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In React, or any frontend framework, oftentimes you'll need to conditionally render one or more components or some HTML. There are a number of ways to achieve this, a few of which we'll see here. If you want to handle the conditional statement within the JSX code, then using the...
Scott Robinson
Pandas DataFrame columns give context into the values of the rows/entries we're working with. Sometimes, we need to remove them, when saving data for proprietary libraries that don't support columns, and sometimes we just want to export them in a different format. In any case - saving the columns...
David Landup
Scikit-Learn offers several datasets to play around with - most of them being toy datasets to learn from and test things out. Some beginners find the comfort of a tabular Pandas DataFrame format more intuitive than NumPy arrays. Thankfully, you can import a dataset as a Bunch object containing a...
Checking for correlation, and quantifying correlation is one of the key steps during exploratory data analysis and forming hypotheses. Pandas is one of the most widely used data manipulation libraries, and it makes calculating correlation coefficients between all numerical variables very straightforward - with a single method call. For more...
When string-based columns have quotes - we'll oftentimes want to get rid of them, in large part because 'string is technically a different string to string, which more often than not isn't a distinction we want to make. Whether you'll be performing NLP and tokenizing words (in which case, you'll...
When working with datasets from external sources - column names can get wild. Different naming conventions, cases (snake_case, CamelCase, etc.), as well as names are common. A common headache is caused by really long column names, that you might have to call on many times in the lifecycle of...
Computer Vision models have come a long way - and you can leverage existing models, pre-trained on a large corpora of data, and just plug them into your prediction pipeline. While fine-tuning a network is the best way to go - importing an existing model and running predictions from the...
Models are more often than not trained to be deployed to production and to give meaningful predictions for new input. To move them outside of your training environment - you'll want to save a trained model and load it in a different one. XGBoost is a great, flexible and blazingly...
Let's say you have a list of individual characters, like this: chars = ['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd'] What if you need to convert...
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