diff --git a/src/ydata_profiling/model/pandas/missing_pandas.py b/src/ydata_profiling/model/pandas/missing_pandas.py
index e9c3a40e0..d2a1303f9 100644
--- a/src/ydata_profiling/model/pandas/missing_pandas.py
+++ b/src/ydata_profiling/model/pandas/missing_pandas.py
@@ -1,3 +1,4 @@
+import numpy as np
 import pandas as pd
 
 from ydata_profiling.config import Settings
@@ -11,14 +12,35 @@
 
 @missing_bar.register
 def pandas_missing_bar(config: Settings, df: pd.DataFrame) -> str:
-    return plot_missing_bar(config, df)
+    notnull_counts = len(df) - df.isnull().sum()
+    return plot_missing_bar(
+        config,
+        notnull_counts=notnull_counts,
+        nrows=len(df),
+        columns=list(df.columns),
+    )
 
 
 @missing_matrix.register
 def pandas_missing_matrix(config: Settings, df: pd.DataFrame) -> str:
-    return plot_missing_matrix(config, df)
+    return plot_missing_matrix(
+        config,
+        columns=list(df.columns),
+        notnull=df.notnull().values,
+        nrows=len(df),
+    )
 
 
 @missing_heatmap.register
 def pandas_missing_heatmap(config: Settings, df: pd.DataFrame) -> str:
-    return plot_missing_heatmap(config, df)
+    # Remove completely filled or completely empty variables.
+    columns = [i for i, n in enumerate(np.var(df.isnull(), axis="rows")) if n > 0]
+    df = df.iloc[:, columns]
+
+    # Create and mask the correlation matrix. Construct the base heatmap.
+    corr_mat = df.isnull().corr()
+    mask = np.zeros_like(corr_mat)
+    mask[np.triu_indices_from(mask)] = True
+    return plot_missing_heatmap(
+        config, corr_mat=corr_mat, mask=mask, columns=list(df.columns)
+    )
diff --git a/src/ydata_profiling/model/spark/missing_spark.py b/src/ydata_profiling/model/spark/missing_spark.py
index 4c268e6a3..a8c46a5a6 100644
--- a/src/ydata_profiling/model/spark/missing_spark.py
+++ b/src/ydata_profiling/model/spark/missing_spark.py
@@ -1,5 +1,6 @@
 from typing import Any, List, Optional
 
+import numpy as np
 from pyspark.sql import DataFrame
 
 from ydata_profiling.config import Settings
@@ -67,18 +68,33 @@ def spark_missing_bar(config: Settings, df: DataFrame) -> str:
     )
 
     return plot_missing_bar(
-        config,
-        MissingnoBarSparkPatch(
-            df=data_nan_counts, columns=df.columns, original_df_size=df.count()
-        ),
+        config, notnull_counts=data_nan_counts, columns=df.columns, nrows=df.count()
     )
 
 
 @missing_matrix.register
 def spark_missing_matrix(config: Settings, df: DataFrame) -> str:
-    return plot_missing_matrix(config, MissingnoBarSparkPatch(df))
+    df = MissingnoBarSparkPatch(df, columns=df.columns, original_df_size=df.count())
+    return plot_missing_matrix(
+        config,
+        columns=df.columns,
+        notnull=df.notnull().values,
+        nrows=len(df),
+    )
 
 
 @missing_heatmap.register
 def spark_missing_heatmap(config: Settings, df: DataFrame) -> str:
-    return plot_missing_heatmap(config, MissingnoBarSparkPatch(df))
+    df = MissingnoBarSparkPatch(df, columns=df.columns, original_df_size=df.count())
+
+    # Remove completely filled or completely empty variables.
+    columns = [i for i, n in enumerate(np.var(df.isnull(), axis="rows")) if n > 0]
+    df = df.iloc[:, columns]
+
+    # Create and mask the correlation matrix. Construct the base heatmap.
+    corr_mat = df.isnull().corr()
+    mask = np.zeros_like(corr_mat)
+    mask[np.triu_indices_from(mask)] = True
+    return plot_missing_heatmap(
+        config, corr_mat=corr_mat, mask=mask, columns=list(df.columns)
+    )
diff --git a/src/ydata_profiling/visualisation/missing.py b/src/ydata_profiling/visualisation/missing.py
index 7632b376d..9dd1068d0 100644
--- a/src/ydata_profiling/visualisation/missing.py
+++ b/src/ydata_profiling/visualisation/missing.py
@@ -1,5 +1,6 @@
 """Plotting functions for the missing values diagrams"""
-import pandas as pd
+from typing import Any, List
+
 from matplotlib import pyplot as plt
 
 from ydata_profiling.config import Settings
@@ -12,22 +13,22 @@
 from ydata_profiling.visualisation.utils import hex_to_rgb, plot_360_n0sc0pe
 
 
-def get_font_size(data: pd.DataFrame) -> float:
+def get_font_size(columns: List[str]) -> float:
     """Calculate font size based on number of columns
 
     Args:
-        data: DataFrame
+        columns: List of column names.
 
     Returns:
         Font size for missing values plots.
     """
-    max_label_length = max(len(label) for label in data.columns)
+    max_label_length = max(len(label) for label in columns)
 
-    if len(data.columns) < 20:
+    if len(columns) < 20:
         font_size = 13.0
-    elif 20 <= len(data.columns) < 40:
+    elif 20 <= len(columns) < 40:
         font_size = 12.0
-    elif 40 <= len(data.columns) < 60:
+    elif 40 <= len(columns) < 60:
         font_size = 10.0
     else:
         font_size = 8.0
@@ -37,21 +38,27 @@ def get_font_size(data: pd.DataFrame) -> float:
 
 
 @manage_matplotlib_context()
-def plot_missing_matrix(config: Settings, data: pd.DataFrame) -> str:
+def plot_missing_matrix(
+    config: Settings, notnull: Any, columns: List[str], nrows: int
+) -> str:
     """Generate missing values matrix plot
 
     Args:
         config: report Settings object
-        data: Pandas DataFrame to generate missing values matrix from.
+        notnull: Missing data indicator matrix.
+        columns: List of column names.
+        nrows: Number of rows in the dataframe.
 
     Returns:
         The resulting missing values matrix encoded as a string.
     """
 
     missing_matrix(
-        data,
+        notnull=notnull,
+        height=nrows,
+        columns=columns,
         figsize=(10, 4),
-        fontsize=get_font_size(data) / 20 * 16,
+        fontsize=get_font_size(columns) / 20 * 16,
         color=hex_to_rgb(config.html.style.primary_colors[0]),
         labels=config.plot.missing.force_labels,
     )
@@ -60,20 +67,25 @@ def plot_missing_matrix(config: Settings, data: pd.DataFrame) -> str:
 
 
 @manage_matplotlib_context()
-def plot_missing_bar(config: Settings, data: pd.DataFrame) -> str:
+def plot_missing_bar(
+    config: Settings, notnull_counts: list, nrows: int, columns: List[str]
+) -> str:
     """Generate missing values bar plot.
 
     Args:
         config: report Settings object
-        data: Pandas DataFrame to generate missing values bar plot from.
+        notnull_counts: Number of nonnull values per column.
+        nrows: Number of rows in the dataframe.
+        columns: List of column names.
 
     Returns:
         The resulting missing values bar plot encoded as a string.
     """
     missing_bar(
-        data,
+        notnull_counts=notnull_counts,
+        nrows=nrows,
         figsize=(10, 5),
-        fontsize=get_font_size(data),
+        fontsize=get_font_size(columns),
         color=hex_to_rgb(config.html.style.primary_colors[0]),
         labels=config.plot.missing.force_labels,
     )
@@ -85,35 +97,40 @@ def plot_missing_bar(config: Settings, data: pd.DataFrame) -> str:
 
 
 @manage_matplotlib_context()
-def plot_missing_heatmap(config: Settings, data: pd.DataFrame) -> str:
+def plot_missing_heatmap(
+    config: Settings, corr_mat: Any, mask: Any, columns: List[str]
+) -> str:
     """Generate missing values heatmap plot.
 
     Args:
         config: report Settings object
-        data: Pandas DataFrame to generate missing values heatmap plot from.
+        corr_mat: Correlation matrix.
+        maks: Upper-triangle mask.
+        columns: List of column names.
 
     Returns:
         The resulting missing values heatmap plot encoded as a string.
     """
 
     height = 4
-    if len(data.columns) > 10:
-        height += int((len(data.columns) - 10) / 5)
+    if len(columns) > 10:
+        height += int((len(columns) - 10) / 5)
     height = min(height, 10)
 
-    font_size = get_font_size(data)
-    if len(data.columns) > 40:
+    font_size = get_font_size(columns)
+    if len(columns) > 40:
         font_size /= 1.4
 
     missing_heatmap(
-        data,
+        corr_mat=corr_mat,
+        mask=mask,
         figsize=(10, height),
         fontsize=font_size,
         cmap=config.plot.missing.cmap,
         labels=config.plot.missing.force_labels,
     )
 
-    if len(data.columns) > 40:
+    if len(columns) > 40:
         plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.3)
     else:
         plt.subplots_adjust(left=0.2, right=0.9, top=0.8, bottom=0.3)
diff --git a/src/ydata_profiling/visualisation/plot.py b/src/ydata_profiling/visualisation/plot.py
index d93f4436f..fd8764b38 100644
--- a/src/ydata_profiling/visualisation/plot.py
+++ b/src/ydata_profiling/visualisation/plot.py
@@ -761,7 +761,8 @@ def _set_visibility(
 
 
 def missing_bar(
-    data: pd.DataFrame,
+    notnull_counts: pd.Series,
+    nrows: int,
     figsize: Tuple[float, float] = (25, 10),
     fontsize: float = 16,
     labels: bool = True,
@@ -774,7 +775,8 @@ def missing_bar(
     Inspired by https://github.com/ResidentMario/missingno
 
     Args:
-        data: The input DataFrame.
+        notnull_counts: Number of nonnull values per column.
+        nrows: Number of rows in the dataframe.
         figsize: The size of the figure to display.
         fontsize: The figure's font size. This default to 16.
         labels: Whether or not to display the column names. Would need to be turned off on particularly large
@@ -784,12 +786,10 @@ def missing_bar(
     Returns:
         The plot axis.
     """
-    null_counts = len(data) - data.isnull().sum()
-    values = null_counts.values
-    null_counts = null_counts / len(data)
+    percentage = notnull_counts / nrows
 
-    if len(values) <= 50:
-        ax0 = null_counts.plot.bar(figsize=figsize, fontsize=fontsize, color=color)
+    if len(notnull_counts) <= 50:
+        ax0 = percentage.plot.bar(figsize=figsize, fontsize=fontsize, color=color)
         ax0.set_xticklabels(
             ax0.get_xticklabels(),
             ha="right",
@@ -801,17 +801,17 @@ def missing_bar(
         ax1.set_xticks(ax0.get_xticks())
         ax1.set_xlim(ax0.get_xlim())
         ax1.set_xticklabels(
-            values, ha="left", fontsize=fontsize, rotation=label_rotation
+            notnull_counts, ha="left", fontsize=fontsize, rotation=label_rotation
         )
     else:
-        ax0 = null_counts.plot.barh(figsize=figsize, fontsize=fontsize, color=color)
+        ax0 = percentage.plot.barh(figsize=figsize, fontsize=fontsize, color=color)
         ylabels = ax0.get_yticklabels() if labels else []
         ax0.set_yticklabels(ylabels, fontsize=fontsize)
 
         ax1 = ax0.twinx()
         ax1.set_yticks(ax0.get_yticks())
         ax1.set_ylim(ax0.get_ylim())
-        ax1.set_yticklabels(values, fontsize=fontsize)
+        ax1.set_yticklabels(notnull_counts, fontsize=fontsize)
 
     for ax in [ax0, ax1]:
         ax = _set_visibility(ax)
@@ -820,7 +820,9 @@ def missing_bar(
 
 
 def missing_matrix(
-    data: pd.DataFrame,
+    notnull: Any,
+    columns: List[str],
+    height: int,
     figsize: Tuple[float, float] = (25, 10),
     color: Tuple[float, ...] = (0.41, 0.41, 0.41),
     fontsize: float = 16,
@@ -833,7 +835,9 @@ def missing_matrix(
     Inspired by https://github.com/ResidentMario/missingno
 
     Args:
-        data: The input DataFrame.
+        notnull: Missing data indicator matrix.
+        columns: List of column names.
+        height: Number of rows in the dataframe.
         figsize: The size of the figure to display.
         fontsize: The figure's font size. Default to 16.
         labels: Whether or not to display the column names when there is more than 50 columns.
@@ -842,9 +846,7 @@ def missing_matrix(
     Returns:
         The plot axis.
     """
-    height, width = data.shape
-
-    notnull = data.notnull().values
+    width = len(columns)
     missing_grid = np.zeros((height, width, 3), dtype=np.float32)
 
     missing_grid[notnull] = color
@@ -860,9 +862,7 @@ def missing_matrix(
 
     ha = "left"
     ax.set_xticks(list(range(0, width)))
-    ax.set_xticklabels(
-        list(data.columns), rotation=label_rotation, ha=ha, fontsize=fontsize
-    )
+    ax.set_xticklabels(columns, rotation=label_rotation, ha=ha, fontsize=fontsize)
     ax.set_yticks([0, height - 1])
     ax.set_yticklabels([1, height], fontsize=fontsize)
 
@@ -878,7 +878,8 @@ def missing_matrix(
 
 
 def missing_heatmap(
-    data: pd.DataFrame,
+    corr_mat: Any,
+    mask: Any,
     figsize: Tuple[float, float] = (20, 12),
     fontsize: float = 16,
     labels: bool = True,
@@ -895,7 +896,8 @@ def missing_heatmap(
     Inspired by https://github.com/ResidentMario/missingno
 
     Args:
-        data: The input DataFrame.
+        corr_mat: correlation matrix.
+        mask: Upper-triangle mask.
         figsize: The size of the figure to display. Defaults to (20, 12).
         fontsize: The figure's font size.
         labels: Whether or not to label each matrix entry with its correlation (default is True).
@@ -906,15 +908,6 @@ def missing_heatmap(
         The plot axis.
     """
     _, ax = plt.subplots(1, 1, figsize=figsize)
-
-    # Remove completely filled or completely empty variables.
-    columns = [i for i, n in enumerate(np.var(data.isnull(), axis="rows")) if n > 0]
-    data = data.iloc[:, columns]
-
-    # Create and mask the correlation matrix. Construct the base heatmap.
-    corr_mat = data.isnull().corr()
-    mask = np.zeros_like(corr_mat)
-    mask[np.triu_indices_from(mask)] = True
     norm_args = {"vmin": -1, "vmax": 1} if normalized_cmap else {}
 
     if labels: