Phenotype table

Constructor, please do not overwrite this method, use the init method instead Leave the constructor without parameters.

Add a new column to the Dataframe.

Add a {key, value} tag to a column at a given index

Add a {key, value} tag to a column

Add a row to the Dataframe.

Add a {key, value} tag to a row at a given index

Add a {key, value} tag to a row

Add a technical information to the resource

Raise an 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

Method that raises an exception if the row doesn't exist.

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

Copy column tag from source_table to self matching by index

Copy column tag 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.

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, ...

Get the complete list of column tags with list of values for each

Get the complete list of row tags with list of values for each

Get the value of a cell at a given index

Get a column as a dataframe

Get a column as a list

Returns the column data of the Dataframe with the given name.

Get the column names

Function to retrieve the column names based on row positions

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 row data of the Dataframe with the given index.

Get the row names

Function to retrieve the row names based on row positions

Get the position of a row from its name

Get tags

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.

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 includes the to_row_id and to_column_id

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 numeric columns.

• 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))

Set the value of a cell at a given index

Set the tags of a column at a given index

Set the tags of a column by name

Set the name of a row at a given index

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.