Spyder同一内核下分块执行脚本：避免重复加载依赖包

Absolutely! Spyder is built for exactly this kind of iterative workflow—you can run parts of your script in the same kernel, skipping repeated dependency reloads, just like Jupyter Notebook cells. Here’s how to make it work for your dashboard:

1. Split Your Script into Code Cells #

Spyder uses # %% as a cell separator, identical to Jupyter’s cell markers. Structure your script like this to separate one-time setup from repeatable update logic:

# %% [markdown]
# Initial Setup - Run once at the start
import numpy as np
import pandas as pd
import statsmodels.api as sm
# Replace with your actual dashboard library (e.g., dash, plotly)
import your_dashboard_tool as dash_tool  

# Load static data or initialize global variables here
df = pd.read_csv("your_dataset.csv")

# %% [markdown]
# Dashboard Update Logic - Run this after modifying statsmodels code
# Re-execute your statsmodels analysis with updated code
model = sm.OLS(df["target"], df[["feature1", "feature2"]])
results = model.fit()

# Refresh your dashboard with new results
dash_tool.update_statistics(results.summary())
dash_tool.refresh_visualizations()

• To run a single cell: Click the green "Run cell" button that appears left of the # %% line, or use the shortcut Ctrl+Enter (Windows/Linux) / Cmd+Enter (Mac).
• The setup cell only needs to run once per kernel session—all subsequent runs of the update cell will reuse already loaded dependencies and data.

2. Keep the Kernel Session Alive #

Don’t restart the IPython console (the bottom-right pane by default). As long as the kernel stays running:

• All imports and variables from the setup cell stay in memory.
• Every update cell run executes in the same environment, so you skip the time-consuming reloading of packages and data.

If you accidentally restart the kernel, just re-run the setup cell once to get back to your working state.

3. Pro Tips for a Smooth Workflow #

• Separate static vs. dynamic code: Put unchanging elements (imports, data loading, global configs) in the first cell. Only include code that updates with your statsmodels changes (model fitting, dashboard refreshes) in later cells.
• Use the Variable Explorer: Keep an eye on your model results and data in Spyder’s Variable Explorer pane to confirm they’re updating correctly after each cell run.
• Skip full script runs: Avoid clicking "Run file" (which executes the entire script). Stick to running individual cells to save time and avoid unnecessary reloads.
• For dashboard frameworks like Dash/Streamlit: If you’re using a server-based framework, structure the update cell to modify the underlying data/model the dashboard uses, rather than restarting the entire server. For example, in Dash, update a dcc.Store component with new results and trigger a callback to refresh the UI.

4. Alternative: Use the Interactive Console #

If you prefer, load your dependencies directly in the IPython console first:

import statsmodels.api as sm
import pandas as pd
df = pd.read_csv("your_dataset.csv")

Then, whenever you modify your statsmodels code, highlight the updated section and press F9 (Run selection), or paste the code directly into the console. This achieves the same goal of reusing the existing kernel session.

内容的提问来源于stack exchange，提问作者fred.schwartz