Taxonomy table tagged

Create a new Table

                                - 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 a new column to the Dataframe.

Add a tag to a column at a given index

Add a tag to a column by name

Add a row to the Dataframe.

Add a tag to a row at a given index

Add a tag to a row by name

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

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

:raises Exception: If the column doesn't exist.

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

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

:raises Exception: If the row doesn't exist.

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

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

Copy column tags from source_table to self matching by index.

Copy column tags from source_table to self matching by name.

Copy row tag from source_table to self matching by index

Copy row tag from source_table to self matching by name

Create a new table from a dataframe and a meta

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.

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.

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

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

                    If none, tag key is used as name, defaults to None

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

                If none, column name is used as key, defaults to None

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

                    If none, tag key is used as name, defaults to None

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

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

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

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

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.

Get the available tags for each column.

Get the available tags for each row.

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

Returns a column with the given name as a DataFrame.

Returns the data of a column with the given name.

Get the index of a column from its name

Get the info of a column by name

Get the column names

Function to retrieve the column names based on column indexes

Get the tags of multiple columns by index

Get the tags of a column at a given index

Get the tags of a column by name

Get the type of a column

Get the info of multiple columns by index

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

Returns the data of a row with the given name.

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

Get the info of a row by name

Get the row names of the table by index

Function to retrieve the row names based on row indexes

Get the tags of a row by name

Get the tags of multiple rows by index

Get the tags of a row at a given index

Get the info of multiple rows by index

Get the tags of a given axis

Get the technical information of the resource

Returns the first n rows for the columns ant targets.

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

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 a column from the Dataframe.

Remove a row from the Dataframe.

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

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

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

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

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

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

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

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

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

Select only numeric columns, return a new table

            if drop_na = 'any', then drop columns where any values are nan (similar to `DataFrame.drop_na(how=all|any)`)

Set the names of all columns

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

Set the names of all rows

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

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

Set the name of a column at a given index

Set the tags of a column at a given index

Set the tags of a column by name

Update the name of a row

Set the tags of a row at a given index

Set the tags of a row by name

Returns the last n rows for the columns ant targets.

Returns the table as a csv string.

Returns the table as a pandas dataframe.

Returns the table as a json string.

Returns the table as a list of lists.

Returns the table as a numpy array.

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