NumPy – 52 – combinare dataset con merge e join – 3

Continuo da qui, copio qui.

Specificare set aritmetici per le unioni
In all the preceding examples we have glossed over one important consideration in performing a join: the type of set arithmetic used in the join. This comes up when a value appears in one key column but not the other. Consider this example:

Here we have merged two datasets that have only a single “name” entry in common: Mary. By default, the result contains the intersection of the two sets of inputs; this is what is known as an inner join. We can specify this explicitly using the how keyword, which defaults to "inner":

Other options for the how keyword are 'outer', 'left', and 'right'. An outer join returns a join over the union of the input columns, and fills in all missing values with NAs:

The left join and right join return joins over the left entries and right entries, respectively. For example:

The output rows now correspond to the entries in the left input. Using how='right' works in a similar manner.

All of these options can be applied straightforwardly to any of the preceding join types.

Conflitti con i nomi delle colonne: la keyword sufixes
Finally, you may end up in a case where your two input DataFrames have conflicting column names. Consider this example:

Because the output would have two conflicting column names, the merge function automatically appends a suffix _x or _y to make the output columns unique. If these defaults are inappropriate, it is possible to specify a custom suffix using the suffixes keyword:

These suffixes work in any of the possible join patterns, and work also if there are multiple overlapping columns.

For more information on these patterns, see Aggregation and Grouping [prossimamente] where we dive a bit deeper into relational algebra. Also see the PandasMerge, Join and Concatenatedocumentation for further discussion of these topics.

:mrgreen;

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