/ 
Alt-Data-Frame-ButMore-0.0043
River stage one • 1 direct dependent • 1 total dependent
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 / Alt::Data::Frame::ButMore

NAME

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

VERSION

version 0.0043

STATUS

This library is current experimental.

SYNOPSIS

    use Alt::Data::Frame::ButMore;
    use Data::Frame;
    use 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->at(0)->length; # 4
    say $df->at('x');       # [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 implenmentation.

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

Document Conventions

Function signatures in docs of this library follow the Function::Parameters conventions, for example,

    function(Type1 $positional_parameter, Type2 :$named_parameter)

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 / BASIC

string

    string() # returns Str

Returns a string representation of the Data::Frame.

ncol / length / number_of_columns

These methods are same,

    # returns Int
    ncol()
    length()
    number_of_columns() # returns Int

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

nrow / number_of_rows

These methods are same,

    # returns Int
    nrow()
    number_of_rows() # returns Int

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

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.

It can take either,

  • A hashref of key mappings.

    If a keys does not exist in the mappings, it would not be renamed.

  • A coderef which transforms each key.

    $df->rename( { $from_key => $to_key, ... } );
    $df->rename( sub { $_[0] . 'foo' } );

set

    set(Indexer $col_name, ColumnLike $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.

names / col_names / column_names

These methods are same

    # returns ArrayRef
    names()
    names( $new_column_names )
    names( @new_column_names )

    col_names()
    col_names( $new_column_names )
    col_names( @new_column_names )

    column_names()
    column_names( $new_column_names )
    column_names( @new_column_names )

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.

row_names

    # returns a PDL::SV
    row_names()
    row_names( Array @new_row_names )
    row_names( ArrayRef $new_row_names )
    row_names( PDL $new_row_names )

Returns an PDL::SV of the names of the rows.

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.

nth_column

    number_of_rows(Int $n) # returns a column

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

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.

copy / clone

These methods are same,

    copy()
    clone()

Make a deep copy of this data frame object.

summary

    summary($percentiles=[0.25, 0.75])

Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding BAD values.

Analyzes numeric datetime columns only. For other column types like PDL::SV and PDL::Factor gets only good value count. Returns a data frame of the summarized statistics.

Parameters:

  • $percentiles

    The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .75], which returns the 25th, 50th, and 75th percentiles (median is always automatically included).

METHODS / SELECTING AND INDEXING

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().

select_rows

    select_rows( Indexer $indexer)

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

    head( Int $n=6 )

If $n ≥ 0, returns a new Data::Frame with the first $n rows of the Data::Frame.

If $n < 0, returns a new Data::Frame with all but the last -$n rows of the Data::Frame.

See also: R's head function.

tail

    tail( Int $n=6 )

If $n ≥ 0, returns a new Data::Frame with the last $n rows of the Data::Frame.

If $n < 0, returns a new Data::Frame with all but the first -$n rows of the Data::Frame.

See also: R's tail function.

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().

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);

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.

METHODS / MERGE

merge / cbind

These methods are same,

    merge($df)
    cbind($df)

append / rbind

These methods are same,

    append($df)
    rbind($df)

METHODS / TRANSFORMATION AND GROUPING

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(ColumnLike $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.

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.

METHODS / OTHERS

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)

MISCELLANEOUS FEATURES

Serialization

See Data::Frame::IO::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.

VARIABLES

doubleformat

This is used when stringifying the data frame. Default is '%.8g'.

TOLERANCE_REL

This is the relative tolerance used when comparing numerical values of two data frames. Default is undef, which means no tolerance at all. You can set it like,

    $Data::Frame::TOLERANCE_REL = 1e-8;

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.