/ 
Alt-Data-Frame-ButMore-0.004_002
River stage zero No dependents
/ Alt::Data::Frame::ButMore

NAME

Alt::Data::Frame::ButMore - Alternative implementation of Data::Frame with more features

VERSION

version 0.004_002

STATUS

This library is current experimental.

SYNOPSIS

use Alt::Data::Frame::ButMore;
use Data::Frame;
use PDL::Core qw(pdl);

my $df = Data::Frame->new(
        columns => [
            z => pdl(1, 2, 3, 4),
            y => ( sequence(4) >= 2 ) ,
            x => [ qw/foo bar baz quux/ ],
        ] );

say $df;
# ---------------
#     z  y  x
# ---------------
#  0  1  0  foo
#  1  2  0  bar
#  2  3  1  baz
#  3  4  1  quux
# ---------------

say $df->at(0);
# [1 2 3 4]

say $df->select_rows( 3,1 )
# ---------------
#     z  y  x
# ---------------
#  3  4  1  quux
#  1  2  0  bar
# ---------------

$df->slice( [0,1], ['z', 'y'] ) .= pdl( 4,3,2,1 );
say $df;
# ---------------
#     z  y  x
# ---------------
#  0  4  2  foo
#  1  3  1  bar
#  2  3  1  baz
#  3  4  1  quux
# ---------------

DESCRIPTION

It's been too long I cannot reach ZMUGHAL. So here I release my [Alt](https://metacpan.org/pod/Alt) implenmentation.

This implements a data frame container that uses PDL for individual columns. As such, it supports marking missing values (BAD values).

The API is currently experimental and is made to work with Statistics::NiceR, so be aware that it could change.

CONSTRUCTION

new( (ArrayRef | HashRef) :$columns,
     ArrayRef :$row_names=undef )

Creates a new Data::Frame when passed the following options as a specification of the columns to add:

  • columns => ArrayRef $columns_array

    When columns is passed an ArrayRef of pairs of the form

    $columns_array = [
    column_name_z => $column_01_data, # first column data
    column_name_y => $column_02_data, # second column data
    column_name_x => $column_03_data, # third column data
]

    then the column data is added to the data frame in the order that the pairs appear in the ArrayRef.

  • columns => HashRef $columns_hash

    $columns_hash = {
    column_name_z => $column_03_data, # third column data
    column_name_y => $column_02_data, # second column data
    column_name_x => $column_01_data, # first column data
}

    then the column data is added to the data frame by the order of the keys in the HashRef (sorted with a stringwise cmp).

  • row_names => ArrayRef $row_names

METHODS

string

string() # returns Str

Returns a string representation of the Data::Frame.

number_of_columns

number_of_columns() # returns Int

Returns the count of the number of columns in the Data::Frame.

ncol

ncol()

This is same as number_of_columns.

length

length()

This is same as number_of_columns.

number_of_rows

number_of_rows() # returns Int

Returns the count of the number of rows in the Data::Frame.

nrow

nrow()

This is same as number_of_rows.

dims

dims()

Returns the dimensions of the data frame object, in an array of ($nrow, $ncol).

shape

shape()

Similar to dims but returns a piddle.

at

my $column_piddle = $df->at($column_indexer);
my $cell_value = $df->at($row_indexer, $column_indexer);

If only one argument is given, it would treat the argument as column indexer to get the column. If two arguments are given, it would treat the arguments for row indexer and column indexer respectively to get the cell value.

If a given argument is non-indexer, it would try guessing whether the argument is numeric or not, and coerce it by either indexer_s() or indexer_i().

exists

exists($col_name)

Returns true if there exists a column named $col_name in the data frame object, false otherwise.

delete

delete($col_name)

In-place delete column given by $col_name.

rename

rename($hashref_or_coderef)

In-place rename columns.

select_columns

select_columns($indexer)

Returns a new data frame object which has the columns selected by $indexer.

If a given argument is non-indexer, it would coerce it by indexer_s().

set

set(Indexer $col_name, Piddle0Dor1D $data)

Sets data to column. If $col_name does not exist, it would add a new column.

isempty

isempty()

Returns true if the data frame has no rows.

nth_columm

number_of_rows(Int $n) # returns a column

Returns column number $n. Supports negative indices (e.g., $n = -1 returns the last column).

column_names

column_names() # returns an ArrayRef

column_names( @new_column_names ) # returns an ArrayRef

Returns an ArrayRef of the names of the columns.

If passed a list of arguments @new_column_names, then the columns will be renamed to the elements of @new_column_names. The length of the argument must match the number of columns in the Data::Frame.

col_names

col_names($new_names)

This is same as column_names.

names

names($new_names)

This is same as column_names.

row_names

row_names() # returns a PDL

row_names( Array @new_row_names ) # returns a PDL

row_names( ArrayRef $new_row_names ) # returns a PDL

row_names( PDL $new_row_names ) # returns a PDL

Returns an ArrayRef of the names of the columns.

If passed a argument, then the rows will be renamed. The length of the argument must match the number of rows in the Data::Frame.

column

column( Str $column_name )

Returns the column with the name $column_name.

add_columns

add_columns( Array @column_pairlist )

Adds all the columns in @column_pairlist to the Data::Frame.

add_column

add_column(Str $name, $data)

Adds a single column to the Data::Frame with the name $name and data $data.

select_rows

select_rows( Indexer $indexer)

# below types would be coerced to Indexer
select_rows( Array @which )
select_rows( ArrayRef $which )
select_rows( Piddle0Dor1D $which )

The argument $indexer is an "Indexer", as defined in Data::Frame::Types. select_rows returns a new Data::Frame that contains rows that match the indices specified by $indexer.

This Data::Frame supports PDL's data flow, meaning that changes to the values in the child data frame columns will appear in the parent data frame.

If no indices are given, a Data::Frame with no rows is returned.

sample

sample($n)

Get a random sample of rows from the data frame object, as a new data frame.

my $sample_df = $df->sample(100);

merge

merge($df)

cbind

cbind($df)

This is same as merge().

append

append($df)

rbind

rbind($df)

This is same as append().

transform

transform($func)

Apply a function to columns of the data frame, and returns a new data frame object.

$func can be one of the following,

  • A function coderef. It would be applied to all columns.

  • A hashref of { $column_name => $coderef, ... }. It allows to apply

    the function to the specified columns. The raw data frame's columns not existing in the hashref be retained unchanged. Hashref keys not yet existing in the raw data frame can be used for creating new columns.

  • An arrayref like [ $column_name => $coderef, ... ]. In this mode

    it's similar as the hasref above, but newly added columns would be in order.

In any of the forms of $func above, if a new column data is calculated to be undef, or in the mappings like hashref or arrayref $coderef is an explicit undef, then the column would be removed from the result data frame.

Here are some examples,

Operate on all data of the data frame,
my $df_new = $df->transform(
        sub {
            my ($col, $df) = @_;
            $col * 2;
        } );
Change some of the existing columns,
my $df_new = $df->transform( {
        foo => sub {
            my ($col, $df) = @_;
            $col * 2;
        },
        bar => sub {
            my ($col, $df) = @_;
            $col * 3;
        } );
Add a new column from existing data,
# Equivalent to: 
# do { my $x = $mtcars->copy;
#      $x->set('kpg', $mtcars->at('mpg') * 1.609); $x; };
my $mtcars_new = $mtcars->transform(
        kpg => sub { 
            my ($col, $df) = @_;    # $col is undef in this case
            $df->at('mpg') * 1.609,
        } );

split

split(Piddle1D $factor)

Splits the data in into groups defined by $factor. In a scalar context it returns a hashref mapping value to data frame. In a list context it returns an assosiative array, which is ordered by values in $factor.

Note that $factor does not necessarily to be PDL::Factor.

slice

my $subset1 = $df->slice($row_indexer, $column_indexer);

# Note that below two cases are different.
my $subset2 = $df->slice($column_indexer);
my $subset3 = $df->slice($row_indexer, undef);

Returns a new dataframe object which is a slice of the raw data frame.

This method returns an lvalue which allows PDL-like .= assignment for changing a subset of the raw data frame. For example,

$df->slice($row_indexer, $column_indexer) .= $another_df;
$df->slice($row_indexer, $column_indexer) .= $piddle;

If a given argument is non-indexer, it would try guessing if the argument is numeric or not, and coerce it by either indexer_s() or indexer_i().

assign

assign( (DataFrame|Piddle) $x )

Assign another data frame or a piddle to this data frame for in-place change.

$x can be,

    *A data frame object having the same dimensions and column names as $self. *A piddle having the same number of elements as $self.

This method is internally used by the .= operation, below are same,

$df->assign($x);
$df .= $x;

is_numeric_column

is_numeric_column($column_name_or_idx)

sort

sort($by_columns, $ascending=true)

Sort rows for given columns. Returns a new data frame.

my $df_sorted1 = $df->sort( [qw(a b)], true );
my $df_sorted2 = $df->sort( [qw(a b)], [1, 0] );
my $df_sorted3 = $df->sort( [qw(a b)], pdl([1, 0]) );

sorti

Similar as this class's sort() method but returns a piddle for row indices.

uniq

uniq()

Returns a new data frame, which has the unique rows. The row names are from the first occurrance of each unique row in the raw data frame.

id

id()

Compute a unique numeric id for each unique row in a data frame.

copy

copy()

Make a deep copy of this data frame object.

clone

clone()

This is same as copy().

which

which(:$bad_to_val=undef, :$ignore_both_bad=true)

Returns a pdl of [[col_idx, row_idx], ...], like the output of "whichND" in PDL::Primitive.

MISCELLANEOUS FEATURES

SERIALIZATION

See Data::Frame::Partial::CSV

SYNTAX SUGAR

See Data::Frame::Partial::Sugar

TIDY EVALUATION

This feature is somewhat similar to R's tidy evaluation.

See Data::Frame::Partial::Eval.

SEE ALSO

AUTHORS

  • Zakariyya Mughal <zmughal@cpan.org>

  • Stephan Loyd <sloyd@cpan.org>

COPYRIGHT AND LICENSE

This software is copyright (c) 2014, 2019 by Zakariyya Mughal, Stephan Loyd.

This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.