[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:import pandas as pd\nimport json\nimport vincent\nfrom vincent import AxisProperties, PropertySet, ValueRef\nvincent.core.initialize_notebook()\nwith open('us_counties.topo.json', 'r') as f:\nget_id = json.load(f)\nnew_geoms = []\nfor geom in get_id['objects']['us_counties.geo']['geometries']:\ngeom['properties']['FIPS'] = int(geom['properties']['FIPS'])\nnew_geoms.append(geom)\nget_id['objects']['us_counties.geo']['geometries'] = new_geoms\nwith open('us_counties.topo.json', 'w') as f:\njson.dump(get_id, f)\ngeometries = get_id['objects']['us_counties.geo']['geometries']\ncounty_codes = [x['properties']['FIPS'] for x in geometries]\ncounty_df = pd.DataFrame({'FIPS': county_codes}, dtype=str)\ncounty_df = county_df.astype(int)\ndf['FIPS'] = df['FIPS'].astype(int)\nmerged = pd.merge(df, county_df, on='FIPS', how='inner')\nmerged = merged.fillna(method='pad')\nstate_topo = 'us_states.topo.json'\ncounty_topo = 'us_counties.topo.json'\ngeo_data = [{'name': 'states',\n'url': state_topo,\n'feature': 'us_states.geo'},\n{'name': 'counties',\n'url': county_topo,\n'feature': 'us_counties.geo'}]\nvis = vincent.Map(data=merged, geo_data=geo_data, scale=1100, projection='albersUsa',\ndata_bind='white_diff', data_key='FIPS',\nmap_key={'counties': 'properties.FIPS'})\nvis.marks[0].properties.enter.stroke_opacity = ValueRef(value=0.5)\nvis.to_json('vega.json')\ndel vis.marks[1].properties.update\nvis.marks[0].properties.update.fill.value = '#fff'\nvis.marks[1].properties.enter.stroke.value = '#000'\nvis.marks[0].properties.enter.stroke.value = '#808080'\nvis.display()"}, {'role': 'assistant', 'content': "['helper_functions', 'load_data', 'data_exploration']"}]