[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:url = ('https://data.detroitmi.gov/resource/i9ph-uyrp.json?'\n'&$select=caseid,address,hour,incidentdate,lat,lon,neighborhood,category,offensedescription'\n'&neighborhood=GREEN%20ACRES'\n'&$limit=100000')\ngreenacres = pd.read_json(url)                           # Reads in file from url in json format\ngreenacres = greenacres.rename(columns={'caseid':'Case ID',   # Renames the columns\n'address':'Address',\n'hour':'Hour',\n'incidentdate':'Incident Date',\n'lat':'Latitude',\n'lon':'Longitude',\n'neighborhood':'Neighborhood',\n'category':'Category',\n'offensedescription':'Offense Description'})\nurl = ('https://data.detroitmi.gov/resource/i9ph-uyrp.json?'\n'&$select=caseid,address,hour,incidentdate,lat,lon,neighborhood,category,offensedescription'\n'&neighborhood=DENBY'\n'&$limit=100000')\ndenby = pd.read_json(url)                           # Reads in file from url in json format\ndenby = denby.rename(columns={'caseid':'Case ID',   # Renames the columns\n'address':'Address',\n'hour':'Hour',\n'incidentdate':'Incident Date',\n'lat':'Latitude',\n'lon':'Longitude',\n'neighborhood':'Neighborhood',\n'category':'Category',\n'offensedescription':'Offense Description'})\ngreenacres['Year'] = pd.DatetimeIndex(greenacres['Incident Date']).year     # Adding a column for the year of the crime\ndenby['Year'] = pd.DatetimeIndex(denby['Incident Date']).year               # Adding a column for the year of the crime\ngreenacres.insert(0, 'Count', 1)                                            # Adds a column containing a 1 for each row\ndenby.insert(0, 'Count', 1)                                                 # Adds a column containing a 1 for each row\ngreenacres = greenacres[greenacres.Year != 2016]                            # Excludes data from 2016 (not a full year)\ndenby = denby[denby.Year != 2016]                                           # Excludes data from 2016 (not a full year)\ngreenacrespivot = pd.pivot_table(greenacres,                             # Creates a pivot table\nindex=['Year','Category'],               # for the year and category of crime\naggfunc=np.sum)                          # by the sum of the count column created above\ngreenacrespivot = greenacrespivot.unstack('Category')           # Unstacks category and moves it to the top\ngreenacrespivot = greenacrespivot['Count'].fillna(value=0)      # Selects only the count columns & fills in NaN values with 0\ngreenacrestop10 = greenacrespivot[['ASSAULT','LARCENY',         # Selects the top 10 Detroit crimes\n'BURGLARY',\n'DAMAGE TO PROPERTY',\n'STOLEN VEHICLE',\n'AGGRAVATED ASSAULT',\n'TRAFFIC',\n'ROBBERY','FRAUD',\n'DANGEROUS DRUGS']]\ndenbypivot = pd.pivot_table(denby,                                      # Creates a pivot table\nindex=['Year','Category'],                  # for the year and category of crime\naggfunc=np.sum)                             # by the sum of the count column created above\ndenbypivot = denbypivot.unstack('Category')                      # Unstacks category and moves it to the top\ndenbypivot = denbypivot['Count'].fillna(value=0)                 # Selects only the count columns & fills in NaN values with 0\ndenbytop10 = denbypivot[['ASSAULT','LARCENY','BURGLARY',         # Selects the top 10 Detroit crimes\n'DAMAGE TO PROPERTY','STOLEN VEHICLE',\n'AGGRAVATED ASSAULT','TRAFFIC',\n'ROBBERY','FRAUD','DANGEROUS DRUGS']]\nfig, ax = plt.subplots()                                   # Creates a figure\ngreenacrestop10.plot(ax=ax,figsize=(12,6))                   # Plots the dataframe\nax.legend(bbox_to_anchor=(1.3, 1))                         # Moves the legend off the graph\nax.set_ylabel('Number of Offenses', fontsize=14)           # Label the y-axis\nax.set_title('Top 10 Offenses Over Time (Green Acres)',       # Title the graph\nfontsize=16, fontweight='bold')\nax.ticklabel_format(useOffset=False)                       # Make sure the years show on the x-axis\nplt.show()\nfig, ax = plt.subplots()                                   # Creates a figure\ndenbytop10.plot(ax=ax,figsize=(12,6))                      # Plots the dataframe\nax.legend(bbox_to_anchor=(1.3, 1))                         # Moves the legend off the graph\nax.set_ylabel('Number of Offenses', fontsize=14)           # Label the y-axis\nax.set_title('Top 10 Offenses Over Time (Denby)',          # Title the graph\nfontsize=16, fontweight='bold')\nax.ticklabel_format(useOffset=False)                       # Make sure the years show on the x-axis\nplt.show()"}, {'role': 'assistant', 'content': 'data_exploration'}]