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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"116rnwsnHWut8LLR4zNihd2p3Md5350xj","authorship_tag":"ABX9TyPM9Fy0sBvZR+786XJHY4L6"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["import json\n","import csv\n","\n","# Load JSON objects as dictionary\n","df = json.load(open('drive/MyDrive/Colab_Notebooks/123/education.json'))\n","df_black = json.load(open('drive/MyDrive/Colab_Notebooks/123/education_black.json'))\n","\n","# Order the datasets by area_name and year collected\n","df = sorted(df, key=lambda x: (x['area_name'], x['year'], x['variable']))\n","df_black = sorted(df_black, key=lambda x: (x['area_name'], x['year']))\n","\n","# Clean the education dataset\n","df = [entry for entry in df if \"25\" in entry['variable'] and\n"," \"Native\" not in entry['variable'] and\n"," \"Black\" not in entry['variable'] and\n"," \"White\" not in entry['variable']]\n","\n","# Rename the variable in both datasets for matching convenience\n","for entry in df:\n"," variable = entry['variable']\n"," if 'College Graduates' in variable:\n"," entry['variable'] = 'College Graduates'\n"," elif 'High School Graduates' in variable:\n"," entry['variable'] = 'High School Graduates'\n"," elif 'Elementary School Education or Less' in variable:\n"," entry['variable'] = 'Elementary School Education'\n"," elif 'Less Than 5 Years of Elementary School' in variable:\n"," entry['variable'] = 'Less Than 5 Years of Elementary School'\n","\n","for entry in df_black:\n"," variable = entry['variable']\n"," if 'College' in variable:\n"," entry['variable'] = 'College Graduates'\n"," elif 'High School Graduate' in variable:\n"," entry['variable'] = 'High School Graduates'\n"," elif 'Less than High School' in variable:\n"," entry['variable'] = 'Elementary School Education'\n","\n","# Re-structure the datasets\n","transformed_df = {}\n","for entry in df:\n"," area_name = entry['area_name']\n"," area_type = entry['area_type']\n"," year = entry['year']\n"," variable = entry['variable']\n"," value = entry['value']\n","\n"," if area_name not in transformed_df:\n"," transformed_df[area_name] = {\n"," \"area_type\": area_type,\n"," \"years\": {}\n"," }\n","\n"," if year not in transformed_df[area_name][\"years\"]:\n"," transformed_df[area_name][\"years\"][year] = []\n","\n"," transformed_df[area_name][\"years\"][year].append({\n"," \"variable\": variable,\n"," \"value\": value\n"," })\n","\n","transformed_df_black = {}\n","for entry in df_black:\n"," area_name = entry['area_name']\n"," area_type = entry['area_type']\n"," year = entry['year']\n"," variable = entry['variable']\n"," value = entry['value']\n","\n"," if area_name not in transformed_df_black:\n"," transformed_df_black[area_name] = {\n"," \"area_type\": area_type,\n"," \"years\": {}\n"," }\n","\n"," if year not in transformed_df_black[area_name][\"years\"]:\n"," transformed_df_black[area_name][\"years\"][year] = []\n","\n"," transformed_df_black[area_name][\"years\"][year].append({\n"," \"variable\": variable,\n"," \"value_black\": value\n"," })\n","\n","# Combine the datasets\n","result = {}\n","for area_name, area_data in transformed_df.items():\n"," if area_name in transformed_df_black:\n"," result[area_name] = {'area_type': area_data['area_type'], 'years': {}}\n"," for year, year_data in area_data['years'].items():\n"," if year in transformed_df_black[area_name]['years']:\n"," result[area_name]['years'][year] = year_data\n","\n","for area_name, area_data in result.items():\n"," for year, year_data in area_data['years'].items():\n"," for entry in year_data:\n"," variable = entry['variable']\n"," black_year_data = transformed_df_black.get(area_name, {}).get('years', {}).get(year, [])\n"," black_entry = next((e for e in black_year_data if e['variable'] == variable), None)\n"," if black_entry:\n"," entry['value_black'] = black_entry['value_black']\n"," else:\n"," entry['value_black'] = None\n","\n","# Save the nested structure data to JSON\n","file_path = '/content/drive/MyDrive/Colab_Notebooks/NC_Education/NC_Education_Nested.json'\n","with open(file_path, 'w') as json_file:\n"," json.dump(result, json_file)"],"metadata":{"id":"77MbG27tJ1-r","executionInfo":{"status":"ok","timestamp":1710779457737,"user_tz":240,"elapsed":2143,"user":{"displayName":"Yangxuan Xu","userId":"16693520489565507742"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["# Flatten the data and write to CSV\n","csv_data = []\n","for area_name, area_data in result.items():\n"," area_type = area_data['area_type']\n"," for year, year_data in area_data['years'].items():\n"," for entry in year_data:\n"," csv_data.append({\n"," 'area_name': area_name,\n"," 'area_type': area_type,\n"," 'year': year,\n"," 'variable': entry['variable'],\n"," 'value': entry['value'],\n"," 'value_black': entry['value_black']\n"," })\n","\n","file_path = '/content/drive/MyDrive/Colab_Notebooks/Previous/NC_Education_Final.csv'\n","with open(file_path, 'w', newline='') as csv_file:\n"," fieldnames = ['area_name', 'area_type', 'year', 'variable', 'value', 'value_black']\n"," writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n","\n"," writer.writeheader()\n"," writer.writerows(csv_data)"],"metadata":{"id":"-Uge7AIgZsZb"},"execution_count":null,"outputs":[]}]} |