IdaDataBase¶

Connect to dashDB/DB2¶

class ibmdbpy.base.IdaDataBase(dsn, uid=u'', pwd=u'', autocommit=True, verbose=False)[source]¶

An IdaDataBase instance represents a reference to a remote dashDB/DB2 instance. This is an abstraction layer for the remote connection. The IdaDataBase interface provides several functions that enable basic database administration in pythonic syntax.

You can use either ODBC or JDBC to connect to the database. The default connection type is ODBC, which is the standard connection type for Windows users. To establish an ODBC connection, download an IBM DB2 driver and set up your ODBC connection by specifying your connection protocol, port, and hostname. An ODBC connection on Linux or Mac might require more settings. For more information about how to establish an ODBC connection, see the pypyodbc documentation.

To connect with JDBC, install the optional external package jaydebeapi, download the ibm jdbc driver, and save it in your local ibmdbpy folder. If you put the jdbc driver in the CLASSPATH variable or the folder that contains it, it will work too. A C++ compiler adapted to the current python version, operating system, and architecture may also be required to install jaydebeapi.

The instantiation of an IdaDataBase object is a mandatory step before creating IdaDataFrame objects because IdaDataFrames require an IdaDataBase as a parameter to be initialized. By convention, use only one instance of IdaDataBase per database. However, you can use several instances of IdaDataFrame per connection.

__init__(dsn, uid=u'', pwd=u'', autocommit=True, verbose=False)[source]¶

Open a database connection.

Parameters:

dsn : str

Data Source Name (as specified in your ODBC settings) or JDBC URL string.

uid : str, optional

User ID.

pwd : str, optional

User password.

autocommit : bool, default: True

If True, automatically commits all operations.

verbose : bool, defaukt: True

If True, prints all SQL requests that are sent to the database.

Returns:

IdaDataBase object

Raises:

ImportError

JayDeBeApi is not installed.

IdaDataBaseError

uid and pwd are defined both in uid, pwd parameters and dsn.

The ‘db2jcc4.jar’ file is not in the ibmdbpy site-package repository.

Examples

ODBC connection, userID and password are stored in ODBC settings:

>>> IdaDataBase(dsn="DASHDB") # ODBC Connection
<ibmdbpy.base.IdaDataBase at 0x9bec860>

ODBC connection, userID and password are not stored in ODBC settings:

>>> IdaDataBase(dsn="DASHDB", uid="<UID>", pwd="<PWD>")
<ibmdbpy.base.IdaDataBase at 0x9bec860>

JDBC connection, full JDBC string:

>>> jdbc='jdbc:db2://<HOST>:<PORT>/<DBNAME>:user=<UID>;password=<PWD>'
>>> IdaDataBase(dsn=jdbc)
<ibmdbpy.base.IdaDataBase at 0x9bec860>

JDBC connectiom, JDBC string and seperate userID and password:

>>> jdbc = 'jdbc:db2://<HOST>:<PORT>/<DBNAME>'
>>> IdaDataBase(dsn=jdbc, uid="<UID>", pwd="<PWD>")
<ibmdbpy.base.IdaDataBase at 0x9bec860>

Attributes

data_source_name	(str) Name of the referring DataBase.
_con_type	(str) Type of the connection, either ‘odbc’ or ‘jdbc’.
_connection_string	(str) Connection string use for connecting via ODBC or JDBC.
_con	(connection object) Connection object to the remote Database.
_idadfs	(list) List of IdaDataFrame objects opened under this connection.

Methods

DataBase Exploration¶

current_schema¶

IdaDataBase.current_schema()[source]¶

Get the current user schema name as a string.

Returns:

str

User’s schema name.

Examples

>>> idadb.current_schema()
'DASHXXXXXX'

show_tables¶

IdaDataBase.show_tables(show_all=False)[source]¶

Show tables and views that are available in self. By default, this function shows only tables that belong to a user’s specific schema.

Parameters:

show_all : bool

If True, all table and view names in the database are returned, not only those that belong to the user’s schema.

Returns:

DataFrame

A data frame containing tables and views names in self with some additional information (TABSCHEMA, TABNAME, OWNER, TYPE).

Notes

show_tables implements a cache strategy. The cache is stored when the user calls the method with the argument show_all set to True. This improves performance because database table look ups are a very common operation. The cache gets updated each time a table or view is created or refreshed, each time a table or view is deleted, or when a new IdaDataFrame is opened.

Examples

>>> ida_db.show_tables()
     TABSCHEMA           TABNAME       OWNER TYPE
0    DASHXXXXXX            SWISS  DASHXXXXXX    T
1    DASHXXXXXX             IRIS  DASHXXXXXX    T
2    DASHXXXXXX     VIEW_TITANIC  DASHXXXXXX    V
...
>>> ida_db.show_tables(show_all = True)
     TABSCHEMA           TABNAME       OWNER TYPE
0    DASHXXXXXX            SWISS  DASHXXXXXX    T
1    DASHXXXXXX             IRIS  DASHXXXXXX    T
2    DASHXXXXXX     VIEW_TITANIC  DASHXXXXXX    V
2      SYSTOOLS      IDAX_MODELS  DASH101631    A
...

show_models¶

IdaDataBase.show_models()[source]¶

Show models that are available in the database.

Returns:	DataFrame

Examples

>>> idadb.show_models()
    MODELSCHEMA               MODELNAME       OWNER
0   DASHXXXXXX  KMEANS_10857_1434974511  DASHXXXXXX
1   DASHXXXXXX  KMEANS_11726_1434977692  DASHXXXXXX
2   DASHXXXXXX  KMEANS_11948_1434976568  DASHXXXXXX

exists_table_or_view¶

IdaDataBase.exists_table_or_view(objectname)[source]¶

Check if a table or view exists in self.

Parameters:

objectname : str

Name of the table or view to check.

Returns:

bool

Raises:

TypeError

The object exists but is not of the expected type.

Examples

>>> idadb.exists_table_or_view("NOT_EXISTING")
False
>>> idadb.exists_table_or_view("TABLE_OR_VIEW")
True
>>> idadb.exists_table_or_view("NO_TABLE_NOR_VIEW")
TypeError : "NO_TABLE_NOR_VIEW" exists in schema '?' but of type '?'

exists_table¶

IdaDataBase.exists_table(tablename)[source]¶

Check if a table exists in self.

Returns:

bool

Raises:

TypeError

The object exists but is not of the expected type.

Examples

>>> idadb.exists_table("NOT_EXISTING")
False
>>> idadb.exists_table("TABLE")
True
>>> idadb.exists_table("NO_TABLE")
TypeError : "tablename" exists in schema "?" but of type '?'

exists_view¶

IdaDataBase.exists_view(viewname)[source]¶

Check if a view exists in self.

Parameters:

viewname : str

Name of the view to check.

Returns:

bool

Raises:

TypeError

The object exists but is not of the expected type.

Examples

>>> idadb.exists_view("NOT_EXISTING")
False
>>> idadb.exists_view("VIEW")
True
>>> idadb.exists_view("NO_VIEW")
TypeError : "viewname" exists in schema "?" but of type '?'

exists_model¶

IdaDataBase.exists_model(modelname)[source]¶

Check if a model exists in self.

Parameters:

modelname : str

Name of the model to check.

Returns:

bool

Raises:

TypeError

The object exists but is not of the expected type.

Examples

>>> idadb.exists_model("MODEL")
True
>>> idadb.exists_model("NOT_EXISTING")
False
>>> idadb.exists_model("NO_MODEL")
TypeError : NO_TABLE exists but is not a model (of type '?')

is_table_or_view¶

IdaDataBase.is_table_or_view(objectname)[source]¶

Check if an object is a table or a view in self.

Parameters:

objectname : str

Name of the object to check.

Returns:

bool

Raises:

ValueError

objectname doesn’t exist in the database.

Examples

>>> idadb.is_table_or_view("NO_TABLE")
False
>>> idadb.is_table_or_view("TABLE")
True
>>> idadb.is_table_or_view("NOT_EXISTING")
ValueError : NO_EXISTING does not exist in database

is_table¶

IdaDataBase.is_table(tablename)[source]¶

Check if an object is a table in self.

Parameters:

tablename : str

Name of the table to check.

Returns:

bool

Raises:

ValueError

The object doesn’t exist in the database.

Examples

>>> idadb.is_table("NO_TABLE")
False
>>> idadb.is_table("TABLE")
True
>>> idadb.is_table("NOT_EXISTING")
ValueError : NO_EXISTING does not exist in database

is_view¶

IdaDataBase.is_view(viewname)[source]¶

Check if an object is a view in self.

Parameters:

viewname : str

Name of the view to check.

Returns:

bool

Raises:

ValueError

The object doesn’t exist in the database.

Examples

>>> idadb.is_view("NO_VIEW")
False
>>> idadb.is_view("VIEW")
True
>>> idadb.is_view("NOT_EXISTING")
ValueError : NO_EXISTING does not exist in database

is_model¶

IdaDataBase.is_model(modelname)[source]¶

Check if an object is a model in self.

Parameters:

modelname : str

Name of the model to check.

Returns:

bool

Raises:

ValueError

The object doesn’t exist in the database.

Examples

>>> idadb.is_model("MODEL")
True
>>> idadb.is_model("NO_MODEL")
False
>>> idadb.is_model("NOT_EXISTING")
ValueError : NO_EXISTING doesn't exist in database

ida_query¶

IdaDataBase.ida_query(query, silent=False, first_row_only=False, autocommit=False)[source]¶

Prepare, execute and format the result of a query in a dataframe or in a Tuple. If nothing is expected to be returned for the SQL command, nothing is returned.

Parameters:

query : str

Query to be executed.

silent: bool, default: False

If True, the query is not printed in the python console even if the verbosity mode is activated (VERBOSE environment variable is equal to “True”).

first_row_only : bool, default: False

If True, only the first row of the result is returned as a Tuple.

autocommit: bool, default: False

If True, the autocommit function is available.

Returns:

DataFrame or Tuple (if first_row_only=False)

Notes

If first_row_only argument is True, then even if the actual result of the query is composed of several rows, only the first row will be returned.

Examples

>>> idadb.ida_query("SELECT * FROM IRIS FETCH FIRST 5 ROWS ONLY")
   sepal_length  sepal_width  petal_length  petal_width species
         5.1          3.5           1.4          0.2  setosa
         4.9          3.0           1.4          0.2  setosa
         4.7          3.2           1.3          0.2  setosa
         4.6          3.1           1.5          0.2  setosa
         5.0          3.6           1.4          0.2  setosa

>>> idadb.ida_query("SELECT COUNT(*) FROM IRIS")
(150, 150, 150, 150)

ida_scalar_query¶

IdaDataBase.ida_scalar_query(query, silent=False, autocommit=False)[source]¶

Prepare and execute a query and return only the first element as a string. If nothing is returned from the SQL query, an error occur.

Parameters:

query : str

Query to be executed.

silent: bool, default: False

If True, the query will not be printed in python console even if verbosity mode is activated.

autocommit: bool, default: False

If True, the autocommit function is available.

Returns:

str or Number

Notes

Even if the actual result of the query is composed of several columns and several rows, only the first element (top-left) will be returned.

Examples

>>> idadb.ida_scalar_query("SELECT TRIM(CURRENT_SCHEMA) from SYSIBM.SYSDUMMY1")
'DASHXXXXX'

Upload DataFrames¶

as_idadataframe¶

IdaDataBase.as_idadataframe(*args, **kwds)[source]¶

Upload a dataframe and return its corresponding IdaDataFrame. The target table (tablename) will be created or replaced if the option clear_existing is set to True.

To add data to an existing tables, see IdaDataBase.append

Parameters:

dataframe : DataFrame

Data to be uploaded, contained in a Pandas DataFrame.

tablename : str, optional

Name to be given to the table created in the database. If not given, a valid tablename is generated (for example, DATA_FRAME_X where X a random number).

clear_existing : bool

If set to True, a table will be replaced when a table with the same name already exists in the database.

primary_key : str

Name of a column to be used as primary key.

Returns:

IdaDataFrame

Raises:

TypeError

Argument dataframe is not of type pandas.DataFrame.

The primary key argument is not a string.

NameError

The name already exists in the database and clear_existing is False.

The primary key argument doesn’t correspond to a column.

PrimaryKeyError

The primary key contains non unique values.

Notes

This function is not intended to be used to add data to an existing table, rather to create a new table from a dataframe. To add data to an existing table, please consider using IdaDataBase.append

Examples

>>> from ibmdbpy.sampledata.iris import iris
>>> idadb.as_idadataframe(iris, "IRIS")
<ibmdbpy.frame.IdaDataFrame at 0xb34a898>
>>> idadb.as_idadataframe(iris, "IRIS")
NameError: IRIS already exists, choose a different name or use clear_existing option.
>>> idadb.as_idadataframe(iris, "IRIS2")
<ibmdbpy.frame.IdaDataFrame at 0xb375940>
>>> idadb.as_idadataframe(iris, "IRIS", clear_existing = True)
<ibmdbpy.frame.IdaDataFrame at 0xb371cf8>

Delete DataBase Objects¶

drop_table¶

IdaDataBase.drop_table(tablename)[source]¶

Drop a table in the database.

Parameters:

tablename : str

Name of the table to drop.

Raises:

ValueError

If the object does not exist.

TypeError

If the object is not a table.

Notes

This operation cannot be undone if autocommit mode is activated.

Examples

>>> idadb.drop_table("TABLE")
True
>>> idadb.drop_table("NO_TABLE")
TypeError : NO_TABLE  exists in schema '?' but of type '?'
>>> idadb.drop_table("NOT_EXISTING")
ValueError : NO_EXISTING doesn't exist in database

drop_view¶

IdaDataBase.drop_view(viewname)[source]¶

Drop a view in the database.

Parameters:

viewname : str

Name of the view to drop.

Raises:

ValueError

If the object does not exist.

TypeError

If the object is not a view.

Notes

This operation cannot be undone if autocommit mode is activated.

Examples

>>> idadb.drop_view("VIEW")
True
>>> idadb.drop_view("NO_VIEW")
TypeError : NO_VIEW exists in schema '?' but of type '?'
>>> idadb.drop_view("NOT_EXISTING")
ValueError : NO_EXISTING doesn't exist in database

drop_model¶

IdaDataBase.drop_model(modelname)[source]¶

Drop a model in the database.

Parameters:

modelname : str

Name of the model to drop.

Raises:

ValueError

If the object does not exist.

TypeError

if the object exists but is not a model.

Notes

This operation cannot be undone if autocommit mode is activated.

Examples

>>> idadb.drop_model("MODEL")
True
>>> idadb.drop_model("NO_MODEL")
TypeError : NO_MODEL exists in schema '?' but of type '?'
>>> idadb.drop_model("NOT_EXISTING")
ValueError : NOT_EXISTING does not exists in database

DataBase Modification¶

rename¶

IdaDataBase.rename(*args, **kwds)[source]¶

Rename a table referenced by an IdaDataFrame in dashDB/DB2.

Parameters:

idadf : IdaDataFrame

IdaDataFrame object referencing the table to rename.

newname : str

Name to be given to self. Should contain only alphanumerical characters and underscores. All lower case characters will be converted to upper case characters. The new name should not already exist in the database.

Raises:

ValueError

The new tablename is not valid.

TypeError

Rename function is supported only for table type.

NameError

The name of the object to be created is identical to an existing name.

Notes

Upper case characters and numbers, optionally separated by underscores “_”, are valid characters.

add_column_id¶

IdaDataBase.add_column_id(*args, **kwds)[source]¶

Add an ID column to an IdaDataFrame.

Raises:

TypeError

idadf is not an IdaDataFrame.

ValueError

The given column name already exists in the DataBase.

Notes

The non-destructive creation of column IDs is not reliable, because row IDs are recalculated on the fly in a non-deterministic way. The only reliable way is to create it destructively, but the row IDs will be created at random. This could be highly improved in the future. An idea is to create ID columns in a non-destructive way and base them on the sorting of a set of columns, defined by the user, or all columns if no column combination results in unique identifiers.

This method is DEPRECATED.

delete_column¶

IdaDataBase.delete_column(idadf, column_name, destructive=False)[source]¶

Delete a column in an idaDataFrame.

Parameters:

idadf : IdaDataFrame

The IdaDataframe in which a column should be deleted.

column_name : str

Name of the column to delete.

destructive : bool

If set to True, the column is deleted in the database. Otherwise, it is deleted virtually, creating a view for the IdaDataFrame.

Raises:

TypeError

column_name should be a string.

ValueError

column_name refers to a column that doesn’t exist in self.

append¶

IdaDataBase.append(idadf, df, maxnrow=None)[source]¶

Append rows of a DataFrame to an IdaDataFrame. The DataFrame must have the same structure (same column names and datatypes). Optionally, the DataFrame to be added can be splitted into several chunks. This improves performance and prevents SQL overflows. By default, chunks are limited to 8000 cells.

Parameters:

idadf : IdaDataFrame

IdaDataFrame that receives data from dataframe df.

df : DataFrame

Dataframe whose rows are added to IdaDataFrame idadf.

maxnrow : int, optional

number corresponding to the maximum number of rows for each chunks.

Raises:

TypeError

maxnrow should be an interger.

Argument idadf should be an IdaDataFrame.

Argument df should be a pandas DataFrame.

ValueErrpr

maxnrow should be greater than 1 or nleft blank.

Other should be a Pandas DataFrame.

Other dataframe has not the same number of columns as self.

Some columns in other have different names that are different from the names of the columns in self.

Connection Management¶

commit¶

IdaDataBase.commit()[source]¶

Commit operations in the database.

Notes

All changes that are made in the database after the last commit, including those in the child IdaDataFrames, are commited.

If the environment variable ‘VERBOSE’ is set to True, the commit operations are notified in the console.

rollback¶

IdaDataBase.rollback()[source]¶

Rollback operations in the database.

Notes

All changes that are made in the database after the last commit, including those in the child IdaDataFrames, are discarded.

close¶

IdaDataBase.close()[source]¶

Close the IdaDataBase connection.

Notes

If the environment variable ‘AUTOCOMMIT’ is set to True, then all changes after the last commit are committed, otherwise they are discarded.

reconnect¶

IdaDataBase.reconnect()[source]¶: Try to reopen the connection.

Private Methods¶

_exists¶

IdaDataBase._exists(objectname, typelist)[source]¶

Check if an object of a certain type exists in dashDB/DB2.

Notes

For more information, see exists_table_or_view, exists_table, exists_view functions.

_is¶

IdaDataBase._is(objectname, typelist)[source]¶

Check if an existing object is of a certain type or in a list of types.

Notes

For more information, see is_table_or_view, is_table, is_view functions.

_drop¶

IdaDataBase._drop(objectname, object_type=u'T')[source]¶

Drop an object in the table depending on its type. Admissible type values are “T” (table) and “V” (view)

Notes

For more information, seedrop_table and drop_view functions.

_upper_columns¶

IdaDataBase._upper_columns(dataframe)[source]¶: Put every column name of a Pandas DataFrame in upper case.

_get_name_and_schema¶

IdaDataBase._get_name_and_schema(objectname)[source]¶

Helper function that returns the name and the schema from an object name. Implicitly, if no schema name was given, it is assumed that user refers to the current schema.

Returns:

tuple

A tuple composed of 2 strings containing the schema and the name.

Examples

>>> _get_name_and_schema(SCHEMA.TABLE)
(SCHEMA, TABLE)
>>> _get_name_and_schema(TABLE)
(<current schema>, TABLE)

_get_valid_tablename¶

IdaDataBase._get_valid_tablename(prefix=u'DATA_FRAME_')[source]¶

Generate a valid database table name.

Parameters:

prefix : str, default: “DATA_FRAME_“

Prefix used to create the table name. The name is constructed using this pattern : <prefix>_X where <prefix> corresponds to the string parameter “prefix” capitalized and X corresponds to a pseudo randomly generated number (0-100000).

Returns:

str

Examples

>>> idadb._get_valid_tablename()
'DATA_FRAME_49537_1434978215'
>>> idadb._get_valid_tablename("MYDATA_")
'MYDATA_65312_1434978215'
>>> idadb._get_valid_tablename("mydata_")
'MYDATA_78425_1434978215'
>>> idadb._get_valid_tablename("mydata$")
ValueError: Table name is not valid, only alphanumeric characters and underscores are allowed.

_get_valid_viewname¶

IdaDataBase._get_valid_viewname(prefix=u'VIEW_')[source]¶

Convenience function : Alternative name for get_valid_tablename.

The parameter prefix has its optional value changed to “VIEW_”.

Examples

>>> idadb._get_valid_viewname()
'VIEW_49537_1434978215'
>>> idadb._get_valid_viewname("MYVIEW_")
'MYVIEW_65312_1434978215'
>>> idadb._get_valid_viewname("myview_")
'MYVIEW_78425_1434978215'
>>> idadb._get_valid_modelname("myview$")
ValueError: View name is not valid, only alphanumeric characters and underscores are allowed.

_get_valid_modelname¶

IdaDataBase._get_valid_modelname(prefix=u'MODEL_')[source]¶

Convenience function : Alternative name for get_valid_tablename.

Parameter prefix has its optional value changed to “MODEL_”.

Examples

>>> idadb._get_valid_modelname()
'MODEL_49537_1434978215'
>>> idadb._get_valid_modelname("TEST_")
'TEST_65312_1434996318'
>>> idadb._get_valid_modelname("test_")
'TEST_78425_1435632423'
>>> idadb._get_valid_tablename("mymodel$")
ValueError: Table name is not valid, only alphanumeric characters and underscores are allowed.

_create_table¶

IdaDataBase._create_table(dataframe, tablename, primary_key=None)[source]¶

Create a new table in the database by declaring its name and columns based on an existing DataFrame. It is possible declare a column as primary key.

Parameters:

dataframe : DataFrame

Pandas DataFrame be used to initiate the table.

tablename : str

Name to be given to the table at its creation.

primary_key: str

Name of a column to declare as primary key.

Notes

The columns and their data type is deducted from the Pandas DataFrame given as parameter.

Examples

>>> from ibmdbpy.sampledata.iris import iris
>>> idadb._create_table(iris, "IRIS")
'IRIS'
>>> idadb._create_table(iris)
'DATA_FRAME_4956'

_create_view¶

IdaDataBase._create_view(idadf, viewname=None)[source]¶

Create a new view in the database from an existing table.

Parameters:

idadf : IdaDataFrame

IdaDataFrame to be duplicated as view.

viewname : str, optional

Name to be given to the view at its creation. If not given, a random name will be generated automatically

Returns:

str

View name.

Examples

>>> idadf = IdaDataFrame(idadb, "IRIS")
>>> idadb._create_view(idadf)
'IDAR_VIEW_4956'

_insert_into_database¶

IdaDataBase._insert_into_database(dataframe, tablename, silent=True)[source]¶

Populate an existing table with data from a dataframe.

Parameters:

dataframe: DataFrame

Data to be inserted into an existing table, contained in a Pandas DataFrame. It is assumed that the structure matches.

tablename: str

Name of the table in which the data is inserted.

silent : bool, default: True

If True, the INSERT statement is not printed. Avoids flooding the console.

_prepare_and_execute¶

IdaDataBase._prepare_and_execute(query, autocommit=True, silent=False)[source]¶

Prepare and execute a query by using the cursor of an idaobject.

Parameters:

idaobject: IdaDataBase or IdaDataFrame

query: str

Query to be executed.

autocommit: bool, default: True

If True, the autocommit function is available.

silent: bool, default: False

If True, the SQL statement is not printed.

_check_procedure¶

IdaDataBase._check_procedure(proc_name, alg_name=None)[source]¶

Check if a procedure is available in the database.

Parameters:

proc_name : str

Name of the procedure to be checked as defined in the underlying database management system.

alg_name : str

Name of the algorithm, human readable.

Returns:

bool

Examples

>>> idadb._check_procedure('KMEANS')
True
>>> idadb._check_procedure('NOT_EXISTING')
IdaDatabaseError: Function 'NOT_EXISTING' is not available.

_call_stored_procedure¶

IdaDataBase._call_stored_procedure(sp_name, **kwargs)[source]¶

Call a specific stored procedure from DashDB/DB2 and return its result.

Parameters:

sp_name : str

Name of the stored procedure.

**kwargs : ...

Additional parameters, specific to the called stored procedure.

_autocommit¶

IdaDataBase._autocommit()[source]¶

Commit changes made to the database in the connection automatically. If the environment variable ‘AUTOCOMMIT’ is set to True, then commit.

Notes

In the case of a commit operation, all changes that are made in the Database after the last commit, including those in the children IdaDataFrames, will be commited.

If the environment variable ‘VERBOSE’ is not set to ‘True’, the autocommit operations will not be notified in the console to the user.

_check_connection¶

IdaDataBase._check_connection()[source]¶: Check if the connection still exists by trying to open a cursor.

_retrieve_cache¶

IdaDataBase._retrieve_cache(cache)[source]¶: Helper function that retrieve cache if available. Cache are just string type values stored in private attributes.

_reset_attributes¶

IdaDataBase._reset_attributes(attributes)[source]¶: Helper function that delete attributes given as parameter if they exists in self. This is used to refresh lazy attributes and caches.