Add performance doc

doc/python/b64.md

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+---
+jupyter:
+  jupytext:
+    notebook_metadata_filter: all
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.16.3
+  kernelspec:
+    display_name: Python 3 (ipykernel)
+    language: python
+    name: python3
+  language_info:
+    codemirror_mode:
+      name: ipython
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+    mimetype: text/x-python
+    name: python
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+    version: 3.10.0
+  plotly:
+    description: How to use typed arrays in Plotly.py
+    display_as: file_settings
+    language: python
+    layout: base
+    name: Improving Performance with Typed Arrays
+    order: 39
+    permalink: python/b64/
+    thumbnail: thumbnail/b64.png
+---
+
+*New in Plotly.py 6.0**
+
+Improve the performance of generating Plotly figures that use a large number of data points by using NumPy arrays and other objects that can be converted to NumPy arrays, such as Pandas Series and Index objects. 
+
+Plotly.py uses Plotly.js for rendering, which supports base64-encoded typed arrays. In Plotly.py, NumPy array and NumPy-convertible arrays are base64 encoded before being passed to Plotly.js for rendering.
+
+
+## Arrays and Data Types Supported
+
+The following types of array objects in Python are supported:
+
+- Numpy `numpy.ndarray` objects.
+- Pandas Index, `pandas.Index`, or Series, `pandas.Series`, objects.
+- Array objects that can be converted to `numpy.ndarray` objects. i.e., they implement `"__array__"` or `"__array_interface__"` and return a `numpy.ndarray.
+
+The following array data types are supported:
+
+- int8
+- uint8
+- int16
+- uint16
+- int32
+- uint32
+- float32
+- float64
+- int64*
+- uint64*
+
+*If the array dtype is **int64** and **uint64**, often the default dtype for arrays in NumPy when no dtype is specified, those dtypes will be changed to other types internally by Plotly.py where possible. 
+
+
+
+## Unsupported Attributes
+
+Arrays passsed to attributes with the following names do not use the Plotly.js base64 typed arrays functionality:
+
+`geojson`, `layers`, and `range`.
+
+
+## Example with NumPy Arrays
+
+Here, we use NumPy arrays with a `go.Scatter3d` figure.
+
+```python
+import plotly.graph_objects as go
+import numpy as np
+
+np.random.seed(1)
+
+# Number of data points
+N = 10000
+
+# Generate random data
+x = np.random.randn(N)
+y = np.random.randn(N).astype('float32')
+z = np.random.randint(size=N, low=0, high=256, dtype='uint8')
+c = np.random.randint(size=N, low=-10, high=10, dtype='int8')
+
+fig = go.Figure(data=[go.Scatter3d(
+    x=x,
+    y=y,
+    z=z,
+    marker=dict(color=c),
+    mode='markers',
+    opacity=0.2
+)])
+
+fig.show()
+```
+
+### Example with Multi-Dimensional Array
+
+Here, we use a multi dimensional array with a `go.Surface` figure.
+
+
+```python
+import plotly.graph_objects as go
+import numpy as np
+
+np.random.seed(1)
+
+# Define the dimensions
+M = 100
+N = 200
+
+x = np.arange(0, M, 1, dtype='int32')
+y = np.arange(0, N, 1, dtype='uint8')
+
+z = np.random.random([N, M])
+
+fig = go.Figure(data=[go.Surface(
+    x=x,
+    y=y,
+    z=z
+)])
+
+fig.show()
+```