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Data Processing Code.ipynb
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"116rnwsnHWut8LLR4zNihd2p3Md5350xj","authorship_tag":"
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"116rnwsnHWut8LLR4zNihd2p3Md5350xj","authorship_tag":"ABX9TyONmx1K/XKi7Wd8yyIElmmd"},"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","# 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":"77MbG27tJ1-r","executionInfo":{"status":"ok","timestamp":1710725001227,"user_tz":240,"elapsed":1453,"user":{"displayName":"Yangxuan Xu","userId":"16693520489565507742"}}},"execution_count":9,"outputs":[]}]}
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Dataset loading script.ipynb
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPWvPiBjqMDQ8utsa3qc6MF"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["import json\n","import os\n","\n","import datasets\n","\n","_DESCRIPTION = \"\"\"\n","The datasets were collected and published to present the educational level of NC population in different areas. The educational attainment for the black population data can raise concern for the educational equity issue in North Carolina. The combined dataset aims to offer a holistic perspective on educational levels and equity, with a specific focus on the educational attainment of the Black population aged 25 and over.\n","\"\"\"\n","\n","_HOMEPAGE = \"https://huggingface.co/datasets/YXu120/NC_Education\"\n","\n","_LICENSE = \"cc-by-sa-4.0\"\n","\n","_URL = \"https://drive.google.com/file/d/1Au9xwsnDkRx4TMWwndWKbzLR5u9WXUQZ\"\n","\n","class NCEducationDataset(datasets.GeneratorBasedBuilder):\n"," VERSION = datasets.Version(\"1.0.0\")\n","\n"," def _info(self):\n"," features = datasets.Features(\n"," {\n"," \"area_name\": datasets.Value(\"string\"),\n"," \"area_type\": datasets.Value(\"string\"),\n"," \"years\": datasets.Sequence(\n"," {\n"," \"year\": datasets.Value(\"string\"),\n"," \"variables\": datasets.Sequence(\n"," {\n"," \"variable\": datasets.Value(\"string\"),\n"," \"value\": datasets.Value(\"int\"),\n"," \"value_black\": datasets.Value(\"int\"),\n"," }\n"," ),\n"," }\n"," ),\n"," }\n"," )\n","\n"," return datasets.DatasetInfo(\n"," description=_DESCRIPTION,\n"," features=features,\n"," homepage=_HOMEPAGE,\n"," license=_LICENSE,\n"," )\n","\n"," def _split_generators(self, dl_manager):\n"," data_file = dl_manager.download(_URL)\n"," return [\n"," datasets.SplitGenerator(\n"," name=\"train\",\n"," gen_kwargs={\n"," \"filepath\": data_file,\n"," },\n"," )\n"," ]\n","\n"," def _generate_examples(self, filepath):\n"," with open(filepath, \"r\", encoding=\"utf-8\") as file:\n"," data = json.load(file)\n"," for idx, (area_name, area_data) in enumerate(data.items()):\n"," years_data = []\n"," for year, variables in area_data[\"years\"].items():\n"," year_data = {\n"," \"year\": year,\n"," \"variables\": [\n"," {\n"," \"variable\": variable[\"variable\"],\n"," \"value\": variable[\"value\"],\n"," \"value_black\": variable.get(\"value_black\"),\n"," }\n"," for variable in variables\n"," ],\n"," }\n"," years_data.append(year_data)\n","\n"," yield idx, {\n"," \"area_name\": area_name,\n"," \"area_type\": area_data[\"area_type\"],\n"," \"years\": years_data,\n"," }"],"metadata":{"id":"EydAuFx4AKlR","executionInfo":{"status":"ok","timestamp":1710724427785,"user_tz":240,"elapsed":132,"user":{"displayName":"Yangxuan Xu","userId":"16693520489565507742"}}},"execution_count":15,"outputs":[]}]}
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