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pandas-stash

A utility to save and load entire workspaces containing pandas objects, numpy arrays and scalars. Inspired by git stash and other programming languages that have simple methods to save and restore the workspace.

import pandas as pd
from pandas_stash import stash, unstash()
df = pd.DataFrame([[1,2],[3,4]])
stash()
del df
unstash()
print(df)

By default the stash will attempt to get variables from the global frame. The keyword argument frame can be used to explicitly pass a particular frame.

See advanced examples for more options.

stash(frame=globals())

Limitations

Currently will store pandas objects:

  • Series
  • DataFrame

Numpy arrays with dimensions 1, 2, 3 and 4 with dtypes:

  • uint8, uint16, uint32, uint64
  • int8, int16, int32, int64
  • float32, float64
  • bool
  • str

Scalar values of the type:

  • int
  • str
  • float
  • unicode

Complex (scalar or numpy) values are NOT supported due to limitations in pandas and pytables. This should be fixed after pandas 0.17 is released

Requirements

  • pandas>=0.15
  • numpy>=1.7
  • pytables>=3.0