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