2020-1-capstone-design2 / 2016104140

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ecf8cf63 1 parent ae7408c4

final ele,middle,high schools dataset configured

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{
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"source": [
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n",
"from tensorflow.keras.optimizers import RMSprop\n",
"from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau\n",
"from tensorflow.keras.backend import square, mean\n",
"\n",
"basic_folder = '/Users/hyewon/Documents/capstone/2016104140/code/dataset/'\n",
"file_name = basic_folder + 'final_middle_school.csv'\n",
"file_name2=basic_folder + 'middle_timetable.csv'\n",
"df =pd.read_csv(file_name)\n",
"df2 =pd.read_csv(file_name2)\n",
"\n",
"target_location=['latitude','longitude']\n",
"target_names=['1_stu_num','2_stu_num','3_stu_num']\n",
"\n",
"\n",
"shift_days = 1\n",
"shift_steps = shift_days * 24\n",
"\n",
"df_targets = df[target_location][target_names].shift(-shift_steps)\n",
"df[target_location][target_names].shift(-shift_steps)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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" ... \n",
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"Name: 위도, Length: 11873, dtype: float64"
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"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'elementary_middle_schools_location.csv'\n",
"df =pd.read_csv(file_name)\n",
"\n",
"latitude=df['위도']\n",
"longitude=df['경도']\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 21398/21398 [00:16<00:00, 1308.22it/s]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name2 = basic_folder + '2019_students_num.csv'\n",
"df2 =pd.read_csv(file_name2)\n",
"\n",
"count_row=df2.shape[0] #number of rows\n",
"elementary=[]\n",
"middle=[]\n",
"high=[]\n",
"\n",
"def type_of_school():\n",
" for x in tqdm(range(count_row)):\n",
" type=df2.loc[x]['학교급']\n",
" if(type=='고등학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['1학년_학생수_계'],df2.loc[x]['2학년_학생수_계'],df2.loc[x]['3학년_학생수_계']]\n",
" high.append(row)\n",
" elif(type=='중학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['1학년_학생수_계'],df2.loc[x]['2학년_학생수_계'],df2.loc[x]['3학년_학생수_계']]\n",
" middle.append(row)\n",
" elif(type=='초등학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['1학년_학생수_계'],df2.loc[x]['2학년_학생수_계'],df2.loc[x]['3학년_학생수_계'],df2.loc[x]['4학년_학생수_계'],df2.loc[x]['5학년_학생수_계'],df2.loc[x]['6학년_학생수_계']]\n",
" elementary.append(row)\n",
"\n",
"type_of_school()\n",
" \n",
"columns=['school_name','1_stu_num','2_stu_num','3_stu_num']\n",
"high_df=pd.DataFrame(high,columns=columns)\n",
"high_df.to_csv(r'high_school_stu_num.csv')\n",
"\n",
"middle_df=pd.DataFrame(middle,columns=columns)\n",
"middle_df.to_csv(r'middle_school_stu_num.csv')\n",
"\n",
"columns1=['school_name','1_stu_num','2_stu_num','3_stu_num','4_stu_num','5_stu_num','6_stu_num']\n",
"ele_df=pd.DataFrame(elementary,columns=columns1)\n",
"ele_df.to_csv(r'elementary_school_stu_num.csv')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/11873 [00:00<?, ?it/s]\n"
]
},
{
"ename": "KeyError",
"evalue": "'도로명주소'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 4409\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4410\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value_at\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4411\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.get_value_at\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.validate_indexer\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-924f5168e0e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0melementary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mtype_of_school\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'school_name'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'school_addr'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mmiddle_df\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmiddle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-9-924f5168e0e8>\u001b[0m in \u001b[0;36mtype_of_school\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mmiddle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32melif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'초등학교'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'학교명'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'도로명주소'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'위도'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'경도'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0melementary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 869\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 870\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 871\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 873\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 4416\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4417\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4418\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4419\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4420\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 4402\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"getitem\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4403\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4404\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"tz\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4405\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4406\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: '도로명주소'"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'elementary_middle_schools_location.csv'\n",
"\n",
"df2 =pd.read_csv(file_name)\n",
"count_row=df2.shape[0]\n",
"\n",
"elementary=[]\n",
"middle=[]\n",
"\n",
"def type_of_school():\n",
" for x in tqdm(range(count_row)):\n",
" type=df2.loc[x]['학교급구분']\n",
" if(type=='중학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['소재지도로명주소'],df2.loc[x]['위도'],df2.loc[x]['경도']]\n",
" middle.append(row)\n",
" elif(type=='초등학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['소재지도로명주소'],df2.loc[x]['위도'],df2.loc[x]['경도']]\n",
" elementary.append(row)\n",
"\n",
"type_of_school()\n",
"columns=['school_name','school_addr','latitude','longitude']\n",
"middle_df=pd.DataFrame(middle,columns=columns)\n",
"middle_df.to_csv(r'middle_school.csv')\n",
"\n",
"elem_df=pd.DataFrame(elementary,columns=columns)\n",
"elem_df.to_csv(r'elementary_school.csv')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 2360/2360 [18:07<00:00, 2.17it/s]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'middle_school.csv'\n",
"file_name2 = basic_folder + 'middle_school_stu_num.csv'\n",
"\n",
"middle_df =pd.read_csv(file_name)\n",
"middle_stu_num_df=pd.read_csv(file_name2)\n",
"\n",
"count_row=middle_df.shape[0]\n",
"count_row2=middle_stu_num_df.shape[0]\n",
"\n",
"middle_arr=[]\n",
"\n",
"def find_middle_student_num():\n",
" for x in tqdm(range(count_row)):\n",
" name=middle_df.loc[x]['school_name']\n",
" for y in range(count_row2):\n",
" if name == middle_stu_num_df.loc[y]['school_name']:\n",
" row=[middle_df.loc[x]['school_name'],middle_df.loc[x]['school_addr'],middle_df.loc[x]['latitude'],middle_df.loc[x]['longitude'],\n",
" middle_stu_num_df.loc[y]['1_stu_num'],middle_stu_num_df.loc[y]['2_stu_num'],middle_stu_num_df.loc[y]['3_stu_num']]\n",
" middle_arr.append(row)\n",
" \n",
"\n",
"find_middle_student_num()\n",
"columns=['school_name','school_addr','latitude','longitude','1_stu_num','2_stu_num','3_stu_num']\n",
"final_middle_df=pd.DataFrame(middle_arr,columns=columns)\n",
"final_middle_df.to_csv(r'final_middle_school.csv')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"0it [00:00, ?it/s]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'elementary_school.csv'\n",
"file_name2 = basic_folder + 'elementary_school_stu_num.csv'\n",
"\n",
"ele_df =pd.read_csv(file_name)\n",
"ele_stu_num_df=pd.read_csv(file_name2)\n",
"\n",
"count_row=ele_df.shape[0]\n",
"count_row2=ele_stu_num_df.shape[0]\n",
"\n",
"ele_arr=[]\n",
"\n",
"def find_ele_student_num():\n",
" for x in tqdm(range(count_row)):\n",
" name=ele_df.loc[x]['school_name']\n",
" for y in range(count_row2):\n",
" if name == ele_stu_num_df.loc[y]['school_name']:\n",
" row=[ele_df.loc[x]['school_name'],ele_df.loc[x]['school_addr'],ele_df.loc[x]['latitude'],ele_df.loc[x]['longitude'],\n",
" ele_stu_num_df.loc[y]['1_stu_num'],ele_stu_num_df.loc[y]['2_stu_num'],ele_stu_num_df.loc[y]['3_stu_num'],\n",
" ele_stu_num_df.loc[y]['4_stu_num'],ele_stu_num_df.loc[y]['5_stu_num'],ele_stu_num_df.loc[y]['6_stu_num']]\n",
" ele_arr.append(row)\n",
" \n",
"find_ele_student_num()\n",
"columns=['school_name','school_addr','latitude','longitude','1_stu_num','2_stu_num','3_stu_num','4_stu_num','5_stu_num','6_stu_num']\n",
"final_ele_df=pd.DataFrame(ele_arr,columns=columns)\n",
"final_ele_df.to_csv(r'final_ele_school.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'high_school.csv'\n",
"file_name2 = basic_folder + 'high_school_stu_num.csv'\n",
"\n",
"middle_df =pd.read_csv(file_name)\n",
"middle_stu_num_df=pd.read_csv(file_name2)\n",
"\n",
"count_row=middle_df.shape[0]\n",
"count_row2=middle_stu_num_df.shape[0]\n",
"\n",
"high_arr=[]\n",
"\n",
"def find_high_student_num():\n",
" for x in tqdm(range(count_row)):\n",
" name=middle_df.loc[x]['school_name']\n",
" for y in range(count_row2):\n",
" if name == middle_stu_num_df.loc[y]['school_name']:\n",
" row=[middle_df.loc[x]['school_name'],middle_df.loc[x]['school_addr'],middle_df.loc[x]['latitude'],middle_df.loc[x]['longitude'],\n",
" middle_stu_num_df.loc[y]['1_stu_num'],middle_stu_num_df.loc[y]['2_stu_num'],middle_stu_num_df.loc[y]['3_stu_num']]\n",
" high_arr.append(row)\n",
" \n",
"find_high_student_num()\n",
"columns=['school_name','school_addr','latitude','longitude','1_stu_num','2_stu_num','3_stu_num']\n",
"final_high_df=pd.DataFrame(middle_arr,columns=columns)\n",
"final_high_df.to_csv(r'final_high_school.csv')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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,시작,끝,총 시간
1교시,9:00,9:40,40
2교시,9:50,10:30,40
3교시,10:40,11:20,40
4교시,11:30,12:10,40
5교시,13:00,13:40,40
6교시,13:50,14:30,40
\ No newline at end of file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 2374/2374 [1:28:59<00:00, 2.25s/it]\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>school_name</th>\n",
" <th>address</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>가락고등학교</td>\n",
" <td>서울특별시 송파구 송이로 42</td>\n",
" <td>37.493</td>\n",
" <td>127.125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>가재울고등학교</td>\n",
" <td>서울특별시 서대문구 수색로 100-35</td>\n",
" <td>37.5773</td>\n",
" <td>126.903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>강동고등학교</td>\n",
" <td>서울특별시 강동구 구천면로 572</td>\n",
" <td>37.5501</td>\n",
" <td>127.147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>강서고등학교</td>\n",
" <td>서울특별시 양천구 목동중앙남로 27</td>\n",
" <td>37.5368</td>\n",
" <td>126.867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>강서공업고등학교</td>\n",
" <td>서울특별시 강서구 방화대로47길 9</td>\n",
" <td>37.5762</td>\n",
" <td>126.815</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2369</th>\n",
" <td>표선고등학교</td>\n",
" <td>제주특별자치도 서귀포시 표선면 표선중앙로 22-15</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2370</th>\n",
" <td>한국뷰티고등학교</td>\n",
" <td>제주특별자치도 제주시 한경면 용고로 70</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2371</th>\n",
" <td>한림고등학교</td>\n",
" <td>제주특별자치도 제주시 한림읍 월계로 74</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2372</th>\n",
" <td>한림공업고등학교</td>\n",
" <td>제주특별자치도 제주시 한림읍 한림중앙로 87</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2373</th>\n",
" <td>함덕고등학교</td>\n",
" <td>제주특별자치도 제주시 조천읍 신흥로 9</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2374 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" school_name address latitude longitude\n",
"0 가락고등학교 서울특별시 송파구 송이로 42 37.493 127.125\n",
"1 가재울고등학교 서울특별시 서대문구 수색로 100-35 37.5773 126.903\n",
"2 강동고등학교 서울특별시 강동구 구천면로 572 37.5501 127.147\n",
"3 강서고등학교 서울특별시 양천구 목동중앙남로 27 37.5368 126.867\n",
"4 강서공업고등학교 서울특별시 강서구 방화대로47길 9 37.5762 126.815\n",
"... ... ... ... ...\n",
"2369 표선고등학교 제주특별자치도 서귀포시 표선면 표선중앙로 22-15 \n",
"2370 한국뷰티고등학교 제주특별자치도 제주시 한경면 용고로 70 \n",
"2371 한림고등학교 제주특별자치도 제주시 한림읍 월계로 74 \n",
"2372 한림공업고등학교 제주특별자치도 제주시 한림읍 한림중앙로 87 \n",
"2373 함덕고등학교 제주특별자치도 제주시 조천읍 신흥로 9 \n",
"\n",
"[2374 rows x 4 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from geopy.geocoders import Nominatim\n",
"from geopy.extra.rate_limiter import RateLimiter\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'elementary_middle_schools_location.csv'\n",
"file_name2 = basic_folder + 'school_info.csv'\n",
"df =pd.read_csv(file_name)\n",
"df2 =pd.read_csv(file_name2)\n",
"\n",
"latitude=df['위도']\n",
"longitude=df['경도']\n",
"\n",
"elementary=[]\n",
"middle=[]\n",
"high=[]\n",
"\n",
"count_row=df2.shape[0] #number of rows\n",
"geolocator = Nominatim(user_agent=\"lms\", timeout=10)\n",
"geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)\n",
"\n",
"def type_of_school():\n",
" for x in range(count_row):\n",
" type=df2.loc[x]['학교종류명']\n",
" if(type=='고등학교'):\n",
" if(df2.loc[x]['도로명주소']):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['도로명주소'],\"\",\"\"]\n",
" high.append(row)\n",
"\n",
"\n",
"type_of_school()\n",
"columns=['school_name','address','latitude','longitude']\n",
"high_df=pd.DataFrame(high,columns=columns)\n",
"\n",
"def find_lat_lon():\n",
" count_rw=high_df.shape[0]\n",
" for x in tqdm(range(count_rw)):\n",
" addr=high_df.loc[x]['address']\n",
" if geolocator.geocode(addr) is not None:\n",
" location=geolocator.geocode(addr)\n",
" high_df.loc[x]['latitude']=location.latitude\n",
" high_df.loc[x]['longitude']=location.longitude\n",
"\n",
"find_lat_lon()\n",
"high_df.to_csv(r'high_school.csv')\n",
"high_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from geopy.geocoders import Nominatim\n",
"from geopy.extra.rate_limiter import RateLimiter\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'high_school.csv'\n",
"\n",
"df =pd.read_csv(file_name)\n",
"count_row=df.shape[0]\n",
"print(count_row)\n",
"df"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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,시작,끝,총 시간
1교시,9:00,9:50,50
2교시,10:00,10:50,50
3교시,11:00,11:50,50
4교시,12:00,12:50,50
5교시,14:00,14:50,50
6교시,15:00,15:50,50
7교시,16:00,16:50,50
\ No newline at end of file
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,시작,끝,총 시간
1교시,9:00,9:45,45
2교시,9:55,10:40,45
3교시,10:50,11:35,45
4교시,11:45,12:30,45
5교시,13:15,14:00,45
6교시,14:10,14:55,45
7교시,15:05,15:50,45
\ No newline at end of file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 21398/21398 [00:16<00:00, 1308.22it/s]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name2 = basic_folder + '2019_students_num.csv'\n",
"df2 =pd.read_csv(file_name2)\n",
"\n",
"count_row=df2.shape[0] #number of rows\n",
"elementary=[]\n",
"middle=[]\n",
"high=[]\n",
"\n",
"def type_of_school():\n",
" for x in tqdm(range(count_row)):\n",
" type=df2.loc[x]['학교급']\n",
" if(type=='고등학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['1학년_학생수_계'],df2.loc[x]['2학년_학생수_계'],df2.loc[x]['3학년_학생수_계']]\n",
" high.append(row)\n",
" elif(type=='중학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['1학년_학생수_계'],df2.loc[x]['2학년_학생수_계'],df2.loc[x]['3학년_학생수_계']]\n",
" middle.append(row)\n",
" elif(type=='초등학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['1학년_학생수_계'],df2.loc[x]['2학년_학생수_계'],df2.loc[x]['3학년_학생수_계'],df2.loc[x]['4학년_학생수_계'],df2.loc[x]['5학년_학생수_계'],df2.loc[x]['6학년_학생수_계']]\n",
" elementary.append(row)\n",
"\n",
"type_of_school()\n",
" \n",
"columns=['school_name','1_stu_num','2_stu_num','3_stu_num']\n",
"high_df=pd.DataFrame(high,columns=columns)\n",
"high_df.to_csv(r'high_school_stu_num.csv')\n",
"\n",
"middle_df=pd.DataFrame(middle,columns=columns)\n",
"middle_df.to_csv(r'middle_school_stu_num.csv')\n",
"\n",
"columns1=['school_name','1_stu_num','2_stu_num','3_stu_num','4_stu_num','5_stu_num','6_stu_num']\n",
"ele_df=pd.DataFrame(elementary,columns=columns1)\n",
"ele_df.to_csv(r'elementary_school_stu_num.csv')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 11873/11873 [00:08<00:00, 1379.25it/s]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'elementary_middle_schools_location.csv'\n",
"\n",
"df2 =pd.read_csv(file_name)\n",
"count_row=df2.shape[0]\n",
"\n",
"elementary=[]\n",
"middle=[]\n",
"\n",
"def type_of_school():\n",
" for x in tqdm(range(count_row)):\n",
" type=df2.loc[x]['학교급구분']\n",
" if(type=='중학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['소재지도로명주소'],df2.loc[x]['위도'],df2.loc[x]['경도']]\n",
" middle.append(row)\n",
" elif(type=='초등학교'):\n",
" row=[df2.loc[x]['학교명'],df2.loc[x]['소재지도로명주소'],df2.loc[x]['위도'],df2.loc[x]['경도']]\n",
" elementary.append(row)\n",
"\n",
"type_of_school()\n",
"columns=['school_name','school_addr','latitude','longitude']\n",
"middle_df=pd.DataFrame(middle,columns=columns)\n",
"middle_df.to_csv(r'middle_school.csv')\n",
"\n",
"elem_df=pd.DataFrame(elementary,columns=columns)\n",
"elem_df.to_csv(r'elementary_school.csv')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 3240/3240 [26:47<00:00, 2.02it/s]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'middle_school.csv'\n",
"file_name2 = basic_folder + 'middle_school_stu_num.csv'\n",
"\n",
"middle_df =pd.read_csv(file_name)\n",
"middle_stu_num_df=pd.read_csv(file_name2)\n",
"\n",
"count_row=middle_df.shape[0]\n",
"count_row2=middle_stu_num_df.shape[0]\n",
"\n",
"middle_arr=[]\n",
"\n",
"def find_middle_student_num():\n",
" for x in tqdm(range(count_row)):\n",
" name=middle_df.loc[x]['school_name']\n",
" for y in range(count_row2):\n",
" if name == middle_stu_num_df.loc[y]['school_name']:\n",
" row=[middle_df.loc[x]['school_name'],middle_df.loc[x]['school_addr'],middle_df.loc[x]['latitude'],middle_df.loc[x]['longitude'],\n",
" middle_stu_num_df.loc[y]['1_stu_num'],middle_stu_num_df.loc[y]['2_stu_num'],middle_stu_num_df.loc[y]['3_stu_num']]\n",
" middle_arr.append(row)\n",
" \n",
"\n",
"find_middle_student_num()\n",
"columns=['school_name','school_addr','latitude','longitude','1_stu_num','2_stu_num','3_stu_num']\n",
"final_middle_df=pd.DataFrame(middle_arr,columns=columns)\n",
"final_middle_df.to_csv(r'final_middle_school.csv')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6278/6278 [1:38:36<00:00, 1.06it/s]\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'elementary_school.csv'\n",
"file_name2 = basic_folder + 'elementary_school_stu_num.csv'\n",
"\n",
"ele_df =pd.read_csv(file_name)\n",
"ele_stu_num_df=pd.read_csv(file_name2)\n",
"\n",
"count_row=ele_df.shape[0]\n",
"count_row2=ele_stu_num_df.shape[0]\n",
"\n",
"ele_arr=[]\n",
"\n",
"def find_ele_student_num():\n",
" for x in tqdm(range(count_row)):\n",
" name=ele_df.loc[x]['school_name']\n",
" for y in range(count_row2):\n",
" if name == ele_stu_num_df.loc[y]['school_name']:\n",
" row=[ele_df.loc[x]['school_name'],ele_df.loc[x]['school_addr'],ele_df.loc[x]['latitude'],ele_df.loc[x]['longitude'],\n",
" ele_stu_num_df.loc[y]['1_stu_num'],ele_stu_num_df.loc[y]['2_stu_num'],ele_stu_num_df.loc[y]['3_stu_num'],\n",
" ele_stu_num_df.loc[y]['4_stu_num'],ele_stu_num_df.loc[y]['5_stu_num'],ele_stu_num_df.loc[y]['6_stu_num']]\n",
" ele_arr.append(row)\n",
" \n",
"find_ele_student_num()\n",
"columns=['school_name','school_addr','latitude','longitude','1_stu_num','2_stu_num','3_stu_num','4_stu_num','5_stu_num','6_stu_num']\n",
"final_ele_df=pd.DataFrame(ele_arr,columns=columns)\n",
"final_ele_df.to_csv(r'final_ele_school.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pyspark\n",
"from tqdm import tqdm\n",
"\n",
"basic_folder = ''\n",
"file_name = basic_folder + 'high_school.csv'\n",
"file_name2 = basic_folder + 'high_school_stu_num.csv'\n",
"\n",
"middle_df =pd.read_csv(file_name)\n",
"middle_stu_num_df=pd.read_csv(file_name2)\n",
"\n",
"count_row=middle_df.shape[0]\n",
"count_row2=middle_stu_num_df.shape[0]\n",
"\n",
"high_arr=[]\n",
"\n",
"def find_high_student_num():\n",
" for x in tqdm(range(count_row)):\n",
" name=middle_df.loc[x]['school_name']\n",
" for y in range(count_row2):\n",
" if name == middle_stu_num_df.loc[y]['school_name']:\n",
" row=[middle_df.loc[x]['school_name'],middle_df.loc[x]['school_addr'],middle_df.loc[x]['latitude'],middle_df.loc[x]['longitude'],\n",
" middle_stu_num_df.loc[y]['1_stu_num'],middle_stu_num_df.loc[y]['2_stu_num'],middle_stu_num_df.loc[y]['3_stu_num']]\n",
" high_arr.append(row)\n",
" \n",
"find_high_student_num()\n",
"columns=['school_name','school_addr','latitude','longitude','1_stu_num','2_stu_num','3_stu_num']\n",
"final_high_df=pd.DataFrame(middle_arr,columns=columns)\n",
"final_high_df.to_csv(r'final_high_school.csv')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
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"name": "python",
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"pygments_lexer": "ipython3",
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},
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}
학구ID,학구명,학구분류,시도코드,시도교육청코드,시도교육청명,교육지원청코드,교육지원청명,생성일자,변경일자,공간객체ID,데이터기준일자,제공기관코드,제공기관명,
Z000300001,동부학교군,0,11,7010000,서울특별시교육청,7021000,서울특별시동부교육지원청,2016-08-12,2016-08-12,28,2019-09-16,7001220,한국교원대학교,
Z000300002,서부학교군,0,11,7010000,서울특별시교육청,7031000,서울특별시서부교육지원청,2016-08-12,2016-08-12,9,2019-09-16,7001220,한국교원대학교,
Z000300003,남부학교군,0,11,7010000,서울특별시교육청,7041000,서울특별시남부교육지원청,2016-08-12,2016-08-12,8,2019-09-16,7001220,한국교원대학교,
Z000300004,북부학교군,0,11,7010000,서울특별시교육청,7051000,서울특별시북부교육지원청,2016-08-12,2016-08-12,14,2019-09-16,7001220,한국교원대학교,
Z000300005,중부학교군,0,11,7010000,서울특별시교육청,7061000,서울특별시중부교육지원청,2016-08-12,2016-08-12,10,2019-09-16,7001220,한국교원대학교,
Z000300006,강동송파학교군,0,11,7010000,서울특별시교육청,7130000,서울특별시강동송파교육지원청,2016-08-12,2016-08-12,15,2019-09-16,7001220,한국교원대학교,
Z000300007,강서학교군,0,11,7010000,서울특별시교육청,7081300,서울특별시강서양천교육지원청,2016-08-12,2016-08-12,26,2019-09-16,7001220,한국교원대학교,
Z000300008,강남학교군,0,11,7010000,서울특별시교육청,7091300,서울특별시강남서초교육지원청,2016-08-12,2016-08-12,16,2019-09-16,7001220,한국교원대학교,
Z000300009,동작관악학교군,0,11,7010000,서울특별시교육청,7132000,서울특별시동작관악교육지원청,2016-08-12,2016-08-12,17,2019-09-16,7001220,한국교원대학교,
Z000300010,성동광진학교군,0,11,7010000,서울특별시교육청,7134000,서울특별시성동광진교육지원청,2016-08-12,2016-08-12,29,2019-09-16,7001220,한국교원대학교,
Z000300011,성북학교군,0,11,7010000,서울특별시교육청,7121200,서울특별시성북강북교육지원청,2016-08-12,2016-08-12,13,2019-09-16,7001220,한국교원대학교,
Z000300012,서부고등학군,0,26,7150000,부산광역시교육청,7171000,부산광역시서부교육지원청,2016-08-12,2016-08-12,53,2019-09-16,7001220,한국교원대학교,
Z000300013,남부고등학군,0,26,7150000,부산광역시교육청,7181000,부산광역시남부교육지원청,2016-08-12,2016-08-12,23,2019-09-16,7001220,한국교원대학교,
Z000300014,동래고등학군,0,26,7150000,부산광역시교육청,7191000,부산광역시동래교육지원청,2016-08-12,2016-08-12,54,2019-09-16,7001220,한국교원대학교,
Z000300015,북부고등학군,0,26,7150000,부산광역시교육청,7201000,부산광역시북부교육지원청,2016-08-12,2016-08-12,21,2019-09-16,7001220,한국교원대학교,
Z000300016,해운대고등학군,0,26,7150000,부산광역시교육청,7211000,부산광역시해운대교육지원청,2016-08-12,2016-08-12,22,2019-09-16,7001220,한국교원대학교,
Z000300017,고등학교1학군,0,27,7240000,대구광역시교육청,7251000,대구광역시동부교육지원청,2016-08-12,2016-08-12,25,2019-09-16,7001220,한국교원대학교,
Z000300018,고등학교2학군,0,27,7240000,대구광역시교육청,7271000,대구광역시남부교육지원청,2016-08-12,2016-08-12,24,2019-09-16,7001220,한국교원대학교,
Z000300019,1학교군,0,28,7310000,인천광역시교육청,7321000,인천광역시남부교육지원청,2016-08-12,2016-08-12,55,2019-09-16,7001220,한국교원대학교,
Z000300020,2학교군,0,28,7310000,인천광역시교육청,7331000,인천광역시북부교육지원청,2016-08-12,2016-08-12,30,2019-09-16,7001220,한국교원대학교,
Z000300021,특수지고등학군,0,28,7310000,인천광역시교육청,7321000,인천광역시남부교육지원청,2016-08-12,2017-02-14,52,2019-09-16,7001220,한국교원대학교,
Z000300022,3학교군,0,28,7310000,인천광역시교육청,7361000,인천광역시서부교육지원청,2016-08-12,2016-08-12,31,2019-09-16,7001220,한국교원대학교,
Z000300023,광주광역시단일학교군,0,29,7380000,광주광역시교육청,7391000,광주광역시동부교육지원청,2016-08-12,2017-02-14,18,2019-09-16,7001220,한국교원대학교,
Z000300024,대전광역시단일학교군,0,30,7430000,대전광역시교육청,7441000,대전광역시동부교육지원청,2016-08-12,2016-08-12,48,2019-09-16,7001220,한국교원대학교,
Z000300025,동부학교군,0,31,7480000,울산광역시교육청,7491000,울산광역시강북교육지원청,2016-08-12,2016-08-12,20,2019-09-16,7001220,한국교원대학교,
Z000300026,북부학교군,0,31,7480000,울산광역시교육청,7491000,울산광역시강북교육지원청,2016-08-12,2016-08-12,19,2019-09-16,7001220,한국교원대학교,
Z000300027,중부학교군,0,31,7480000,울산광역시교육청,7491000,울산광역시강북교육지원청,2018-07-12,2018-07-12,58,2019-09-16,7001220,한국교원대학교,
Z000300028,남부학교군,0,31,7480000,울산광역시교육청,7501000,울산광역시강남교육지원청,2018-07-12,2018-07-12,59,2019-09-16,7001220,한국교원대학교,
Z000300029,언양특수학교군,0,31,7480000,울산광역시교육청,7501000,울산광역시강남교육지원청,2016-08-12,2016-08-12,27,2019-09-16,7001220,한국교원대학교,
Z000300030,수원1구역,0,41,7530000,경기도교육청,7541000,경기도수원교육지원청,2016-08-12,2016-08-12,51,2019-09-16,7001220,한국교원대학교,
Z000300031,수원2구역,0,41,7530000,경기도교육청,7541000,경기도수원교육지원청,2016-08-12,2016-08-12,50,2019-09-16,7001220,한국교원대학교,
Z000300032,성남1구역,0,41,7530000,경기도교육청,7551000,경기도성남교육지원청,2016-08-12,2016-08-12,47,2019-09-16,7001220,한국교원대학교,
Z000300033,성남2구역,0,41,7530000,경기도교육청,7551000,경기도성남교육지원청,2016-08-12,2016-08-12,39,2019-09-16,7001220,한국교원대학교,
Z000300034,의정부학군,0,41,7530000,경기도교육청,7561000,경기도의정부교육지원청,2016-08-12,2016-08-12,32,2019-09-16,7001220,한국교원대학교,
Z000300035,안양권1구역,0,41,7530000,경기도교육청,7569000,경기도안양과천교육지원청,2016-08-12,2016-08-12,45,2019-09-16,7001220,한국교원대학교,
Z000300036,안양권2구역,0,41,7530000,경기도교육청,7569000,경기도안양과천교육지원청,2016-08-12,2016-08-12,49,2019-09-16,7001220,한국교원대학교,
Z000300037,부천학군,0,41,7530000,경기도교육청,7581000,경기도부천교육지원청,2016-08-12,2016-08-12,34,2019-09-16,7001220,한국교원대학교,
Z000300038,광명학군,0,41,7530000,경기도교육청,7591000,경기도광명교육지원청,2016-08-12,2016-08-12,33,2019-09-16,7001220,한국교원대학교,
Z000300039,안산1구역,0,41,7530000,경기도교육청,7611000,경기도안산교육지원청,2016-08-12,2016-08-12,42,2019-09-16,7001220,한국교원대학교,
Z000300040,안산2구역,0,41,7530000,경기도교육청,7611000,경기도안산교육지원청,2016-08-12,2016-08-12,44,2019-09-16,7001220,한국교원대학교,
Z000300041,고양1구역,0,41,7530000,경기도교육청,7621000,경기도고양교육지원청,2016-08-12,2016-08-12,41,2019-09-16,7001220,한국교원대학교,
Z000300042,고양2구역,0,41,7530000,경기도교육청,7621000,경기도고양교육지원청,2016-08-12,2016-08-12,40,2019-09-16,7001220,한국교원대학교,
Z000300043,안양권3구역,0,41,7530000,경기도교육청,7642000,경기도군포의왕교육지원청,2016-08-12,2016-08-12,35,2019-09-16,7001220,한국교원대학교,
Z000300044,안양권4구역,0,41,7530000,경기도교육청,7642000,경기도군포의왕교육지원청,2016-08-12,2016-08-12,46,2019-09-16,7001220,한국교원대학교,
Z000300045,용인1구역,0,41,7530000,경기도교육청,7751000,경기도용인교육지원청,2016-08-12,2016-08-12,43,2019-09-16,7001220,한국교원대학교,
Z000300046,용인2구역,0,41,7530000,경기도교육청,7751000,경기도용인교육지원청,2016-08-12,2016-08-12,36,2019-09-16,7001220,한국교원대학교,
Z000300047,용인3구역,0,41,7530000,경기도교육청,7751000,경기도용인교육지원청,2016-08-12,2016-08-12,37,2019-09-16,7001220,한국교원대학교,
Z000300048,청주시고등학군,0,43,8000000,충청북도교육청,8011000,충청북도청주교육지원청,2016-08-12,2016-08-12,38,2019-09-16,7001220,한국교원대학교,
Z000300049,천안시학교군,0,44,8140000,충청남도교육청,8151000,충청남도천안교육지원청,2016-08-12,2016-08-12,4,2019-09-16,7001220,한국교원대학교,
Z000300050,전주시학교군,0,45,8320000,전라북도교육청,8331000,전라북도전주교육지원청,2016-08-12,2017-02-14,1,2019-09-16,7001220,한국교원대학교,
Z000300051,군산시학교군,0,45,8320000,전라북도교육청,8341000,전라북도군산교육지원청,2016-08-12,2016-08-12,5,2019-09-16,7001220,한국교원대학교,
Z000300052,익산시학교군,0,45,8320000,전라북도교육청,8351000,전라북도익산교육지원청,2016-08-12,2016-08-12,12,2019-09-16,7001220,한국교원대학교,
Z000300053,목포시제1학교군,0,46,8490000,전라남도교육청,8501000,전라남도목포교육지원청,2016-08-12,2016-08-12,6,2019-09-16,7001220,한국교원대학교,
Z000300054,여수시제2학교군,0,46,8490000,전라남도교육청,8511000,전라남도여수교육지원청,2016-08-12,2016-08-12,7,2019-09-16,7001220,한국교원대학교,
Z000300055,순천시제3학교군,0,46,8490000,전라남도교육청,8521000,전라남도순천교육지원청,2016-08-12,2016-08-12,2,2019-09-16,7001220,한국교원대학교,
Z000300056,포항시제1학교군,0,47,8750000,경상북도교육청,8761000,경상북도포항교육지원청,2016-08-12,2016-08-12,11,2019-09-16,7001220,한국교원대학교,
Z000300057,창원시제1학교군,0,48,9010000,경상남도교육청,9022000,경상남도창원교육지원청,2016-08-12,2019-07-16,62,2019-09-16,7001220,한국교원대학교,
Z000300058,창원시제2학교군,0,48,9010000,경상남도교육청,9022000,경상남도창원교육지원청,2016-08-12,2019-07-16,61,2019-09-16,7001220,한국교원대학교,
Z000300059,진주시제3학교군,0,48,9010000,경상남도교육청,9051000,경상남도진주교육지원청,2016-08-12,2016-08-12,57,2019-09-16,7001220,한국교원대학교,
Z000300060,김해시제4학교군,0,48,9010000,경상남도교육청,9091000,경상남도김해교육지원청,2016-08-12,2016-08-12,56,2019-09-16,7001220,한국교원대학교,
Z000300061,제주시학교군,0,50,9290000,제주특별자치도교육청,9296000,제주특별자치도제주시교육지원청,2016-08-12,2017-02-14,3,2019-09-16,7001220,한국교원대학교,
Z000300062,세종시고등학군,0,36,9300000,세종특별자치시교육청,9300000,세종특별자치시교육청,2018-11-05,2018-11-06,60,2019-09-16,7001220,한국교원대학교,
Z000300063,거제시제5학교군,0,48,9010000,경상남도교육청,9111000,경상남도거제교육지원청,2019-07-22,2019-07-22,63,2019-09-16,7001220,한국교원대학교,
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{
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]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n",
"from tensorflow.keras.optimizers import RMSprop\n",
"from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau\n",
"from tensorflow.keras.backend import square, mean\n",
"\n",
"basic_folder = '/Users/hyewon/Documents/capstone/2016104140/code/dataset/'\n",
"file_name = basic_folder + 'final_middle_school.csv'\n",
"file_name2=basic_folder + 'middle_timetable.csv'\n",
"df =pd.read_csv(file_name)\n",
"df2 =pd.read_csv(file_name2)\n",
"\n",
"target_location=['latitude','longitude']\n",
"target_names=['1_stu_num','2_stu_num','3_stu_num']\n",
"\n",
"\n",
"shift_days = 1\n",
"shift_steps = shift_days * 24\n",
"\n",
"df_targets = df[target_location][target_names].shift(-shift_steps)\n",
"df[target_location][target_names].shift(-shift_steps)\n",
"\n"
]
},
{
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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