Icon

kn_​example_​python_​pickle_​dictionary

KNIME/Python - write and read back a dictionary

KNIME/Python - write and read back a dictionary


Pickle any Python object you want, save it as a ZIP file with the model wriiter node and read it back



save a dictionary in Python to disc and reload it laterhttps://forum.knime.com/t/output-a-dictionary-in-python-snippet/13543/4?u=mlauber71 import knime.scripting.io as knioimport numpy as npimport pandas as pdfrom pandas import DataFrameimport pickle# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_dict.html# https://stackoverflow.com/questions/26716616/convert-a-pandas-dataframe-to-a-dictionary/26716774a = {'a': ['red', 'yellow', 'blue'], 'b': [0.5, 0.25, 0.125]}pkl_file = knio.flow_variables['context.workflow.data-path'] + 'my_dictionary.pickle' with open(pkl_file, 'wb') as handle: pickle.dump(a, handle, protocol=pickle.HIGHEST_PROTOCOL)output_table = pd.DataFrame.from_dict(a, orient='columns')output_table = output_table.set_index(output_table.index.astype(str) + '_i1')knio.output_tables[0] = knio.Table.from_pandas(output_table) import knime.scripting.io as knioimport numpy as npimport pandas as pdfrom pandas import DataFrameimport pickle# reimport the listfrom dataframeinput_table = knio.input_tables[0].to_pandas()a = input_table.to_dict('list')pkl_file = knio.flow_variables['context.workflow.data-path'] + 'my_dictionary.pickle' with open(pkl_file, 'rb') as handle: b = pickle.load(handle)# c = pd.DataFrame.from_dict(b, orient='index')print(a == b)output_table = pd.DataFrame.from_dict(b, orient='columns')output_table = output_table.set_index(output_table.index.astype(str) + '_i2')knio.output_tables[0] = knio.Table.from_pandas(output_table) Pickle any Python object you want, save it as a ZIP file with the model writer node and read it back import knime.scripting.io as knioimport pandas as pdmy_dictionary = {'a': ['red', 'yellow', 'blue'], 'b': [0.5, 0.25, 0.125]}output_table = pd.DataFrame.from_dict(my_dictionary, orient='columns')output_table = output_table.set_index(output_table.index.astype(str) + '_i3')# These are the node's outputs that need to be populated:knio.output_tables[0] = knio.Table.from_pandas(output_table)knio.output_objects[0] = my_dictionary import knime.scripting.io as knioimport pandas as pdmy_dictionary_imported = knio.input_objects[0]output_table = pd.DataFrame.from_dict(my_dictionary_imported, orient='columns')# These are the node's outputs that need to be populated:output_table = output_table.set_index(output_table.index.astype(str) + '_i4')# output_table = output_table.reset_index(drop=True)knio.output_tables[0] = knio.Table.from_pandas(output_table) import knime.scripting.io as knio# bring the new style of an import object to the tranditional syntaxinput_object = knio.input_objects[0] import json# set the path for the pickel filepath = knio.flow_variables['context.workflow.data-path'] + 'my_dictionary.json'# Write the dictionary to a JSON filewith open(path, "w") as f: json.dump(input_object, f) import knime.scripting.io as knioimport json# set the path for the pickel filepath = knio.flow_variables['context.workflow.data-path'] + 'my_dictionary.json'with open(path , "r") as f: v_variable_list = json.load(f) knio.output_objects[0] = v_variable_list import knime.scripting.io as knioimport pandas as pdmy_dictionary_imported = knio.input_objects[0]output_table = pd.DataFrame.from_dict(my_dictionary_imported, orient='columns')# These are the node's outputs that need to be populated:output_table = output_table.set_index(output_table.index.astype(str) + '_i5')# output_table = output_table.reset_index(drop=True)knio.output_tables[0] = knio.Table.from_pandas(output_table) locate and create/data/ folderwith absolute pathssee if the results matchwrite dictionarymy_dictionary.zipPath variable created forvar_model_pathmy_dictionary.zipread dictionarysee if the results matchwrite dictionaryread dictionarymy_dictionary.jsonmy_dictionary.jsonsee if the results matchread dictionary Collect LocalMetadata Concatenate Python Script Model Writer Model Reader Python Script Concatenate Python Script Python Script Python Script Python Script Concatenate Python Script save a dictionary in Python to disc and reload it laterhttps://forum.knime.com/t/output-a-dictionary-in-python-snippet/13543/4?u=mlauber71 import knime.scripting.io as knioimport numpy as npimport pandas as pdfrom pandas import DataFrameimport pickle# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_dict.html# https://stackoverflow.com/questions/26716616/convert-a-pandas-dataframe-to-a-dictionary/26716774a = {'a': ['red', 'yellow', 'blue'], 'b': [0.5, 0.25, 0.125]}pkl_file = knio.flow_variables['context.workflow.data-path'] + 'my_dictionary.pickle' with open(pkl_file, 'wb') as handle: pickle.dump(a, handle, protocol=pickle.HIGHEST_PROTOCOL)output_table = pd.DataFrame.from_dict(a, orient='columns')output_table = output_table.set_index(output_table.index.astype(str) + '_i1')knio.output_tables[0] = knio.Table.from_pandas(output_table) import knime.scripting.io as knioimport numpy as npimport pandas as pdfrom pandas import DataFrameimport pickle# reimport the listfrom dataframeinput_table = knio.input_tables[0].to_pandas()a = input_table.to_dict('list')pkl_file = knio.flow_variables['context.workflow.data-path'] + 'my_dictionary.pickle' with open(pkl_file, 'rb') as handle: b = pickle.load(handle)# c = pd.DataFrame.from_dict(b, orient='index')print(a == b)output_table = pd.DataFrame.from_dict(b, orient='columns')output_table = output_table.set_index(output_table.index.astype(str) + '_i2')knio.output_tables[0] = knio.Table.from_pandas(output_table) Pickle any Python object you want, save it as a ZIP file with the model writer node and read it back import knime.scripting.io as knioimport pandas as pdmy_dictionary = {'a': ['red', 'yellow', 'blue'], 'b': [0.5, 0.25, 0.125]}output_table = pd.DataFrame.from_dict(my_dictionary, orient='columns')output_table = output_table.set_index(output_table.index.astype(str) + '_i3')# These are the node's outputs that need to be populated:knio.output_tables[0] = knio.Table.from_pandas(output_table)knio.output_objects[0] = my_dictionary import knime.scripting.io as knioimport pandas as pdmy_dictionary_imported = knio.input_objects[0]output_table = pd.DataFrame.from_dict(my_dictionary_imported, orient='columns')# These are the node's outputs that need to be populated:output_table = output_table.set_index(output_table.index.astype(str) + '_i4')# output_table = output_table.reset_index(drop=True)knio.output_tables[0] = knio.Table.from_pandas(output_table) import knime.scripting.io as knio# bring the new style of an import object to the tranditional syntaxinput_object = knio.input_objects[0] import json# set the path for the pickel filepath = knio.flow_variables['context.workflow.data-path'] + 'my_dictionary.json'# Write the dictionary to a JSON filewith open(path, "w") as f: json.dump(input_object, f) import knime.scripting.io as knioimport json# set the path for the pickel filepath = knio.flow_variables['context.workflow.data-path'] + 'my_dictionary.json'with open(path , "r") as f: v_variable_list = json.load(f) knio.output_objects[0] = v_variable_list import knime.scripting.io as knioimport pandas as pdmy_dictionary_imported = knio.input_objects[0]output_table = pd.DataFrame.from_dict(my_dictionary_imported, orient='columns')# These are the node's outputs that need to be populated:output_table = output_table.set_index(output_table.index.astype(str) + '_i5')# output_table = output_table.reset_index(drop=True)knio.output_tables[0] = knio.Table.from_pandas(output_table) locate and create/data/ folderwith absolute pathssee if the results matchwrite dictionarymy_dictionary.zipPath variable created forvar_model_pathmy_dictionary.zipread dictionarysee if the results matchwrite dictionaryread dictionarymy_dictionary.jsonmy_dictionary.jsonsee if the results matchread dictionaryCollect LocalMetadata Concatenate Python Script Model Writer Model Reader Python Script Concatenate Python Script Python Script Python Script Python Script Concatenate Python Script

Nodes

Extensions

  • No modules found

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: m_lauber
Created at: 2019-02-21
On NodePit since: 2019-10-05
Last update: 2026-07-23
Created with KNIME version: v4.7.0
Tags: pythonknimepicklesavedictionarymodelwriterzip

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!