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Scala code analyzer targets case variable names that are identical to the outer matched varables - "suspicous shado


Scala code analyzer targets case variable names that are identical to the outer matched varables - "suspicous shado

By : Gunnar
Date : November 22 2020, 10:40 AM
will help you In the following code snippet in which the outer match vars (x,y) are case matched by (xx,yy): , This could be confusing:
code :
val x = Some(1)
val y = Some(2)

(x, y) match {
    case (Some(x), Some(y)) => println(s"x=$x y=$y")
}
val list = List(1,2,3)
list match {
    case x :: y :: list => list // List(3) and not List(1,2,3)
    case x :: list => list      // List with 1 element, should the outer list have size 2
    case _ => list              // Returns the outer list when empty
}


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Can "core data generated code" be moved to a superclass shared by 2 targets with identical data models?

Can "core data generated code" be moved to a superclass shared by 2 targets with identical data models?


By : j.s12
Date : March 29 2020, 07:55 AM
hope this fix your issue If the classes are identical use only one, but include it in both targets. Each target can have its own data model file, but share the same class module.
In Scala, is it possible to have a case class containing methods with identical names to fields?

In Scala, is it possible to have a case class containing methods with identical names to fields?


By : bouchidor takashi
Date : March 29 2020, 07:55 AM
it fixes the issue Scala automatically creates a method with the same name as any val declared in a class (including the fields of case classes) to support a concept called referential transparency. This is also why you can override a def with a val. If you're still skeptical, you can test it yourself like this:
First, create a Scala file with a single case class.
code :
// MyCase.scala
case class MyCase(myField1: Int, myField2: String)
$ scalac MyCase.scala 
$ ls
MyCase$.class MyCase.class  MyCase.scala
$ javap -private MyCase
Compiled from "MyCase.scala"
public class MyCase extends java.lang.Object implements scala.Product,scala.Serializable{
    private final int myField1;
    private final java.lang.String myField2;
    public static final scala.Function1 tupled();
    public static final scala.Function1 curry();
    public static final scala.Function1 curried();
    public scala.collection.Iterator productIterator();
    public scala.collection.Iterator productElements();
    public int myField1();
    public java.lang.String myField2();
    public MyCase copy(int, java.lang.String);
    public java.lang.String copy$default$2();
    public int copy$default$1();
    public int hashCode();
    public java.lang.String toString();
    public boolean equals(java.lang.Object);
    public java.lang.String productPrefix();
    public int productArity();
    public java.lang.Object productElement(int);
    public boolean canEqual(java.lang.Object);
    private final boolean gd1$1(int, java.lang.String);
    public MyCase(int, java.lang.String);
}
How to overwrite identical column names when performing an "outer" join in Pandas?

How to overwrite identical column names when performing an "outer" join in Pandas?


By : Cousin Hoang
Date : March 29 2020, 07:55 AM
Hope that helps Since the column names for both dataframes are the same you could stack them and then do a drop_duplicates or groupby
For example:
code :
result = pd.concat([left, right]).reset_index()
result.drop_duplicates(['city','country','state'], keep='first', inplace=True)
df_stacked = pd.concat([left, right]).reset_index()
result = df_stacked.groupby(['city','country','state']).first()
df_stacked = pd.concat([right, left]).reset_index()
result = df_stacked.groupby(['city','country','state']).first()
result = result.reset_index()
# sort our descending population, and if populations are equal (or NaN), sort by ascending city name
result = result.sort_values(['pop', 'city'], ascending=[False, True])
result.drop('index', axis=1, inplace=True)
result.to_csv('result.csv', index=False)
In CMake how to create targets with identical names?

In CMake how to create targets with identical names?


By : Vyacheslav
Date : March 29 2020, 07:55 AM
Does that help According with CMake policy CMP0002 (introduced by CMake 2.6, emphasis mine):
code :
set(ALLOW_DUPLICATE_CUSTOM_TARGETS TRUE)
How to look up identical column names in two dataframes and combine the matched columns

How to look up identical column names in two dataframes and combine the matched columns


By : Dinesh chadokar
Date : October 04 2020, 09:00 PM
wish of those help Here is one way similar to @ALollz but save the subdf in multiple index dataframe
code :
s = pd.concat([df1, df2], keys=['df1', 'df2']).unstack(0)
s.loc[:,'A']
Out[390]: 
        df1  df2
2019Q1    1    4
2019Q2    2    5
2019Q3    3    6
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