And depending on your personality and interests, you might not find any of these much of a downfall at all which is a go

Now this tree traversal query could be the basis to augment the query with some other information of interest. For example, having a birth year in the table we can calculate how old the parent was when the child was born. Next query do exactly that, together with showing lineages. To do that it traverses the tree from top to bottom. parentAge is zero in the first row because we don’t know when Alice was born from the data we have.

If you have identified a rough number of significant phases of your project where you will change the dataset significantly, make sure to save it as another name by the of the operation, such as:

Did you write a forever-looping algorithm by mistake? Do you suspect your current algorithm is not going to output what you expect? You don’t need to close everything or delete the cell. Look for the Interrupt Kernel button on the Jupyter menu bar and interrupt the currently running cell to save your project from data loss.

There is an obvious trade-off here between saving copies and memory usage. If you find yourself with a heavy Jupyter file, always remember to delete those copies (as something called garbage collection) as you go along and validate them, and become certain that you won’t need them anymore.

Base query finds Frank’s parent — Mary, recursive query takes this result under the Ancestor name and finds parents of Mary, which are Dave and Eve and this continues until we can’t find any parents anymore.

Did you accidentally delete a cell, or made a mistake with your dataset? You can look for “Undo Cell Operation” in the Jupyter menu bar to bring back the cell you deleted, or undo the cell operation of your data.

Now this tree traversal query could be the basis to augment the query with some other information of interest. For example, having a birth year in the table we can calculate how old the parent was when the child was born. Next query do exactly that, together with showing lineages. To do that it traverses the tree from top to bottom. parentAge is zero in the first row because we don’t know when Alice was born from the data we have.

Having been working with Jupyter almost daily for the past three years, I have experienced serious undesired situations that took me back days, or even weeks in my project completion time. These are simple things that I have learned to do to prevent serious data loss, time loss, and project phase confusion.

Label parts of your Jupyter Notebook by creating a cell between them, where you will label what you are doing for this next phase. Also include a conclusion cell at the end, with a short summary of what you understood and any questions you have left.

One of the most annoying things that can happen is to spend hours and hours investigating and understanding your dataset, and arrive the next morning or the next week, and not understand anymore what you did or what you concluded. Even worse, if when another colleague has to take over, and you can’t explain your work anymore.

Sometimes, you want to do a quick operation on an earlier copy of your dataset, and you don’t think you will keep it in your file anyway — so you pick a random cell and write the code there. We have all been there, but try to form a habit of organizing cell operations chronologically and where they would be if you were to hand in the project today as it is, or had to explain what/how/why you wrote the code that way.

If you have a large folder containing dozens of files, it can be extremely tedious to import each one of them individually. What you can do instead, is transform that folder into a .zip file and open that in Jupyter:

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about selling a product — not about getting thousands of likes from other designers. A very fresh example — using 3D illustrations (because everyone does that now!) won’t necessarily guarantee you lots of sales. It can even work quite the opposite.

You can then choose to work on the copy of the original data frame or the original data frame (there’s no difference) but the important thing is that you will be able to go back to one of those original files if you make an irreparable mistake and only notice way down the line when it’s too late or too confusing to “undo cell operation”.

Open a draft file for every main file of your project, and copy-paste any unwanted code you wrote in there. It happens more often than not that you can use some of what you wrote previously on your new algorithm, or have to go back to see what didn’t work in your previous code. It occupies very little extra space and can save you a lot of time and confusion.