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Twin reduction table

RESOURCE
Typing name :  RESOURCE.gws_gena.TwinReductionTable Brick :  gws_gena
Parent :
Table

Twin reduction table

Represents a twin reduction table

Views
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VIEW RESOURCE
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Functions
__init__

Create a new Table

data : Union[pandas.core.frame.DataFrame, numpy.ndarray, list] - None
data use to initilialize the table
row_names : List - None
set the row names for the table. Must be the same size as the number of rows
column_names : List - None
set the column names for the table. Must be the same size as the number of columns
row_tags : List - None
set the row tags for the table. Must be the same size as the number of rows
column_tags : List - None
set the column tags for the table. Must be the same size as the number of columns
strict_format_header_names : bool - False
If false, the following rules are applied to headers (columns and rows) names: - convert to string - remove leading and trailing whitespaces - rename duplicates by adding _1, _2, _3, ... at the end of the name If true, the previous rules apply plus : - replace ' ', '-', '.' with underscores - remove all other special characters - remove all accents
add_column

Add a new column to the Dataframe.

name : str
name of the column
data : Union[list, pandas.core.series.Series] - None
data for the column, must be the same length as other colums
index : int - None
index for the column, if none, the column is append to the end
add_column_tag_by_index

Add a tag to a column at a given index

column_index : int
index of the column
key : str
key of the tag
value : str
value of the tag
add_column_tag_by_name

Add a tag to a column by name

column_name : str
name of the column
key : str
key of the tag
value : str
value of the tag
add_row

Add a row to the Dataframe.

name : str
name of the row
data : Union[list, pandas.core.series.Series] - None
data of the row
index : int - None
index of the row, if none, the row is append to the end
add_row_tag_by_index

Add a tag to a row at a given index

row_index : int
index of the row
key : str
key of the tag
value : str
value of the tag
add_row_tag_by_name

Add a tag to a row by name

row_name : str
name of the row
key : str
key of the tag
value : str
value of the tag
add_technical_info

Add technical information on the resource. Technical info are useful to set additional information on the resource.

technical_info : TechnicalInfo
technical information to add (key, value)
check_column_exists

Checks if a column with the given name exists in the Table and raises an exception if it doesn't.

name : str
The name of the column to check.
case_sensitive : bool - True
Whether the check should be case sensitive. Defaults to True.
check_resource

You can redefine this method to define custom logic to check this resource. If there is a problem with the resource, return a string that define the error, otherwise return None This method is called on output resources of a task. If there is an error returned, the task will be set to error and next proceses will not be run. It is also call when uploading a resource (usually for files or folder), if there is an error returned, the resource will not be uploaded

Return type : Union[str, NoneType]
check_row_exists

Checks if a row with the given name exists in the Table and raises an exception if it doesn't.

name : str
The name of the row to check.
case_sensitive : bool - True
Whether the check should be case sensitive. Defaults to True.
clone

Clone the resource to create a new instance with a new id. It copies the RFields.

Return type : ResourceType
column_exists

Checks if a column with the given name exists in the Table.

name : str
The name of the column to check.
case_sensitive : bool - True
Whether the check should be case sensitive. Defaults to True.
Return type : bool
copy_column_tags_by_index

Copy column tags from source_table to self matching by index.

source_table : Table
source table to copy tags from
from_index : int - None
start index of the columns to copy
to_index : int - None
end index of the columns to copy
copy_column_tags_by_name

Copy column tags from source_table to self matching by name.

source_table : Table
source table to copy tags from
copy_row_tags_by_index

Copy row tag from source_table to self matching by index

source_table : Table
source table to copy tags from
from_index : int - None
start index of the rows to copy
to_index : int - None
end index of the rows to copy
copy_row_tags_by_name

Copy row tag from source_table to self matching by name

source_table : Table
source table to copy tags from
create_sub_table

Create a new table from a dataframe and a meta

dataframe : DataFrame
The dataframe
row_tags : List
The list of row tags
column_tags : List
The list of column tags
Return type : Table
create_sub_table_filtered_by_columns

Create a sub Table based on a subset Dataframe of this original table filtered by columns It copies the tags of this table into the new table based on column names that matched between filtered_df and this dataframe.

filtered_df : DataFrame
The filtered dataframe
Return type : Table
create_sub_table_filtered_by_rows

Create a sub Table based on a subset Dataframe of this original table filtered by rows. It copies the tags of this table into the new table based on row names that matched between filtered_df and this dataframe.

filtered_df : DataFrame
The filtered dataframe
Return type : Table
equals

Check if the table is equal to another table. It compares the data, row tags and column tags.

o : object
The other table
Return type : bool
extract_column_tags_to_new_row

Create a new row and fill it with the values of the tag of each column.

tag_key : str
new_row_name : str - None
name of the new row that will contains tag values. If none, tag key is used as name
extract_column_values_to_row_tags

Create a new tag for each row and fill it with the values of the provided column

column_name : str
name of the column to extract
new_tag_key : str - None
key of the new tag that will contains column values. If none, column name is used as key
delete_column : bool - False
if true, delete the column after the extraction
extract_row_tags_to_new_column

Create a new columns and fill it with the values of the tag of each row

tag_key : str
new_column_name : str - None
name of the new column that will contains tag values. If none, tag key is used as name
extract_row_values_to_column_tags

Create a new tag for each column and fill it with the values of the row.

row_name : str
name of the row to extract values from
new_tag_key : str - None
key of the new tag that will contains row values. If none, row name is used as key
delete_row : bool - False
if true, delete the row after the extraction
filter_out_by_column_names

Filter out table columns matching a list of names, return a new table

filters : List
The list of names
Return type : Table
filter_out_by_row_names

Filter out table rows matching a list of names, return a new table

filters : List
The list of names
Return type : Table
filter_out_by_tags

Filter out table rows or columns matching a list of tags and return a new table. The row or column that matches the tags are removed.

Example of search tags are:

  • tags = [ {"key1": "value1"} ] to filter out rows or columns having a tag {"key1": "value1"}
  • tags = [ {"key1": "value1", "key2": "value2"} ] to filter out rows or columns having tags {"key1": "value1"} AND {"key2": "value2"}
  • tags = [ {"key1": "value1"}, {"key2": "value2"} ] to filter out rows or columns having tags {"key1": "value1"} OR {"key2": "value2"}
  • tags = [ {"key1": "value1", "key2": "value2"}, {"key3": "value3"} ] to filter out rows or columns having tags ({"key1": "value1"} AND {"key2": "value2"}) OR {"key2": "value2"}
  • AND and OR logics can further be combined to perform complex selects
axis : Literal[0, 1, 'index', 'columns']
axis to filter out the tags
tags : List
The of tags
Return type : Table
generate_new_column_name

Generates a column name that is unique in the Dataframe base on name. If the column name doesn't exist, return name, otherwise return name_1 or name_2, ... Only the name is returned, the column is not added to the Dataframe.

name : str
name of the column
Return type : str
get_available_column_tags

Get the available tags for each column.

Return type : Dict
get_available_row_tags

Get the available tags for each row.

Return type : Dict
get_cell_value_at

Get the value of a cell at a given coordonate (row, column)

row_index : int
index of the row
column_index : int
index of the column
Return type : Any
get_column_as_dataframe

Returns a column with the given name as a DataFrame.

column_name : str
The name of the column.
skip_nan : bool - False
Whether to skip NaN values. Defaults to False.
Return type : DataFrame
get_column_data

Returns the data of a column with the given name.

column_name : str
The name of the column.
skip_nan : bool - False
Whether to skip NaN values. Defaults to False.
Return type : List
get_column_index_from_name

Get the index of a column from its name

column_name : str
name of the column
Return type : int
get_column_info

Get the info of a column by name

column_name : str
name of the column
Return type : TableColumnInfo
get_column_names

Get the column names

from_index : int - None
start index of the columns to retrieve
to_index : int - None
end index of the columns to retrieve
Return type : List
get_column_names_by_indexes

Function to retrieve the column names based on column indexes

indexes : List
list of column indexes
Return type : List
get_column_tags

Get the tags of multiple columns by index

from_index : int - None
start index of the columns to retrieve
to_index : int - None
end index of the columns to retrieve, to_index is included
none_if_empty : bool - False
if true, return None if no tags are found
Return type : List
get_column_tags_by_index

Get the tags of a column at a given index

column_index : int
index of the column
Return type : Dict
get_column_tags_by_name

Get the tags of a column by name

column_name : str
name of the column
Return type : Dict
get_column_type

Get the type of a column

column_name : Any
name of the column
Return type : TableColumnType
get_columns_info

Get the info of multiple columns by index

from_index : int - None
start index of the columns to retrieve
to_index : int - None
end index of the columns to retrieve
Return type : List
get_data
Return type : DataFrame
get_default_name

You can redefine this method to set a name of the resource. When saving the resource the name will be saved automatically This can be useful to distinguish this resource from another one or to search for the resource

Return type : str
get_row_data

Returns the data of a row with the given name.

row_name : str
The name of the row.
skip_na : bool - False
Whether to skip NaN values. Defaults to False.
Return type : List
get_row_index_by_name

Get the index of a row from its name. Raise an exception if the row doesn't exist

row_name : str
name of the row
Return type : int
get_row_info

Get the info of a row by name

row_name : str
name of the row
Return type : TableHeaderInfo
get_row_names

Get the row names of the table by index

from_index : int - None
start index of the rows to retrieve
to_index : int - None
end index of the rows to retrieve
Return type : List
get_row_names_by_indexes

Function to retrieve the row names based on row indexes

indexes : List
list of row indexes
Return type : List
get_row_tag_by_name

Get the tags of a row by name

row_name : str
name of the row
Return type : Dict
get_row_tags

Get the tags of multiple rows by index

from_index : int - None
start index of the rows to retrieve
to_index : int - None
end index of the rows to retrieve, to_index is included
none_if_empty : bool - False
if true, return None if no tags are found
Return type : List
get_row_tags_by_index

Get the tags of a row at a given index

row_index : int
index of the row
Return type : Dict
get_rows_info

Get the info of multiple rows by index

from_index : int - None
start index of the rows to retrieve
to_index : int - None
end index of the rows to retrieve
Return type : List
get_tags

Get the tags of a given axis

axis : Literal[0, 1, 'index', 'columns']
axis to retrieve the tags
Return type : List
get_technical_info

Get the technical information of the resource

key : str
key of the technical information
Return type : TechnicalInfo
infer_objects

Call infer_objects on the underlying dataframe, it modifies the table dataframe.

Return type : Table
init

This can be overwritten to perform custom initialization of the resource. This method is called just after the init (constructor) of the resource. The default values of RFields are set before this method is called.

remove_column

Remove a column from the Dataframe.

column_name : str
name of the column
remove_row

Remove a row from the Dataframe.

row_name : str
name of the row to remove
row_exists

Checks if a row with the given name exists in the Table.

name : str
The name of the row to check.
case_sensitive : bool - True
Whether the check should be case sensitive. Defaults to True.
Return type : bool
select_by_column_indexes

Select table columns matching a list of indexes, return a new table

indexes : List
The list of indexes
Return type : Table
select_by_column_names

Select table columns matching a list of names, return a new table

filters : List
The list of names
Return type : Table
select_by_column_tags

Select table columns matching a list of tags

Example of search tags are:

  • tags = [ {"key1": "value1"} ] to select columns having a tag {"key1": "value1"}
  • tags = [ {"key1": "value1", "key2": "value2"} ] to select columns having tags {"key1": "value1"} AND {"key2": "value2"}
  • tags = [ {"key1": "value1"}, {"key2": "value2"} ] to select columns having tags {"key1": "value1"} OR {"key2": "value2"}
  • tags = [ {"key1": "value1", "key2": "value2"}, {"key3": "value3"} ] to select columns having tags ({"key1": "value1"} AND {"key2": "value2"}) OR {"key2": "value2"}
  • AND and OR logics can further be combined to perform complex selects
tags : List
The of tags
Return type : Table
select_by_coords

Create a new table from coords. It does not includes the to_row_id and to_column_id

from_row_id : int
start row index
from_column_id : int
start column index
to_row_id : int
end row index
to_column_id : int
end column index
Return type : Table
select_by_row_indexes

Select table rows matching a list of indexes, return a new table

indexes : List
The list of indexes
Return type : Table
select_by_row_names

Select table rows matching a list of names, return a new table

filters : List
The list of names
Return type : Table
select_by_row_tags

Select table rows matching a list of tags

Example of search tags are:

  • tags = [ {"key1": "value1"} ] to select rows having a tag {"key1": "value1"}
  • tags = [ {"key1": "value1", "key2": "value2"} ] to select rows having tags {"key1": "value1"} AND {"key2": "value2"}
  • tags = [ {"key1": "value1"}, {"key2": "value2"} ] to select rows having tags {"key1": "value1"} OR {"key2": "value2"}
  • tags = [ {"key1": "value1", "key2": "value2"}, {"key3": "value3"} ] to select rows having tags ({"key1": "value1"} AND {"key2": "value2"}) OR {"key2": "value2"}
  • AND and OR logics can further be combined to perform complex selects
tags : List
The of tags
Return type : Table
select_by_tags

Select table rows or columns matching a list of tags and return a new table

Example of search tags are:

  • tags = [ {"key1": "value1"} ] to select rows or columns having a tag {"key1": "value1"}
  • tags = [ {"key1": "value1", "key2": "value2"} ] to select rows or columns having tags {"key1": "value1"} AND {"key2": "value2"}
  • tags = [ {"key1": "value1"}, {"key2": "value2"} ] to select rows or columns having tags {"key1": "value1"} OR {"key2": "value2"}
  • tags = [ {"key1": "value1", "key2": "value2"}, {"key3": "value3"} ] to select rows or columns having tags ({"key1": "value1"} AND {"key2": "value2"}) OR {"key2": "value2"}
  • AND and OR logics can further be combined to perform complex selects
axis : Literal[0, 1, 'index', 'columns']
axis to select the tags
tags : List
The of tags
Return type : Table
select_numeric_columns

Select only numeric columns, return a new table

drop_na : Literal['all', 'any'] - all
if drop_na = 'all', then drops columns where all values are nan (similar to `DataFrame.drop_na(how=all|any)`) if drop_na = 'any', then drop columns where any values are nan (similar to `DataFrame.drop_na(how=all|any)`)
Return type : Table
set_all_column_names

Set the names of all columns

column_names : List
list of column names
set_all_column_tags

Set the tags of all columns, the length of the list must be equal to the number of columns

tags : List
list of tags
set_all_row_names

Set the names of all rows

row_names : List
list of row names, must be the same length number of rows
set_all_row_tags

Set the tags of all rows, the length of the list must be equal to the number of rows

tags : List
list of tags
set_cell_value_at

Set the value of a cell at a given coordonate (row, column)

row_index : int
index of the row
column_index : int
index of the column
value : Any
value to set
set_column_name

Set the name of a column at a given index

current_name : str
current name of the column
new_name : str
new name of the column
set_column_tags_by_index

Set the tags of a column at a given index

column_index : int
index of the column
tags : Dict
tags to set
set_column_tags_by_name

Set the tags of a column by name

column_name : str
name of the column
tags : Dict
tags to set
set_comments
comments : str - ''
set_row_name

Update the name of a row

current_name : Any
current name of the row
new_name : str
new name of the row
set_row_tags_by_index

Set the tags of a row at a given index

row_index : int
index of the row
tags : Dict
tags to set
set_row_tags_by_name

Set the tags of a row by name

row_name : str
name of the row
tags : Dict
tags to set
tail

Returns the last n rows for the columns ant targets.

nrows : int - 5
Number of rows
Return type : DataFrame
to_csv

Returns the table as a csv string.

Return type : str
to_dataframe

Returns the table as a pandas dataframe.

Return type : DataFrame
to_json

Returns the table as a json string.

Return type : dict
to_list

Returns the table as a list of lists.

Return type : List
to_numpy

Returns the table as a numpy array.

Return type : ndarray
transpose

Transpose the table, it returnes a new Table, the original table is not modified.

infer_objects : bool - False
Return type : Table