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fix(manual): typos

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Louis Heredero
2026-06-28 22:30:09 +02:00
parent 31e696c938
commit e28f324a85
1 changed files with 2 additions and 2 deletions
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docs/manual.typ
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@@ -46,9 +46,9 @@ What this means is that in Python, type checks are deferred to runtime when oper
For developers, it might seem like a great way of simplifying the language and making it very flexible, but it does come with a cost.
Indeed, type errors are very easy to make in Python. While passing an integer where a string is expected might not be an issue in some cases, these are the sort of thing that can cause crashes or incorrect results without a clear diagnostic to help the user fix it.
Fortunately, developers using IDEs or properly configured text editors can benefit from external type checkers such as MyPy which will perform static type analysis of their Python code. Some can also be configured to be very strict, forcing the user to make the whole code typable statically, thus avoiding any runtime type errors.
Fortunately, developers using IDEs or properly configured text editors can benefit from external type checkers such as MyPy which will perform static type analysis of their Python code. Some can also be configured to be very strict, forcing the user to make the whole code typeable statically, thus avoiding any runtime type errors.
This is not the end of the problem though. Some parts of a program, especially in data related fields, may not be available at "compile-time". For example, a dataset can be loaded from an external file, or data can be fetched from an API, with no guarantees of having the expected format when analysing the code statically.
This is not the end of the problem though. Some parts of a program, especially in data related fields, may not be available at "compile-time". For example, a dataset can be loaded from an external file, or data can be fetched from an API, with no guarantees of having the expected format when analyzing the code statically.
In turn, that can cause a range of loud and silent errors at runtime. A malformed number will probably crash the program when trying to convert it, but a NaN in a series of value might just produce wrong results without any exception. Combine this with often long-running data-processing pipelines and this is how developers can waste hours of precious computation time.