Python & SQL vs Carnets - Jupyter (with scipy) Uso & Estadísticas

• Learn programming with the best code recipes. • Python: - How to get a substring? - How to declare a generalized method? - How to perform a database query? • SQL: - How to combine multiple tables in a single query? - How to sort the result of grouping data? - What types of data are there in SQL Server? • The best examples of code from books and specific internet resources are selected in the app. Finding the best solution on the internet can take hours of work. Ready-to-use and tested "recipes" for code are included in the app. • The app is a great tool for passing exams or preparing for interviews. It helps you study the typical tasks you might face. • One app for iOS and macOS. Buy once and use it on your phone, tablet, or computer. • Quick search of the correct example by code or name of topic. • All examples in the app are available offline. The app can be used when an internet connection is not available. • As a developer, I use this app when I need to quickly remember how to solve a given task in SQL or Python. If you can help to translate the app into your language, please contact me by email. In the application, some examples are available for an additional fee (approximately 45%).

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Carnets - Jupyter (with scipy)

Jupyter notebooks are a powerful tool used in education and research. You can write small snippets of Python code and observe the result on screen, combine with paragraphs of text, using Markdown. Carnets provides a complete, stand-alone, implementation of Jupyter notebooks. Everything runs on your device, using the embedded Python interpreter; you do not need an internet connection. Compared to the standard version (Carnets), this app adds several packages targeted for scientific computation and learning: scipy, sklearn, seaborn and coremltools. You can switch between standard notebooks and the more modern jupyterlab using the Settings app. Of course, all the packages included with the standard app are present too: numpy, simply, matplotlib... To see the full list of installed packages, type "%pip list" in a code window. You can add more packages using "%pip install packageName", but only if they are pure Python. You can share your notebooks with other apps and also open notebooks or directories managed by other apps. Partial list of installed packages: astropy, babel, bokeh, cartopy, cryptography, cvxopt, Fiona, geopandas, geopy, gym, inequality, libpysal, lxml, mapclassify, matplotlib, networkx, nltk, numpy, openCV, pandas, parso, pillow, pyFFTW, pyproj, qutip, rasterio, regex, Rtree, scikit-learn, scipy, seaborn, segregation, shapely, soupsieve, spopt, statsmodels, sympy, wordcloud.