scrapbook | A library for recording and reading data in notebooks

StandardScaler is a preprocessing class from sklearn that takes numeric entries and convert them to a likely Gaussian distribution with 0 mean and unit variance. It doesn't deal with text data. That explains the first error.

LabelEncoder is another preprocessing class from sklearn that takes data and maps them to a numeric encoded representation. Ex: ["apple","banana","apple","banana"] to [0,1,0,1]

Your dataset has missing values, you should deal with them first. By means of imputing, droping or some similar approach.

Then you should convert the types (all but rating are considered object or string) from each column to handle properly each datatype.

I think what you are looking for is a masonry grid. It's worth a google search. What these packages do is calculate positions within a container on page load and resize. One example for react could be react-responsive-masonry

Example usage with react-responsive-masonry

The actual problem is that Windows cannot start a program whose command line is longer than about 32000 characters. This can happen if you have so many dependencies that the command line arguments reach the limit. This is why changing the directory to C:\tmp did not help, the size of this part is negligible when compared to all the dependencies on the command line.

There's a nice answer to a different question which contains some useful tips which can help out.

I initially worked around the issue by running the code snippet in a newly-created project, but this was only possible because I did not need any parts of the project I'm working on.

The real solution was to switch to Linux because pretty much all distributions have limits which exceed those of Windows by an order of magnitude or more.

It is not necessary to create a class that inherits from QGroupBox and QAbstractButton (plus it is not possible in pyqt or Qt/C++). The solution is to create a QObject that handles the states of the other QGroupBox when any QGroupBox is checked, and I implemented that for an old answer for Qt/C++ so this answer is just a translation:

There is no leak here. In general an object owned by a std::unique_ptr is never leaked, as long as .release() is not called on the owning std::unique_ptr.

Your program does however have undefined behavior because the lifetimes are not correct. If you store a reference or pointer to the owned object you take over some responsibility to assure correct object lifetimes.

give_a_number() returns, by-value, a std::unique_ptr owning the int object. This std::unique_ptr is therefore materialized as a temporary object in the statement

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Explanation:

As of now, nope. You can't do that directly. What you can do though (what we are doing) is as follows :

1. Create a python environment on your EMR masternode using the hadoop user
2. Install sparkmagic in your environment and configure all kernels as described in the README.md file for sparkmagic
3. Copy your notebook to master node/use it directly from s3 location

4. Install papermill and run with papermill :

   papermill s3://path/to/notebook/input.ipynb s3://path/to/notebook/output.ipynb -p param=1