Groups
With pandapower version > 2.10.1, functionality to group network elements is available. Basically, these are helper functions to better handle and interact with multiple elements, even of different element types, such as buses, generators and lines, at once.
For first steps with group functionality, as often, it is recommended to have a look to the tutorial.
Plenty of group related functions are presented in the following.
Create and delete groups
- pandapower.create_group(net, element_types, element_indices, name='', reference_columns=None, index=None, **kwargs)
Add a new group to net[‘group’] dataframe.
Attention
If you declare a group but forget to declare all connected elements although you wants to (e.g. declaring lines but forgetting to mention the connected switches), you may get problems after using drop_elements_and_group() or other functions. There are different pandapower toolbox functions which may help you to define 'elements_dict', such as get_connecting_branches(), get_inner_branches(), get_connecting_elements_dict().
- Parameters:
- netpandapowerNet
pandapower net
- element_typesstr or list of strings
defines, together with ‘elements’, which net elements belong to the group
- element_indiceslist of list of indices
defines, together with ‘element_types’, which net elements belong to the group
- namestr, optional
name of the group, by default “”
- reference_columnsstring or list of strings, optional
If given, the elements_dict should not refer to DataFrames index but to another column. It is highly relevant that the reference_column exists in all DataFrames of the grouped elements and have the same dtype, by default None
- indexint, optional
index for the dataframe net.group, by default None
- EXAMPLES:
>>> create_group(net, ["bus", "gen"], [[10, 12], [1, 2]]) >>> create_group(net, ["bus", "gen"], [["Berlin", "Paris"], ["Wind_1", "Nuclear1"]], reference_columns="name")
- pandapower.create_group_from_dict(net, elements_dict, name='', reference_column=None, index=None, **kwargs)
Wrapper function of create_group().
- pandapower.drop_group(net, index)
Drops the group of given index.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the group which should be dropped
- pandapower.drop_group_and_elements(net, index)
Drops all elements of the group and in net.group the group itself.
Adapt group members
- pandapower.attach_to_group(net, index, element_types, element_indices, reference_columns=None, take_existing_reference_columns=True)
Appends the group by the elements given.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the considered group
- element_typesstr or list of strings
defines, together with ‘elements’, which net elements belong to the group
- element_indiceslist of list of indices
defines, together with ‘element_types’, which net elements belong to the group
- reference_columnsstring or list of strings, optional
If given, the elements_dict should not refer to DataFrames index but to another column. It is highly relevant that the reference_column exists in all DataFrames of the grouped elements and have the same dtype, by default False
- take_existing_reference_columnsbool, optional
Determines the behavior if the given reference_columns does not match the reference_column existing in net.group. If True, the existing reference_column is taken for both. If False, an error is raised.
- pandapower.attach_to_groups(net, index, element_types, element_indices, reference_columns=None)
Appends the groups by the elements given.
- Parameters:
- netpandapowerNet
pandapower net
- indexlist[int]
index of the considered group
- element_typesstr or list of strings
defines, together with ‘elements’, which net elements belong to the group
- element_indiceslist of list of indices
defines, together with ‘element_types’, which net elements belong to the group
- reference_columnsstring or list of strings, optional
If given, the elements_dict should not refer to DataFrames index but to another column. It is highly relevant that the reference_column exists in all DataFrames of the grouped elements and have the same dtype, by default None
See also
- pandapower.detach_from_group(net, index, element_type, element_index)
Detaches elements from the group with the given group index ‘index’. No errors are raised if elements are passed to be drop from groups which alread don’t have these elements as members. A reverse function is available -> pp.group.attach_to_group().
- Parameters:
- netpandapowerNet
pandapower net
- indexint
Index of the group from which the element should be dropped
- element_typestr
The element type of which elements should be dropped from the group(s), e.g. “bus”
- element_indexint or list of integers
indices of the elements which should be dropped from the group
- pandapower.detach_from_groups(net, element_type, element_index, index=None)
Detaches elements from one or multiple groups, defined by ‘index’. No errors are raised if elements are passed to be dropped from groups which alread don’t have these elements as members. A reverse function is available -> pp.group.attach_to_group().
- Parameters:
- netpandapowerNet
pandapower net
- element_typestr
The element type of which elements should be dropped from the group(s), e.g. “bus”
- element_indexint or list of integers
indices of the elements which should be dropped from the group
- indexint or list of integers, optional
Indices of the group(s) from which the element should be dropped. If None, the elements are dropped from all groups, by default None
Access group data and evaluate membership
- pandapower.group_name(net, index)
Returns the name of the group and checks that all group rows include the same name
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the group
- verbosebool, optional
Setting to False, accelerate the code but don’t check inconsistent names, by default True
See also
group_indexrecursive function
- pandapower.group_index(net, name)
Returns the index of the group named as given
- Parameters:
- netpandapowerNet
pandapower net
- namestr
name of the requested group
See also
group_namerecursive function
- pandapower.group_element_index(net, index, element_type)
Returns the indices of the elements of the group in the element table net[element_type]. This function considers net.group.reference_column.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
Index of the group
- element_typestr
name of the element table to which the returned indices of the elements of the group belong to
- Returns:
- pd.Index
indices of the elements of the group in the element table net[element_type]
- pandapower.group_row(net, index, element_type)
Returns the row which consists the data of the requested group index and element type.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the group
- element_typestr
element type (defines which row of the data of the group should be returned)
- Returns:
- pandas.Series
data of net.group, defined by the index of the group and the element type
- Raises:
- KeyError
Now row exist for the requested group and element type
- ValueError
Multiple rows exist for the requested group and element type
- pandapower.element_associated_groups(net, element_type, element_index, return_empties=True, drop_empty_lines=True)
Returns to which groups given elements belong to.
- Parameters:
- netpandapowerNet
pandapower net
- element_typestr
element type of the elements to be found in the groups, e.g. “gen” or “load”
- element_indexstr
indices of the element table which should be found in the groups
- return_emptiesbool, optional
whether entries with an empty list of assiciated groups should be returned, by default True
- drop_empty_linesbool, optional
This parameter decides whether empty entries should be removed (the complete row in net.group), by default True
- Returns:
- dict[int, list[int]]
for each element index a list of associated group indices is returned as a dict
- pandapower.isin_group(net, element_type, element_index, index=None, drop_empty_lines=True)
Returns whether elements are in group(s).
- Parameters:
- netpandapowerNet
pandapower net
- element_typestr
element type of the elements to be found in the groups, e.g. “gen” or “load”
- element_indexint or list of integers
indices of the element table which should be found in the groups
- indexint or list of integers, optional
Can narrow the number of groups in which the elements are searched, by default None
- drop_empty_linesbool, optional
This parameter decides whether empty entries should be removed (the complete row in net.group), by default True
- Returns:
- boolean or boolean numpy.array
Information whether the element are in any group
- pandapower.count_group_elements(net, index)
Returns a Series concluding the number of included elements in self.elements_dict
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the considered group
- Returns:
- pd.Series
number of elements per element type existing in the group
See also
count_elements
Compare groups
- pandapower.groups_equal(net, index1, index2, **kwargs)
Returns a boolean whether both group are equal.
- Parameters:
- netpandapowerNet
pandapower net
- index1int
index of the first group to compare
- index2int
index of the second group to compare
- pandapower.compare_group_elements(net, index1, index2)
allow_cross_reference_column_comparison
- Parameters:
- netpandapowerNet
pandapower net
- index1int
index of the first group to compare
- index2int
index of the second group to compare
Fix group data
- pandapower.check_unique_group_rows(net, raise_error=True, log_level='warning')
Checks whether all groups have unique names. raise_error decides whether duplicated names lead to error or log message.
- Parameters:
- netpandapowerNet
pandapower net
- raise_errorbool, optional
decides whether duplicated names lead to error or log message., by default False
- log_levelstr, optional
the level for logs, relevant if raise_error is False
Notes
Using different reference_columns for the same group and element_type is not supported.
- pandapower.remove_not_existing_group_members(net, verbose=True)
Remove group members from net.group that do not exist in the elements tables.
- Parameters:
- netpandapowerNet
pandapower net
- verbosebool, optional
Steers amount of logging messages, by default True
- pandapower.ensure_lists_in_group_element_column(net, drop_empty_lines=True)
Ensure that all entries in net.group.element are of type list.
- Parameters:
- netpandapowerNet
pandapower net
- drop_empty_linesbool, optional
This parameter decides whether empty entries should be removed (the complete row in net.group), by default True
- pandapower.group_entries_exist_in_element_table(net, index, element_type)
Returns an array of booleans whether the entries in net.group.element exist in net[element_type], also considering reference_column
- Parameters:
- netpandapowerNet
pandapower net
- indexint
Index of the group
- element_typestr
element type which entries should be checked, e.g. “bus”
- Returns:
- array of booleans
Whether the entries in net.group.element exist in net[element_type]
Further group functions
- pandapower.set_group_in_service(net, index)
Sets all elements of the group in service.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the considered group
- pandapower.set_group_out_of_service(net, index)
Sets all elements of the group out of service.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the considered group
- pandapower.set_value_to_group(net, index, value, column, replace=True, append_column=True)
Sets the same value to the column of the element tables of all elements/members of the group.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the considered group
- valueint/float/object
value to be written to all group members into the element tables
- columnstr
column to be manipulated
- replacebool, optional
If False, value is only written to places where no value exist before (column doesn’t exist before or value is nan), by default True
- append_columnbool, optional
Decides whether the column should be added to element tables where this doesn’t exist before, by default True
- pandapower.group_res_p_mw(net, index)
Sums all result table values p_mw of group members.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the considered group
Examples
>>> import pandapower as pp >>> import pandapower.networks as nw >>> net = nw.create_cigre_network_mv(with_der="all") >>> pp.runpp(net) >>> gr_idx = pp.create_group(net, ["sgen", "line"], [[0, 1], [0, 1]], name="test group") >>> net.res_line.pl_mw.loc[[0, 1]].sum() - net.res_sgen.p_mw.loc[[0, 1]].sum() # expected value 0.057938 >>> pp.group_res_p_mw(net, gr_idx) 0.057938
- pandapower.group_res_q_mvar(net, index)
Sums all result table values q_mvar of group members.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the considered group
Examples
>>> import pandapower as pp >>> import pandapower.networks as nw >>> net = nw.create_cigre_network_mv(with_der="all") >>> pp.runpp(net) >>> gr_idx = pp.create_group(net, ["sgen", "line"], [[0, 1], [0, 1]], name="test group") >>> net.res_line.ql_mvar.loc[[0, 1]].sum() - net.res_sgen.q_mvar.loc[[0, 1]].sum() # expected value 0.010114 >>> pp.group_res_q_mvar(net, gr_idx) 0.010114
- pandapower.group_res_power_per_bus(net, index)
Returns active and reactive power consumptions of given group per bus
- Parameters:
- netpandapowerNet
pandapower net
- indexint
_description_
- Returns:
- pd.DataFrame
power consumption of the group per bus (index=bus, columns=[“p_mw”, “q_mvar”])
Examples
>>> import pandapower as pp >>> import pandapower.networks as nw >>> net = nw.create_cigre_network_mv(with_der="all") >>> pp.runpp(net) >>> gr_idx = pp.create_group(net, ["sgen", "line"], [[0, 1], [0, 1]], name="test group") >>> pp.group_res_power_per_bus(net, gr_idx) p_mw q_mvar bus 1 2.953004e+00 1.328978e+00 2 -3.641532e-14 4.618528e-14 3 -2.875066e+00 -1.318864e+00 4 -2.000000e-02 0.000000e+00
- pandapower.set_group_reference_column(net, index, reference_column, element_type=None)
Set a reference_column to the group of given index. The values in net.group.element get updated.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
Index of the group
- reference_columnstr
column in the elemt tables which should be used as reference to link the group members. If no column but the index should be used as the link (is the default), reference_column should be None.
- element_typestr, optional
Type of element which should get a new column to reference. If None, all element types are considered, by default None
- Raises:
- ValueError
net[element_type][reference_column] has duplicated values.
- pandapower.return_group_as_net(net, index, keep_everything_else=False, verbose=True, **kwargs)
Returns a pandapower net consisting of the members of this group.
- Parameters:
- netpandapowerNet
pandapower net
- indexint
index of the considered group
- keep_everything_elsebool, optional
Decides whether other data than element tables are kept, by default False
- verbosebool, optional
Decides whether logging messages are triggered, by default True
- Returns:
- pandapowerNet
pandapower net, only with elements that are members of the group