Monty  Boehm

Monty Boehm

1659649320

Common Table Operations on Tables.jl interface Implementations

TableOperations

Common table operations on Tables.jl compatible sources

Installation: at the Julia REPL, import Pkg; Pkg.add("TableOperations")

Maintenance: TableOperations is maintained collectively by the JuliaData collaborators. Responsiveness to pull requests and issues can vary, depending on the availability of key collaborators.

Documentation

TableOperations.select

The TableOperations.select function allows specifying a custom subset and order of columns from a Tables.jl source, like:

ctable = (A=[1, missing, 3], B=[1.0, 2.0, 3.0], C=["hey", "there", "sailor"])

table_subset = ctable |> TableOperations.select(:C, :A) |> Tables.columntable

This "selects" the C and A columns from the original table, and re-orders them with C first. The column names can be provided as Strings, Symbols, or Integers.

TableOperations.transform

The TableOperations.transform function allows specifying a "transform" function per column that will be applied per element. This is handy when a simple transformation is needed for a specific column (or columns). Note that this doesn't allow the creation of new columns, but only applies the transform function to the specified column, and thus, replacing the original column. Usage is like:

ctable = (A=[1, missing, 3], B=[1.0, 2.0, 3.0], C=["hey", "there", "sailor"])

table = ctable |> TableOperations.transform(C=x->Symbol(x)) |> Tables.columntable

Here, we're providing the transform function x->Symbol(x), which turns an argument into a Symbol, and saying we should apply it to the C column. Multiple tranfrom functions can be provided for multiple columns and the column to transform function can also be provided in Dicts that map column names as Strings, Symbols, or even Ints (referring to the column index).

TableOperations.filter

The TableOperations.filter function allows applying a "filter" function to each row in the input table source, keeping rows for which f(row) is true. Usage is like:

ctable = (A=[1, missing, 3], B=[1.0, 2.0, 3.0], C=["hey", "there", "sailor"])

table = ctable |> TableOperations.filter(x->Tables.getcolumn(x, :B) > 2.0) |> Tables.columntable

TableOperations.map

The TableOperations.map function allows applying a "mapping" function to each row in the input table source; the function f should take and return a Tables.jl Row compatible object. Usage is like:

ctable = (A=[1, missing, 3], B=[1.0, 2.0, 3.0], C=["hey", "there", "sailor"])

table = ctable |> TableOperations.map(x->(A=Tables.getcolumn(x, :A), C=Tables.getcolumn(x, :C), B=Tables.getcolumn(x, :B) * 2)) |> Tables.columntable

Contributing and Questions

Contributions are very welcome, as are feature requests and suggestions. Please open an issue if you encounter any problems or would just like to ask a question.

Author: JuliaData
Source Code: https://github.com/JuliaData/TableOperations.jl 
License: View license

#julia #table 

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Buddha Community

Common Table Operations on Tables.jl interface Implementations
Monty  Boehm

Monty Boehm

1659649320

Common Table Operations on Tables.jl interface Implementations

TableOperations

Common table operations on Tables.jl compatible sources

Installation: at the Julia REPL, import Pkg; Pkg.add("TableOperations")

Maintenance: TableOperations is maintained collectively by the JuliaData collaborators. Responsiveness to pull requests and issues can vary, depending on the availability of key collaborators.

Documentation

TableOperations.select

The TableOperations.select function allows specifying a custom subset and order of columns from a Tables.jl source, like:

ctable = (A=[1, missing, 3], B=[1.0, 2.0, 3.0], C=["hey", "there", "sailor"])

table_subset = ctable |> TableOperations.select(:C, :A) |> Tables.columntable

This "selects" the C and A columns from the original table, and re-orders them with C first. The column names can be provided as Strings, Symbols, or Integers.

TableOperations.transform

The TableOperations.transform function allows specifying a "transform" function per column that will be applied per element. This is handy when a simple transformation is needed for a specific column (or columns). Note that this doesn't allow the creation of new columns, but only applies the transform function to the specified column, and thus, replacing the original column. Usage is like:

ctable = (A=[1, missing, 3], B=[1.0, 2.0, 3.0], C=["hey", "there", "sailor"])

table = ctable |> TableOperations.transform(C=x->Symbol(x)) |> Tables.columntable

Here, we're providing the transform function x->Symbol(x), which turns an argument into a Symbol, and saying we should apply it to the C column. Multiple tranfrom functions can be provided for multiple columns and the column to transform function can also be provided in Dicts that map column names as Strings, Symbols, or even Ints (referring to the column index).

TableOperations.filter

The TableOperations.filter function allows applying a "filter" function to each row in the input table source, keeping rows for which f(row) is true. Usage is like:

ctable = (A=[1, missing, 3], B=[1.0, 2.0, 3.0], C=["hey", "there", "sailor"])

table = ctable |> TableOperations.filter(x->Tables.getcolumn(x, :B) > 2.0) |> Tables.columntable

TableOperations.map

The TableOperations.map function allows applying a "mapping" function to each row in the input table source; the function f should take and return a Tables.jl Row compatible object. Usage is like:

ctable = (A=[1, missing, 3], B=[1.0, 2.0, 3.0], C=["hey", "there", "sailor"])

table = ctable |> TableOperations.map(x->(A=Tables.getcolumn(x, :A), C=Tables.getcolumn(x, :C), B=Tables.getcolumn(x, :B) * 2)) |> Tables.columntable

Contributing and Questions

Contributions are very welcome, as are feature requests and suggestions. Please open an issue if you encounter any problems or would just like to ask a question.

Author: JuliaData
Source Code: https://github.com/JuliaData/TableOperations.jl 
License: View license

#julia #table 

Ray  Patel

Ray Patel

1619565060

Ternary operator in Python?

  1. Ternary Operator in Python

What is a ternary operator: The ternary operator is a conditional expression that means this is a comparison operator and results come on a true or false condition and it is the shortest way to writing an if-else statement. It is a condition in a single line replacing the multiline if-else code.

syntax : condition ? value_if_true : value_if_false

condition: A boolean expression evaluates true or false

value_if_true: a value to be assigned if the expression is evaluated to true.

value_if_false: A value to be assigned if the expression is evaluated to false.

How to use ternary operator in python here are some examples of Python ternary operator if-else.

Brief description of examples we have to take two variables a and b. The value of a is 10 and b is 20. find the minimum number using a ternary operator with one line of code. ( **min = a if a < b else b ) **. if a less than b then print a otherwise print b and second examples are the same as first and the third example is check number is even or odd.

#python #python ternary operator #ternary operator #ternary operator in if-else #ternary operator in python #ternary operator with dict #ternary operator with lambda

Fredy  Larson

Fredy Larson

1595209620

How to alter tables in production when records are in millions

As a developer, I have experienced changes in app when it is in production and the records have grown up to millions. In this specific case if you want to alter a column using simple migrations that will not work because of the following reasons:

It is not so easy if your production servers are under heavy load and the database tables have 100 million rows. Because such a migration will run for some seconds or even minutes and the database table can be locked for this time period – a no-go on a zero-downtime environment.

In this specific case you can use MySQL’s algorithms: Online DDL operations. That’s how you can do it in Laravel.

First of all create migration. For example I want to modify a column’s name the traditional migration will be:

Schema::table('users', function (Blueprint $table) {
            $table->renameColumn('name', 'first_name');
        });

Run the following command php artisan migrate –pretend this command will not run the migration rather it will print out it’s raw sql:

ALTER TABLE users CHANGE name first_name VARCHAR(191) NOT NULL

Copy that raw sql, remove following code:

Schema::table('users', function (Blueprint $table) {
            $table->renameColumn('name', 'first_name');
        });

Replace it with following in migrations up method:

\DB::statement('ALTER TABLE users CHANGE name first_name VARCHAR(191) NOT NULL');

Add desired algorithm, in my case query will look like this:

\DB::statement('ALTER TABLE users CHANGE name first_name VARCHAR(191) NOT NULL, ALGORITHM=INPLACE, LOCK=NONE;');

#laravel #mysql #php #alter heavy tables in production laravel #alter table in production laravel #alter tables with million of records in laravel #how to alter heavy table in production laravel #how to alter table in production larave #mysql online ddl operations

Monty  Boehm

Monty Boehm

1659641700

Tables.jl: An interface for Tables In Julia

Tables.jl

The Tables.jl package provides simple, yet powerful interface functions for working with all kinds tabular data.

Installation: at the Julia REPL, import Pkg; Pkg.add("Tables")

Maintenance: Tables is maintained collectively by the JuliaData collaborators. Responsiveness to pull requests and issues can vary, depending on the availability of key collaborators.

Documentation

List of packages which support Tables.jl interface can be found in INTEGRATIONS.md.

Additional Resources

Please refer to TableOperations.jl for common table operations (select, filter, transform, map, etc.)

Author: JuliaData
Source Code: https://github.com/JuliaData/Tables.jl 
License: MIT license

#julia #data #interfaces #table 

Abdullah  Kozey

Abdullah Kozey

1617738420

Unformatted input/output operations In C++

In this article, we will discuss the unformatted Input/Output operations In C++. Using objects cin and cout for the input and the output of data of various types is possible because of overloading of operator >> and << to recognize all the basic C++ types. The operator >> is overloaded in the istream class and operator << is overloaded in the ostream class.

The general format for reading data from the keyboard:

cin >> var1 >> var2 >> …. >> var_n;

  • Here, var1var2, ……, varn are the variable names that are declared already.
  • The input data must be separated by white space characters and the data type of user input must be similar to the data types of the variables which are declared in the program.
  • The operator >> reads the data character by character and assigns it to the indicated location.
  • Reading of variables terminates when white space occurs or character type occurs that does not match the destination type.

#c++ #c++ programs #c++-operator overloading #cpp-input-output #cpp-operator #cpp-operator-overloading #operators