Based on the provided documentation and code snippet, it appears that you are trying to create a custom help page for creating scatter plots using the `sns.scatterplot()` function in SeabornoPython. Here's an example of how you could structure this help page:

```python

from IPython.display import HTML

import numpy as np

# Create some sample data

x = np.random.normal(0, 1, size=100)

y = np.random.normal(x + .5, .25)

# Create the plot

fig, ax = plt.subplots()

sns.scatterplot(data=pd.DataFrame({'A': x,'B': y}), 'A', 'B', alpha=.8,

label='Scatter Plot')

ax.legend();

```

This code will create a scatter plot using Seaborn's `scatterplot()` function and display it in an IPython cell. The result is a scatter plot with blue markers, where each marker represents one data point.

To customize this example further, you can modify the arguments passed to the `sns.scatterplot()` function:

* Use different types of markers by passing various string values for the `marker` argument (e.g., 'o', '+', '.', '*').

* Change the size and transparency of the markers using the `s` and `alpha` keywords respectively.

* Add labels, titles, and legend to your plot as needed.

Additionally, you can add more features like:

1. Creating multiple scatter plots on one figure

2. Using different colors for each group in a single dataset

Here's an example of how you could modify the previous code snippet to achieve these additional goals:

```python

# Create some sample data with two groups (A and B)

x = np.random.normal(0, 1, size=50).repeat(2)

y_A = np.random.normal(x + .5, .25, size=(100,))

y_B = y_A - np.abs(np.random.normal(.75,.25,size=y_A.shape'0'))

# Create the plot

fig, ax = plt.subplots(figsize=(8,6))

sns.scatterplot(data=pd.DataFrame({'Group': ''A''*50+''B''*50,

'x' : x.repeat(2),

'y1' : np.concatenate((y_A,y_B))}),

x='x', y='y1',

hue='Group', alpha=.8, markers=''o','D'', s=60)

ax.legend();

```

This code will create two scatter plots on the same figure with different colors for each group (A and B). Each marker represents one data point.

These are just basic examples of what you can do using SeabornoPython's `sns.scatterplot()` function. For more advanced customization options, please refer to the 'Seaborn documentation'(https://seaborn.pydata.org/generated/seaborn散点图.html#seaborn-scatterplot) for additional arguments and parameters that are available.

You can also use other libraries like Matplotlib or Plotly if you need even more control over your plots.

Scatter plot correlation python