DataFrames

DataFrames store data in rows and columns, where cells are scalars. The idea in Arc-Lang is to build up DataFrames incrementally.

extern type DataFrame;

extern def df(): DataFrame;
extern def append[T: BaseRow](DataFrame, T);
extern def head(DataFrame, i32): DataFrame;
extern def tail(DataFrame, i32): DataFrame;
extern def take(DataFrame, i32): DataFrame;
extern def join(DataFrame, DataFrame, [String], [String]): DataFrame;

val df = DataFrame(
  {name: 
"John"
, age: 
30
, city: 
"New York"
},
  {name: 
"Jane"
, age: 
22
, city: 
"San Francisco"
},
  {name: 
"Joe"
,  age: 
49
, city: 
"Boston"
},
  {name: 
"Jill"
, age: 
35
, city: 
"New York"
},
);
df.append({name: 
"Mary"
, age: 
12
});

val df = DataFrame({
  
"name"
: [
"John"
, 
"Jane"
, 
"Joe"
, 
"Jill"
],
   
"age"
: [
30
, 
22
, 
49
, 
35
],
  
"city"
: [
"New York"
, 
"San Francisco"
, 
"Boston"
, 
"New York"
]}
);
df.append({
"name"
: 
"Mary"
, 
"age"
: 
12
});
extern type Series;
extern def df(data: Array[Series]?): DataFrame;
extern def series[T: Scalar](name: String, data: Array[T]);
extern def Series[T: Scalar](name: String, values: Array[T]): Series;
extern def __index__(df: DataFrame, col: i32): Array[String];

extern def ds(): DataSet;
extern def append[T](DataSet[T], T);
extern def head[T](DataSet[T], i32): DataSet[T];
extern def tail[T](DataSet[T], i32): DataSet[T];
extern def take[T](DataSet[T], i32): DataSet[T];
extern def join_df[A, B](DataSet[A], DataSet[B], [String], [String]): DataSet[A ++ B];

A series is a named one-dimensional array of data representing a column in a DataFrame.

val obj = {
"name"
: 
"John"
, 
"age"
: 
30
, 
"city"
: 
"New York"
};

val name = obj[
"name"
];