ydata_profiling.model.summary_algorithms import describe_text_1d
+from ydata_profiling.model.var_description.default import VarDescriptionHashable
 
 
 @describe_text_1d.register
 def describe_text_1d_spark(
-    config: Settings, df: DataFrame, summary: dict
-) -> Tuple[Settings, DataFrame, dict]:
+    config: Settings, df: DataFrame, summary: VarDescriptionHashable
+) -> Tuple[Settings, DataFrame, VarDescriptionHashable]:
     """Describe a categorical series.
 
     Args:

src/ydata_profiling/model/spark/var_description/counts_spark.py

@@ -4,69 +4,52 @@
 from ydata_profiling.model.var_description.counts import VarCounts
 
 
-class VarCountsSpark(VarCounts):
-    value_counts_without_nan: DataFrame
-    """Counts of values in the series without NaN."""
-    value_counts_index_sorted: DataFrame
-    """Sorted counts of values in the series without NaN."""
-    value_counts: DataFrame
-
-    def __init__(self, config: Settings, series: DataFrame):
-        """Counts the values in a series (with and without NaN, distinct).
-
-        Args:
-            config: report Settings object
-            series: Series for which we want to calculate the values.
-            summary: series' summary
-
-        Returns:
-            A dictionary with the count values (with and without NaN, distinct).
-        """
-        length = series.count()
-
-        value_counts = series.groupBy(series.columns).count()
-        value_counts = value_counts.sort("count", ascending=False).persist()
-        value_counts_index_sorted = value_counts.sort(series.columns[0], ascending=True)
-
-        n_missing = value_counts.where(value_counts[series.columns[0]].isNull()).first()
-        if n_missing is None:
-            n_missing = 0
-        else:
-            n_missing = n_missing["count"]
-
-        # FIXME: reduce to top-n and bottom-n
-        value_counts_index_sorted = (
-            value_counts_index_sorted.limit(200)
-            .toPandas()
-            .set_index(series.columns[0], drop=True)
-            .squeeze(axis="columns")
-        )
-
-        # this is necessary as freqtables requires value_counts_without_nan
-        # to be a pandas series. However, if we try to get everything into
-        # pandas we will definitly crash the server
-        value_counts_without_nan = (
-            value_counts.dropna()
-            .limit(200)
-            .toPandas()
-            .set_index(series.columns[0], drop=True)
-            .squeeze(axis="columns")
-        )
-
-        # FIXME: This is not correct, but used to fulfil render expectations
-        # @chanedwin
-        memory_size = 0
-
-        self.value_counts = value_counts
-        super().__init__(
-            hashable=False,
-            value_counts_without_nan=value_counts_without_nan,
-            value_counts_index_sorted=value_counts_index_sorted,
-            ordering=False,
-            n_missing=n_missing,
-            n=length,
-            p_missing=n_missing / length,
-            count=length - n_missing,
-            memory_size=memory_size,
-            value_counts=value_counts.persist(),
-        )
+def get_counts_spark(config: Settings, series: DataFrame) -> VarCounts:
+    """Get a VarCounts object for a spark series."""
+    length = series.count()
+
+    value_counts = series.groupBy(series.columns).count()
+    value_counts = value_counts.sort("count", ascending=False).persist()
+    value_counts_index_sorted = value_counts.sort(series.columns[0], ascending=True)
+
+    n_missing = value_counts.where(value_counts[series.columns[0]].isNull()).first()
+    if n_missing is None:
+        n_missing = 0
+    else:
+        n_missing = n_missing["count"]
+
+    # FIXME: reduce to top-n and bottom-n
+    value_counts_index_sorted = (
+        value_counts_index_sorted.limit(200)
+        .toPandas()
+        .set_index(series.columns[0], drop=True)
+        .squeeze(axis="columns")
+    )
+
+    # this is necessary as freqtables requires value_counts_without_nan
+    # to be a pandas series. However, if we try to get everything into
+    # pandas we will definitly crash the server
+    value_counts_without_nan = (
+        value_counts.dropna()
+        .limit(200)
+        .toPandas()
+        .set_index(series.columns[0], drop=True)
+        .squeeze(axis="columns")
+    )
+
+    # FIXME: This is not correct, but used to fulfil render expectations
+    # @chanedwin
+    memory_size = 0
+
+    return VarCounts(
+        hashable=False,
+        value_counts_without_nan=value_counts_without_nan,
+        value_counts_index_sorted=value_counts_index_sorted,
+        ordering=False,
+        n_missing=n_missing,
+        n=length,
+        p_missing=n_missing / length,
+        count=length - n_missing,
+        memory_size=memory_size,
+        value_counts=value_counts.persist(),
+    )

src/ydata_profiling/model/spark/var_description/default_spark.py

@@ -1,55 +1,41 @@
 from __future__ import annotations
 
-from dataclasses import dataclass
-
 from pyspark.sql import DataFrame
 
 from ydata_profiling.config import Settings
-from ydata_profiling.model.spark.var_description.counts_spark import VarCountsSpark
+from ydata_profiling.model.spark.var_description.counts_spark import get_counts_spark
 from ydata_profiling.model.var_description.default import VarDescriptionHashable
 
 
-@dataclass
-class VarDescriptionSparkHashable(VarDescriptionHashable):
-    """Default description for pandas columns."""
-
-    @classmethod
-    def from_var_counts(
-        cls, var_counts: VarCountsSpark, init_dict: dict
-    ) -> VarDescriptionSparkHashable:
-        """Get a default description from a VarCountsPandas object."""
-
-        count = var_counts.count
-        n_distinct = var_counts.value_counts.count()
-
-        p_distinct = n_distinct / count if count > 0 else 0
-
-        n_unique = var_counts.value_counts.where("count == 1").count()
-        is_unique = n_unique == count
-        p_unique = n_unique / count
-
-        return VarDescriptionSparkHashable(
-            n=var_counts.n,
-            count=var_counts.count,
-            n_missing=var_counts.n_missing,
-            p_missing=var_counts.p_missing,
-            hashable=var_counts.hashable,
-            memory_size=var_counts.memory_size,
-            ordering=var_counts.ordering,
-            value_counts_index_sorted=var_counts.value_counts_index_sorted,
-            value_counts_without_nan=var_counts.value_counts_without_nan,
-            var_specific=init_dict,
-            is_unique=is_unique,
-            n_unique=n_unique,
-            n_distinct=n_distinct,
-            p_distinct=p_distinct,
-            p_unique=p_unique,
-            value_counts=var_counts.value_counts,
-        )
-
-
 def get_default_spark_description(
     config: Settings, series: DataFrame, init_dict: dict
-) -> VarDescriptionSparkHashable:
-    _var_counts = VarCountsSpark(config, series)
-    return VarDescriptionSparkHashable.from_var_counts(_var_counts, init_dict)
+) -> VarDescriptionHashable:
+    var_counts = get_counts_spark(config, series)
+
+    count = var_counts.count
+    n_distinct = var_counts.value_counts.count()
+
+    p_distinct = n_distinct / count if count > 0 else 0
+
+    n_unique = var_counts.value_counts.where("count == 1").count()
+    is_unique = n_unique == count
+    p_unique = n_unique / count
+
+    return VarDescriptionHashable(
+        n=var_counts.n,
+        count=var_counts.count,
+        n_missing=var_counts.n_missing,
+        p_missing=var_counts.p_missing,
+        hashable=var_counts.hashable,
+        memory_size=var_counts.memory_size,
+        ordering=var_counts.ordering,
+        value_counts_index_sorted=var_counts.value_counts_index_sorted,
+        value_counts_without_nan=var_counts.value_counts_without_nan,
+        var_specific=init_dict,
+        is_unique=is_unique,
+        n_unique=n_unique,
+        n_distinct=n_distinct,
+        p_distinct=p_distinct,
+        p_unique=p_unique,
+        value_counts=var_counts.value_counts,
+    )