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Magic numbers become particularly confusing when the same number is used for different purposes in one section of code. It is easier to alter the value of the number, as it is not duplicated. Changing the value of a magic number is error-prone, because the same value is often used several times in different places within a program. [6]
Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values. For example, if s is a Series, s['a'] will return the data point at index a. Unlike dictionary keys, index values are not guaranteed to be unique.
The same technique can be used to map two-letter country codes like "us" or "za" to country names (26 2 = 676 table entries), 5-digit ZIP codes like 13083 to city names (100 000 entries), etc. Invalid data values (such as the country code "xx" or the ZIP code 00000) may be left undefined in the table or mapped to some appropriate "null" value.
Using a limited amount of NaN representations allows the system to use other possible NaN values for non-arithmetic purposes, the most important being "NaN-boxing", i.e. using the payload for arbitrary data. [23] (This concept of "canonical NaN" is not the same as the concept of a "canonical encoding" in IEEE 754.)
In Python, mangling is used for class attributes that one does not want subclasses to use [6] which are designated as such by giving them a name with two or more leading underscores and no more than one trailing underscore. For example, __thing will be mangled, as will ___thing and __thing_, but __thing__ and __thing___ will not. Python's ...
In general, NaNs will be propagated, i.e. most operations involving a NaN will result in a NaN, although functions that would give some defined result for any given floating-point value will do so for NaNs as well, e.g. NaN ^ 0 = 1.
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This sort of quantification is known as uniqueness quantification or unique existential quantification, and is often denoted with the symbols "∃!" [ 2 ] or "∃ =1 ". For example, the formal statement