parameters: (self, backend)
return_statement: return backend.make_dataframe(snapshot_chain)
docstring: With local RDataFrame, Snapshot returns another RDataFrame object that
          can be used to continue the application. The equivalent in the
          distributed scenario is to create a distributed RDataFrame.
  
          This is done by constructing a TChain with the name and the list of
          paths stored in this object. The chain is then passed to the
          `make_dataframe` function that changes depending on the backend.
  
          For example, if the original RDataFrame that triggered the distributed
          computation was created via a Spark backend, then this function will
          return another distributed RDataFrame build from a Spark backend
          instance. And so on for all other DistRDF backends.
docstring_summary: With local RDataFrame, Snapshot returns another RDataFrame object that
          can be used to continue the application. The equivalent in the
          distributed scenario is to create a distributed RDataFrame.
docstring_tokens: With local RDataFrame Snapshot returns another RDataFrame object that can be used to continue the application . The equivalent in the distributed scenario is to create a distributed RDataFrame .
function: def GetValue(self, backend):
          """
          With local RDataFrame, Snapshot returns another RDataFrame object that
          can be used to continue the application. The equivalent in the
          distributed scenario is to create a distributed RDataFrame.
  
          This is done by constructing a TChain with the name and the list of
          paths stored in this object. The chain is then passed to the
          `make_dataframe` function that changes depending on the backend.
  
          For example, if the original RDataFrame that triggered the distributed
          computation was created via a Spark backend, then this function will
          return another distributed RDataFrame build from a Spark backend
          instance. And so on for all other DistRDF backends.
          """
          snapshot_chain = ROOT.TChain(self.treename)
          # Add partial snapshot files to the chain
          for filename in self.filenames:
              snapshot_chain.Add(filename)
          # Create a new rdf with the chain and return that to user
          return backend.make_dataframe(snapshot_chain)
function_tokens: def GetValue ( self , backend ) : snapshot_chain = ROOT . TChain ( self . treename ) # Add partial snapshot files to the chain for filename in self . filenames : snapshot_chain . Add ( filename ) # Create a new rdf with the chain and return that to user return backend . make_dataframe ( snapshot_chain )