data_base
❭ IO
❭ LoaderDumper
LoaderDumper¶
Read and write data in various formats.
This package provides IO modules that always contain three components:
A
dump()
function to write out the data, and its correspondingLoader
object.A
Loader
class that can load the data back into memory.a
check()
method that checks whether the object can be saved with this dumper.
To save an object, the dump method is called:
>>> import my_dumper
>>> my_dumper.dump(obj, savedir)
This saves the object as specified in the respective dump()
method.
In addition, a Loader.json
is saved alongside the data.
This file contains the specification of a Loader
object,
which can then be initialized and contains all the mechanisms to load the object back into memory.
Functions¶
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Resolve a loader path to an absolute path. |
|
Standard interface to load data. |
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Convert a dumper submodule to a string. |
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Get the dumper string from a filepath. |
Modules¶
Save and load |
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Save and load dask dataframes to msgpack with categorical columns. |
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Save and load dask dataframes to msgpack. |
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Save and load dask dataframes to and from Apache parquet format. |
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Create a folder and return it as a ManagedFolder object. |
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Create and load |
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Read and write numpy arrays to msgpack files |
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Read and write a numpy array to |
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Read and write a numpy array to the compressed |
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Read and write a numpy array to the |
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Save and load pandas dataframes to msgpack files. |
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Read and write a pandas DataFrame to the parquet format. |
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Read and write a pandas DataFrame to the pickle format. |
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Base class for child Loader classes |
|
Read and write a |
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Read and write numpy arrays to and from shared memory. |
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Read and write an object to the cloudpickle format. |
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Read and write objects to the msgpack format. |
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Read and write objects to the pickle format. |
|
Convenience methods for data IO. |