diff --git a/copulas/visualization.py b/copulas/visualization.py
index ca754793..6df130f0 100644
--- a/copulas/visualization.py
+++ b/copulas/visualization.py
@@ -2,135 +2,274 @@
 
 import pandas as pd
 
-try:
-    import matplotlib.pyplot as plt
-except RuntimeError as e:
-    if 'Python is not installed as a framework.' in e.message:
-        import matplotlib
-        matplotlib.use('PS')   # Avoid crash on macos
-        import matplotlib.pyplot as plt
+from copulas.utils2 import PlotConfig
+import plotly.express as px
+from pandas.api.types import is_datetime64_dtype
 
 
-def scatter_3d(data, columns=None, fig=None, title=None, position=None):
-    """Plot 3 dimensional data in a scatter plot."""
-    fig = fig or plt.figure()
-    position = position or 111
-
-    ax = fig.add_subplot(position, projection='3d')
-    ax.scatter(*(
-        data[column]
-        for column in columns or data.columns
-    ))
-    if title:
-        ax.set_title(title)
-        ax.title.set_position([.5, 1.05])
-
-    return ax
-
-
-def scatter_2d(data, columns=None, fig=None, title=None, position=None):
-    """Plot 2 dimensional data in a scatter plot."""
-    fig = fig or plt.figure()
-    position = position or 111
-
-    ax = fig.add_subplot(position)
-    columns = columns or data.columns
-    if len(columns) != 2:
-        raise ValueError('Only 2 columns can be plotted')
-
-    x, y = columns
-
-    ax.scatter(data[x], data[y])
-    plt.xlabel(x)
-    plt.ylabel(y)
-
-    if title:
-        ax.set_title(title)
-        ax.title.set_position([.5, 1.05])
-
-    return ax
+def _generate_column_bar_plot(real_data, synthetic_data, plot_kwargs={}):
+    """Generate a bar plot of the real and synthetic data.
 
+    Args:
+        real_column (pandas.Series):
+            The real data for the desired column.
+        synthetic_column (pandas.Series):
+            The synthetic data for the desired column.
+        plot_kwargs (dict, optional):
+            Dictionary of keyword arguments to pass to px.histogram. Keyword arguments
+            provided this way will overwrite defaults.
+
+    Returns:
+        plotly.graph_objects._figure.Figure
+    """
+    all_data = pd.concat([real_data, synthetic_data], axis=0, ignore_index=True)
+    histogram_kwargs = {
+        'x': 'values',
+        'barmode': 'group',
+        'color_discrete_sequence': [PlotConfig.DATACEBO_DARK, PlotConfig.DATACEBO_GREEN],
+        'pattern_shape': 'Data',
+        'pattern_shape_sequence': ['', '/'],
+        'histnorm': 'probability density',
+    }
+    histogram_kwargs.update(plot_kwargs)
+    fig = px.histogram(
+        all_data,
+        **histogram_kwargs
+    )
+
+    return fig
+
+def _generate_scatter_plot(all_data, columns):
+    """Generate a scatter plot for column pair plot.
 
-def hist_1d(data, fig=None, title=None, position=None, bins=20, label=None):
-    """Plot 1 dimensional data in a histogram."""
-    fig = fig or plt.figure()
-    position = position or 111
+    Args:
+        all_data (pandas.DataFrame):
+            The real and synthetic data for the desired column pair containing a
+            ``Data`` column that indicates whether is real or synthetic.
+        columns (list):
+            A list of the columns being plotted.
 
-    ax = fig.add_subplot(position)
-    ax.hist(data, density=True, bins=bins, alpha=0.8, label=label)
+    Returns:
+        plotly.graph_objects._figure.Figure
+    """
+    fig = px.scatter(
+        all_data,
+        x=columns[0],
+        y=columns[1],
+        color='Data',
+        color_discrete_map={
+            'Real': PlotConfig.DATACEBO_DARK,
+            'Synthetic': PlotConfig.DATACEBO_GREEN
+        },
+        symbol='Data'
+    )
+
+    fig.update_layout(
+        title=f"Real vs. Synthetic Data for columns '{columns[0]}' and '{columns[1]}'",
+        plot_bgcolor=PlotConfig.BACKGROUND_COLOR,
+        font={'size': PlotConfig.FONT_SIZE},
+    )
+
+    return fig
+
+def _generate_column_plot(real_column,
+                          synthetic_column,
+                          plot_kwargs={},
+                          plot_title=None,
+                          x_label=None):
+    """Generate a plot of the real and synthetic data.
 
-    if label:
-        ax.legend()
+    Args:
+        real_column (pandas.Series):
+            The real data for the desired column.
+        synthetic_column (pandas.Series):
+            The synthetic data for the desired column.
+        hist_kwargs (dict, optional):
+            Dictionary of keyword arguments to pass to px.histogram. Keyword arguments
+            provided this way will overwrite defaults.
+        plot_title (str, optional):
+            Title to use for the plot. Defaults to 'Real vs. Synthetic Data for column {column}'
+        x_label (str, optional):
+            Label to use for x-axis. Defaults to 'Category'.
+
+    Returns:
+        plotly.graph_objects._figure.Figure
+    """
+    column_name = real_column.name if hasattr(real_column, 'name') else ''
+
+    real_data = pd.DataFrame({'values': real_column.copy().dropna()})
+    real_data['Data'] = 'Real'
+    synthetic_data = pd.DataFrame({'values': synthetic_column.copy().dropna()})
+    synthetic_data['Data'] = 'Synthetic'
+
+    is_datetime_sdtype = False
+    if is_datetime64_dtype(real_column.dtype):
+        is_datetime_sdtype = True
+        real_data['values'] = real_data['values'].astype('int64')
+        synthetic_data['values'] = synthetic_data['values'].astype('int64')
+
+    trace_args = {}
+
+    fig = _generate_column_bar_plot(real_data, synthetic_data, plot_kwargs)
+
+    for i, name in enumerate(['Real', 'Synthetic']):
+        fig.update_traces(
+            x=pd.to_datetime(fig.data[i].x) if is_datetime_sdtype else fig.data[i].x,
+            hovertemplate=f'<b>{name}</b><br>Frequency: %{{y}}<extra></extra>',
+            selector={'name': name},
+            **trace_args
+        )
+
+    if not plot_title:
+        plot_title = f"Real vs. Synthetic Data for column '{column_name}'"
+
+    if not x_label:
+        x_label = 'Category'
+
+    fig.update_layout(
+        title=plot_title,
+        xaxis_title=x_label,
+        yaxis_title='Frequency',
+        plot_bgcolor=PlotConfig.BACKGROUND_COLOR,
+        annotations=[],
+        font={'size': PlotConfig.FONT_SIZE},
+    )
+    return fig
+
+def hist_1d(data, title=None, bins=20, label=None):
+    """Plot 1 dimensional data in a histogram.
+    
+    Args:
+        data (pd.DataFrame):
+            The table data.
+        title (str):
+            The title of the plot.
+        bins (int):
+            The number of bins to use for the histogram.
+        label (str):
+            The label of the plot.
+    
+    Returns:
+        plotly.graph_objects._figure.Figure
+    """
+    if not isinstance(data, pd.DataFrame):
+        data = pd.DataFrame(data)
+    if len(data.columns) > 1:
+        raise ValueError('Only 1 column can be plotted')
+
+    fig = px.histogram(
+        data_frame=data,
+        barmode='group',
+        color_discrete_sequence=[PlotConfig.DATACEBO_DARK],
+        histnorm='probability density',
+        title=title,
+        nbins=bins,
+    )
+    fig.update_layout(
+        xaxis_title='',
+        yaxis_title='',
+        legend_title=label,
+        showlegend=True if label else False
+    )
+
+    return fig
+
+def compare_1d(real, synth):
+    """Return a plot of the real and synthetic data for a given column.
 
-    if title:
-        ax.set_title(title)
-        ax.title.set_position([.5, 1.05])
+    Args:
+        real (pandas.DataFrame):
+            The real table data.
+        synth (pandas.DataFrame):
+            The synthetic table data.
 
-    return ax
+    Returns:
+        plotly.graph_objects._figure.Figure
+    """
+    if not isinstance(real, pd.Series):
+        real = pd.Series(real)
+    if not isinstance(synth, pd.Series):
+        synth = pd.Series(synth)
 
+    return _generate_column_plot(real, synth, plot_type='bar')
 
-def side_by_side(plotting_func, arrays):
-    """Plot side-by-side figures.
+def scatter_2d(data, columns=None):
+    """Plot 2 dimensional data in a scatter plot.
 
     Args:
-        plotting_func (callable):
-            A matplotlib function which takes in the standard plot kwargs.
-        arrays (dict[str, np.ndarray]):
-            A mapping from the name of the subplot to the values.
-    """
-    fig = plt.figure(figsize=(10, 4))
+        data (pandas.DataFrame):
+            The table data.
+        columns (list[string]):
+            The names of the two columns to plot.
 
-    position_base = f'1{len(arrays)}'
-    for index, (title, array) in enumerate(arrays.items()):
-        position = int(position_base + str(index + 1))
-        plotting_func(array, fig=fig, title=title, position=position)
+    Returns:
+        plotly.graph_objects._figure.Figure
+    """
+    data = data[columns]
+    columns = list(data.columns)
+    data['Data'] = 'Real'
 
-    plt.tight_layout()
+    return _generate_scatter_plot(data, columns)
 
-
-def compare_3d(real, synth, columns=None, figsize=(10, 4)):
-    """Generate a 3d scatter plot comparing real/synthetic data.
+def compare_2d_(real, synth, columns=None):
+    """Return a plot of the real and synthetic data for a given column pair.
 
     Args:
-        real (pd.DataFrame):
-            The real data.
-        synth (pd.DataFrame):
-            The synthetic data.
-        columns (list):
-            The name of the columns to plot.
-        figsize:
-            Figure size, passed to matplotlib.
+        real (pandas.DataFrame):
+            The real table data.
+        synth (pandas.Dataframe):
+            The synthetic table data.
+        columns (list[string]):
+            The names of the two columns to plot.
+
+    Returns:
+        plotly.graph_objects._figure.Figure
     """
-    columns = columns or real.columns
-    fig = plt.figure(figsize=figsize)
-
-    scatter_3d(real[columns], fig=fig, title='Real Data', position=121)
-    scatter_3d(synth[columns], fig=fig, title='Synthetic Data', position=122)
+    real_data = real_data[columns]
+    synthetic_data = synthetic_data[columns]
+    columns = list(real_data.columns)
+    real_data['Data'] = 'Real'
+    synthetic_data['Data'] = 'Synthetic'
+    all_data = pd.concat([real_data, synthetic_data], axis=0, ignore_index=True)
 
-    plt.tight_layout()
+    return _generate_scatter_plot(all_data, columns)
 
-
-def compare_2d(real, synth, columns=None, figsize=None):
-    """Generate a 2d scatter plot comparing real/synthetic data.
+def scatter_3d_plotly(data, columns=None):
+    """Return a 3D scatter plot of the data.
 
     Args:
-        real (pd.DataFrame):
-            The real data.
-        synth (pd.DataFrame):
-            The synthetic data.
-        columns (list):
-            The name of the columns to plot.
-        figsize:
-            Figure size, passed to matplotlib.
+        data (pandas.DataFrame):
+            The table data. Must have at least 3 columns.
+        column_names (list[string]):
+            The names of the three columns to plot. If not passed,
+            the first three columns of the data will be used.
+
+    Returns:
+        plotly.graph_objects._figure.Figure
     """
-    x, y = columns or real.columns
-    ax = real.plot.scatter(x, y, color='blue', alpha=0.5, figsize=figsize)
-    ax = synth.plot.scatter(x, y, ax=ax, color='orange', alpha=0.5, figsize=figsize)
-    ax.legend(['Real', 'Synthetic'])
-
-
-def compare_1d(real, synth, columns=None, figsize=None):
-    """Generate a 1d scatter plot comparing real/synthetic data.
+    fig = px.scatter(
+        data,
+        x=columns[0],
+        y=columns[1],
+        z=columns[2],
+        color='Data',
+        color_discrete_map={
+            'Real': PlotConfig.DATACEBO_DARK,
+            'Synthetic': PlotConfig.DATACEBO_GREEN
+        },
+        symbol='Data'
+    )
+
+    fig.update_layout(
+        title=f"Data for columns '{columns[0]}', '{columns[1]}' and '{columns[2]}'",
+        plot_bgcolor=PlotConfig.BACKGROUND_COLOR,
+        font={'size': PlotConfig.FONT_SIZE},
+    )
+
+    return fig
+
+def compare_3d(real, synth, columns=None):
+    """Generate a 3d scatter plot comparing real/synthetic data.
 
     Args:
         real (pd.DataFrame):
@@ -139,26 +278,10 @@ def compare_1d(real, synth, columns=None, figsize=None):
             The synthetic data.
         columns (list):
             The name of the columns to plot.
-        figsize:
-            Figure size, passed to matplotlib.
     """
-    if len(real.shape) == 1:
-        real = pd.DataFrame({'': real})
-        synth = pd.DataFrame({'': synth})
-
     columns = columns or real.columns
 
-    num_cols = len(columns)
-    fig_cols = min(2, num_cols)
-    fig_rows = (num_cols // fig_cols) + 1
-    prefix = f'{fig_rows}{fig_cols}'
-
-    figsize = figsize or (5 * fig_cols, 3 * fig_rows)
-    fig = plt.figure(figsize=figsize)
-
-    for idx, column in enumerate(columns):
-        position = int(prefix + str(idx + 1))
-        hist_1d(real[column], fig=fig, position=position, title=column, label='Real')
-        hist_1d(synth[column], fig=fig, position=position, title=column, label='Synthetic')
+    fig = scatter_3d_plotly(real[columns])
+    fig = scatter_3d_plotly(synth[columns], fig=fig)
 
-    plt.tight_layout()
+    return fig
diff --git a/setup.py b/setup.py
index 1a757695..e2897af9 100644
--- a/setup.py
+++ b/setup.py
@@ -12,8 +12,6 @@
     history = history_file.read()
 
 install_requires = [
-    "matplotlib>=3.4.0,<4;python_version<'3.10'",
-    "matplotlib>=3.6.0,<4;python_version>='3.10'",
     "numpy>=1.20.0,<2;python_version<'3.10'",
     "numpy>=1.23.3,<2;python_version>='3.10'",
     "pandas>=1.1.3;python_version<'3.10'",
@@ -21,6 +19,7 @@
     "pandas>=1.5.0;python_version>='3.11'",
     "scipy>=1.5.4,<2;python_version<'3.10'",
     "scipy>=1.9.2,<2;python_version>='3.10'",
+    'plotly>=5.10.0,<6',
 ]
 
 development_requires = [