Use Napa cabbage. Add 1 cinnamon stick, 4 cloves, and 4 pieces star anise. Substitute 8 ounces thinly sliced beef for sh

The analytics server was developed with an intent to enable users to test the strategy as quickly as possible with minimal frontend and backend development. Enabling users to focus on the core of their concept.

Use Napa cabbage. Brown 8 ounces ground pork in the oil before adding the garlic and fresh ginger. Skip the mushrooms and shrimp (add a handful of peanuts when you’d add the shrimp, and cook until lightly toasted). Garnish: scallions.

The pandas analytics server is intended to simplify the transfer of code from the Jupiter notebook to the server-side. The main benefit is code reusability. The project is open source under the MIT license. Pull requests and forks are welcomed.

All of this requires a lot of development time, which would be better spent on improving the strategy or focusing on a new project in case it doesn’t work. Is there a better way?

Put 3 tablespoons neutral oil in a large skillet over high heat. When hot, add 1 tablespoon each minced garlic and fresh ginger and cook for 15 seconds; add 1 small shredded Napa cabbage and 8 ounces sliced mushrooms; cook, stirring, until soft and slightly brown, 5 minutes. Add 8 ounces shrimp and cook until pink, 3 to 5 minutes. Turn off the heat and stir soy sauce to taste. Garnish: scallions.

The current code generates random data each second to simulate a data point update using get requests. Strategy service takes the latest data points and stores them into a DataFrame. The current code doesn’t test the hypothesis but it is trivial to add one.

Use Napa cabbage; skip the mushrooms and shrimp. Once the cabbage is soft, push it to one side of the pan, add more oil, and scramble 2 or 3 eggs in the empty spot until firm. Stir in 1 cup chopped tomatoes. Garnish: scallions and soy.

You’ve found an interesting pattern in a certain dataset (eg. stocks, cryptocurrencies, weather data). You have written a quick-and-dirty code in a Jupyter Notebook to analyze and plot the data. The results of the analysis seem promising and you would like to test your hypothesis on live data.

Porting the Jupyter Notebook code to the server-side is not a trivial process. A webserver needs to fetch new data, process it, apply the strategy and test the hypothesis. It also needs to serve a status page so that we can observe the progress (not necessary, but it is a nice thing to have). Also, the question arises where to host the server? It needs to be always on so a laptop is out of the question.

Keep the stock, skip the rest. Brown 8 ounces chopped ham and 1 chopped onion in olive oil. Add the stock and 3 cups cooked white beans; bring to a boil. Add green cabbage and some thyme; simmer until tender. Garnish: Parmesan.

To host the analytics server, users can create an AWS account and start an instance anywhere in the world for free. pandas-analytics-server already has the installation instructions for CentOS-based operating systems. Let me know in the comments if you need some help with it.

Use Napa cabbage; skip the mushrooms and shrimp. Once the cabbage is soft, push it to one side of the pan, add more oil, and scramble 2 or 3 eggs in the empty spot until firm. Stir in 1 cup chopped tomatoes. Garnish: scallions and soy.

To test the hypothesis on live data, you need to port the Jupyter Notebook code to the server-side. This is a cumbersome process if you are not well versed in backend development. So much time went into tweaking matplotlib parameters, now you need to reimplement them in JavaScript. If you are like me and don’t know JavaScript, learning a new plotting framework will take some time.

It enables us to copy-paste pandas code from Jupyter Notebook, start the server and observe how our strategy performs in the real world. It enables us to reuse matplotlib code as pandas-analytics-server renders it on a web page.

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the name of the book, so here you go: Several Short Sentences About Writing by Verlyn Klinkenborg. It’s sort of a weird book, to be honest. It contains no paragraphs, just sentences stacked on top of each other in austere columns. (The whole thing sort of reads like a poem.) And yes, it’s about the mechanics of putting words together, but it’s also about other things: thinking, noticing, mapping the mess inside your head. Also, trusting yourself — something I have a difficult time doing.