Module 04 · Jupyter Notebooks and JupyterLab
Jupyter Notebooks on the Internet互联网 Jupyter Notebook 资源
There are thousands of interesting Jupyter Notebooks available on the internet for you to learn from. One of the best sources is: https://github.com/jupyter/jupyter/wiki
互联网上有很多有趣的 Jupyter Notebooks 供您学习。其中一个最好的资源是：https://github.com/jupyter/jupyter/wiki
It is important to notice that you can download such notebooks to your local computer or import them to a cloud based notebook tool so that you can rerun, modify, and apply what's explained in the notebook.
需要注意的是，您可以下载这些 Notebooks 到本地计算机或导入到基于云的 Notebooks 工具中，以便您可以重新运行、修改并应用 Notebooks 中所讲解的内容。
Very often, Jupyter Notebooks are already shared in a rendered view. This means that you can look at them as if they were running locally on your machine. But sometimes, folks only share a link to the Jupyter file (which you can make out by the *.ipynb extension). In this case, you can pick the URL to that file and paste it to the NB-Viewer => https://nbviewer.jupyter.org/
通常情况下，Jupyter Notebooks 已经以渲染视图的形式共享。这意味着您可以直接查看它们，就像它们在您的本地机器上运行一样。但有时，人们只共享 Jupyter 文件的链接（您可以通过 *.ipynb 扩展名识别）。在这种情况下，您可以获取该文件的 URL 并将其粘贴到 NB-Viewer => https://nbviewer.jupyter.org/
The list of Jupyter Notebooks provides you with a huge collection of materials to explore. Therefore, it might be useful to give you some pointers to interesting notebooks. You have covered some examples with data in the labs. Let's highlight some useful data that further explores data science. In addition, as we have covered different tasks in data science, we will also provide a sample notebook for each task.
Jupyter Notebooks 列表为您提供大量可供探索的资料。因此，提供一些有趣的 Notebooks 指针可能对您有所帮助。您在实验中已经接触了一些包含数据的示例。让我们重点介绍一些进一步探索数据科学的实用数据。此外，由于我们已经涵盖了数据科学中的不同任务，我们还将为每个任务提供一个示例 Notebooks。
First, you start with exploratory data analysis, for which this notebook is highly recommended: https://nbviewer.jupyter.org/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb
首先，您可以从探索性数据分析开始，强烈推荐使用这个笔记本：https://nbviewer.jupyter.org/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb
For data integration/cleansing at a smaller scale, the python library pandas is often used. For this task, you can have a look at this notebook: https://towardsdatascience.com/data-cleaning-with-python-using-pandas-library-c6f4a68ea8eb
对于小规模的数据集成/清洗，通常使用 Python 库 pandas。对于这项任务，您可以查看这个笔记本：https://towardsdatascience.com/data-cleaning-with-python-using-pandas-library-c6f4a68ea8eb
If you want to know more about clustering, have a look at this notebook: https://nbviewer.jupyter.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb
如果您想了解更多关于聚类的内容，可以查看这个笔记本：https://nbviewer.jupyter.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb
And finally, if you want an in-depth notebook on the iris dataset, have a look at this: https://www.kaggle.com/lalitharajesh/iris-dataset-exploratory-data-analysis
最后，如果您想查看关于 iris 数据集的深入笔记本，可以查看这个：https://www.kaggle.com/lalitharajesh/iris-dataset-exploratory-data-analysis