Quality check table - gws_ubiome

Typing name : RESOURCE.gws_ubiome.QualityCheckTable

Brick : gws_ubiome

Parent :

Table

QualityCheckTable class

Views

SEQUENCING QUALITY BOXPLOT

Default view

Boxplot of the reads sequencing quality

SMART INTERACTIVE PLOT

Generate an interactive plot using an AI (OpenAI).

VIEW RESOURCE

View the complete resource as json

QUALITY LINE PLOT

Line plots of the reads sequencing quality

TABULAR

View as a table

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

head

Returns the first n rows for the columns ant targets.

nrows : int - 5

Number of rows

Return type : DataFrame

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