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‎README.md

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# [![arctic](logo/arctic_50.png)](https://github.com/manahl/arctic) [Arctic TimeSeries and Tick store](https://github.com/manahl/arctic)
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[![Documentation Status](https://readthedocs.org/projects/arctic/badge/?version=latest)](https://arctic.readthedocs.io/en/latest/?badge=latest)
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[![Travis CI](https://travis-ci.org/manahl/arctic.svg?branch=master)](https://travis-ci.org/manahl/arctic)
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[![Coverage Status](https://coveralls.io/repos/github/manahl/arctic/badge.svg?branch=master)](https://coveralls.io/github/manahl/arctic?branch=master)
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[![Code Health](https://landscape.io/github/manahl/arctic/master/landscape.svg?style=flat)](https://landscape.io/github/manahl/arctic/master)
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Plugging a custom class in as a library type is straightforward. [This example
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shows how.](howtos/how_to_custom_arctic_library.py)
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## Documentation
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You can find complete documentation at [Arctic docs](https://arctic.readthedocs.io/en/latest/)
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## Concepts
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‎docs/chunkstore_api.md

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# [append](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L376)
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```
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append(symbol, item)
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Appends data from item to symbol's data in the database.
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Is not idempotent
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Parameters
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----------
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symbol: str
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the symbol for the given item in the DB
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item: DataFrame or Series
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the data to append
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```
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Example usage:
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```
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>>> df = DataFrame(data={'data': [100, 200, 300]},
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index=MultiIndex.from_tuples([(dt(2016, 1, 1), 1),
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(dt(2016, 1, 2), 1),
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(dt(2016, 1, 3), 1)],
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names=['date', 'id']))
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>>> lib.write('test', df, chunk_size='M')
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>>> lib.read('test')
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data
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date id
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2016-01-01 1 100
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2016-01-02 1 200
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2016-01-03 1 300
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>>> lib.append('test', df)
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>>> lib.append('test', df)
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>>> lib.read('test')
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data
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date id
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2016-01-01 1 100
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1 100
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1 100
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2016-01-02 1 200
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1 200
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1 200
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2016-01-03 1 300
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1 300
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1 300
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```
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# [delete](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L102)
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```
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delete(symbol, chunk_range=None)
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Delete all chunks for a symbol, or optionally, chunks within a range
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Parameters
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----------
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symbol : str
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symbol name for the item
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chunk_range: range object
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a date range to delete
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```
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Example usage:
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```
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>>> lib.read('test')
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data
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date id
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2016-01-01 1 100
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2016-01-03 1 300
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>>> lib.delete('test', chunk_range=pd.date_range('2016-01-01', '2016-01-01'))
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>>> lib.read('test')
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data
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date id
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2016-01-03 1 300
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```
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# [get_chunk_ranges](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L452)
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```
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get_chunk_ranges(symbol, chunk_range=None, reverse=False)
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Returns a generator of (Start, End) tuples for each chunk in the symbol
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Parameters
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----------
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symbol: str
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the symbol for the given item in the DB
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chunk_range: None, or a range object
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allows you to subset the chunks by range
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reverse: boolean
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Returns
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-------
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generator
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```
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Example usage:
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```
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>>> list(lib.get_chunk_ranges('new_name'))
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[('2016-01-01', '2016-01-01'), ('2016-01-03', '2016-01-03')]
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```
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# [get_info](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L431)
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```
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get_info(symbol)
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Returns information about the symbol, in a dictionary
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Parameters
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----------
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symbol: str
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the symbol for the given item in the DB
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Returns
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-------
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dictionary
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```
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# [iterator](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L477)
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```
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iterator(symbol, chunk_range=None):
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Returns a generator that accesses each chunk in ascending order
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Parameters
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----------
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symbol: str
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the symbol for the given item in the DB
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chunk_range: None, or a range object
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allows you to subset the chunks by range
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Returns
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-------
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generator
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```
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# [list_symbols](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L138)
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```
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list_symbols()
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Returns all symbols in the library
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Returns
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-------
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list of str
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```
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# [read](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L176)
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```
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read(symbol, chunk_range=None, filter_data=True, **kwargs)
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Reads data for a given symbol from the database.
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Parameters
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----------
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symbol: str
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the symbol to retrieve
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chunk_range: object
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corresponding range object for the specified chunker (for
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DateChunker it is a DateRange object or a DatetimeIndex,
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as returned by pandas.date_range
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filter_data: boolean
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perform chunk level filtering on the data (see filter in _chunker)
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only applicable when chunk_range is specified
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kwargs: ?
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values passed to the serializer. Varies by serializer
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Returns
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-------
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DataFrame or Series
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```
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Example usage:
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```
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>>> dr = pd.date_range(start='2010-01-01', periods=1000, freq='D')
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>>> df = DataFrame(data={'data': np.random.randint(0, 100, size=1000),
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'date': dr
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})
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>>> lib.write('symbol_name', df, chunk_size='M')
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>>> lib.read('symbol_name', chunk_range=pd.date_range('2012-09-01', '2016-01-01'))
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data date
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0 61 2012-09-01
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1 69 2012-09-02
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2 96 2012-09-03
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3 23 2012-09-04
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4 66 2012-09-05
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5 54 2012-09-06
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6 21 2012-09-07
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7 92 2012-09-08
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8 95 2012-09-09
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9 24 2012-09-10
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10 87 2012-09-11
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11 33 2012-09-12
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12 59 2012-09-13
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13 54 2012-09-14
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14 48 2012-09-15
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15 67 2012-09-16
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16 73 2012-09-17
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17 72 2012-09-18
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18 6 2012-09-19
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19 24 2012-09-20
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20 8 2012-09-21
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21 50 2012-09-22
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22 40 2012-09-23
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23 45 2012-09-24
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24 8 2012-09-25
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25 73 2012-09-26
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```
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# [rename](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L151)
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```
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rename(from_symbol, to_symbol)
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Rename a symbol
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Parameters
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----------
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from_symbol: str
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the existing symbol that will be renamed
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to_symbol: str
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the new symbol name
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```
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# [reverse_iterator](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L498)
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```
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reverse_iterator(symbol, chunk_range=None):
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Returns a generator that accesses each chunk in descending order
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Parameters
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----------
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symbol: str
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the symbol for the given item in the DB
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chunk_range: None, or a range object
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allows you to subset the chunks by range
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Returns
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-------
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generator
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```
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# [update](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L394)
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```
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update(symbol, item, chunk_range=None, upsert=False, **kwargs)
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Overwrites data in DB with data in item for the given symbol.
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Is idempotent
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Parameters
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----------
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symbol: str
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the symbol for the given item in the DB
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item: DataFrame or Series
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the data to update
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chunk_range: None, or a range object
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If a range is specified, it will clear/delete the data within the
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range and overwrite it with the data in item. This allows the user
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to update with data that might only be a subset of the
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original data.
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upsert: bool
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if True, will write the data even if the symbol does not exist.
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kwargs:
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optional keyword args passed to write during an upsert. Includes:
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chunk_size
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chunker
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```
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# [write](https://github.com/manahl/arctic/blob/master/arctic/chunkstore/chunkstore.py#L230)
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```
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write(symbol, item, chunker=DateChunker(), **kwargs)
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Writes data from item to symbol in the database
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Parameters
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----------
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symbol: str
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the symbol that will be used to reference the written data
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item: Dataframe or Series
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the data to write the database
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chunker: Object of type Chunker
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A chunker that chunks the data in item
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kwargs:
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optional keyword args that are passed to the chunker. Includes:
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chunk_size:
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used by chunker to break data into discrete chunks.
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see specific chunkers for more information about this param.
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```
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Example usage:
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```
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>>> dr = pd.date_range(start='2010-01-01', periods=1000, freq='D')
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>>> df = DataFrame(data={'data': np.random.randint(0, 100, size=1000),
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'date': dr
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})
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>>> lib.write('symbol_name', df, chunk_size='M')
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```
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‎docs/quickstart.md

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## Quickstart
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### Install Arctic
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```
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pip install git+https://github.com/manahl/arctic.git
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```
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### Run a MongoDB
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```
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mongod --dbpath <path/to/db_directory>
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```
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### Using VersionStore
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```
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from arctic import Arctic
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import quandl
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# Connect to Local MONGODB
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store = Arctic('localhost')
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# Create the library - defaults to VersionStore
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store.initialize_library('NASDAQ')
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# Access the library
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library = store['NASDAQ']
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# Load some data - maybe from Quandl
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aapl = quandl.get("WIKI/AAPL", authtoken="your token here")
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# Store the data in the library
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library.write('AAPL', aapl, metadata={'source': 'Quandl'})
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# Reading the data
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item = library.read('AAPL')
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aapl = item.data
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metadata = item.metadata
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```
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VersionStore supports much more: [See the HowTo](howtos/how_to_use_arctic.py)!
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### Adding your own storage engine
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Plugging a custom class in as a library type is straightforward. [This example

‎docs/releasing.md

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## PyPI
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Package is hosted here: https://pypi.python.org/pypi/arctic/
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## General upload and packaging docs
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http://peterdowns.com/posts/first-time-with-pypi.html
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## Pre-requisites
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* Ensure you have pypandoc installed for converting the `README.md` to rst for pypi.
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* Configure `.pypirc` to have appropriate credentials for upload. `@burrowsa` `@jamesblackburn` have access
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```
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pip install pypandoc
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```
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## Procedure
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1. Confirm the version number is sane: `grep version= setup.py`
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1. Ensure the working directory is clean: `git status`
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1. Register the egg: `python setup.py register -r pypi`
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1. Upload the source-dist: `python setup.py sdist upload -r pypi`
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1. Upload the egg: `python setup.py build bdist_egg upload -r pypi`
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1. Tag the package: `git tag v1.0.0 -m "Tagging v1.0.0" && git push --tags`
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1. Update the version number in setup.py and push to master

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