pip install jupyter

pip install nbconvert

jupyter nbconvert --to script your_notebook.ipynb

# 進入文件目錄
cd D:\Projects\MyProject
# 執(zhí)行轉(zhuǎn)換
jupyter nbconvert --to script data_analysis.ipynb

[NbConvertApp] Converting notebook data_analysis.ipynb to script
[NbConvertApp] Writing 15170 bytes to data_analysis.py

Get-ChildItem *.ipynb | ForEach-Object { jupyter nbconvert --to script $_.Name }

for file in *.ipynb; do jupyter nbconvert --to script "$file"; done

import json
def convert_ipynb_to_py(ipynb_file, py_file):
    """
    將 Jupyter Notebook 文件轉(zhuǎn)換為 Python 腳本文件
    參數(shù):
        ipynb_file: 輸入的 .ipynb 文件路徑
        py_file: 輸出的 .py 文件路徑
    """
    # 讀取 .ipynb 文件（JSON 格式）
    with open(ipynb_file, 'r', encoding='utf-8') as f:
        notebook = json.load(f)
    # 寫入 .py 文件
    with open(py_file, 'w', encoding='utf-8') as f:
        for cell in notebook['cells']:
            # 只提取 code 類型的單元格
            if cell['cell_type'] == 'code':
                # 將 source 列表拼接為字符串
                source = ''.join(cell['source'])
                f.write(source + '\n\n')
# 使用示例
if __name__ == "__main__":
    convert_ipynb_to_py('your_notebook.ipynb', 'your_notebook.py')
    print("轉(zhuǎn)換完成！")

import json
import re

def convert_ipynb_to_py_advanced(ipynb_file, py_file):
    """
    高級轉(zhuǎn)換：保留 Markdown 作為注釋，過濾 Jupyter 魔法命令
    """
    with open(ipynb_file, 'r', encoding='utf-8') as f:
        notebook = json.load(f)
    
    with open(py_file, 'w', encoding='utf-8') as f:
        # 寫入文件頭
        f.write('# -*- coding: utf-8 -*-\n')
        f.write(f'# Converted from: {ipynb_file}\n')
        f.write('# ============================================\n\n')
        
        for i, cell in enumerate(notebook['cells']):
            if cell['cell_type'] == 'markdown':
                # 將 Markdown 轉(zhuǎn)換為 Python 注釋
                source = ''.join(cell['source'])
                comment_lines = ['# ' + line for line in source.split('\n')]
                f.write('\n'.join(comment_lines) + '\n\n')
                
            elif cell['cell_type'] == 'code':
                source = ''.join(cell['source'])
                
                # 過濾 Jupyter 魔法命令（如 %matplotlib inline）
                lines = source.split('\n')
                filtered_lines = []
                for line in lines:
                    if not line.strip().startswith('%') and not line.strip().startswith('!'):
                        filtered_lines.append(line)
                
                if filtered_lines:
                    f.write('\n'.join(filtered_lines) + '\n\n')

# 使用示例
convert_ipynb_to_py_advanced('data_analysis.ipynb', 'data_analysis_clean.py')

# Cell 1 (Markdown)
# ## 數(shù)據(jù)分析示例

# Cell 2 (Code)
import pandas as pd
import matplotlib.pyplot as plt

# Cell 3 (Code)
%matplotlib inline
df = pd.read_csv('data.csv')
df.head()

# Cell 4 (Code)
plt.plot(df['x'], df['y'])
plt.show()

# -*- coding: utf-8 -*-
# Converted from: analysis.ipynb
# ============================================

# ## 數(shù)據(jù)分析示例

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data.csv')
df.head()

plt.plot(df['x'], df['y'])
plt.show()

pip install jupytext
jupytext --to notebook your_script.py