Carnets - Jupyter (with scipy) 1.8.4

Nicolas Holzschuch ❘ Freeware ❘
iOS
out of 63 votes

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Jupyter notebooks are a valuable tool extensively employed in educational and research contexts. It allows users to write and execute small Python code snippets while simultaneously observing the output on the screen. Additionally, it enables the integration of explanatory text sections using Markdown.

Carnets provides a comprehensive, self-contained implementation of Jupyter notebooks. What sets Carnets apart is its ability to run entirely on the user's own device, eliminating the need for internet connectivity. In comparison to the standard version, Carnets offers several additional packages specifically designed for scientific computation and learning, such as scipy, sklearn, seaborn, and coremltools. Moreover, users have the flexibility to switch between traditional notebooks and the more advanced jupyterlab interface through the user-friendly Settings app.

Naturally, Carnets includes all the standard packages found in the regular Jupyter notebook application, including numpy, simply, and matplotlib. By typing "%pip list" in a code window, users can obtain a comprehensive list of installed packages. Furthermore, users have the ability to expand this list by utilizing the "%pip install packageName" command; however, it is important to note that only pure Python packages are compatible with this feature.

One of the notable features of Carnets is its compatibility with other applications, allowing users to seamlessly share their notebooks or open notebooks and directories managed by other applications.

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<li>Bokeh</li>
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<li>Cryptography</li>
<li>Cvxopt</li>
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<li>Geopandas</li>
<li>Geopy</li>
<lI>Gym</lI>
<lI>Inequality</lI>
<lI>Libpysal</lI>
<lI>Lxml</lI>
<lI>Mapclassify</lI>
<lI>Matplotlib</lI>
<lI>Networkx</lI>
<lI>Nltk</lI>
<lI>Numpy</lI>
<lI>Opencv</lI>
<lI>Pandas</lI>
<lI>Parso</lI>
<li>Pillow</li>
<li>Pyfftw</li>
<li>Pyproj</li>
<li>Qutip</li>
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<li>Regex</li>
<li>Rtree</li>
<li>Scikit-learn</li>
<li>Scipy</li>
<li>Seaborn</li>
<li>Segregation</li>
<li>Shapely</li>
<li>Soupsieve</li>
<li>Spopt</li>
<li>Statsmodels</li>
<li>Sympy</li>
<li>Wordcloud</li>

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