cdm_reader_mapper.DataBundle

class cdm_reader_mapper.DataBundle(*args, **kwargs)

Class for manipulating the MDF data and mapping it to the CDM.

Parameters:

• data (pd.DataFrame or Iterable[pd.DataFrame], optional) – MDF DataFrame.

• columns (pd.Index, pd.MultiIndex or list, optional) – Column labels of data

• dtypes (pd.Series or dict, optional) – Data types of data.

• parse_dates (list or bool, optional) – Information how to parse dates on data

• mask (pandas.DataFrame, optional) – MDF validation mask

• imodel (str, optional) – Name of the MFD/CDM data model.

• mode (str) – Data mode (“data” or “tables”) Default: “data”

Examples

Getting a DataBundle while reading data from disk.

>>> from cdm_reader_mapper import read_mdf
>>> db = read_mdf(source="file_on_disk", imodel="custom_model_name")

Constructing a DataBundle from already read MDf data.

>>> from cdm_reader_mapper import DataBundle
>>> read = read_mdf(source="file_on_disk", imodel="custom_model_name")
>>> data_ = read.data
>>> mask_ = read.mask
>>> db = DataBundle(data=data_, mask=mask_)

Constructing a DataBundle from already read CDM data.

>>> from cdm_reader_mapper import read_tables
>>> tables = read_tables("path_to_files").data
>>> db = DataBundle(data=tables, mode="tables")

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
add(addition[, inplace])	Adding information to a DataBundle.
copy()	Make deep copy of a DataBundle.
correct_datetime([imodel, inplace])	Correct datetime information in data.
correct_pt([imodel, inplace])	Correct platform type information in data.
duplicate_check([inplace])	Duplicate check in data.
flag_duplicates([inplace])	Flag detected duplicates in data.
get_duplicates(**kwargs)	Get duplicate matches in data.
map_model([imodel, inplace])	Map data to the Common Data Model.
remove_duplicates([inplace])	Remove detected duplicates in data.
replace_columns(df_corr[, subset, inplace])	Replace columns in data.
select_where_all_false([inplace, do_mask])	Select rows from data where all column entries in mask are False.
select_where_all_true([inplace, do_mask])	Select rows from data where all column entries in mask are True.
select_where_entry_isin(selection[, ...])	Select rows from data where column entries are in a specific value list.
select_where_index_isin(index[, inplace, ...])	Select rows from data where indexes within a specific index list.
split_by_boolean_false([do_mask])	Split data by rows where all column entries in mask are False.
split_by_boolean_true([do_mask])	Split data by rows where all column entries in mask are True.
split_by_column_entries(selection[, do_mask])	Split data by rows where column entries are in a specific value list.
split_by_index(index[, do_mask])	Split data by rows within specific index list.
stack_h(other[, datasets, inplace])	Stack multiple DataBundle's horizontally.
stack_v(other[, datasets, inplace])	Stack multiple DataBundle's vertically.
unique(**kwargs)	Get unique values of data.
validate_datetime([imodel])	Validate datetime information in data.
validate_id([imodel])	Validate station id information in data.
write([dtypes, parse_dates, encoding, mode])	Write data on disk.

Attributes

columns	Column labels of data.
data	MDF pandas.DataFrame data.
dtypes	Dictionary of data types on data.
encoding	A string representing the encoding to use in the data.
imodel	Name of the MDF/CDM input model.
mask	MDF pandas.DataFrame validation mask.
mode	Data mode.
parse_dates	Information of how to parse dates in data.