diff --git a/.gitignore b/.gitignore
index 2d2cfe2b9..71fb75bd1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -24,6 +24,7 @@ var/
 *.egg-info/
 .installed.cfg
 *.egg
+uv.lock
 
 # PyInstaller
 #  Usually these files are written by a python script from a template
@@ -85,3 +86,6 @@ docsrc/source/pages/api/_autosummary/
 # User created
 VERSION
 version.py
+
+# local java/spark sdk environment files
+.sdkmanrc
diff --git a/pyproject.toml b/pyproject.toml
index eae6b3dd4..e46cb237c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -87,6 +87,7 @@ dev = [
     "sphinx-autodoc-typehints>=1.10.3",
     "sphinx-multiversion>=0.2.3",
     "autodoc_pydantic",
+    "standard-imghdr"
 ]
 
 docs = [
diff --git a/src/ydata_profiling/model/spark/correlations_spark.py b/src/ydata_profiling/model/spark/correlations_spark.py
index f9f1d1ecb..441dfbf0f 100644
--- a/src/ydata_profiling/model/spark/correlations_spark.py
+++ b/src/ydata_profiling/model/spark/correlations_spark.py
@@ -45,10 +45,23 @@ def _compute_corr_natively(df: DataFrame, summary: dict, corr_type: str) -> Arra
     exact same workflow. The syntax is Correlation.corr(dataframe, method="pearson" OR "spearman"),
     and Correlation is from pyspark.ml.stat
     """
-    variables = {column: description["type"] for column, description in summary.items()}
+    variables = {
+        column: {
+            "type": description["type"],
+            "all_missing": description.get("n", 0) - description.get("n_missing", 0)
+            == 0,
+        }
+        for column, description in summary.items()
+    }
     interval_columns = [
-        column for column, type_name in variables.items() if type_name == "Numeric"
+        column
+        for column, mapping in variables.items()
+        if mapping["type"] == "Numeric" and not mapping["all_missing"]
     ]
+
+    if not interval_columns:
+        return [], interval_columns
+
     df = df.select(*interval_columns)
 
     # convert to vector column first
@@ -63,6 +76,9 @@ def _compute_corr_natively(df: DataFrame, summary: dict, corr_type: str) -> Arra
     assembler = VectorAssembler(**assembler_args)
     df_vector = assembler.transform(df).select(vector_col)
 
+    if df_vector.count() <= 1:
+        return [], interval_columns
+
     # get correlation matrix
     matrix = (
         Correlation.corr(df_vector, vector_col, method=corr_type).head()[0].toArray()
diff --git a/src/ydata_profiling/model/spark/describe_counts_spark.py b/src/ydata_profiling/model/spark/describe_counts_spark.py
index d7a091e7f..f02d1043c 100644
--- a/src/ydata_profiling/model/spark/describe_counts_spark.py
+++ b/src/ydata_profiling/model/spark/describe_counts_spark.py
@@ -6,6 +6,7 @@
 import pandas as pd
 from pyspark.sql import DataFrame
 from pyspark.sql import functions as F
+from pyspark.sql import types as T
 
 from ydata_profiling.config import Settings
 from ydata_profiling.model.summary_algorithms import describe_counts
@@ -25,6 +26,11 @@ def describe_counts_spark(
     Returns:
         Updated settings, input series, and summary dictionary.
     """
+    # Cast Decimal Type s
+    if isinstance(series.schema.fields[0].dataType, T.DecimalType):
+        series = series.select(
+            F.col(series.columns[0]).cast(T.DoubleType()).alias(series.columns[0])
+        )
 
     # Count occurrences of each value
     value_counts = series.groupBy(series.columns[0]).count()
@@ -36,9 +42,23 @@ def describe_counts_spark(
     value_counts_index_sorted = value_counts.orderBy(F.asc(series.columns[0]))
 
     # Count missing values
-    n_missing = (
-        value_counts.filter(F.col(series.columns[0]).isNull()).select("count").first()
-    )
+    if series.dtypes[0][1] in ("int", "float", "bigint", "double"):
+        n_missing = (
+            # Need to add the isnan() check because Pandas isnull check will count NaN as null, but Spark does not
+            value_counts.filter(
+                F.col(series.columns[0]).isNull() | F.isnan(F.col(series.columns[0]))
+            )
+            .select("count")
+            .first()
+        )
+    else:
+        n_missing = (
+            # Need to add the isnan() check because Pandas isnull check will count NaN as null, but Spark does not
+            value_counts.filter(F.col(series.columns[0]).isNull())
+            .select("count")
+            .first()
+        )
+
     n_missing = n_missing["count"] if n_missing else 0
 
     # Convert top 200 values to Pandas for frequency table display
@@ -60,17 +80,15 @@ def describe_counts_spark(
             value_counts.filter(F.col(column).isNotNull())  # Exclude NaNs
             .filter(~F.isnan(F.col(column)))  # Remove implicit NaNs (if numeric column)
             .groupBy(column)  # Group by unique values
-            .count()  # Count occurrences
+            .agg(F.sum("count").alias("count"))  # Sum of count
             .orderBy(F.desc("count"))  # Sort in descending order
-            .limit(200)  # Limit for performance
         )
     else:
         value_counts_no_nan = (
             value_counts.filter(F.col(column).isNotNull())  # Exclude NULLs
             .groupBy(column)  # Group by unique timestamp values
-            .count()  # Count occurrences
+            .agg(F.sum("count").alias("count"))  # Sum of count
             .orderBy(F.desc("count"))  # Sort by most frequent timestamps
-            .limit(200)  # Limit for performance
         )
 
     # Convert to Pandas Series, forcing proper structure
diff --git a/src/ydata_profiling/model/spark/describe_numeric_spark.py b/src/ydata_profiling/model/spark/describe_numeric_spark.py
index 5e30c7539..8c299577e 100644
--- a/src/ydata_profiling/model/spark/describe_numeric_spark.py
+++ b/src/ydata_profiling/model/spark/describe_numeric_spark.py
@@ -11,6 +11,14 @@
 def numeric_stats_spark(df: DataFrame, summary: dict) -> dict:
     column = df.columns[0]
 
+    # Removing null types from numeric summary stats to match Pandas defaults which skip na's (skipna=False)
+    finite_filter = (
+        F.col(column).isNotNull()
+        & ~F.isnan(F.col(column))
+        & ~F.col(column).isin([np.inf, -np.inf])
+    )
+    non_null_df = df.filter(finite_filter)
+
     expr = [
         F.mean(F.col(column)).alias("mean"),
         F.stddev(F.col(column)).alias("std"),
@@ -21,7 +29,7 @@ def numeric_stats_spark(df: DataFrame, summary: dict) -> dict:
         F.skewness(F.col(column)).alias("skewness"),
         F.sum(F.col(column)).alias("sum"),
     ]
-    return df.agg(*expr).first().asDict()
+    return non_null_df.agg(*expr).first().asDict()
 
 
 def describe_numeric_1d_spark(
@@ -81,30 +89,42 @@ def describe_numeric_1d_spark(
     quantiles = config.vars.num.quantiles
     quantile_threshold = 0.05
 
-    summary.update(
-        {
-            f"{percentile:.0%}": value
-            for percentile, value in zip(
-                quantiles,
-                df.stat.approxQuantile(
-                    f"{df.columns[0]}",
+    if summary.get("n") == summary.get("n_missing"):
+        # This means the entire column is null/nan, so summary values need to be hard-coded:
+        summary.update({f"{percentile:.0%}": np.nan for percentile in quantiles})
+
+        summary["mad"] = np.nan
+        summary["iqr"] = np.nan
+
+    else:
+        summary.update(
+            {
+                f"{percentile:.0%}": value
+                for percentile, value in zip(
                     quantiles,
-                    quantile_threshold,
-                ),
-            )
-        }
-    )
+                    df.stat.approxQuantile(
+                        f"{df.columns[0]}",
+                        quantiles,
+                        quantile_threshold,
+                    ),
+                )
+            }
+        )
 
-    median = summary["50%"]
+        median = summary.get("50%")
 
-    summary["mad"] = df.select(
-        (F.abs(F.col(f"{df.columns[0]}").cast("int") - median)).alias("abs_dev")
-    ).stat.approxQuantile("abs_dev", [0.5], quantile_threshold)[0]
+        summary["mad"] = df.select(
+            (F.abs(F.col(f"{df.columns[0]}").cast("int") - median)).alias("abs_dev")
+        ).stat.approxQuantile("abs_dev", [0.5], quantile_threshold)[0]
+
+        summary["iqr"] = summary["75%"] - summary["25%"]
 
     # FIXME: move to fmt
     summary["p_negative"] = summary["n_negative"] / summary["n"]
-    summary["range"] = summary["max"] - summary["min"]
-    summary["iqr"] = summary["75%"] - summary["25%"]
+    if summary["min"] is None or summary["max"] is None:
+        summary["range"] = np.nan
+    else:
+        summary["range"] = summary["max"] - summary["min"]
     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"]
diff --git a/src/ydata_profiling/model/spark/describe_supported_spark.py b/src/ydata_profiling/model/spark/describe_supported_spark.py
index 1758f668d..7dcaff62c 100644
--- a/src/ydata_profiling/model/spark/describe_supported_spark.py
+++ b/src/ydata_profiling/model/spark/describe_supported_spark.py
@@ -19,15 +19,15 @@ def describe_supported_spark(
     """
 
     # number of non-NaN observations in the Series
-    count = summary["count"]
-    n_distinct = summary["value_counts"].count()
+    n = summary["n"]
+    n_distinct = summary["value_counts"].count()  # Null/nan's count in value_counts
 
     summary["n_distinct"] = n_distinct
-    summary["p_distinct"] = n_distinct / count if count > 0 else 0
+    summary["p_distinct"] = n_distinct / n if n > 0 else 0
 
     n_unique = summary["value_counts"].where("count == 1").count()
-    summary["is_unique"] = n_unique == count
+    summary["is_unique"] = n_unique == n
     summary["n_unique"] = n_unique
-    summary["p_unique"] = n_unique / count if count > 0 else 0
+    summary["p_unique"] = n_unique / n if n > 0 else 0
 
     return config, series, summary
diff --git a/src/ydata_profiling/model/spark/summary_spark.py b/src/ydata_profiling/model/spark/summary_spark.py
index 5a033b1d5..75e01e240 100644
--- a/src/ydata_profiling/model/spark/summary_spark.py
+++ b/src/ydata_profiling/model/spark/summary_spark.py
@@ -43,6 +43,8 @@ def spark_describe_1d(
 
         if str(series.schema[0].dataType).startswith("ArrayType"):
             dtype = "ArrayType"
+        elif str(series.schema[0].dataType).startswith("Decimal"):
+            dtype = "decimal"
         else:
             dtype = series.schema[0].dataType.simpleString()
 
@@ -56,6 +58,7 @@ def spark_describe_1d(
             "boolean": "Boolean",
             "date": "DateTime",
             "timestamp": "DateTime",
+            "decimal": "Numeric",
         }[dtype]
 
     return summarizer.summarize(config, series, dtype=vtype)
diff --git a/src/ydata_profiling/report/formatters.py b/src/ydata_profiling/report/formatters.py
index d2c4657e0..199ea854d 100644
--- a/src/ydata_profiling/report/formatters.py
+++ b/src/ydata_profiling/report/formatters.py
@@ -245,13 +245,16 @@ def fmt_numeric(value: float, precision: int = 10) -> str:
     Returns:
         The numeric value with the given precision.
     """
-    fmtted = f"{{:.{precision}g}}".format(value)
-    for v in ["e+", "e-"]:
-        if v in fmtted:
-            sign = "-" if v in "e-" else ""
-            fmtted = fmtted.replace(v, " × 10<sup>") + "</sup>"
-            fmtted = fmtted.replace("<sup>0", "<sup>")
-            fmtted = fmtted.replace("<sup>", f"<sup>{sign}")
+    if value is None:
+        fmtted = "N/A"
+    else:
+        fmtted = f"{{:.{precision}g}}".format(value)
+        for v in ["e+", "e-"]:
+            if v in fmtted:
+                sign = "-" if v in "e-" else ""
+                fmtted = fmtted.replace(v, " × 10<sup>") + "</sup>"
+                fmtted = fmtted.replace("<sup>0", "<sup>")
+                fmtted = fmtted.replace("<sup>", f"<sup>{sign}")
 
     return fmtted
 
diff --git a/src/ydata_profiling/report/structure/variables/render_categorical.py b/src/ydata_profiling/report/structure/variables/render_categorical.py
index 86f5a262a..8fa6b6ae0 100644
--- a/src/ydata_profiling/report/structure/variables/render_categorical.py
+++ b/src/ydata_profiling/report/structure/variables/render_categorical.py
@@ -23,6 +23,7 @@
 from ydata_profiling.report.presentation.core.renderable import Renderable
 from ydata_profiling.report.presentation.frequency_table_utils import freq_table
 from ydata_profiling.report.structure.variables.render_common import render_common
+from ydata_profiling.report.utils import image_or_empty
 from ydata_profiling.visualisation.plot import cat_frequency_plot, histogram
 
 
@@ -95,7 +96,7 @@ def render_categorical_length(
     else:
         hist_data = histogram(config, *summary["histogram_length"])
 
-    length_histo = Image(
+    length_histo = image_or_empty(
         hist_data,
         image_format=config.plot.image_format,
         alt="length histogram",
diff --git a/src/ydata_profiling/report/structure/variables/render_count.py b/src/ydata_profiling/report/structure/variables/render_count.py
index e11e9913e..776bb6763 100644
--- a/src/ydata_profiling/report/structure/variables/render_count.py
+++ b/src/ydata_profiling/report/structure/variables/render_count.py
@@ -8,11 +8,11 @@
 from ydata_profiling.report.presentation.core import (
     Container,
     FrequencyTable,
-    Image,
     Table,
     VariableInfo,
 )
 from ydata_profiling.report.structure.variables.render_common import render_common
+from ydata_profiling.report.utils import image_or_empty
 from ydata_profiling.visualisation.plot import histogram, mini_histogram
 
 
@@ -94,26 +94,41 @@ def render_count(config: Settings, summary: dict) -> dict:
         style=config.html.style,
     )
 
-    mini_histo = Image(
-        mini_histogram(config, *summary["histogram"]),
-        image_format=image_format,
+    summary_histogram = summary.get("histogram", [])
+
+    mini_hist_data = None
+
+    if summary_histogram:
+        mini_hist_data = mini_histogram(config, *summary["histogram"])
+
+    mini_histo = image_or_empty(
+        mini_hist_data,
         alt="Mini histogram",
+        image_format=image_format,
+        name="Mini Histogram",
     )
 
     template_variables["top"] = Container(
         [info, table1, table2, mini_histo], sequence_type="grid"
     )
 
-    seqs = [
-        Image(
-            histogram(config, *summary["histogram"]),
-            image_format=image_format,
-            alt="Histogram",
-            caption=f"<strong>Histogram with fixed size bins</strong> (bins={len(summary['histogram'][1]) - 1})",
-            name="Histogram",
-            anchor_id="histogram",
-        )
-    ]
+    hist_data = None
+    hist_caption = None
+
+    if summary_histogram:
+        hist_data = histogram(config, *summary["histogram"])
+        hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={len(summary['histogram'][1]) - 1})"
+
+    hist = image_or_empty(
+        hist_data,
+        alt="Histogram",
+        image_format=image_format,
+        caption=hist_caption,
+        name="Histogram",
+        anchor_id="histogram",
+    )
+
+    seqs = [hist]
 
     fq = FrequencyTable(
         template_variables["freq_table_rows"],
diff --git a/src/ydata_profiling/report/structure/variables/render_date.py b/src/ydata_profiling/report/structure/variables/render_date.py
index 1f142daae..8d002d885 100644
--- a/src/ydata_profiling/report/structure/variables/render_date.py
+++ b/src/ydata_profiling/report/structure/variables/render_date.py
@@ -2,12 +2,8 @@
 
 from ydata_profiling.config import Settings
 from ydata_profiling.report.formatters import fmt, fmt_bytesize, fmt_percent
-from ydata_profiling.report.presentation.core import (
-    Container,
-    Image,
-    Table,
-    VariableInfo,
-)
+from ydata_profiling.report.presentation.core import Container, Table, VariableInfo
+from ydata_profiling.report.utils import image_or_empty
 from ydata_profiling.visualisation.plot import histogram, mini_histogram
 
 
@@ -76,55 +72,68 @@ def render_date(config: Settings, summary: Dict[str, Any]) -> Dict[str, Any]:
         style=config.html.style,
     )
 
-    if isinstance(summary["histogram"], list):
-        mini_histo = Image(
-            mini_histogram(
+    summary_histogram = summary.get("histogram", [])
+
+    mini_hist_data = None
+
+    if summary_histogram:
+        if isinstance(summary_histogram, list):
+            mini_hist_data = mini_histogram(
                 config,
                 [x[0] for x in summary["histogram"]],
                 [x[1] for x in summary["histogram"]],
                 date=True,
-            ),
-            image_format=image_format,
-            alt="Mini histogram",
-        )
-    else:
-        mini_histo = Image(
-            mini_histogram(
+            )
+        else:
+            mini_hist_data = mini_histogram(
                 config, summary["histogram"][0], summary["histogram"][1], date=True
-            ),
-            image_format=image_format,
-            alt="Mini histogram",
-        )
+            )
+
+    mini_histo = image_or_empty(
+        mini_hist_data,
+        alt="Mini histogram",
+        image_format=image_format,
+        name="Mini Histogram",
+        anchor_id=f"{varid}minihistogram",
+    )
 
     template_variables["top"] = Container(
         [info, table1, table2, mini_histo], sequence_type="grid"
     )
 
-    if isinstance(summary["histogram"], list):
-        hist_data = histogram(
-            config,
-            [x[0] for x in summary["histogram"]],
-            [x[1] for x in summary["histogram"]],
-            date=True,
-        )
-    else:
-        hist_data = histogram(
-            config, summary["histogram"][0], summary["histogram"][1], date=True
+    hist_data = None
+    hist_caption = None
+
+    if summary_histogram:
+        if isinstance(summary_histogram, list):
+            hist_data = histogram(
+                config,
+                [x[0] for x in summary["histogram"]],
+                [x[1] for x in summary["histogram"]],
+                date=True,
+            )
+        else:
+            hist_data = histogram(
+                config, summary["histogram"][0], summary["histogram"][1], date=True
+            )
+
+        n_bins = len(summary["histogram"][1]) - 1 if summary["histogram"] else 0
+        hist_caption = (
+            f"<strong>Histogram with fixed size bins</strong> (bins={n_bins})"
         )
 
+    hist = image_or_empty(
+        hist_data,
+        image_format=image_format,
+        alt="Histogram",
+        caption=hist_caption,
+        name="Histogram",
+        anchor_id=f"{varid}histogram",
+    )
+
     # Bottom
-    n_bins = len(summary["histogram"][1]) - 1 if summary["histogram"] else 0
     bottom = Container(
-        [
-            Image(
-                hist_data,
-                image_format=image_format,
-                alt="Histogram",
-                caption=f"<strong>Histogram with fixed size bins</strong> (bins={n_bins})",
-                name="Histogram",
-                anchor_id=f"{varid}histogram",
-            )
-        ],
+        [hist],
         sequence_type="tabs",
         anchor_id=summary["varid"],
     )
diff --git a/src/ydata_profiling/report/structure/variables/render_file.py b/src/ydata_profiling/report/structure/variables/render_file.py
index 81379a41f..e014434ab 100644
--- a/src/ydata_profiling/report/structure/variables/render_file.py
+++ b/src/ydata_profiling/report/structure/variables/render_file.py
@@ -1,10 +1,11 @@
 from typing import List
 
 from ydata_profiling.config import Settings
-from ydata_profiling.report.presentation.core import Container, FrequencyTable, Image
+from ydata_profiling.report.presentation.core import Container, FrequencyTable
 from ydata_profiling.report.presentation.core.renderable import Renderable
 from ydata_profiling.report.presentation.frequency_table_utils import freq_table
 from ydata_profiling.report.structure.variables.render_path import render_path
+from ydata_profiling.report.utils import image_or_empty
 from ydata_profiling.visualisation.plot import histogram
 
 
@@ -20,17 +21,21 @@ def render_file(config: Settings, summary: dict) -> dict:
     image_format = config.plot.image_format
 
     file_tabs: List[Renderable] = []
+
+    hist_data = None
+    if summary["histogram_file_size"]:
+        hist_data = histogram(config, *summary["histogram_file_size"])
+
     if "file_size" in summary:
-        file_tabs.append(
-            Image(
-                histogram(config, *summary["histogram_file_size"]),
-                image_format=image_format,
-                alt="Size",
-                caption=f"<strong>Histogram with fixed size bins of file sizes (in bytes)</strong> (bins={len(summary['histogram_file_size'][1]) - 1})",
-                name="File size",
-                anchor_id=f"{varid}file_size_histogram",
-            )
+        hist = image_or_empty(
+            hist_data,
+            image_format=image_format,
+            alt="Size",
+            caption=f"<strong>Histogram with fixed size bins of file sizes (in bytes)</strong> (bins={len(summary['histogram_file_size'][1]) - 1})",
+            name="File size",
+            anchor_id=f"{varid}file_size_histogram",
         )
+        file_tabs.append(hist)
 
     file_dates = {
         "file_created_time": "Created",
diff --git a/src/ydata_profiling/report/structure/variables/render_real.py b/src/ydata_profiling/report/structure/variables/render_real.py
index 227200c27..92c73fcf3 100644
--- a/src/ydata_profiling/report/structure/variables/render_real.py
+++ b/src/ydata_profiling/report/structure/variables/render_real.py
@@ -9,11 +9,11 @@
 from ydata_profiling.report.presentation.core import (
     Container,
     FrequencyTable,
-    Image,
     Table,
     VariableInfo,
 )
 from ydata_profiling.report.structure.variables.render_common import render_common
+from ydata_profiling.report.utils import image_or_empty
 from ydata_profiling.visualisation.plot import histogram, mini_histogram
 
 
@@ -118,22 +118,27 @@ def render_real(config: Settings, summary: dict) -> dict:
         style=config.html.style,
     )
 
-    if isinstance(summary.get("histogram", []), list):
-        mini_histo = Image(
-            mini_histogram(
+    summary_histogram = summary.get("histogram", [])
+
+    mini_hist_data = None
+
+    if summary_histogram:
+        if isinstance(summary_histogram, list):
+            mini_hist_data = mini_histogram(
                 config,
-                [x[0] for x in summary.get("histogram", [])],
-                [x[1] for x in summary.get("histogram", [])],
-            ),
-            image_format=image_format,
-            alt="Mini histogram",
-        )
-    else:
-        mini_histo = Image(
-            mini_histogram(config, *summary["histogram"]),
-            image_format=image_format,
-            alt="Mini histogram",
-        )
+                [x[0] for x in summary_histogram],
+                [x[1] for x in summary_histogram],
+            )
+        else:
+            mini_hist_data = mini_histogram(config, *summary_histogram)
+
+    mini_histo = image_or_empty(
+        mini_hist_data,
+        alt="Mini histogram",
+        image_format=image_format,
+        name="Mini Histogram",
+        anchor_id=f"{varid}minihistogram",
+    )
 
     template_variables["top"] = Container(
         [info, table1, table2, mini_histo], sequence_type="grid"
@@ -243,22 +248,31 @@ def render_real(config: Settings, summary: dict) -> dict:
         sequence_type="grid",
     )
 
-    if isinstance(summary.get("histogram", []), list):
-        hist_data = histogram(
-            config,
-            [x[0] for x in summary.get("histogram", [])],
-            [x[1] for x in summary.get("histogram", [])],
-        )
-        bins = len(summary["histogram"][0][1]) - 1 if "histogram" in summary else 0
-        hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={bins})"
-    else:
-        hist_data = histogram(config, *summary["histogram"])
-        hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={len(summary['histogram'][1]) - 1})"
+    hist_data = None
+    hist_caption = None
+
+    if summary_histogram:
+        if isinstance(summary_histogram, list):
+            hist_data = histogram(
+                config,
+                [x[0] for x in summary_histogram],
+                [x[1] for x in summary_histogram],
+            )
+            bins = len(summary_histogram[0][1])
+            hist_caption = (
+                f"<strong>Histogram with fixed size bins</strong> (bins={bins - 1})"
+            )
+        else:
+            hist_data = histogram(config, *summary_histogram)
+            hist_caption = (
+                f"<strong>Histogram with fixed size bins</strong> "
+                f"(bins={len(summary_histogram[1]) - 1})"
+            )
 
-    hist = Image(
+    hist = image_or_empty(
         hist_data,
-        image_format=image_format,
         alt="Histogram",
+        image_format=image_format,
         caption=hist_caption,
         name="Histogram",
         anchor_id=f"{varid}histogram",
diff --git a/src/ydata_profiling/report/structure/variables/render_timeseries.py b/src/ydata_profiling/report/structure/variables/render_timeseries.py
index 6f3bc27cd..17f396ac0 100644
--- a/src/ydata_profiling/report/structure/variables/render_timeseries.py
+++ b/src/ydata_profiling/report/structure/variables/render_timeseries.py
@@ -15,6 +15,7 @@
     VariableInfo,
 )
 from ydata_profiling.report.structure.variables.render_common import render_common
+from ydata_profiling.report.utils import image_or_empty
 from ydata_profiling.visualisation.plot import (
     histogram,
     mini_ts_plot,
@@ -289,18 +290,24 @@ def render_timeseries(config: Settings, summary: dict) -> dict:
         sequence_type="grid",
     )
 
-    if isinstance(summary["histogram"], list):
-        hist_data = histogram(
-            config,
-            [x[0] for x in summary["histogram"]],
-            [x[1] for x in summary["histogram"]],
-        )
-        hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={len(summary['histogram'][0][1]) - 1})"
-    else:
-        hist_data = histogram(config, *summary["histogram"])
-        hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={len(summary['histogram'][1]) - 1})"
+    summary_histogram = summary.get("histogram", [])
 
-    hist = Image(
+    hist_data = None
+    hist_caption = None
+
+    if summary_histogram:
+        if isinstance(summary_histogram, list):
+            hist_data = histogram(
+                config,
+                [x[0] for x in summary["histogram"]],
+                [x[1] for x in summary["histogram"]],
+            )
+            hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={len(summary['histogram'][0][1]) - 1})"
+        else:
+            hist_data = histogram(config, *summary["histogram"])
+            hist_caption = f"<strong>Histogram with fixed size bins</strong> (bins={len(summary['histogram'][1]) - 1})"
+
+    hist = image_or_empty(
         hist_data,
         image_format=image_format,
         alt="Histogram",
diff --git a/src/ydata_profiling/report/utils.py b/src/ydata_profiling/report/utils.py
new file mode 100644
index 000000000..9d8a09b04
--- /dev/null
+++ b/src/ydata_profiling/report/utils.py
@@ -0,0 +1,34 @@
+from typing import Optional
+
+from ydata_profiling.config import ImageType
+from ydata_profiling.report.presentation.core import Container, Image
+
+
+def image_or_empty(
+    image: Optional[str],
+    *,
+    alt: str,
+    image_format: ImageType,
+    caption: Optional[str] = None,
+    name: Optional[str] = None,
+    anchor_id: Optional[str] = None,
+) -> Container | Image:
+    """
+    Add helper to render an image or an empty container when necessary.
+    """
+    if image is None:
+        return Container(
+            items=[],
+            name=name or f"{alt}_empty",
+            anchor_id=anchor_id,
+            sequence_type="grid",
+        )
+
+    return Image(
+        image=image,
+        image_format=image_format,
+        alt=alt,
+        caption=caption,
+        name=name,
+        anchor_id=anchor_id,
+    )
diff --git a/src/ydata_profiling/visualisation/plot.py b/src/ydata_profiling/visualisation/plot.py
index a2df1fd5d..b3dc5338e 100644
--- a/src/ydata_profiling/visualisation/plot.py
+++ b/src/ydata_profiling/visualisation/plot.py
@@ -134,13 +134,49 @@ def plot_word_cloud(config: Settings, word_counts: pd.Series) -> str:
     return plot_360_n0sc0pe(config)
 
 
+def _is_valid_hist_data(series: np.ndarray, bins: Union[int, np.ndarray]) -> bool:
+    """
+    Returns True if the series and bins contain enough usable numeric data
+    to produce a histogram without matplotlib errors.
+    """
+    if series is None or bins is None:
+        return False
+
+    if len(series) == 0:
+        return False
+
+    try:
+        series_arr = np.asarray(series, dtype=float)
+    except Exception:
+        return False
+
+    if not np.isfinite(series_arr).any():
+        return False
+
+    # Handle bins type
+    if isinstance(bins, int):
+        if bins < 1:
+            return False
+    else:
+        try:
+            bins_arr = np.asarray(bins, dtype=float)
+        except Exception:
+            return False
+        if len(bins_arr) < 2:
+            return False
+        if not np.isfinite(bins_arr).all():
+            return False
+
+    return True
+
+
 @manage_matplotlib_context()
 def histogram(
     config: Settings,
     series: np.ndarray,
     bins: Union[int, np.ndarray],
     date: bool = False,
-) -> str:
+) -> str | None:
     """Plot an histogram of the data.
 
     Args:
@@ -153,6 +189,9 @@ def histogram(
       The resulting histogram encoded as a string.
 
     """
+
+    if not _is_valid_hist_data(series, bins):
+        return None
     plot = _plot_histogram(config, series, bins, date=date, figsize=(7, 3))
     plot.xaxis.set_tick_params(rotation=90 if date else 45)
     plot.figure.tight_layout()
@@ -165,7 +204,7 @@ def mini_histogram(
     series: np.ndarray,
     bins: Union[int, np.ndarray],
     date: bool = False,
-) -> str:
+) -> str | None:
     """Plot a small (mini) histogram of the data.
 
     Args:
@@ -176,6 +215,9 @@ def mini_histogram(
     Returns:
       The resulting mini histogram encoded as a string.
     """
+    if not _is_valid_hist_data(series, bins):
+        return None
+
     plot = _plot_histogram(
         config, series, bins, figsize=(3, 2.25), date=date, hide_yaxis=True
     )
@@ -253,8 +295,16 @@ def correlation_matrix(config: Settings, data: pd.DataFrame, vmin: int = -1) ->
     cmap.set_bad(config.plot.correlation.bad)
 
     labels = data.columns
+
+    try:
+        matrix = np.asarray(data, dtype=float)
+    except Exception:
+        # If conversion fails, create an all-NaN matrix of the appropriate shape
+        n = len(data)
+        matrix = np.full((n, n), np.nan, dtype=float)
+
     matrix_image = axes_cor.imshow(
-        data, vmin=vmin, vmax=1, interpolation="nearest", cmap=cmap
+        matrix, vmin=vmin, vmax=1, interpolation="nearest", cmap=cmap
     )
     plt.colorbar(matrix_image)
 
diff --git a/tests/backends/spark_backend/test_correlations_spark.py b/tests/backends/spark_backend/test_correlations_spark.py
index ef16224a1..02ab6886c 100644
--- a/tests/backends/spark_backend/test_correlations_spark.py
+++ b/tests/backends/spark_backend/test_correlations_spark.py
@@ -43,7 +43,10 @@ def correlation_data_cat(spark_session):
 
 @pytest.fixture
 def correlation_var_types():
-    return {"test_num_1": {"type": "Numeric"}, "test_num_2": {"type": "Numeric"}}
+    return {
+        "test_num_1": {"type": "Numeric", "n": 7, "n_missing": 0},
+        "test_num_2": {"type": "Numeric", "n": 7, "n_missing": 0},
+    }
 
 
 def test_spearman_spark(correlation_data_num, correlation_var_types):
diff --git a/tests/backends/spark_backend/test_descriptions_spark.py b/tests/backends/spark_backend/test_descriptions_spark.py
index 24de9afbb..63aabbe4e 100644
--- a/tests/backends/spark_backend/test_descriptions_spark.py
+++ b/tests/backends/spark_backend/test_descriptions_spark.py
@@ -110,8 +110,8 @@ def expected_results():
         },
         "x": {
             "n": 9,
-            "count": 9,
-            "p_missing": 0.0,
+            "count": 8,
+            "p_missing": 0.1111111111111111,
             "n_distinct": 7,
             "n_unique": 5,
             "p_distinct": 0.7777777777777778,
@@ -130,21 +130,21 @@ def expected_results():
             "95%": 50.0,
             "mad": 5.0,
             "min": -10.0,
-            "max": check_is_NaN,
-            "mean": check_is_NaN,
-            "std": check_is_NaN,
-            "variance": check_is_NaN,
-            "kurtosis": check_is_NaN,
-            "skewness": check_is_NaN,
-            "sum": check_is_NaN,
-            "range": check_is_NaN,
+            "max": 50.0,
+            "mean": 13.375,
+            "std": 23.68807716974934,
+            "variance": 561.125,
+            "kurtosis": -0.9061564710904939,
+            "skewness": 0.8700654233008702,
+            "sum": 107.0,
+            "range": 60.0,
             "iqr": 18.0,
-            "cv": check_is_NaN,
+            "cv": 1.771071190261633,
         },
         "y": {
             "n": 9,
-            "count": 9,
-            "p_missing": 0.0,
+            "count": 8,
+            "p_missing": 0.1111111111111111,
             "n_distinct": 9,
             "n_unique": 9,
             "p_distinct": 1.0,
@@ -163,16 +163,16 @@ def expected_results():
             "95%": 3122.0,
             "mad": 15.9,
             "min": -3.1415926535,
-            "max": check_is_NaN,
-            "mean": check_is_NaN,
-            "std": check_is_NaN,
-            "variance": check_is_NaN,
-            "kurtosis": check_is_NaN,
-            "skewness": check_is_NaN,
-            "sum": check_is_NaN,
-            "range": check_is_NaN,
+            "max": 3122.0,
+            "mean": 491.1743650433125,
+            "std": 1086.1335236468508,
+            "variance": 1179686.0311895243,
+            "kurtosis": 2.6543509612939804,
+            "skewness": 2.097192909339154,
+            "sum": 3929.3949203465,
+            "range": 3125.1415926535,
             "iqr": 110.999999,
-            "cv": check_is_NaN,
+            "cv": 2.211299287883385,
         },
         "cat": {
             "n": 9,
@@ -180,9 +180,9 @@ def expected_results():
             "p_missing": 0.1111111111111111,
             "n_distinct": 7,
             "n_unique": 6,
-            "p_distinct": 0.875,
+            "p_distinct": 0.7777777777777778,
             "is_unique": False,
-            "p_unique": 0.75,
+            "p_unique": 0.6666666666666666,
         },
         "s1": {
             "n": 9,
@@ -247,9 +247,9 @@ def expected_results():
             "p_missing": 0.1111111111111111,
             "n_distinct": 6,
             "n_unique": 4,
-            "p_distinct": 0.75,
+            "p_distinct": 0.6666666666666666,
             "is_unique": False,
-            "p_unique": 0.5,
+            "p_unique": 0.4444444444444444,
         },
         "bool_tf": {
             "count": 9,
@@ -287,8 +287,8 @@ def expected_results():
         },
         "bool_01_with_nan": {
             "n": 9,
-            "count": 9,
-            "p_missing": 0.0,
+            "count": 8,
+            "p_missing": 0.1111111111111111,
             "n_distinct": 3,
             "n_unique": 1,
             "p_distinct": 0.3333333333333333,
@@ -307,16 +307,16 @@ def expected_results():
             "95%": 1.0,
             "mad": 0.0,
             "min": 0.0,
-            "max": check_is_NaN,
-            "mean": check_is_NaN,
-            "std": check_is_NaN,
-            "variance": check_is_NaN,
-            "kurtosis": check_is_NaN,
-            "skewness": check_is_NaN,
-            "sum": check_is_NaN,
-            "range": check_is_NaN,
+            "max": 1.0,
+            "mean": 0.5,
+            "std": 0.5345224838248488,
+            "variance": 0.2857142857142857,
+            "kurtosis": -2.0,
+            "skewness": 1.1102230246251565e-16,
+            "sum": 4.0,
+            "range": 1.0,
             "iqr": 1.0,
-            "cv": check_is_NaN,
+            "cv": 1.0690449676496976,
         },
         "list": {
             "n": 9,
diff --git a/tests/backends/spark_backend/test_issue1429.py b/tests/backends/spark_backend/test_issue1429.py
new file mode 100644
index 000000000..3d17b8cef
--- /dev/null
+++ b/tests/backends/spark_backend/test_issue1429.py
@@ -0,0 +1,138 @@
+"""
+Test for issue 1429:
+https://github.com/ydataai/ydata-profiling/issues/1429
+"""
+from typing import List, Optional, Tuple
+
+import numpy as np
+from pyspark.sql import DataFrame, SparkSession
+from pyspark.sql import types as T
+
+from ydata_profiling import ProfileReport
+from ydata_profiling.config import SparkSettings
+from ydata_profiling.model.spark.describe_counts_spark import describe_counts_spark
+from ydata_profiling.model.spark.describe_generic_spark import describe_generic_spark
+from ydata_profiling.model.spark.describe_numeric_spark import (
+    describe_numeric_1d_spark,
+    numeric_stats_spark,
+)
+from ydata_profiling.model.spark.describe_supported_spark import (
+    describe_supported_spark,
+)
+
+RowType = Tuple[
+    Optional[str],
+    Optional[float],
+    Optional[int],
+    Optional[bool],
+    Optional[float],
+    Optional[str],
+]
+
+config = SparkSettings()
+
+
+def create_test_df(spark: SparkSession) -> DataFrame:
+    schema = T.StructType(
+        [
+            T.StructField("category", T.StringType(), True),
+            T.StructField("double", T.DoubleType(), True),
+            T.StructField("int", T.IntegerType(), True),
+            T.StructField("boolean", T.BooleanType(), True),
+            T.StructField("null_double", T.DoubleType(), True),
+            T.StructField("null_string", T.StringType(), True),
+        ]
+    )
+
+    data: List[RowType] = [
+        (f"test_{num + 1}", float(num), int(num), True, None, None)
+        for num in range(205)
+    ]
+
+    # Adding dupes
+    data.extend([("test_1", float(1), int(1), False, None, None) for _ in range(205)])
+
+    # Adding nulls
+    data.extend([(None, None, None, None, None, None) for _ in range(100)])
+
+    return spark.createDataFrame(data, schema=schema)
+
+
+def test_describe_numeric_spark(spark_session):
+    test_df = create_test_df(spark_session)
+
+    numeric_stats = numeric_stats_spark(df=test_df.select("double"), summary={})
+
+    for _, value in numeric_stats.items():
+        assert value is not None
+
+
+def test_describe_numeric_1d_spark_for_null_column_edge_case(
+    spark_session, test_output_dir
+):
+    spark = spark_session
+    test_df = create_test_df(spark)
+
+    _, _, summary = describe_counts_spark(
+        config=config, series=test_df.select("null_double"), summary={}
+    )
+
+    _, _, summary = describe_generic_spark(
+        config=config, df=test_df.select("null_double"), summary=summary
+    )
+
+    _, _, summary = describe_supported_spark(
+        config=config, series=test_df.select("null_double"), summary=summary
+    )
+
+    _, _, summary = describe_numeric_1d_spark(
+        config=config, df=test_df.select("null_double"), summary=summary
+    )
+
+    assert summary["iqr"] is np.nan
+    assert summary["mad"] is np.nan
+    assert summary["cv"] is np.nan
+    assert summary["mean"] is None
+    assert summary["histogram"] == []
+
+    profile = ProfileReport(test_df, title="1429_edge_case.html", config=config)
+
+    output_file = test_output_dir / "1429_edge_case.html"
+
+    profile.to_file(output_file)
+
+    assert output_file.exists()
+
+
+def test_describe_counts_spark(spark_session):
+    test_df = create_test_df(spark_session)
+
+    _, _, summary = describe_counts_spark(
+        config=config, series=test_df.select("category"), summary={}
+    )
+
+    assert summary["value_counts_without_nan"].loc["test_1"] == 206
+
+    _, _, summary = describe_counts_spark(
+        config=config, series=test_df.select("double"), summary={}
+    )
+
+    assert summary["value_counts_without_nan"].loc[float(1)] == 206
+
+    _, _, summary = describe_counts_spark(
+        config=config, series=test_df.select("int"), summary={}
+    )
+
+    assert summary["value_counts_without_nan"].loc[int(1)] == 206
+
+    _, _, summary = describe_counts_spark(
+        config=config, series=test_df.select("boolean"), summary={}
+    )
+
+    assert summary["value_counts_without_nan"].loc[True] == 205
+
+    _, _, summary = describe_counts_spark(
+        config=config, series=test_df.select("null_double"), summary={}
+    )
+
+    assert summary["value_counts_without_nan"].size == 0
diff --git a/tests/backends/spark_backend/test_issue1602.py b/tests/backends/spark_backend/test_issue1602.py
new file mode 100644
index 000000000..b25ae1873
--- /dev/null
+++ b/tests/backends/spark_backend/test_issue1602.py
@@ -0,0 +1,33 @@
+"""
+Test for issue 1602:
+https://github.com/ydataai/ydata-profiling/issues/1602
+"""
+
+from pyspark.sql import types as T
+
+from ydata_profiling import ProfileReport
+
+
+def test_spark_handles_decimal_type(test_output_dir, spark_session):
+    from decimal import Decimal
+
+    spark = spark_session
+
+    schema = T.StructType(
+        [
+            T.StructField("number", T.StringType(), True),
+            T.StructField("decimal", T.DecimalType(10, 2), True),
+        ]
+    )
+
+    data = [(f"test_{num + 1}", Decimal(num + 1)) for num in range(205)]
+
+    data.extend([("test_1", Decimal("1.05")) for _ in range(205)])
+
+    test_df = spark.createDataFrame(data, schema=schema)
+
+    profile = ProfileReport(test_df, title="decimal_handling", explorative=True)
+    output_file = test_output_dir / "decimal_handling.html"
+    profile.to_file(output_file)
+
+    assert output_file.exists()
diff --git a/tests/backends/spark_backend/test_issue1722.py b/tests/backends/spark_backend/test_issue1722.py
new file mode 100644
index 000000000..6cb2d5596
--- /dev/null
+++ b/tests/backends/spark_backend/test_issue1722.py
@@ -0,0 +1,90 @@
+"""
+Test for issue 1722:
+https://github.com/ydataai/ydata-profiling/issues/1722
+"""
+
+from datetime import date, datetime
+
+from pyspark.sql import SparkSession
+from pyspark.sql import types as T
+
+from ydata_profiling import ProfileReport
+
+
+def make_non_numeric_df(spark: SparkSession):
+    # Intentionally not including any numeric types
+    schema = T.StructType(
+        [
+            T.StructField("id", T.StringType(), False),
+            T.StructField("d", T.DateType(), False),
+            T.StructField("ts", T.TimestampType(), False),
+            T.StructField("arr", T.ArrayType(T.IntegerType()), False),
+            T.StructField("mp", T.MapType(T.StringType(), T.IntegerType()), False),
+            T.StructField(
+                "struct",
+                T.StructType(
+                    [
+                        T.StructField("a", T.IntegerType(), False),
+                        T.StructField("b", T.StringType(), False),
+                    ]
+                ),
+                False,
+            ),
+        ]
+    )
+
+    data = [
+        (
+            "r1",
+            date(2020, 1, 1),
+            datetime(2020, 1, 1, 12, 0),
+            [1, 2],
+            {"x": 1, "y": 2},
+            (10, "aa"),
+        ),
+        (
+            "r2",
+            date(2021, 6, 15),
+            datetime(2021, 6, 15, 8, 30),
+            [3],
+            {"z": 3},
+            (20, "bb"),
+        ),
+        ("r3", date(2022, 12, 31), datetime(2022, 12, 31, 23, 59), [], {}, (30, "cc")),
+    ]
+
+    return spark.createDataFrame(data, schema=schema)
+
+
+def test_issue1722(test_output_dir, spark_session):
+    from pyspark.sql import functions as F
+
+    spark = spark_session
+
+    non_numeric_df = make_non_numeric_df(spark)
+
+    # type casting 1
+    df_casted = non_numeric_df.select(
+        [
+            (
+                F.col(field.name).cast("string").alias(field.name)
+                if isinstance(field.dataType, (T.DateType, T.TimestampType))
+                else F.col(field.name)
+            )
+            for field in non_numeric_df.schema
+        ]
+    )
+    # type casting 2
+    complex_columns = [
+        field.name
+        for field in non_numeric_df.schema.fields
+        if isinstance(field.dataType, (T.ArrayType, T.MapType, T.StructType))
+    ]
+    for col_name in complex_columns:
+        df_casted = df_casted.withColumn(col_name, F.to_json(F.col(col_name)))
+
+    profile = ProfileReport(df_casted, title="non_numeric_1722", explorative=True)
+    output_file = test_output_dir / "non_numeric_1722.html"
+    profile.to_file(output_file)
+
+    assert output_file.exists()
diff --git a/tests/conftest.py b/tests/conftest.py
index 28ce21215..7db073a3c 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -88,6 +88,10 @@ def spark_context():
         .setAppName("pytest-pyspark-tests")
         .setMaster("local[*]")
         .set("spark.sql.ansi.enabled", "false")
+        .set("spark.driver.host", "127.0.0.1")
+        .set("spark.driver.bindAddress", "127.0.0.1")
+        .set("spark.driver.port", "4040")
+        .set("spark.blockManager.port", "4041")
     )
 
     # Check if SparkContext exists before creating a new one
@@ -112,6 +116,8 @@ def spark_session(spark_context):
         pytest.skip("Skipping Spark tests because PySpark is not installed.")
     spark = (
         SparkSession.builder.master("local[*]")
+        .config("spark.driver.host", "127.0.0.1")
+        .config("spark.driver.bindAddress", "127.0.0.1")
         .appName("pytest")
         .config("spark.sql.ansi.enabled", "false")  # <-- restore permissive casts
         .getOrCreate()
diff --git a/tests/unit/test_formatters.py b/tests/unit/test_formatters.py
index 156cc4385..f49e693d8 100644
--- a/tests/unit/test_formatters.py
+++ b/tests/unit/test_formatters.py
@@ -83,6 +83,7 @@ def test_fmt_array(array, threshold, expected):
         (1e20, 10, "1 × 10<sup>20</sup>"),
         (1e-20, 10, "1 × 10<sup>-20</sup>"),
         (1e8, 3, "1 × 10<sup>8</sup>"),
+        (None, 3, "N/A"),
     ],
 )
 def test_fmt_numeric(value, precision, expected):