diff --git a/packages/python/plotly/plotly/basedatatypes.py b/packages/python/plotly/plotly/basedatatypes.py
index 5897b504002..077b5efc5e0 100644
--- a/packages/python/plotly/plotly/basedatatypes.py
+++ b/packages/python/plotly/plotly/basedatatypes.py
@@ -1593,16 +1593,19 @@ def add_trace(self, trace, row=None, col=None, secondary_y=None):
 
         Examples
         --------
+
         >>> from plotly import subplots
         >>> import plotly.graph_objs as go
 
         Add two Scatter traces to a figure
+
         >>> fig = go.Figure()
         >>> fig.add_trace(go.Scatter(x=[1,2,3], y=[2,1,2]))
         >>> fig.add_trace(go.Scatter(x=[1,2,3], y=[2,1,2]))
 
 
         Add two Scatter traces to vertically stacked subplots
+
         >>> fig = subplots.make_subplots(rows=2)
         >>> fig.add_trace(go.Scatter(x=[1,2,3], y=[2,1,2]), row=1, col=1)
         >>> fig.add_trace(go.Scatter(x=[1,2,3], y=[2,1,2]), row=2, col=1)
@@ -1665,15 +1668,18 @@ def add_traces(self, data, rows=None, cols=None, secondary_ys=None):
 
         Examples
         --------
+
         >>> from plotly import subplots
         >>> import plotly.graph_objs as go
 
         Add two Scatter traces to a figure
+
         >>> fig = go.Figure()
         >>> fig.add_traces([go.Scatter(x=[1,2,3], y=[2,1,2]),
         ...                 go.Scatter(x=[1,2,3], y=[2,1,2])])
 
         Add two Scatter traces to vertically stacked subplots
+
         >>> fig = subplots.make_subplots(rows=2)
         >>> fig.add_traces([go.Scatter(x=[1,2,3], y=[2,1,2]),
         ...                 go.Scatter(x=[1,2,3], y=[2,1,2])],
@@ -1773,10 +1779,12 @@ def append_trace(self, trace, row, col):
 
         Examples
         --------
+
         >>> from plotly import tools
         >>> import plotly.graph_objs as go
-        # stack two subplots vertically
+        >>> # stack two subplots vertically
         >>> fig = tools.make_subplots(rows=2)
+
         This is the format of your plot grid:
         [ (1,1) x1,y1 ]
         [ (2,1) x2,y2 ]
@@ -2425,6 +2433,7 @@ def batch_update(self):
         --------
         For example, suppose we have a figure widget, `fig`, with a single
         trace.
+
         >>> import plotly.graph_objs as go
         >>> fig = go.FigureWidget(data=[{'y': [3, 4, 2]}])
 
@@ -2593,6 +2602,7 @@ def batch_animate(self, duration=500, easing="cubic-in-out"):
 
         2) Animate a change in the size and color of the trace's markers
         over 2 seconds using the elastic-in-out easing method
+
         >>> with fig.batch_update(duration=2000, easing='elastic-in-out'):
         ...     fig.data[0].marker.color = 'green'
         ...     fig.data[0].marker.size = 20
@@ -3055,6 +3065,7 @@ def _parent_path_str(self):
 
         Examples
         --------
+
         >>> import plotly.graph_objs as go
         >>> go.Layout()._parent_path_str
         ''
@@ -4582,6 +4593,7 @@ def on_hover(self, callback, append=False):
 
         Examples
         --------
+
         >>> from plotly.callbacks import Points, InputDeviceState
         >>> points, state = Points(), InputDeviceState()
 
@@ -4641,6 +4653,7 @@ def on_unhover(self, callback, append=False):
 
         Examples
         --------
+
         >>> from plotly.callbacks import Points, InputDeviceState
         >>> points, state = Points(), InputDeviceState()
 
@@ -4700,6 +4713,7 @@ def on_click(self, callback, append=False):
 
         Examples
         --------
+
         >>> from plotly.callbacks import Points, InputDeviceState
         >>> points, state = Points(), InputDeviceState()
 
@@ -4758,6 +4772,7 @@ def on_selection(self, callback, append=False):
 
         Examples
         --------
+
         >>> from plotly.callbacks import Points
         >>> points = Points()
 
@@ -4823,6 +4838,7 @@ def on_deselect(self, callback, append=False):
 
         Examples
         --------
+
         >>> from plotly.callbacks import Points
         >>> points = Points()
 
diff --git a/packages/python/plotly/plotly/express/_doc.py b/packages/python/plotly/plotly/express/_doc.py
index 39a133ef225..950a3953be7 100644
--- a/packages/python/plotly/plotly/express/_doc.py
+++ b/packages/python/plotly/plotly/express/_doc.py
@@ -27,13 +27,13 @@
         colref_type,
         colref_desc,
         "Values from this column or array_like are used to position marks along the x axis in cartesian coordinates.",
-        "For horizontal `histogram`s, these values are used as inputs to `histfunc`.",
+        "For horizontal histograms, these values are used as inputs to `histfunc`.",
     ],
     y=[
         colref_type,
         colref_desc,
         "Values from this column or array_like are used to position marks along the y axis in cartesian coordinates.",
-        "For vertical `histogram`s, these values are used as inputs to `histfunc`.",
+        "For vertical histograms, these values are used as inputs to `histfunc`.",
     ],
     z=[
         colref_type,
@@ -455,6 +455,7 @@ def make_docstring(fn):
             if param in docs
             else "(documentation missing from map)"
         )
+
         param_type = docs[param][0]
         result += "%s: %s\n%s\n" % (param, param_type, param_desc)
     result += "\nReturns\n-------\n"
diff --git a/packages/python/plotly/plotly/figure_factory/_2d_density.py b/packages/python/plotly/plotly/figure_factory/_2d_density.py
index 59c5559a8c8..5a96d4b3718 100644
--- a/packages/python/plotly/plotly/figure_factory/_2d_density.py
+++ b/packages/python/plotly/plotly/figure_factory/_2d_density.py
@@ -51,49 +51,47 @@ def create_2d_density(
     :param (float) height: the height of the chart
     :param (float) width: the width of the chart
 
+    Examples
+    --------
+
     Example 1: Simple 2D Density Plot
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory create_2d_density
 
-    import numpy as np
+    >>> from plotly.figure_factory create_2d_density
+
+    >>> import numpy as np
 
-    # Make data points
-    t = np.linspace(-1,1.2,2000)
-    x = (t**3)+(0.3*np.random.randn(2000))
-    y = (t**6)+(0.3*np.random.randn(2000))
+    >>> # Make data points
+    >>> t = np.linspace(-1,1.2,2000)
+    >>> x = (t**3)+(0.3*np.random.randn(2000))
+    >>> y = (t**6)+(0.3*np.random.randn(2000))
 
-    # Create a figure
-    fig = create_2D_density(x, y)
+    >>> # Create a figure
+    >>> fig = create_2D_density(x, y)
 
-    # Plot the data
-    py.iplot(fig, filename='simple-2d-density')
-    ```
+    >>> # Plot the data
+    >>> fig.show()
 
     Example 2: Using Parameters
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory create_2d_density
 
-    import numpy as np
+    >>> from plotly.figure_factory create_2d_density
+
+    >>> import numpy as np
 
-    # Make data points
-    t = np.linspace(-1,1.2,2000)
-    x = (t**3)+(0.3*np.random.randn(2000))
-    y = (t**6)+(0.3*np.random.randn(2000))
+    >>> # Make data points
+    >>> t = np.linspace(-1,1.2,2000)
+    >>> x = (t**3)+(0.3*np.random.randn(2000))
+    >>> y = (t**6)+(0.3*np.random.randn(2000))
 
-    # Create custom colorscale
-    colorscale = ['#7A4579', '#D56073', 'rgb(236,158,105)',
-                  (1, 1, 0.2), (0.98,0.98,0.98)]
+    >>> # Create custom colorscale
+    >>> colorscale = ['#7A4579', '#D56073', 'rgb(236,158,105)',
+    ...              (1, 1, 0.2), (0.98,0.98,0.98)]
 
-    # Create a figure
-    fig = create_2D_density(
-        x, y, colorscale=colorscale,
-        hist_color='rgb(255, 237, 222)', point_size=3)
+    >>> # Create a figure
+    >>> fig = create_2D_density(x, y, colorscale=colorscale,
+    ...       hist_color='rgb(255, 237, 222)', point_size=3)
 
-    # Plot the data
-    py.iplot(fig, filename='use-parameters')
-    ```
+    >>> # Plot the data
+    >>> fig.show()
     """
 
     # validate x and y are filled with numbers only
diff --git a/packages/python/plotly/plotly/figure_factory/_annotated_heatmap.py b/packages/python/plotly/plotly/figure_factory/_annotated_heatmap.py
index ec61aac8af8..c3b1e2fa989 100644
--- a/packages/python/plotly/plotly/figure_factory/_annotated_heatmap.py
+++ b/packages/python/plotly/plotly/figure_factory/_annotated_heatmap.py
@@ -84,17 +84,16 @@ def create_annotated_heatmap(
         call help(plotly.graph_objs.Heatmap)
 
     Example 1: Simple annotated heatmap with default configuration
-    ```
-    import plotly.plotly as py
-    import plotly.figure_factory as FF
 
-    z = [[0.300000, 0.00000, 0.65, 0.300000],
-         [1, 0.100005, 0.45, 0.4300],
-         [0.300000, 0.00000, 0.65, 0.300000],
-         [1, 0.100005, 0.45, 0.00000]]
+    >>> import plotly.figure_factory as ff
+
+    >>> z = [[0.300000, 0.00000, 0.65, 0.300000],
+    ...      [1, 0.100005, 0.45, 0.4300],
+    ...      [0.300000, 0.00000, 0.65, 0.300000],
+    ...      [1, 0.100005, 0.45, 0.00000]]
 
-    figure = FF.create_annotated_heatmap(z)
-    py.iplot(figure)
+    >>> fig = ff.create_annotated_heatmap(z)
+    >>> fig.show()
     ```
     """
 
diff --git a/packages/python/plotly/plotly/figure_factory/_bullet.py b/packages/python/plotly/plotly/figure_factory/_bullet.py
index 5daaa823f60..111e713a3a4 100644
--- a/packages/python/plotly/plotly/figure_factory/_bullet.py
+++ b/packages/python/plotly/plotly/figure_factory/_bullet.py
@@ -238,49 +238,40 @@ def create_bullet(
         for more information on valid params.
 
     Example 1: Use a Dictionary
-    ```
-    import plotly
-    import plotly.plotly as py
-    import plotly.figure_factory as ff
-
-    data = [
-      {"label": "Revenue", "sublabel": "US$, in thousands",
-       "range": [150, 225, 300], "performance": [220,270], "point": [250]},
-      {"label": "Profit", "sublabel": "%", "range": [20, 25, 30],
-       "performance": [21, 23], "point": [26]},
-      {"label": "Order Size", "sublabel":"US$, average","range": [350, 500, 600],
-       "performance": [100,320],"point": [550]},
-      {"label": "New Customers", "sublabel": "count", "range": [1400, 2000, 2500],
-       "performance": [1000, 1650],"point": [2100]},
-      {"label": "Satisfaction", "sublabel": "out of 5","range": [3.5, 4.25, 5],
-       "performance": [3.2, 4.7], "point": [4.4]}
-    ]
-
-    fig = ff.create_bullet(
-        data, titles='label', subtitles='sublabel', markers='point',
-        measures='performance', ranges='range', orientation='h',
-        title='my simple bullet chart'
-    )
-    py.iplot(fig)
-    ```
+    
+    >>> import plotly.figure_factory as ff
+
+    >>> data = [
+    ...   {"label": "revenue", "sublabel": "us$, in thousands",
+    ...    "range": [150, 225, 300], "performance": [220,270], "point": [250]},
+    ...   {"label": "Profit", "sublabel": "%", "range": [20, 25, 30],
+    ...    "performance": [21, 23], "point": [26]},
+    ...   {"label": "Order Size", "sublabel":"US$, average","range": [350, 500, 600],
+    ...    "performance": [100,320],"point": [550]},
+    ...   {"label": "New Customers", "sublabel": "count", "range": [1400, 2000, 2500],
+    ...    "performance": [1000, 1650],"point": [2100]},
+    ...   {"label": "Satisfaction", "sublabel": "out of 5","range": [3.5, 4.25, 5],
+    ...    "performance": [3.2, 4.7], "point": [4.4]}
+    ... ]
+
+    >>> fig = ff.create_bullet(
+    ...     data, titles='label', subtitles='sublabel', markers='point',
+    ...     measures='performance', ranges='range', orientation='h',
+    ...     title='my simple bullet chart'
+    ... )
+    >>> fig.show()
 
     Example 2: Use a DataFrame with Custom Colors
-    ```
-    import plotly.plotly as py
-    import plotly.figure_factory as ff
-
-    import pandas as pd
 
-    data = pd.read_json('https://cdn.rawgit.com/plotly/datasets/master/BulletData.json')
+    >>> import plotly.figure_factory as ff
+    >>> import pandas as pd
+    >>> data = pd.read_json('https://cdn.rawgit.com/plotly/datasets/master/BulletData.json')
 
-    fig = ff.create_bullet(
-        data, titles='title', markers='markers', measures='measures',
-        orientation='v', measure_colors=['rgb(14, 52, 75)', 'rgb(31, 141, 127)'],
-        scatter_options={'marker': {'symbol': 'circle'}}, width=700
-
-    )
-    py.iplot(fig)
-    ```
+    >>> fig = ff.create_bullet(
+    ...     data, titles='title', markers='markers', measures='measures',
+    ...     orientation='v', measure_colors=['rgb(14, 52, 75)', 'rgb(31, 141, 127)'],
+    ...     scatter_options={'marker': {'symbol': 'circle'}}, width=700)
+    >>> fig.show()
     """
     # validate df
     if not pd:
diff --git a/packages/python/plotly/plotly/figure_factory/_candlestick.py b/packages/python/plotly/plotly/figure_factory/_candlestick.py
index b012a60a596..db4cc507355 100644
--- a/packages/python/plotly/plotly/figure_factory/_candlestick.py
+++ b/packages/python/plotly/plotly/figure_factory/_candlestick.py
@@ -122,23 +122,21 @@ def create_candlestick(open, high, low, close, dates=None, direction="both", **k
     :rtype (dict): returns a representation of candlestick chart figure.
 
     Example 1: Simple candlestick chart from a Pandas DataFrame
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_candlestick
-    from datetime import datetime
 
-    import pandas.io.data as web
+    >>> from plotly.figure_factory import create_candlestick
+    >>> from datetime import datetime
 
-    df = web.DataReader("aapl", 'yahoo', datetime(2007, 10, 1), datetime(2009, 4, 1))
-    fig = create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index)
-    py.plot(fig, filename='finance/aapl-candlestick', validate=False)
-    ```
+    >>> import pandas.io.data as web
+
+    >>> df = web.DataReader("aapl", 'yahoo', datetime(2007, 10, 1), datetime(2009, 4, 1))
+    >>> fig = create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index)
+    >>> fig.show()
 
     Example 2: Add text and annotations to the candlestick chart
-    ```
-    fig = create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index)
-    # Update the fig - all options here: https://plot.ly/python/reference/#Layout
-    fig['layout'].update({
+    
+    >>> fig = create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index)
+    >>> # Update the fig - all options here: https://plot.ly/python/reference/#Layout
+    >>> fig['layout'].update({
         'title': 'The Great Recession',
         'yaxis': {'title': 'AAPL Stock'},
         'shapes': [{
@@ -152,65 +150,59 @@ def create_candlestick(open, high, low, close, dates=None, direction="both", **k
             'text': 'Official start of the recession'
         }]
     })
-    py.plot(fig, filename='finance/aapl-recession-candlestick', validate=False)
-    ```
+    >>> fig.show()
 
     Example 3: Customize the candlestick colors
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_candlestick
-    from plotly.graph_objs import Line, Marker
-    from datetime import datetime
-
-    import pandas.io.data as web
+    
+    >>> from plotly.figure_factory import create_candlestick
+    >>> from plotly.graph_objs import Line, Marker
+    >>> from datetime import datetime
 
-    df = web.DataReader("aapl", 'yahoo', datetime(2008, 1, 1), datetime(2009, 4, 1))
+    >>> import pandas.io.data as web
 
-    # Make increasing candlesticks and customize their color and name
-    fig_increasing = create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index,
-        direction='increasing', name='AAPL',
-        marker=Marker(color='rgb(150, 200, 250)'),
-        line=Line(color='rgb(150, 200, 250)'))
+    >>> df = web.DataReader("aapl", 'yahoo', datetime(2008, 1, 1), datetime(2009, 4, 1))
 
-    # Make decreasing candlesticks and customize their color and name
-    fig_decreasing = create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index,
-        direction='decreasing',
-        marker=Marker(color='rgb(128, 128, 128)'),
-        line=Line(color='rgb(128, 128, 128)'))
+    >>> # Make increasing candlesticks and customize their color and name
+    >>> fig_increasing = create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index,
+    ...     direction='increasing', name='AAPL',
+    ...     marker=Marker(color='rgb(150, 200, 250)'),
+    ...     line=Line(color='rgb(150, 200, 250)'))
 
-    # Initialize the figure
-    fig = fig_increasing
+    >>> # Make decreasing candlesticks and customize their color and name
+    >>> fig_decreasing = create_candlestick(df.Open, df.High, df.Low, df.Close, dates=df.index,
+    ...     direction='decreasing',
+    ...     marker=Marker(color='rgb(128, 128, 128)'),
+    ...     line=Line(color='rgb(128, 128, 128)'))
 
-    # Add decreasing data with .extend()
-    fig['data'].extend(fig_decreasing['data'])
+    >>> # Initialize the figure
+    >>> fig = fig_increasing
 
-    py.iplot(fig, filename='finance/aapl-candlestick-custom', validate=False)
-    ```
+    >>> # Add decreasing data with .extend()
+    >>> fig['data'].extend(fig_decreasing['data'])
+    >>> fig.show()
 
     Example 4: Candlestick chart with datetime objects
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_candlestick
-
-    from datetime import datetime
-
-    # Add data
-    open_data = [33.0, 33.3, 33.5, 33.0, 34.1]
-    high_data = [33.1, 33.3, 33.6, 33.2, 34.8]
-    low_data = [32.7, 32.7, 32.8, 32.6, 32.8]
-    close_data = [33.0, 32.9, 33.3, 33.1, 33.1]
-    dates = [datetime(year=2013, month=10, day=10),
-             datetime(year=2013, month=11, day=10),
-             datetime(year=2013, month=12, day=10),
-             datetime(year=2014, month=1, day=10),
-             datetime(year=2014, month=2, day=10)]
-
-    # Create ohlc
-    fig = create_candlestick(open_data, high_data,
-        low_data, close_data, dates=dates)
-
-    py.iplot(fig, filename='finance/simple-candlestick', validate=False)
-    ```
+    
+    >>> from plotly.figure_factory import create_candlestick
+
+    >>> from datetime import datetime
+
+    >>> # Add data
+    >>> open_data = [33.0, 33.3, 33.5, 33.0, 34.1]
+    >>> high_data = [33.1, 33.3, 33.6, 33.2, 34.8]
+    >>> low_data = [32.7, 32.7, 32.8, 32.6, 32.8]
+    >>> close_data = [33.0, 32.9, 33.3, 33.1, 33.1]
+    >>> dates = [datetime(year=2013, month=10, day=10),
+    ...          datetime(year=2013, month=11, day=10),
+    ...          datetime(year=2013, month=12, day=10),
+    ...          datetime(year=2014, month=1, day=10),
+    ...          datetime(year=2014, month=2, day=10)]
+
+    >>> # Create ohlc
+    >>> fig = create_candlestick(open_data, high_data,
+    ...     low_data, close_data, dates=dates)
+    >>> fig.show()
+
     """
     if dates is not None:
         utils.validate_equal_length(open, high, low, close, dates)
diff --git a/packages/python/plotly/plotly/figure_factory/_county_choropleth.py b/packages/python/plotly/plotly/figure_factory/_county_choropleth.py
index 5e8dfea47a0..eb0d6b45111 100644
--- a/packages/python/plotly/plotly/figure_factory/_county_choropleth.py
+++ b/packages/python/plotly/plotly/figure_factory/_county_choropleth.py
@@ -478,35 +478,33 @@ def create_choropleth(
     :param **layout_options: a **kwargs argument for all layout parameters
 
 
-    Example 1: Florida
-    ```
-    import plotly.plotly as py
-    import plotly.figure_factory as ff
-
-    import numpy as np
-    import pandas as pd
-
-    df_sample = pd.read_csv(
-        'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
-    )
-    df_sample_r = df_sample[df_sample['STNAME'] == 'Florida']
+    Example 1: Florida:: 
+    
+        import plotly.plotly as py
+        import plotly.figure_factory as ff
 
-    values = df_sample_r['TOT_POP'].tolist()
-    fips = df_sample_r['FIPS'].tolist()
+        import numpy as np
+        import pandas as pd
 
-    binning_endpoints = list(np.mgrid[min(values):max(values):4j])
-    colorscale = ["#030512","#1d1d3b","#323268","#3d4b94","#3e6ab0",
-                  "#4989bc","#60a7c7","#85c5d3","#b7e0e4","#eafcfd"]
-    fig = ff.create_choropleth(
-        fips=fips, values=values, scope=['Florida'], show_state_data=True,
-        colorscale=colorscale, binning_endpoints=binning_endpoints,
-        round_legend_values=True, plot_bgcolor='rgb(229,229,229)',
-        paper_bgcolor='rgb(229,229,229)', legend_title='Florida Population',
-        county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
-        exponent_format=True,
-    )
-    py.iplot(fig, filename='choropleth_florida')
-    ```
+        df_sample = pd.read_csv(
+            'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
+        )
+        df_sample_r = df_sample[df_sample['STNAME'] == 'Florida']
+
+        values = df_sample_r['TOT_POP'].tolist()
+        fips = df_sample_r['FIPS'].tolist()
+
+        binning_endpoints = list(np.mgrid[min(values):max(values):4j])
+        colorscale = ["#030512","#1d1d3b","#323268","#3d4b94","#3e6ab0",
+                    "#4989bc","#60a7c7","#85c5d3","#b7e0e4","#eafcfd"]
+        fig = ff.create_choropleth(
+            fips=fips, values=values, scope=['Florida'], show_state_data=True,
+            colorscale=colorscale, binning_endpoints=binning_endpoints,
+            round_legend_values=True, plot_bgcolor='rgb(229,229,229)',
+            paper_bgcolor='rgb(229,229,229)', legend_title='Florida Population',
+            county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
+            exponent_format=True,
+        )
 
     Example 2: New England
     ```
diff --git a/packages/python/plotly/plotly/figure_factory/_dendrogram.py b/packages/python/plotly/plotly/figure_factory/_dendrogram.py
index 8ce371261f9..14ae27bccce 100644
--- a/packages/python/plotly/plotly/figure_factory/_dendrogram.py
+++ b/packages/python/plotly/plotly/figure_factory/_dendrogram.py
@@ -40,46 +40,38 @@ def create_dendrogram(
     :param (double) color_threshold: Value at which the separation of clusters will be made
 
     Example 1: Simple bottom oriented dendrogram
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_dendrogram
 
-    import numpy as np
+    >>> from plotly.figure_factory import create_dendrogram
 
-    X = np.random.rand(10,10)
-    dendro = create_dendrogram(X)
-    plot_url = py.plot(dendro, filename='simple-dendrogram')
+    >>> import numpy as np
 
-    ```
+    >>> X = np.random.rand(10,10)
+    >>> fig = create_dendrogram(X)
+    >>> fig.show()
 
     Example 2: Dendrogram to put on the left of the heatmap
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_dendrogram
+    
+    >>> from plotly.figure_factory import create_dendrogram
 
-    import numpy as np
+    >>> import numpy as np
 
-    X = np.random.rand(5,5)
-    names = ['Jack', 'Oxana', 'John', 'Chelsea', 'Mark']
-    dendro = create_dendrogram(X, orientation='right', labels=names)
-    dendro['layout'].update({'width':700, 'height':500})
-
-    py.iplot(dendro, filename='vertical-dendrogram')
-    ```
+    >>> X = np.random.rand(5,5)
+    >>> names = ['Jack', 'Oxana', 'John', 'Chelsea', 'Mark']
+    >>> dendro = create_dendrogram(X, orientation='right', labels=names)
+    >>> dendro['layout'].update({'width':700, 'height':500})
+    >>> dendro.show()
 
     Example 3: Dendrogram with Pandas
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_dendrogram
-
-    import numpy as np
-    import pandas as pd
-
-    Index= ['A','B','C','D','E','F','G','H','I','J']
-    df = pd.DataFrame(abs(np.random.randn(10, 10)), index=Index)
-    fig = create_dendrogram(df, labels=Index)
-    url = py.plot(fig, filename='pandas-dendrogram')
-    ```
+    
+    >>> from plotly.figure_factory import create_dendrogram
+
+    >>> import numpy as np
+    >>> import pandas as pd
+
+    >>> Index= ['A','B','C','D','E','F','G','H','I','J']
+    >>> df = pd.DataFrame(abs(np.random.randn(10, 10)), index=Index)
+    >>> fig = create_dendrogram(df, labels=Index)
+    >>> fig.show()
     """
     if not scp or not scs or not sch:
         raise ImportError(
diff --git a/packages/python/plotly/plotly/figure_factory/_distplot.py b/packages/python/plotly/plotly/figure_factory/_distplot.py
index dd7d3adb1d9..2808931abe9 100644
--- a/packages/python/plotly/plotly/figure_factory/_distplot.py
+++ b/packages/python/plotly/plotly/figure_factory/_distplot.py
@@ -85,96 +85,83 @@ def create_distplot(
     :return (dict): Representation of a distplot figure.
 
     Example 1: Simple distplot of 1 data set
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_distplot
 
-    hist_data = [[1.1, 1.1, 2.5, 3.0, 3.5,
-                  3.5, 4.1, 4.4, 4.5, 4.5,
-                  5.0, 5.0, 5.2, 5.5, 5.5,
-                  5.5, 5.5, 5.5, 6.1, 7.0]]
+    >>> from plotly.figure_factory import create_distplot
 
-    group_labels = ['distplot example']
+    >>> hist_data = [[1.1, 1.1, 2.5, 3.0, 3.5,
+    ...               3.5, 4.1, 4.4, 4.5, 4.5,
+    ...               5.0, 5.0, 5.2, 5.5, 5.5,
+    ...               5.5, 5.5, 5.5, 6.1, 7.0]]
+    >>> group_labels = ['distplot example']
+    >>> fig = create_distplot(hist_data, group_labels)
+    >>> fig.show()
 
-    fig = create_distplot(hist_data, group_labels)
-
-    url = py.plot(fig, filename='Simple distplot', validate=False)
-    ```
 
     Example 2: Two data sets and added rug text
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_distplot
-
-    # Add histogram data
-    hist1_x = [0.8, 1.2, 0.2, 0.6, 1.6,
-               -0.9, -0.07, 1.95, 0.9, -0.2,
-               -0.5, 0.3, 0.4, -0.37, 0.6]
-    hist2_x = [0.8, 1.5, 1.5, 0.6, 0.59,
-               1.0, 0.8, 1.7, 0.5, 0.8,
-               -0.3, 1.2, 0.56, 0.3, 2.2]
+    
+    >>> from plotly.figure_factory import create_distplot
+    >>> # Add histogram data
+    >>> hist1_x = [0.8, 1.2, 0.2, 0.6, 1.6,
+    ...            -0.9, -0.07, 1.95, 0.9, -0.2,
+    ...            -0.5, 0.3, 0.4, -0.37, 0.6]
+    >>> hist2_x = [0.8, 1.5, 1.5, 0.6, 0.59,
+    ...            1.0, 0.8, 1.7, 0.5, 0.8,
+    ...            -0.3, 1.2, 0.56, 0.3, 2.2]
 
-    # Group data together
-    hist_data = [hist1_x, hist2_x]
+    >>> # Group data together
+    >>> hist_data = [hist1_x, hist2_x]
 
-    group_labels = ['2012', '2013']
+    >>> group_labels = ['2012', '2013']
 
-    # Add text
-    rug_text_1 = ['a1', 'b1', 'c1', 'd1', 'e1',
-          'f1', 'g1', 'h1', 'i1', 'j1',
-          'k1', 'l1', 'm1', 'n1', 'o1']
+    >>> # Add text
+    >>> rug_text_1 = ['a1', 'b1', 'c1', 'd1', 'e1',
+    ...       'f1', 'g1', 'h1', 'i1', 'j1',
+    ...       'k1', 'l1', 'm1', 'n1', 'o1']
 
-    rug_text_2 = ['a2', 'b2', 'c2', 'd2', 'e2',
-          'f2', 'g2', 'h2', 'i2', 'j2',
-          'k2', 'l2', 'm2', 'n2', 'o2']
+    >>> rug_text_2 = ['a2', 'b2', 'c2', 'd2', 'e2',
+    ...       'f2', 'g2', 'h2', 'i2', 'j2',
+    ...       'k2', 'l2', 'm2', 'n2', 'o2']
 
-    # Group text together
-    rug_text_all = [rug_text_1, rug_text_2]
+    >>> # Group text together
+    >>> rug_text_all = [rug_text_1, rug_text_2]
 
-    # Create distplot
-    fig = create_distplot(
-        hist_data, group_labels, rug_text=rug_text_all, bin_size=.2)
+    >>> # Create distplot
+    >>> fig = create_distplot(
+    ...     hist_data, group_labels, rug_text=rug_text_all, bin_size=.2)
 
-    # Add title
-    fig['layout'].update(title='Dist Plot')
+    >>> # Add title
+    >>> fig['layout'].update(title='Dist Plot')
+    >>> fig.show()
 
-    # Plot!
-    url = py.plot(fig, filename='Distplot with rug text', validate=False)
-    ```
 
     Example 3: Plot with normal curve and hide rug plot
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_distplot
-    import numpy as np
+    
+    >>> from plotly.figure_factory import create_distplot
+    >>> import numpy as np
 
-    x1 = np.random.randn(190)
-    x2 = np.random.randn(200)+1
-    x3 = np.random.randn(200)-1
-    x4 = np.random.randn(210)+2
+    >>> x1 = np.random.randn(190)
+    >>> x2 = np.random.randn(200)+1
+    >>> x3 = np.random.randn(200)-1
+    >>> x4 = np.random.randn(210)+2
 
-    hist_data = [x1, x2, x3, x4]
-    group_labels = ['2012', '2013', '2014', '2015']
+    >>> hist_data = [x1, x2, x3, x4]
+    >>> group_labels = ['2012', '2013', '2014', '2015']
 
-    fig = create_distplot(
-        hist_data, group_labels, curve_type='normal',
-        show_rug=False, bin_size=.4)
+    >>> fig = create_distplot(
+    ...     hist_data, group_labels, curve_type='normal',
+    ...     show_rug=False, bin_size=.4)
 
-    url = py.plot(fig, filename='hist and normal curve', validate=False)
 
     Example 4: Distplot with Pandas
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_distplot
-    import numpy as np
-    import pandas as pd
-
-    df = pd.DataFrame({'2012': np.random.randn(200),
-                       '2013': np.random.randn(200)+1})
-    py.iplot(create_distplot([df[c] for c in df.columns], df.columns),
-                             filename='examples/distplot with pandas',
-                             validate=False)
-    ```
+    
+    >>> from plotly.figure_factory import create_distplot
+    >>> import numpy as np
+    >>> import pandas as pd
+
+    >>> df = pd.DataFrame({'2012': np.random.randn(200),
+    >>>                    '2013': np.random.randn(200)+1})
+    >>> fig = create_distplot([df[c] for c in df.columns], df.columns)
+    >>> fig.show()
     """
     if colors is None:
         colors = []
diff --git a/packages/python/plotly/plotly/figure_factory/_facet_grid.py b/packages/python/plotly/plotly/figure_factory/_facet_grid.py
index cc129468b1a..95e0f70edec 100644
--- a/packages/python/plotly/plotly/figure_factory/_facet_grid.py
+++ b/packages/python/plotly/plotly/figure_factory/_facet_grid.py
@@ -720,121 +720,94 @@ def create_facet_grid(
     :param (dict) kwargs: a dictionary of scatterplot arguments.
 
     Examples 1: One Way Faceting
-    ```
-    import plotly.plotly as py
-    import plotly.figure_factory as ff
 
-    import pandas as pd
+    >>> import plotly.figure_factory as ff
+    >>> import pandas as pd
+    >>> mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
 
-    mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
-
-    fig = ff.create_facet_grid(
-        mpg,
-        x='displ',
-        y='cty',
-        facet_col='cyl',
-    )
-    py.iplot(fig, filename='facet_grid_mpg_one_way_facet')
-    ```
+    >>> fig = ff.create_facet_grid(
+    ...     mpg,
+    ...     x='displ',
+    ...     y='cty',
+    ...     facet_col='cyl',
+    ... )
+    >>> fig.show()
 
     Example 2: Two Way Faceting
-    ```
-    import plotly.plotly as py
-    import plotly.figure_factory as ff
 
-    import pandas as pd
+    >>> import plotly.figure_factory as ff
 
-    mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
+    >>> import pandas as pd
 
-    fig = ff.create_facet_grid(
-        mpg,
-        x='displ',
-        y='cty',
-        facet_row='drv',
-        facet_col='cyl',
-    )
-    py.iplot(fig, filename='facet_grid_mpg_two_way_facet')
-    ```
-
-    Example 3: Categorical Coloring
-    ```
-    import plotly.plotly as py
-    import plotly.figure_factory as ff
+    >>> mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
 
-    import pandas as pd
+    >>> fig = ff.create_facet_grid(
+    ...     mpg,
+    ...     x='displ',
+    ...     y='cty',
+    ...     facet_row='drv',
+    ...     facet_col='cyl',
+    ... )
+    >>> fig.show()
 
-    mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
+    Example 3: Categorical Coloring
 
-    fig = ff.create_facet_grid(
-        mtcars,
-        x='mpg',
-        y='wt',
-        facet_col='cyl',
-        color_name='cyl',
-        color_is_cat=True,
-    )
-    py.iplot(fig, filename='facet_grid_mpg_default_colors')
-    ```
+    >>> import plotly.figure_factory as ff
+    >>> import pandas as pd
+    >>> mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
+    >>> fig = ff.create_facet_grid(
+    ...     mtcars,
+    ...     x='mpg',
+    ...     y='wt',
+    ...     facet_col='cyl',
+    ...     color_name='cyl',
+    ...     color_is_cat=True,
+    ... )
+    >>> fig.show()
 
     Example 4: Sequential Coloring
-    ```
-    import plotly.plotly as py
-    import plotly.figure_factory as ff
-
-    import pandas as pd
-
-    tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv')
-
-    fig = ff.create_facet_grid(
-        tips,
-        x='total_bill',
-        y='tip',
-        facet_row='sex',
-        facet_col='smoker',
-        color_name='size',
-        colormap='Viridis',
-    )
-    py.iplot(fig, filename='facet_grid_tips_sequential_colors')
-    ```
 
-    Example 5: Custom labels
-    ```
-    import plotly.plotly as py
-    import plotly.figure_factory as ff
+    >>> import plotly.figure_factory as ff
+    >>> import pandas as pd
+    >>> tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv')
+
+    >>> fig = ff.create_facet_grid(
+    >>>     tips,
+    ...     x='total_bill',
+    ...     y='tip',
+    ...     facet_row='sex',
+    ...     facet_col='smoker',
+    ...     color_name='size',
+    ...     colormap='Viridis',
+    ... )
+    >>> fig.show()
 
-    import pandas as pd
-
-    mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv')
-
-    fig = ff.create_facet_grid(
-        mtcars,
-        x='wt',
-        y='mpg',
-        facet_col='cyl',
-        facet_col_labels={4: "$\\alpha$", 6: '$\\beta$', 8: '$\sqrt[y]{x}$'},
-    )
+    Example 5: Custom labels
 
-    py.iplot(fig, filename='facet_grid_mtcars_custom_labels')
-    ```
+    >>> import plotly.figure_factory as ff
+    >>> import pandas as pd
+    >>> mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv')
+    >>> fig = ff.create_facet_grid(
+    ...     mtcars,
+    ...     x='wt',
+    ...     y='mpg',
+    ...     facet_col='cyl',
+    ...     facet_col_labels={4: "$\\alpha$", 6: '$\\beta$', 8: '$\sqrt[y]{x}$'},
+    ... )
+    >>> fig.show()
 
     Example 6: Other Trace Type
-    ```
-    import plotly.plotly as py
-    import plotly.figure_factory as ff
-
-    import pandas as pd
-
-    mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv')
-
-    fig = ff.create_facet_grid(
-        mtcars,
-        x='wt',
-        facet_col='cyl',
-        trace_type='histogram',
-    )
 
-    py.iplot(fig, filename='facet_grid_mtcars_other_trace_type')
-    ```
+    >>> import plotly.figure_factory as ff
+    >>> import pandas as pd
+    >>> mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv')
+    >>> fig = ff.create_facet_grid(
+    ...     mtcars,
+    ...     x='wt',
+    ...     facet_col='cyl',
+    ...     trace_type='histogram',
+    ... )
+    >>> fig.show()
     """
     if not pd:
         raise ImportError("'pandas' must be installed for this figure_factory.")
diff --git a/packages/python/plotly/plotly/figure_factory/_gantt.py b/packages/python/plotly/plotly/figure_factory/_gantt.py
index 8af7a435381..f4ec5f58281 100644
--- a/packages/python/plotly/plotly/figure_factory/_gantt.py
+++ b/packages/python/plotly/plotly/figure_factory/_gantt.py
@@ -850,113 +850,95 @@ def create_gantt(
     :param (float) width: the width of the chart
 
     Example 1: Simple Gantt Chart
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_gantt
 
-    # Make data for chart
-    df = [dict(Task="Job A", Start='2009-01-01', Finish='2009-02-30'),
-          dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15'),
-          dict(Task="Job C", Start='2009-02-20', Finish='2009-05-30')]
+    >>> from plotly.figure_factory import create_gantt
 
-    # Create a figure
-    fig = create_gantt(df)
+    >>> # Make data for chart
+    >>> df = [dict(Task="Job A", Start='2009-01-01', Finish='2009-02-30'),
+    ...       dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15'),
+    ...       dict(Task="Job C", Start='2009-02-20', Finish='2009-05-30')]
+
+    >>> # Create a figure
+    >>> fig = create_gantt(df)
+    >>> fig.show()
 
-    # Plot the data
-    py.iplot(fig, filename='Simple Gantt Chart', world_readable=True)
-    ```
 
     Example 2: Index by Column with Numerical Entries
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_gantt
-
-    # Make data for chart
-    df = [dict(Task="Job A", Start='2009-01-01',
-               Finish='2009-02-30', Complete=10),
-          dict(Task="Job B", Start='2009-03-05',
-               Finish='2009-04-15', Complete=60),
-          dict(Task="Job C", Start='2009-02-20',
-               Finish='2009-05-30', Complete=95)]
-
-    # Create a figure with Plotly colorscale
-    fig = create_gantt(df, colors='Blues', index_col='Complete',
-                       show_colorbar=True, bar_width=0.5,
-                       showgrid_x=True, showgrid_y=True)
-
-    # Plot the data
-    py.iplot(fig, filename='Numerical Entries', world_readable=True)
-    ```
+
+    >>> from plotly.figure_factory import create_gantt
+
+    >>> # Make data for chart
+    >>> df = [dict(Task="Job A", Start='2009-01-01',
+    ...            Finish='2009-02-30', Complete=10),
+    ...       dict(Task="Job B", Start='2009-03-05',
+    ...            Finish='2009-04-15', Complete=60),
+    ...       dict(Task="Job C", Start='2009-02-20',
+    ...            Finish='2009-05-30', Complete=95)]
+
+    >>> # Create a figure with Plotly colorscale
+    >>> fig = create_gantt(df, colors='Blues', index_col='Complete',
+    ...                    show_colorbar=True, bar_width=0.5,
+    ...                    showgrid_x=True, showgrid_y=True)
+    >>> fig.show()
+
 
     Example 3: Index by Column with String Entries
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_gantt
-
-    # Make data for chart
-    df = [dict(Task="Job A", Start='2009-01-01',
-               Finish='2009-02-30', Resource='Apple'),
-          dict(Task="Job B", Start='2009-03-05',
-               Finish='2009-04-15', Resource='Grape'),
-          dict(Task="Job C", Start='2009-02-20',
-               Finish='2009-05-30', Resource='Banana')]
-
-    # Create a figure with Plotly colorscale
-    fig = create_gantt(df, colors=['rgb(200, 50, 25)', (1, 0, 1), '#6c4774'],
-                       index_col='Resource', reverse_colors=True,
-                       show_colorbar=True)
-
-    # Plot the data
-    py.iplot(fig, filename='String Entries', world_readable=True)
-    ```
+
+    >>> from plotly.figure_factory import create_gantt
+
+    >>> # Make data for chart
+    >>> df = [dict(Task="Job A", Start='2009-01-01',
+    ...            Finish='2009-02-30', Resource='Apple'),
+    ...       dict(Task="Job B", Start='2009-03-05',
+    ...            Finish='2009-04-15', Resource='Grape'),
+    ...       dict(Task="Job C", Start='2009-02-20',
+    ...            Finish='2009-05-30', Resource='Banana')]
+
+    >>> # Create a figure with Plotly colorscale
+    >>> fig = create_gantt(df, colors=['rgb(200, 50, 25)', (1, 0, 1), '#6c4774'],
+    ...                    index_col='Resource', reverse_colors=True,
+    ...                    show_colorbar=True)
+    >>> fig.show()
+
 
     Example 4: Use a dictionary for colors
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_gantt
-
-    # Make data for chart
-    df = [dict(Task="Job A", Start='2009-01-01',
-               Finish='2009-02-30', Resource='Apple'),
-          dict(Task="Job B", Start='2009-03-05',
-               Finish='2009-04-15', Resource='Grape'),
-          dict(Task="Job C", Start='2009-02-20',
-               Finish='2009-05-30', Resource='Banana')]
-
-    # Make a dictionary of colors
-    colors = {'Apple': 'rgb(255, 0, 0)',
-              'Grape': 'rgb(170, 14, 200)',
-              'Banana': (1, 1, 0.2)}
-
-    # Create a figure with Plotly colorscale
-    fig = create_gantt(df, colors=colors, index_col='Resource',
-                       show_colorbar=True)
-
-    # Plot the data
-    py.iplot(fig, filename='dictioanry colors', world_readable=True)
-    ```
+
+    >>> from plotly.figure_factory import create_gantt
+    >>> # Make data for chart
+    >>> df = [dict(Task="Job A", Start='2009-01-01',
+    ...            Finish='2009-02-30', Resource='Apple'),
+    ...       dict(Task="Job B", Start='2009-03-05',
+    ...            Finish='2009-04-15', Resource='Grape'),
+    ...       dict(Task="Job C", Start='2009-02-20',
+    ...            Finish='2009-05-30', Resource='Banana')]
+
+    >>> # Make a dictionary of colors
+    >>> colors = {'Apple': 'rgb(255, 0, 0)',
+    ...           'Grape': 'rgb(170, 14, 200)',
+    ...           'Banana': (1, 1, 0.2)}
+
+    >>> # Create a figure with Plotly colorscale
+    >>> fig = create_gantt(df, colors=colors, index_col='Resource',
+    ...                    show_colorbar=True)
+
+    >>> fig.show()
 
     Example 5: Use a pandas dataframe
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_gantt
-
-    import pandas as pd
-
-    # Make data as a dataframe
-    df = pd.DataFrame([['Run', '2010-01-01', '2011-02-02', 10],
-                       ['Fast', '2011-01-01', '2012-06-05', 55],
-                       ['Eat', '2012-01-05', '2013-07-05', 94]],
-                      columns=['Task', 'Start', 'Finish', 'Complete'])
-
-    # Create a figure with Plotly colorscale
-    fig = create_gantt(df, colors='Blues', index_col='Complete',
-                       show_colorbar=True, bar_width=0.5,
-                       showgrid_x=True, showgrid_y=True)
-
-    # Plot the data
-    py.iplot(fig, filename='data with dataframe', world_readable=True)
-    ```
+
+    >>> from plotly.figure_factory import create_gantt
+    >>> import pandas as pd
+
+    >>> # Make data as a dataframe
+    >>> df = pd.DataFrame([['Run', '2010-01-01', '2011-02-02', 10],
+    ...                    ['Fast', '2011-01-01', '2012-06-05', 55],
+    ...                    ['Eat', '2012-01-05', '2013-07-05', 94]],
+    ...                   columns=['Task', 'Start', 'Finish', 'Complete'])
+
+    >>> # Create a figure with Plotly colorscale
+    >>> fig = create_gantt(df, colors='Blues', index_col='Complete',
+    ...                    show_colorbar=True, bar_width=0.5,
+    ...                    showgrid_x=True, showgrid_y=True)
+    >>> fig.show()
     """
     # validate gantt input data
     chart = validate_gantt(df)
diff --git a/packages/python/plotly/plotly/figure_factory/_ohlc.py b/packages/python/plotly/plotly/figure_factory/_ohlc.py
index 131d3df2d88..3122bb0a6c5 100644
--- a/packages/python/plotly/plotly/figure_factory/_ohlc.py
+++ b/packages/python/plotly/plotly/figure_factory/_ohlc.py
@@ -157,107 +157,96 @@ def create_ohlc(open, high, low, close, dates=None, direction="both", **kwargs):
     :rtype (dict): returns a representation of an ohlc chart figure.
 
     Example 1: Simple OHLC chart from a Pandas DataFrame
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_ohlc
-    from datetime import datetime
 
-    import pandas.io.data as web
+    >>> from plotly.figure_factory import create_ohlc
+    >>> from datetime import datetime
 
-    df = web.DataReader("aapl", 'yahoo', datetime(2008, 8, 15),
-                        datetime(2008, 10, 15))
-    fig = create_ohlc(df.Open, df.High, df.Low, df.Close, dates=df.index)
+    >>> import pandas.io.data as web
 
-    py.plot(fig, filename='finance/aapl-ohlc')
-    ```
+    >>> df = web.DataReader("aapl", 'yahoo', datetime(2008, 8, 15),
+    ...                     datetime(2008, 10, 15))
+    >>> fig = create_ohlc(df.Open, df.High, df.Low, df.Close, dates=df.index)
+    >>> fig.show()
 
     Example 2: Add text and annotations to the OHLC chart
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_ohlc
-    from datetime import datetime
-
-    import pandas.io.data as web
-
-    df = web.DataReader("aapl", 'yahoo', datetime(2008, 8, 15),
-                        datetime(2008, 10, 15))
-    fig = create_ohlc(df.Open, df.High, df.Low, df.Close, dates=df.index)
-
-    # Update the fig - options here: https://plot.ly/python/reference/#Layout
-    fig['layout'].update({
-        'title': 'The Great Recession',
-        'yaxis': {'title': 'AAPL Stock'},
-        'shapes': [{
-            'x0': '2008-09-15', 'x1': '2008-09-15', 'type': 'line',
-            'y0': 0, 'y1': 1, 'xref': 'x', 'yref': 'paper',
-            'line': {'color': 'rgb(40,40,40)', 'width': 0.5}
-        }],
-        'annotations': [{
-            'text': "the fall of Lehman Brothers",
-            'x': '2008-09-15', 'y': 1.02,
-            'xref': 'x', 'yref': 'paper',
-            'showarrow': False, 'xanchor': 'left'
-        }]
-    })
-
-    py.plot(fig, filename='finance/aapl-recession-ohlc', validate=False)
-    ```
+
+    >>> from plotly.figure_factory import create_ohlc
+    >>> from datetime import datetime
+
+    >>> import pandas.io.data as web
+
+    >>> df = web.datareader("aapl", 'yahoo', datetime(2008, 8, 15),
+    ...                     datetime(2008, 10, 15))
+    >>> fig = create_ohlc(df.open, df.high, df.low, df.close, dates=df.index)
+
+    >>> # update the fig - options here: https://plot.ly/python/reference/#layout
+    >>> fig['layout'].update({
+    ...     'title': 'the great recession',
+    ...     'yaxis': {'title': 'aapl stock'},
+    ...     'shapes': [{
+    ...         'x0': '2008-09-15', 'x1': '2008-09-15', 'type': 'line',
+    ...         'y0': 0, 'y1': 1, 'xref': 'x', 'yref': 'paper',
+    ...         'line': {'color': 'rgb(40,40,40)', 'width': 0.5}
+    ...     }],
+    ...     'annotations': [{
+    ...         'text': "the fall of lehman brothers",
+    ...         'x': '2008-09-15', 'y': 1.02,
+    ...         'xref': 'x', 'yref': 'paper',
+    ...         'showarrow': false, 'xanchor': 'left'
+    ...     }]
+    ... })
+    >>> fig.show()
 
     Example 3: Customize the OHLC colors
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_ohlc
-    from plotly.graph_objs import Line, Marker
-    from datetime import datetime
 
-    import pandas.io.data as web
+    >>> from plotly.figure_factory import create_ohlc
+    >>> from plotly.graph_objs import Line, Marker
+    >>> from datetime import datetime
+
+    >>> import pandas.io.data as web
 
-    df = web.DataReader("aapl", 'yahoo', datetime(2008, 1, 1),
-                        datetime(2009, 4, 1))
+    >>> df = web.DataReader("aapl", 'yahoo', datetime(2008, 1, 1),
+    ...                     datetime(2009, 4, 1))
 
-    # Make increasing ohlc sticks and customize their color and name
-    fig_increasing = create_ohlc(df.Open, df.High, df.Low, df.Close,
-                                 dates=df.index, direction='increasing',
-                                 name='AAPL',
-                                 line=Line(color='rgb(150, 200, 250)'))
+    >>> # Make increasing ohlc sticks and customize their color and name
+    >>> fig_increasing = create_ohlc(df.Open, df.High, df.Low, df.Close,
+    ...                              dates=df.index, direction='increasing',
+    ...                              name='AAPL',
+    ...                              line=Line(color='rgb(150, 200, 250)'))
 
-    # Make decreasing ohlc sticks and customize their color and name
-    fig_decreasing = create_ohlc(df.Open, df.High, df.Low, df.Close,
-                                 dates=df.index, direction='decreasing',
-                                 line=Line(color='rgb(128, 128, 128)'))
+    >>> # Make decreasing ohlc sticks and customize their color and name
+    >>> fig_decreasing = create_ohlc(df.Open, df.High, df.Low, df.Close,
+    ...                              dates=df.index, direction='decreasing',
+    ...                              line=Line(color='rgb(128, 128, 128)'))
 
-    # Initialize the figure
-    fig = fig_increasing
+    >>> # Initialize the figure
+    >>> fig = fig_increasing
 
-    # Add decreasing data with .extend()
-    fig['data'].extend(fig_decreasing['data'])
+    >>> # Add decreasing data with .extend()
+    >>> fig['data'].extend(fig_decreasing['data'])
+    >>> fig.show()
 
-    py.iplot(fig, filename='finance/aapl-ohlc-colors', validate=False)
-    ```
 
     Example 4: OHLC chart with datetime objects
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_ohlc
-
-    from datetime import datetime
-
-    # Add data
-    open_data = [33.0, 33.3, 33.5, 33.0, 34.1]
-    high_data = [33.1, 33.3, 33.6, 33.2, 34.8]
-    low_data = [32.7, 32.7, 32.8, 32.6, 32.8]
-    close_data = [33.0, 32.9, 33.3, 33.1, 33.1]
-    dates = [datetime(year=2013, month=10, day=10),
-             datetime(year=2013, month=11, day=10),
-             datetime(year=2013, month=12, day=10),
-             datetime(year=2014, month=1, day=10),
-             datetime(year=2014, month=2, day=10)]
-
-    # Create ohlc
-    fig = create_ohlc(open_data, high_data, low_data, close_data, dates=dates)
-
-    py.iplot(fig, filename='finance/simple-ohlc', validate=False)
-    ```
+
+    >>> from plotly.figure_factory import create_ohlc
+
+    >>> from datetime import datetime
+
+    >>> # Add data
+    >>> open_data = [33.0, 33.3, 33.5, 33.0, 34.1]
+    >>> high_data = [33.1, 33.3, 33.6, 33.2, 34.8]
+    >>> low_data = [32.7, 32.7, 32.8, 32.6, 32.8]
+    >>> close_data = [33.0, 32.9, 33.3, 33.1, 33.1]
+    >>> dates = [datetime(year=2013, month=10, day=10),
+    ...          datetime(year=2013, month=11, day=10),
+    ...          datetime(year=2013, month=12, day=10),
+    ...          datetime(year=2014, month=1, day=10),
+    ...          datetime(year=2014, month=2, day=10)]
+
+    >>> # Create ohlc
+    >>> fig = create_ohlc(open_data, high_data, low_data, close_data, dates=dates)
+    >>> fig.show()
     """
     if dates is not None:
         utils.validate_equal_length(open, high, low, close, dates)
diff --git a/packages/python/plotly/plotly/figure_factory/_quiver.py b/packages/python/plotly/plotly/figure_factory/_quiver.py
index e64f7eebfcb..e9503515ea1 100644
--- a/packages/python/plotly/plotly/figure_factory/_quiver.py
+++ b/packages/python/plotly/plotly/figure_factory/_quiver.py
@@ -32,84 +32,70 @@ def create_quiver(
     :rtype (dict): returns a representation of quiver figure.
 
     Example 1: Trivial Quiver
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_quiver
 
-    import math
+    >>> from plotly.figure_factory import create_quiver
+    >>> import math
 
-    # 1 Arrow from (0,0) to (1,1)
-    fig = create_quiver(x=[0], y=[0], u=[1], v=[1], scale=1)
+    >>> # 1 Arrow from (0,0) to (1,1)
+    >>> fig = create_quiver(x=[0], y=[0], u=[1], v=[1], scale=1)
+    >>> fig.show()
 
-    py.plot(fig, filename='quiver')
-    ```
 
     Example 2: Quiver plot using meshgrid
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_quiver
 
-    import numpy as np
-    import math
+    >>> from plotly.figure_factory import create_quiver
 
-    # Add data
-    x,y = np.meshgrid(np.arange(0, 2, .2), np.arange(0, 2, .2))
-    u = np.cos(x)*y
-    v = np.sin(x)*y
+    >>> import numpy as np
+    >>> import math
 
-    #Create quiver
-    fig = create_quiver(x, y, u, v)
+    >>> # Add data
+    >>> x,y = np.meshgrid(np.arange(0, 2, .2), np.arange(0, 2, .2))
+    >>> u = np.cos(x)*y
+    >>> v = np.sin(x)*y
+
+    >>> #Create quiver
+    >>> fig = create_quiver(x, y, u, v)
+    >>> fig.show()
 
-    # Plot
-    py.plot(fig, filename='quiver')
-    ```
 
     Example 3: Styling the quiver plot
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_quiver
-    import numpy as np
-    import math
 
-    # Add data
-    x, y = np.meshgrid(np.arange(-np.pi, math.pi, .5),
-                       np.arange(-math.pi, math.pi, .5))
-    u = np.cos(x)*y
-    v = np.sin(x)*y
+    >>> from plotly.figure_factory import create_quiver
+    >>> import numpy as np
+    >>> import math
+
+    >>> # Add data
+    >>> x, y = np.meshgrid(np.arange(-np.pi, math.pi, .5),
+    ...                    np.arange(-math.pi, math.pi, .5))
+    >>> u = np.cos(x)*y
+    >>> v = np.sin(x)*y
 
-    # Create quiver
-    fig = create_quiver(x, y, u, v, scale=.2, arrow_scale=.3, angle=math.pi/6,
-                        name='Wind Velocity', line=dict(width=1))
+    >>> # Create quiver
+    >>> fig = create_quiver(x, y, u, v, scale=.2, arrow_scale=.3, angle=math.pi/6,
+    ...                     name='Wind Velocity', line=dict(width=1))
 
-    # Add title to layout
-    fig['layout'].update(title='Quiver Plot')
+    >>> # Add title to layout
+    >>> fig['layout'].update(title='Quiver Plot')
+    >>> fig.show()
 
-    # Plot
-    py.plot(fig, filename='quiver')
-    ```
 
     Example 4: Forcing a fix scale ratio to maintain the arrow length
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_quiver
-
-    import numpy as np
-
-    # Add data
-    x,y = np.meshgrid(np.arange(0.5, 3.5, .5), np.arange(0.5, 4.5, .5))
-    u = x
-    v = y
-    angle = np.arctan(v / u)
-    norm = 0.25
-    u = norm * np.cos(angle)
-    v = norm * np.sin(angle)
-
-    # Create quiver with a fix scale ratio
-    fig = create_quiver(x, y, u, v, scale = 1, scaleratio = 0.5)
-
-    # Plot
-    py.plot(fig, filename='quiver')
-    ```
+
+    >>> from plotly.figure_factory import create_quiver
+    >>> import numpy as np
+
+    >>> # Add data
+    >>> x,y = np.meshgrid(np.arange(0.5, 3.5, .5), np.arange(0.5, 4.5, .5))
+    >>> u = x
+    >>> v = y
+    >>> angle = np.arctan(v / u)
+    >>> norm = 0.25
+    >>> u = norm * np.cos(angle)
+    >>> v = norm * np.sin(angle)
+
+    >>> # Create quiver with a fix scale ratio
+    >>> fig = create_quiver(x, y, u, v, scale = 1, scaleratio = 0.5)
+    >>> fig.show()
     """
     utils.validate_equal_length(x, y, u, v)
     utils.validate_positive_scalars(arrow_scale=arrow_scale, scale=scale)
diff --git a/packages/python/plotly/plotly/figure_factory/_scatterplot.py b/packages/python/plotly/plotly/figure_factory/_scatterplot.py
index f7975d77dbf..9d606c1d7ac 100644
--- a/packages/python/plotly/plotly/figure_factory/_scatterplot.py
+++ b/packages/python/plotly/plotly/figure_factory/_scatterplot.py
@@ -892,161 +892,142 @@ def create_scatterplotmatrix(
         'colorscale' in 'marker'
 
     Example 1: Vanilla Scatterplot Matrix
-    ```
-    import plotly.plotly as py
-    from plotly.graph_objs import graph_objs
-    from plotly.figure_factory import create_scatterplotmatrix
 
-    import numpy as np
-    import pandas as pd
+    >>> from plotly.graph_objs import graph_objs
+    >>> from plotly.figure_factory import create_scatterplotmatrix
 
-    # Create dataframe
-    df = pd.DataFrame(np.random.randn(10, 2),
-                    columns=['Column 1', 'Column 2'])
+    >>> import numpy as np
+    >>> import pandas as pd
 
-    # Create scatterplot matrix
-    fig = create_scatterplotmatrix(df)
+    >>> # Create dataframe
+    >>> df = pd.DataFrame(np.random.randn(10, 2),
+    ...                 columns=['Column 1', 'Column 2'])
+
+    >>> # Create scatterplot matrix
+    >>> fig = create_scatterplotmatrix(df)
+    >>> fig.show()
 
-    # Plot
-    py.iplot(fig, filename='Vanilla Scatterplot Matrix')
-    ```
 
     Example 2: Indexing a Column
-    ```
-    import plotly.plotly as py
-    from plotly.graph_objs import graph_objs
-    from plotly.figure_factory import create_scatterplotmatrix
 
-    import numpy as np
-    import pandas as pd
+    >>> from plotly.graph_objs import graph_objs
+    >>> from plotly.figure_factory import create_scatterplotmatrix
+
+    >>> import numpy as np
+    >>> import pandas as pd
 
-    # Create dataframe with index
-    df = pd.DataFrame(np.random.randn(10, 2),
-                       columns=['A', 'B'])
+    >>> # Create dataframe with index
+    >>> df = pd.DataFrame(np.random.randn(10, 2),
+    ...                    columns=['A', 'B'])
 
-    # Add another column of strings to the dataframe
-    df['Fruit'] = pd.Series(['apple', 'apple', 'grape', 'apple', 'apple',
-                             'grape', 'pear', 'pear', 'apple', 'pear'])
+    >>> # Add another column of strings to the dataframe
+    >>> df['Fruit'] = pd.Series(['apple', 'apple', 'grape', 'apple', 'apple',
+    ...                          'grape', 'pear', 'pear', 'apple', 'pear'])
 
-    # Create scatterplot matrix
-    fig = create_scatterplotmatrix(df, index='Fruit', size=10)
+    >>> # Create scatterplot matrix
+    >>> fig = create_scatterplotmatrix(df, index='Fruit', size=10)
+    >>> fig.show()
 
-    # Plot
-    py.iplot(fig, filename = 'Scatterplot Matrix with Index')
-    ```
 
     Example 3: Styling the Diagonal Subplots
-    ```
-    import plotly.plotly as py
-    from plotly.graph_objs import graph_objs
-    from plotly.figure_factory import create_scatterplotmatrix
 
-    import numpy as np
-    import pandas as pd
+    >>> from plotly.graph_objs import graph_objs
+    >>> from plotly.figure_factory import create_scatterplotmatrix
+
+    >>> import numpy as np
+    >>> import pandas as pd
 
-    # Create dataframe with index
-    df = pd.DataFrame(np.random.randn(10, 4),
-                       columns=['A', 'B', 'C', 'D'])
+    >>> # Create dataframe with index
+    >>> df = pd.DataFrame(np.random.randn(10, 4),
+    ...                    columns=['A', 'B', 'C', 'D'])
 
-    # Add another column of strings to the dataframe
-    df['Fruit'] = pd.Series(['apple', 'apple', 'grape', 'apple', 'apple',
-                             'grape', 'pear', 'pear', 'apple', 'pear'])
+    >>> # Add another column of strings to the dataframe
+    >>> df['Fruit'] = pd.Series(['apple', 'apple', 'grape', 'apple', 'apple',
+    ...                          'grape', 'pear', 'pear', 'apple', 'pear'])
 
-    # Create scatterplot matrix
-    fig = create_scatterplotmatrix(df, diag='box', index='Fruit', height=1000,
-                                   width=1000)
+    >>> # Create scatterplot matrix
+    >>> fig = create_scatterplotmatrix(df, diag='box', index='Fruit', height=1000,
+    ...                                width=1000)
+    >>> fig.show()
 
-    # Plot
-    py.iplot(fig, filename = 'Scatterplot Matrix - Diagonal Styling')
-    ```
 
     Example 4: Use a Theme to Style the Subplots
-    ```
-    import plotly.plotly as py
-    from plotly.graph_objs import graph_objs
-    from plotly.figure_factory import create_scatterplotmatrix
 
-    import numpy as np
-    import pandas as pd
+    >>> from plotly.graph_objs import graph_objs
+    >>> from plotly.figure_factory import create_scatterplotmatrix
 
-    # Create dataframe with random data
-    df = pd.DataFrame(np.random.randn(100, 3),
-                       columns=['A', 'B', 'C'])
+    >>> import numpy as np
+    >>> import pandas as pd
 
-    # Create scatterplot matrix using a built-in
-    # Plotly palette scale and indexing column 'A'
-    fig = create_scatterplotmatrix(df, diag='histogram', index='A',
-                                   colormap='Blues', height=800, width=800)
+    >>> # Create dataframe with random data
+    >>> df = pd.DataFrame(np.random.randn(100, 3),
+    ...                    columns=['A', 'B', 'C'])
+
+    >>> # Create scatterplot matrix using a built-in
+    >>> # Plotly palette scale and indexing column 'A'
+    >>> fig = create_scatterplotmatrix(df, diag='histogram', index='A',
+    ...                                colormap='Blues', height=800, width=800)
+    >>> fig.show()
 
-    # Plot
-    py.iplot(fig, filename = 'Scatterplot Matrix - Colormap Theme')
-    ```
 
     Example 5: Example 4 with Interval Factoring
-    ```
-    import plotly.plotly as py
-    from plotly.graph_objs import graph_objs
-    from plotly.figure_factory import create_scatterplotmatrix
-
-    import numpy as np
-    import pandas as pd
-
-    # Create dataframe with random data
-    df = pd.DataFrame(np.random.randn(100, 3),
-                       columns=['A', 'B', 'C'])
-
-    # Create scatterplot matrix using a list of 2 rgb tuples
-    # and endpoints at -1, 0 and 1
-    fig = create_scatterplotmatrix(df, diag='histogram', index='A',
-                                   colormap=['rgb(140, 255, 50)',
-                                             'rgb(170, 60, 115)', '#6c4774',
-                                             (0.5, 0.1, 0.8)],
-                                   endpts=[-1, 0, 1], height=800, width=800)
-
-    # Plot
-    py.iplot(fig, filename = 'Scatterplot Matrix - Intervals')
-    ```
+
+    >>> from plotly.graph_objs import graph_objs
+    >>> from plotly.figure_factory import create_scatterplotmatrix
+
+    >>> import numpy as np
+    >>> import pandas as pd
+
+    >>> # Create dataframe with random data
+    >>> df = pd.DataFrame(np.random.randn(100, 3),
+    ...                    columns=['A', 'B', 'C'])
+
+    >>> # Create scatterplot matrix using a list of 2 rgb tuples
+    >>> # and endpoints at -1, 0 and 1
+    >>> fig = create_scatterplotmatrix(df, diag='histogram', index='A',
+    ...                                colormap=['rgb(140, 255, 50)',
+    ...                                          'rgb(170, 60, 115)', '#6c4774',
+    ...                                          (0.5, 0.1, 0.8)],
+    ...                                endpts=[-1, 0, 1], height=800, width=800)
+    >>> fig.show()
+
 
     Example 6: Using the colormap as a Dictionary
-    ```
-    import plotly.plotly as py
-    from plotly.graph_objs import graph_objs
-    from plotly.figure_factory import create_scatterplotmatrix
-
-    import numpy as np
-    import pandas as pd
-    import random
-
-    # Create dataframe with random data
-    df = pd.DataFrame(np.random.randn(100, 3),
-                       columns=['Column A',
-                                'Column B',
-                                'Column C'])
-
-    # Add new color column to dataframe
-    new_column = []
-    strange_colors = ['turquoise', 'limegreen', 'goldenrod']
-
-    for j in range(100):
-        new_column.append(random.choice(strange_colors))
-    df['Colors'] = pd.Series(new_column, index=df.index)
-
-    # Create scatterplot matrix using a dictionary of hex color values
-    # which correspond to actual color names in 'Colors' column
-    fig = create_scatterplotmatrix(
-        df, diag='box', index='Colors',
-        colormap= dict(
-            turquoise = '#00F5FF',
-            limegreen = '#32CD32',
-            goldenrod = '#DAA520'
-        ),
-        colormap_type='cat',
-        height=800, width=800
-    )
-
-    # Plot
-    py.iplot(fig, filename = 'Scatterplot Matrix - colormap dictionary ')
-    ```
+
+    >>> from plotly.graph_objs import graph_objs
+    >>> from plotly.figure_factory import create_scatterplotmatrix
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> import random
+
+    >>> # Create dataframe with random data
+    >>> df = pd.DataFrame(np.random.randn(100, 3),
+    ...                    columns=['Column A',
+    ...                             'Column B',
+    ...                             'Column C'])
+
+    >>> # Add new color column to dataframe
+    >>> new_column = []
+    >>> strange_colors = ['turquoise', 'limegreen', 'goldenrod']
+
+    >>> for j in range(100):
+    ...     new_column.append(random.choice(strange_colors))
+    >>> df['Colors'] = pd.Series(new_column, index=df.index)
+
+    >>> # Create scatterplot matrix using a dictionary of hex color values
+    >>> # which correspond to actual color names in 'Colors' column
+    >>> fig = create_scatterplotmatrix(
+    ...     df, diag='box', index='Colors',
+    ...     colormap= dict(
+    ...         turquoise = '#00F5FF',
+    ...         limegreen = '#32CD32',
+    ...         goldenrod = '#DAA520'
+    ...     ),
+    ...     colormap_type='cat',
+    ...     height=800, width=800
+    ... )
+    >>> fig.show()
     """
     # TODO: protected until #282
     if dataframe is None:
diff --git a/packages/python/plotly/plotly/figure_factory/_streamline.py b/packages/python/plotly/plotly/figure_factory/_streamline.py
index 88a790d87fe..caf42d704e4 100644
--- a/packages/python/plotly/plotly/figure_factory/_streamline.py
+++ b/packages/python/plotly/plotly/figure_factory/_streamline.py
@@ -60,62 +60,53 @@ def create_streamline(
     :rtype (dict): returns a representation of streamline figure.
 
     Example 1: Plot simple streamline and increase arrow size
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_streamline
 
-    import numpy as np
-    import math
+    >>> from plotly.figure_factory import create_streamline
+    >>> import numpy as np
+    >>> import math
 
-    # Add data
-    x = np.linspace(-3, 3, 100)
-    y = np.linspace(-3, 3, 100)
-    Y, X = np.meshgrid(x, y)
-    u = -1 - X**2 + Y
-    v = 1 + X - Y**2
-    u = u.T  # Transpose
-    v = v.T  # Transpose
+    >>> # Add data
+    >>> x = np.linspace(-3, 3, 100)
+    >>> y = np.linspace(-3, 3, 100)
+    >>> Y, X = np.meshgrid(x, y)
+    >>> u = -1 - X**2 + Y
+    >>> v = 1 + X - Y**2
+    >>> u = u.T  # Transpose
+    >>> v = v.T  # Transpose
 
-    # Create streamline
-    fig = create_streamline(x, y, u, v, arrow_scale=.1)
-
-    # Plot
-    py.plot(fig, filename='streamline')
-    ```
+    >>> # Create streamline
+    >>> fig = create_streamline(x, y, u, v, arrow_scale=.1)
+    >>> fig.show()
 
     Example 2: from nbviewer.ipython.org/github/barbagroup/AeroPython
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_streamline
-
-    import numpy as np
-    import math
-
-    # Add data
-    N = 50
-    x_start, x_end = -2.0, 2.0
-    y_start, y_end = -1.0, 1.0
-    x = np.linspace(x_start, x_end, N)
-    y = np.linspace(y_start, y_end, N)
-    X, Y = np.meshgrid(x, y)
-    ss = 5.0
-    x_s, y_s = -1.0, 0.0
-
-    # Compute the velocity field on the mesh grid
-    u_s = ss/(2*np.pi) * (X-x_s)/((X-x_s)**2 + (Y-y_s)**2)
-    v_s = ss/(2*np.pi) * (Y-y_s)/((X-x_s)**2 + (Y-y_s)**2)
-
-    # Create streamline
-    fig = create_streamline(x, y, u_s, v_s, density=2, name='streamline')
-
-    # Add source point
-    point = Scatter(x=[x_s], y=[y_s], mode='markers',
-                    marker=Marker(size=14), name='source point')
-
-    # Plot
-    fig['data'].append(point)
-    py.plot(fig, filename='streamline')
-    ```
+
+    >>> from plotly.figure_factory import create_streamline
+    >>> import numpy as np
+    >>> import math
+
+    >>> # Add data
+    >>> N = 50
+    >>> x_start, x_end = -2.0, 2.0
+    >>> y_start, y_end = -1.0, 1.0
+    >>> x = np.linspace(x_start, x_end, N)
+    >>> y = np.linspace(y_start, y_end, N)
+    >>> X, Y = np.meshgrid(x, y)
+    >>> ss = 5.0
+    >>> x_s, y_s = -1.0, 0.0
+
+    >>> # Compute the velocity field on the mesh grid
+    >>> u_s = ss/(2*np.pi) * (X-x_s)/((X-x_s)**2 + (Y-y_s)**2)
+    >>> v_s = ss/(2*np.pi) * (Y-y_s)/((X-x_s)**2 + (Y-y_s)**2)
+
+    >>> # Create streamline
+    >>> fig = create_streamline(x, y, u_s, v_s, density=2, name='streamline')
+
+    >>> # Add source point
+    >>> point = Scatter(x=[x_s], y=[y_s], mode='markers',
+    ...                 marker=Marker(size=14), name='source point')
+
+    >>> fig['data'].append(point)
+    >>> fig.show()
     """
     utils.validate_equal_length(x, y)
     utils.validate_equal_length(u, v)
diff --git a/packages/python/plotly/plotly/figure_factory/_table.py b/packages/python/plotly/plotly/figure_factory/_table.py
index c4570e2016a..8e3087410d0 100644
--- a/packages/python/plotly/plotly/figure_factory/_table.py
+++ b/packages/python/plotly/plotly/figure_factory/_table.py
@@ -58,52 +58,46 @@ def create_table(
         call help(plotly.graph_objs.Heatmap)
 
     Example 1: Simple Plotly Table
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_table
 
-    text = [['Country', 'Year', 'Population'],
-            ['US', 2000, 282200000],
-            ['Canada', 2000, 27790000],
-            ['US', 2010, 309000000],
-            ['Canada', 2010, 34000000]]
+    >>> from plotly.figure_factory import create_table
 
-    table = create_table(text)
-    py.iplot(table)
-    ```
+    >>> text = [['Country', 'Year', 'Population'],
+    ...         ['US', 2000, 282200000],
+    ...         ['Canada', 2000, 27790000],
+    ...         ['US', 2010, 309000000],
+    ...         ['Canada', 2010, 34000000]]
+
+    >>> table = create_table(text)
+    >>> table.show()
 
     Example 2: Table with Custom Coloring
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_table
-
-    text = [['Country', 'Year', 'Population'],
-            ['US', 2000, 282200000],
-            ['Canada', 2000, 27790000],
-            ['US', 2010, 309000000],
-            ['Canada', 2010, 34000000]]
-
-    table = create_table(text,
-                         colorscale=[[0, '#000000'],
-                                     [.5, '#80beff'],
-                                     [1, '#cce5ff']],
-                         font_colors=['#ffffff', '#000000',
-                                    '#000000'])
-    py.iplot(table)
-    ```
+
+    >>> from plotly.figure_factory import create_table
+
+    >>> text = [['Country', 'Year', 'Population'],
+    ...         ['US', 2000, 282200000],
+    ...         ['Canada', 2000, 27790000],
+    ...         ['US', 2010, 309000000],
+    ...         ['Canada', 2010, 34000000]]
+
+    >>> table = create_table(text,
+    ...                      colorscale=[[0, '#000000'],
+    ...                                  [.5, '#80beff'],
+    ...                                  [1, '#cce5ff']],
+    ...                      font_colors=['#ffffff', '#000000',
+    ...                                 '#000000'])
+    >>> table.show()
+
     Example 3: Simple Plotly Table with Pandas
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_table
 
-    import pandas as pd
+    >>> from plotly.figure_factory import create_table
+    >>> import pandas as pd
 
-    df = pd.read_csv('http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt', sep='\t')
-    df_p = df[0:25]
+    >>> df = pd.read_csv('http://www.stat.ubc.ca/~jenny/notOcto/STAT545A/examples/gapminder/data/gapminderDataFiveYear.txt', sep='\t')
+    >>> df_p = df[0:25]
 
-    table_simple = create_table(df_p)
-    py.iplot(table_simple)
-    ```
+    >>> table_simple = create_table(df_p)
+    >>> table_simple.show()
     """
 
     # Avoiding mutables in the call signature
diff --git a/packages/python/plotly/plotly/figure_factory/_ternary_contour.py b/packages/python/plotly/plotly/figure_factory/_ternary_contour.py
index 52c7099bced..e48fea65673 100644
--- a/packages/python/plotly/plotly/figure_factory/_ternary_contour.py
+++ b/packages/python/plotly/plotly/figure_factory/_ternary_contour.py
@@ -574,40 +574,43 @@ def create_ternary_contour(
 
     Example 1: ternary contour plot with filled contours
 
-    # Define coordinates
-    a, b = np.mgrid[0:1:20j, 0:1:20j]
-    mask = a + b <= 1
-    a = a[mask].ravel()
-    b = b[mask].ravel()
-    c = 1 - a - b
-    # Values to be displayed as contours
-    z = a * b * c
-    fig = ff.create_ternarycontour(np.stack((a, b, c)), z)
+    >>> import plotly.figure_factory as ff
+    >>> import numpy as np
+    >>> # Define coordinates
+    >>> a, b = np.mgrid[0:1:20j, 0:1:20j]
+    >>> mask = a + b <= 1
+    >>> a = a[mask].ravel()
+    >>> b = b[mask].ravel()
+    >>> c = 1 - a - b
+    >>> # Values to be displayed as contours
+    >>> z = a * b * c
+    >>> fig = ff.create_ternarycontour(np.stack((a, b, c)), z)
+    >>> fig.show()
 
     It is also possible to give only two barycentric coordinates for each
     point, since the sum of the three coordinates is one:
 
-    fig = ff.create_ternarycontour(np.stack((a, b)), z)
-    plotly.iplot(fig)
+    >>> fig = ff.create_ternarycontour(np.stack((a, b)), z)
+
 
     Example 2: ternary contour plot with line contours
 
-    fig = ff.create_ternarycontour(np.stack((a, b, c)), z, coloring='lines')
+    >>> fig = ff.create_ternarycontour(np.stack((a, b, c)), z, coloring='lines')
 
     Example 3: customize number of contours
 
-    fig = ff.create_ternarycontour(np.stack((a, b, c)), z, ncontours=8)
+    >>> fig = ff.create_ternarycontour(np.stack((a, b, c)), z, ncontours=8)
 
     Example 4: superimpose contour plot and original data as markers
 
-    fig = ff.create_ternarycontour(np.stack((a, b, c)), z, coloring='lines',
+    >>> fig = ff.create_ternarycontour(np.stack((a, b, c)), z, coloring='lines',
                                    showmarkers=True)
 
     Example 5: customize title and pole labels
 
-    fig = ff.create_ternarycontour(np.stack((a, b, c)), z,
-                                   title='Ternary plot',
-                                   pole_labels=['clay', 'quartz', 'fledspar'])
+    >>> fig = ff.create_ternarycontour(np.stack((a, b, c)), z,
+    ...                               title='Ternary plot',
+    ...                               pole_labels=['clay', 'quartz', 'fledspar'])
     """
     if scipy_interp is None:
         raise ImportError(
diff --git a/packages/python/plotly/plotly/figure_factory/_trisurf.py b/packages/python/plotly/plotly/figure_factory/_trisurf.py
index 9efc255fbbd..caf7353d837 100644
--- a/packages/python/plotly/plotly/figure_factory/_trisurf.py
+++ b/packages/python/plotly/plotly/figure_factory/_trisurf.py
@@ -305,186 +305,166 @@ def create_trisurf(
         the x, y and z axes. 'x', 'y' and 'z' take (int|float) values
 
     Example 1: Sphere
-    ```
-    # Necessary Imports for Trisurf
-    import numpy as np
-    from scipy.spatial import Delaunay
-
-    import plotly.plotly as py
-    from plotly.figure_factory import create_trisurf
-    from plotly.graph_objs import graph_objs
-
-    # Make data for plot
-    u = np.linspace(0, 2*np.pi, 20)
-    v = np.linspace(0, np.pi, 20)
-    u,v = np.meshgrid(u,v)
-    u = u.flatten()
-    v = v.flatten()
-
-    x = np.sin(v)*np.cos(u)
-    y = np.sin(v)*np.sin(u)
-    z = np.cos(v)
-
-    points2D = np.vstack([u,v]).T
-    tri = Delaunay(points2D)
-    simplices = tri.simplices
-
-    # Create a figure
-    fig1 = create_trisurf(x=x, y=y, z=z, colormap="Rainbow",
-                          simplices=simplices)
-    # Plot the data
-    py.iplot(fig1, filename='trisurf-plot-sphere')
-    ```
+
+    >>> # Necessary Imports for Trisurf
+    >>> import numpy as np
+    >>> from scipy.spatial import Delaunay
+
+    >>> from plotly.figure_factory import create_trisurf
+    >>> from plotly.graph_objs import graph_objs
+
+    >>> # Make data for plot
+    >>> u = np.linspace(0, 2*np.pi, 20)
+    >>> v = np.linspace(0, np.pi, 20)
+    >>> u,v = np.meshgrid(u,v)
+    >>> u = u.flatten()
+    >>> v = v.flatten()
+
+    >>> x = np.sin(v)*np.cos(u)
+    >>> y = np.sin(v)*np.sin(u)
+    >>> z = np.cos(v)
+
+    >>> points2D = np.vstack([u,v]).T
+    >>> tri = Delaunay(points2D)
+    >>> simplices = tri.simplices
+
+    >>> # Create a figure
+    >>> fig1 = create_trisurf(x=x, y=y, z=z, colormap="Rainbow",
+    ...                       simplices=simplices)
 
     Example 2: Torus
-    ```
-    # Necessary Imports for Trisurf
-    import numpy as np
-    from scipy.spatial import Delaunay
-
-    import plotly.plotly as py
-    from plotly.figure_factory import create_trisurf
-    from plotly.graph_objs import graph_objs
-
-    # Make data for plot
-    u = np.linspace(0, 2*np.pi, 20)
-    v = np.linspace(0, 2*np.pi, 20)
-    u,v = np.meshgrid(u,v)
-    u = u.flatten()
-    v = v.flatten()
-
-    x = (3 + (np.cos(v)))*np.cos(u)
-    y = (3 + (np.cos(v)))*np.sin(u)
-    z = np.sin(v)
-
-    points2D = np.vstack([u,v]).T
-    tri = Delaunay(points2D)
-    simplices = tri.simplices
-
-    # Create a figure
-    fig1 = create_trisurf(x=x, y=y, z=z, colormap="Viridis",
-                          simplices=simplices)
-    # Plot the data
-    py.iplot(fig1, filename='trisurf-plot-torus')
-    ```
+
+    >>> # Necessary Imports for Trisurf
+    >>> import numpy as np
+    >>> from scipy.spatial import Delaunay
+
+    >>> from plotly.figure_factory import create_trisurf
+    >>> from plotly.graph_objs import graph_objs
+
+    >>> # Make data for plot
+    >>> u = np.linspace(0, 2*np.pi, 20)
+    >>> v = np.linspace(0, 2*np.pi, 20)
+    >>> u,v = np.meshgrid(u,v)
+    >>> u = u.flatten()
+    >>> v = v.flatten()
+
+    >>> x = (3 + (np.cos(v)))*np.cos(u)
+    >>> y = (3 + (np.cos(v)))*np.sin(u)
+    >>> z = np.sin(v)
+
+    >>> points2D = np.vstack([u,v]).T
+    >>> tri = Delaunay(points2D)
+    >>> simplices = tri.simplices
+
+    >>> # Create a figure
+    >>> fig1 = create_trisurf(x=x, y=y, z=z, colormap="Viridis",
+    ...                       simplices=simplices)
 
     Example 3: Mobius Band
-    ```
-    # Necessary Imports for Trisurf
-    import numpy as np
-    from scipy.spatial import Delaunay
-
-    import plotly.plotly as py
-    from plotly.figure_factory import create_trisurf
-    from plotly.graph_objs import graph_objs
-
-    # Make data for plot
-    u = np.linspace(0, 2*np.pi, 24)
-    v = np.linspace(-1, 1, 8)
-    u,v = np.meshgrid(u,v)
-    u = u.flatten()
-    v = v.flatten()
-
-    tp = 1 + 0.5*v*np.cos(u/2.)
-    x = tp*np.cos(u)
-    y = tp*np.sin(u)
-    z = 0.5*v*np.sin(u/2.)
-
-    points2D = np.vstack([u,v]).T
-    tri = Delaunay(points2D)
-    simplices = tri.simplices
-
-    # Create a figure
-    fig1 = create_trisurf(x=x, y=y, z=z, colormap=[(0.2, 0.4, 0.6), (1, 1, 1)],
-                          simplices=simplices)
-    # Plot the data
-    py.iplot(fig1, filename='trisurf-plot-mobius-band')
-    ```
+
+    >>> # Necessary Imports for Trisurf
+    >>> import numpy as np
+    >>> from scipy.spatial import Delaunay
+
+    >>> from plotly.figure_factory import create_trisurf
+    >>> from plotly.graph_objs import graph_objs
+
+    >>> # Make data for plot
+    >>> u = np.linspace(0, 2*np.pi, 24)
+    >>> v = np.linspace(-1, 1, 8)
+    >>> u,v = np.meshgrid(u,v)
+    >>> u = u.flatten()
+    >>> v = v.flatten()
+
+    >>> tp = 1 + 0.5*v*np.cos(u/2.)
+    >>> x = tp*np.cos(u)
+    >>> y = tp*np.sin(u)
+    >>> z = 0.5*v*np.sin(u/2.)
+
+    >>> points2D = np.vstack([u,v]).T
+    >>> tri = Delaunay(points2D)
+    >>> simplices = tri.simplices
+
+    >>> # Create a figure
+    >>> fig1 = create_trisurf(x=x, y=y, z=z, colormap=[(0.2, 0.4, 0.6), (1, 1, 1)],
+    ...                       simplices=simplices)
 
     Example 4: Using a Custom Colormap Function with Light Cone
-    ```
-    # Necessary Imports for Trisurf
-    import numpy as np
-    from scipy.spatial import Delaunay
-
-    import plotly.plotly as py
-    from plotly.figure_factory import create_trisurf
-    from plotly.graph_objs import graph_objs
-
-    # Make data for plot
-    u=np.linspace(-np.pi, np.pi, 30)
-    v=np.linspace(-np.pi, np.pi, 30)
-    u,v=np.meshgrid(u,v)
-    u=u.flatten()
-    v=v.flatten()
-
-    x = u
-    y = u*np.cos(v)
-    z = u*np.sin(v)
-
-    points2D = np.vstack([u,v]).T
-    tri = Delaunay(points2D)
-    simplices = tri.simplices
-
-    # Define distance function
-    def dist_origin(x, y, z):
-        return np.sqrt((1.0 * x)**2 + (1.0 * y)**2 + (1.0 * z)**2)
-
-    # Create a figure
-    fig1 = create_trisurf(x=x, y=y, z=z,
-                          colormap=['#FFFFFF', '#E4FFFE',
-                                    '#A4F6F9', '#FF99FE',
-                                    '#BA52ED'],
-                          scale=[0, 0.6, 0.71, 0.89, 1],
-                          simplices=simplices,
-                          color_func=dist_origin)
-    # Plot the data
-    py.iplot(fig1, filename='trisurf-plot-custom-coloring')
-    ```
+
+    >>> # Necessary Imports for Trisurf
+    >>> import numpy as np
+    >>> from scipy.spatial import Delaunay
+
+    >>> from plotly.figure_factory import create_trisurf
+    >>> from plotly.graph_objs import graph_objs
+
+    >>> # Make data for plot
+    >>> u=np.linspace(-np.pi, np.pi, 30)
+    >>> v=np.linspace(-np.pi, np.pi, 30)
+    >>> u,v=np.meshgrid(u,v)
+    >>> u=u.flatten()
+    >>> v=v.flatten()
+
+    >>> x = u
+    >>> y = u*np.cos(v)
+    >>> z = u*np.sin(v)
+
+    >>> points2D = np.vstack([u,v]).T
+    >>> tri = Delaunay(points2D)
+    >>> simplices = tri.simplices
+
+    >>> # Define distance function
+    >>> def dist_origin(x, y, z):
+    ...     return np.sqrt((1.0 * x)**2 + (1.0 * y)**2 + (1.0 * z)**2)
+
+    >>> # Create a figure
+    >>> fig1 = create_trisurf(x=x, y=y, z=z,
+    ...                       colormap=['#FFFFFF', '#E4FFFE',
+    ...                                 '#A4F6F9', '#FF99FE',
+    ...                                 '#BA52ED'],
+    ...                       scale=[0, 0.6, 0.71, 0.89, 1],
+    ...                       simplices=simplices,
+    ...                       color_func=dist_origin)
 
     Example 5: Enter color_func as a list of colors
-    ```
-    # Necessary Imports for Trisurf
-    import numpy as np
-    from scipy.spatial import Delaunay
-    import random
-
-    import plotly.plotly as py
-    from plotly.figure_factory import create_trisurf
-    from plotly.graph_objs import graph_objs
-
-    # Make data for plot
-    u=np.linspace(-np.pi, np.pi, 30)
-    v=np.linspace(-np.pi, np.pi, 30)
-    u,v=np.meshgrid(u,v)
-    u=u.flatten()
-    v=v.flatten()
-
-    x = u
-    y = u*np.cos(v)
-    z = u*np.sin(v)
-
-    points2D = np.vstack([u,v]).T
-    tri = Delaunay(points2D)
-    simplices = tri.simplices
-
-
-    colors = []
-    color_choices = ['rgb(0, 0, 0)', '#6c4774', '#d6c7dd']
-
-    for index in range(len(simplices)):
-        colors.append(random.choice(color_choices))
-
-    fig = create_trisurf(
-        x, y, z, simplices,
-        color_func=colors,
-        show_colorbar=True,
-        edges_color='rgb(2, 85, 180)',
-        title=' Modern Art'
-    )
 
-    py.iplot(fig, filename="trisurf-plot-modern-art")
-    ```
+    >>> # Necessary Imports for Trisurf
+    >>> import numpy as np
+    >>> from scipy.spatial import Delaunay
+    >>> import random
+
+    >>> from plotly.figure_factory import create_trisurf
+    >>> from plotly.graph_objs import graph_objs
+
+    >>> # Make data for plot
+    >>> u=np.linspace(-np.pi, np.pi, 30)
+    >>> v=np.linspace(-np.pi, np.pi, 30)
+    >>> u,v=np.meshgrid(u,v)
+    >>> u=u.flatten()
+    >>> v=v.flatten()
+
+    >>> x = u
+    >>> y = u*np.cos(v)
+    >>> z = u*np.sin(v)
+
+    >>> points2D = np.vstack([u,v]).T
+    >>> tri = Delaunay(points2D)
+    >>> simplices = tri.simplices
+
+
+    >>> colors = []
+    >>> color_choices = ['rgb(0, 0, 0)', '#6c4774', '#d6c7dd']
+
+    >>> for index in range(len(simplices)):
+    >>>     colors.append(random.choice(color_choices))
+
+    >>> fig = create_trisurf(
+    ...     x, y, z, simplices,
+    ...     color_func=colors,
+    ...     show_colorbar=True,
+    ...     edges_color='rgb(2, 85, 180)',
+    ...     title=' Modern Art'
+    ... )
     """
     if aspectratio is None:
         aspectratio = {"x": 1, "y": 1, "z": 1}
diff --git a/packages/python/plotly/plotly/figure_factory/_violin.py b/packages/python/plotly/plotly/figure_factory/_violin.py
index 520a8fa5517..c2bc612cfce 100644
--- a/packages/python/plotly/plotly/figure_factory/_violin.py
+++ b/packages/python/plotly/plotly/figure_factory/_violin.py
@@ -487,98 +487,92 @@ def create_violin(
     :param (str) title: the title of the violin plot.
 
     Example 1: Single Violin Plot
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_violin
-    from plotly.graph_objs import graph_objs
 
-    import numpy as np
-    from scipy import stats
+    >>> from plotly.figure_factory import create_violin
+    >>> import plotly.graph_objs as graph_objects
 
-    # create list of random values
-    data_list = np.random.randn(100)
-    data_list.tolist()
+    >>> import numpy as np
+    >>> from scipy import stats
 
-    # create violin fig
-    fig = create_violin(data_list, colors='#604d9e')
+    >>> # create list of random values
+    >>> data_list = np.random.randn(100)
+    >>> data_list.tolist()
 
-    # plot
-    py.iplot(fig, filename='Violin Plot')
-    ```
+    >>> # create violin fig
+    >>> fig = create_violin(data_list, colors='#604d9e')
+
+    >>> # plot
+    >>> fig.show()
 
     Example 2: Multiple Violin Plots with Qualitative Coloring
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_violin
-    from plotly.graph_objs import graph_objs
-
-    import numpy as np
-    import pandas as pd
-    from scipy import stats
-
-    # create dataframe
-    np.random.seed(619517)
-    Nr=250
-    y = np.random.randn(Nr)
-    gr = np.random.choice(list("ABCDE"), Nr)
-    norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)]
-
-    for i, letter in enumerate("ABCDE"):
-        y[gr == letter] *=norm_params[i][1]+ norm_params[i][0]
-    df = pd.DataFrame(dict(Score=y, Group=gr))
-
-    # create violin fig
-    fig = create_violin(df, data_header='Score', group_header='Group',
-                        sort=True, height=600, width=1000)
-
-    # plot
-    py.iplot(fig, filename='Violin Plot with Coloring')
-    ```
+
+    >>> from plotly.figure_factory import create_violin
+    >>> import plotly.graph_objs as graph_objects
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from scipy import stats
+
+    >>> # create dataframe
+    >>> np.random.seed(619517)
+    >>> Nr=250
+    >>> y = np.random.randn(Nr)
+    >>> gr = np.random.choice(list("ABCDE"), Nr)
+    >>> norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)]
+
+    >>> for i, letter in enumerate("ABCDE"):
+    ...     y[gr == letter] *=norm_params[i][1]+ norm_params[i][0]
+    >>> df = pd.DataFrame(dict(Score=y, Group=gr))
+
+    >>> # create violin fig
+    >>> fig = create_violin(df, data_header='Score', group_header='Group',
+    ...                    sort=True, height=600, width=1000)
+
+    >>> # plot
+    >>> fig.show()
 
     Example 3: Violin Plots with Colorscale
-    ```
-    import plotly.plotly as py
-    from plotly.figure_factory import create_violin
-    from plotly.graph_objs import graph_objs
-
-    import numpy as np
-    import pandas as pd
-    from scipy import stats
-
-    # create dataframe
-    np.random.seed(619517)
-    Nr=250
-    y = np.random.randn(Nr)
-    gr = np.random.choice(list("ABCDE"), Nr)
-    norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)]
-
-    for i, letter in enumerate("ABCDE"):
-        y[gr == letter] *=norm_params[i][1]+ norm_params[i][0]
-    df = pd.DataFrame(dict(Score=y, Group=gr))
-
-    # define header params
-    data_header = 'Score'
-    group_header = 'Group'
-
-    # make groupby object with pandas
-    group_stats = {}
-    groupby_data = df.groupby([group_header])
-
-    for group in "ABCDE":
-        data_from_group = groupby_data.get_group(group)[data_header]
-        # take a stat of the grouped data
-        stat = np.median(data_from_group)
-        # add to dictionary
-        group_stats[group] = stat
-
-    # create violin fig
-    fig = create_violin(df, data_header='Score', group_header='Group',
-                        height=600, width=1000, use_colorscale=True,
-                        group_stats=group_stats)
-
-    # plot
-    py.iplot(fig, filename='Violin Plot with Colorscale')
-    ```
+
+    >>> from plotly.figure_factory import create_violin
+    >>> import plotly.graph_objs as graph_objects
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from scipy import stats
+
+    >>> # create dataframe
+    >>> np.random.seed(619517)
+    >>> Nr=250
+    >>> y = np.random.randn(Nr)
+    >>> gr = np.random.choice(list("ABCDE"), Nr)
+    >>> norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)]
+
+    >>> for i, letter in enumerate("ABCDE"):
+    ...     y[gr == letter] *=norm_params[i][1]+ norm_params[i][0]
+    >>> df = pd.DataFrame(dict(Score=y, Group=gr))
+
+    >>> # define header params
+    >>> data_header = 'Score'
+    >>> group_header = 'Group'
+
+    >>> # make groupby object with pandas
+    >>> group_stats = {}
+    >>> groupby_data = df.groupby([group_header])
+
+    >>> for group in "ABCDE":
+    ...     data_from_group = groupby_data.get_group(group)[data_header]
+    ...     # take a stat of the grouped data
+    ...     stat = np.median(data_from_group)
+    ...     # add to dictionary
+    ...     group_stats[group] = stat
+
+    >>> # create violin fig
+    >>> fig = create_violin(df, data_header='Score', group_header='Group',
+    ...                     height=600, width=1000, use_colorscale=True,
+    ...                     group_stats=group_stats)
+
+    >>> # plot
+    >>> fig.show()
     """
 
     # Validate colors
diff --git a/packages/python/plotly/plotly/graph_objs/_figure.py b/packages/python/plotly/plotly/graph_objs/_figure.py
index 4c2a0d295c0..942720030c4 100644
--- a/packages/python/plotly/plotly/graph_objs/_figure.py
+++ b/packages/python/plotly/plotly/graph_objs/_figure.py
@@ -857,6 +857,7 @@ def add_bar(
 
         Parameters
         ----------
+
         alignmentgroup
             Set several traces linked to the same position axis or
             matching axes to the same alignmentgroup. This controls
@@ -4824,9 +4825,10 @@ def add_funnel(
             If True, associate this trace with the secondary y-axis of the
             subplot at the specified row and col. Only valid if all of the
             following conditions are satisfied:
-              * The figure was created using `plotly.subplots.make_subplots`.
-              * The row and col arguments are not None
-              * The subplot at the specified row and col has type xy
+
+             * The figure was created using `plotly.subplots.make_subplots`.
+             * The row and col arguments are not None
+             * The subplot at the specified row and col has type xy
                 (which is the default) and secondary_y True.  These
                 properties are specified in the specs argument to
                 make_subplots. See the make_subplots docstring for more info.
diff --git a/packages/python/plotly/plotly/io/_json.py b/packages/python/plotly/plotly/io/_json.py
index f5b7fbb5a60..f67dbab3eb6 100644
--- a/packages/python/plotly/plotly/io/_json.py
+++ b/packages/python/plotly/plotly/io/_json.py
@@ -111,8 +111,7 @@ def from_json(value, output_type="Figure", skip_invalid=False):
 
     output_type: type or str (default 'Figure')
         The output figure type or type name.
-        One of:  graph_objs.Figure, 'Figure',
-                 graph_objs.FigureWidget, 'FigureWidget'
+        One of:  graph_objs.Figure, 'Figure', graph_objs.FigureWidget, 'FigureWidget'
 
     skip_invalid: bool (default False)
         False if invalid figure properties should result in an exception.
@@ -167,8 +166,7 @@ def read_json(file, output_type="Figure", skip_invalid=False):
 
     output_type: type or str (default 'Figure')
         The output figure type or type name.
-        One of:  graph_objs.Figure, 'Figure',
-                 graph_objs.FigureWidget, 'FigureWidget'
+        One of:  graph_objs.Figure, 'Figure', graph_objs.FigureWidget, 'FigureWidget'
 
     skip_invalid: bool (default False)
         False if invalid figure properties should result in an exception.
diff --git a/packages/python/plotly/plotly/io/_orca.py b/packages/python/plotly/plotly/io/_orca.py
index ae4c25494f5..771259ed34b 100644
--- a/packages/python/plotly/plotly/io/_orca.py
+++ b/packages/python/plotly/plotly/io/_orca.py
@@ -1131,6 +1131,7 @@ def validate_executable():
     >>> pio.orca.config.use_xvfb = True
     
 You can save this configuration for use in future sessions as follows:
+
     >>> pio.orca.config.save() 
     
 See https://www.x.org/releases/X11R7.6/doc/man/man1/Xvfb.1.xhtml
diff --git a/packages/python/plotly/plotly/io/_templates.py b/packages/python/plotly/plotly/io/_templates.py
index 9b8589c2af2..34110d09a02 100644
--- a/packages/python/plotly/plotly/io/_templates.py
+++ b/packages/python/plotly/plotly/io/_templates.py
@@ -213,6 +213,7 @@ def merge_templates(self, *args):
 
         Examples
         --------
+
         >>> pio.templates.merge_templates(
         ...     go.layout.Template(layout={'font': {'size': 20}}),
         ...     go.layout.Template(data={'scatter': [{'mode': 'markers'}]}),
@@ -367,10 +368,12 @@ def to_templated(fig, skip=("title", "text")):
     Examples
     --------
     Imports
+
     >>> import plotly.graph_objs as go
     >>> import plotly.io as pio
 
     Construct a figure with large courier text
+
     >>> fig = go.Figure(layout={'title': 'Figure Title',
     ...                         'font': {'size': 20, 'family': 'Courier'}})
     >>> fig
@@ -382,6 +385,7 @@ def to_templated(fig, skip=("title", "text")):
 
     Convert to a figure with a template. Note how the 'font' properties have
     been moved into the template property.
+
     >>> templated_fig = pio.to_templated(fig)
     >>> templated_fig
     Figure({
@@ -407,6 +411,7 @@ def to_templated(fig, skip=("title", "text")):
     The default font in fig2 will now be size 20 Courier.
 
     Next, register as a named template...
+
     >>> pio.templates['large_courier'] = templated_fig.layout.template
 
     and specify this template by name when constructing a figure.
@@ -434,7 +439,7 @@ def to_templated(fig, skip=("title", "text")):
 
     Returns
     -------
-    figure
+    go.Figure
     """
 
     # process fig
diff --git a/packages/python/plotly/plotly/subplots.py b/packages/python/plotly/plotly/subplots.py
index e158a42bfe6..63adab47e3a 100644
--- a/packages/python/plotly/plotly/subplots.py
+++ b/packages/python/plotly/plotly/subplots.py
@@ -230,11 +230,13 @@ def make_subplots(
 
     Examples
     --------
+
     Example 1:
-    # Stack two subplots vertically, and add a scatter trace to each
+
+    >>> # Stack two subplots vertically, and add a scatter trace to each
     >>> from plotly.subplots import make_subplots
     ... import plotly.graph_objs as go
-    ... fig = tools.make_subplots(rows=2)
+    ... fig = make_subplots(rows=2)
 
     This is the format of your plot grid:
     [ (1,1) xaxis1,yaxis1 ]
@@ -243,10 +245,11 @@ def make_subplots(
     >>> fig.add_scatter(y=[2, 1, 3], row=1, col=1)
     ... fig.add_scatter(y=[1, 3, 2], row=2, col=1)
 
-    # or see Figure.append_trace
+    or see Figure.append_trace
 
     Example 2:
-    # Stack a scatter plot
+
+    >>> # Stack a scatter plot
     >>> fig = make_subplots(rows=2, shared_xaxes=True)
 
     This is the format of your plot grid:
@@ -257,10 +260,11 @@ def make_subplots(
     ... fig.add_scatter(y=[1, 3, 2], row=2, col=1)
 
     Example 3:
-    # irregular subplot layout (more examples below under 'specs')
-    fig = tools.make_subplots(rows=2, cols=2,
-                              specs=[[{}, {}],
-                                     [{'colspan': 2}, None]])
+
+    >>> # irregular subplot layout (more examples below under 'specs')
+    >>> fig = make_subplots(rows=2, cols=2,
+                            specs=[[{}, {}],
+                                   [{'colspan': 2}, None]])
 
     This is the format of your plot grid:
     [ (1,1) xaxis1,yaxis1 ]  [ (1,2) xaxis2,yaxis2 ]
@@ -271,7 +275,8 @@ def make_subplots(
     ... fig.add_trace(go.Scatter(x=[1,2,3], y=[2,1,2]), row=2, col=1)
 
     Example 4:
-    # insets
+
+    >>> # insets
     >>> fig = make_subplots(insets=[{'cell': (1,1), 'l': 0.7, 'b': 0.3}])
 
     This is the format of your plot grid:
@@ -284,7 +289,8 @@ def make_subplots(
     ... fig.add_scatter(x=[1,2,3], y=[2,1,2], xaxis='x2', yaxis='y2')
 
     Example 5:
-    # include subplot titles
+
+    >>> # include subplot titles
     >>> fig = make_subplots(rows=2, subplot_titles=('Plot 1','Plot 2'))
 
     This is the format of your plot grid:
@@ -295,6 +301,7 @@ def make_subplots(
     >>> fig.add_bar(x=[1,2,3], y=[2,1,2], row=2, col=1)
 
     Example 6:
+
     Subplot with mixed subplot types
 
     >>> fig = make_subplots(rows=2, cols=2,