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Python's built-in data type is dictionary, which defines one-to-one relationships between keys and values.
Dictionaries consist of pairs of keys and their corresponding values.
Dictionaries are indexed by keys.
Dictionary is similar to associative array or hash table of other languages.
As following example explains further- India, Angel & Cartoon are keys & their corresponding values are Bharat, Mother Teresa & Mickey respectively.
>>> dict = {'India': 'Bharat', 'Angel': ‘Mother Teresa’, 'Cartoon': 'Mickey'}
>>>print dict[India]
Bharat
>>>print dict[Angel]
Mother Teresa

To extract the value it requires the object type to be defined and according to the object type only the values will be fetched. The values will be extracted as:
• If the object is a tuple then PyTuple_Size() method is used that returns the length of the values and another method PyTuple_GetItem() returns the data item that is stored at a specific index.
• If the object is a list then PyListSize() is having the same function that is defined for the tuple and PyList_GetItem() that also return the data items at a specified index.
• Strings uses PyString_Size() to return the length of the value and PyString_AsString() that return the pointer to its value.
• To check the type of the object and the extracted values use of methods like PyString_Check(), PyTuple_Check(), PyList_Check(), etc are used.

In Python, every new name introduced has its place where it lives and can be hooked. It is called a namespace. It is like a box where a variable name is graphed to the object placed. Whenever the variable is searched out in the bar, this box will be searched, to get the related object. Name space in Python is the frequently asked question in Python interview.

Memory management in Python involves a private heap containing all Python objects and data structures. Interpreter takes care of Python heap and that the programmer has no access to it.
The allocation of heap space for Python objects is done by Python memory manager. The core API of Python provides some tools for the programmer to code reliable and more robust program.
Python also has a build-in garbage collector which recycles all the unused memory. When an object is no longer referenced by the program, the heap space it occupies can be freed. The garbage collector determines objects which are no longer referenced by the sprogram frees the occupied memory and make it available to the heap space.
The gc module defines functions to enable /disable garbage collector:
gc.enable() -Enables automatic garbage collection.
gc.disable() - Disables automatic garbage collection.