yoojaesang

knn first commit

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"name": "generate_prediction.ipynb",
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"name": "python3",
"display_name": "Python 3"
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"metadata": {
"id": "TB2mZGOKD5lp",
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"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/gdrive/')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/gdrive/\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6toBcxODD60f"
},
"source": [
"folder = '/content/gdrive/My Drive/colab_data/cap_di/'\n",
"weather = folder+'세종시 날씨.csv'\n",
"generate = folder+'세종시.csv'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "kkN1rP34E7WC"
},
"source": [
"import pandas as pd"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8NENGSIfE9rH",
"outputId": "83e74f5a-52f7-41bb-eead-0b56d0f5cab0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"wt = pd.read_csv(weather,encoding='CP949')\n",
"wt.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>일시</th>\n",
" <th>기온(°C)</th>\n",
" <th>강수량(mm)</th>\n",
" <th>습도(%)</th>\n",
" <th>현지기압(hPa)</th>\n",
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" <th>0</th>\n",
" <td>2019-07-01 1:00</td>\n",
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" <td>993.5</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
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" <th>1</th>\n",
" <td>2019-07-01 2:00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-07-01 3:00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-07-01 4:00</td>\n",
" <td>21.9</td>\n",
" <td>NaN</td>\n",
" <td>91</td>\n",
" <td>993.4</td>\n",
" <td>NaN</td>\n",
" <td>21.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-07-01 5:00</td>\n",
" <td>21.8</td>\n",
" <td>NaN</td>\n",
" <td>91</td>\n",
" <td>993.9</td>\n",
" <td>NaN</td>\n",
" <td>21.2</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" 일시 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 일조(hr) 지면온도(°C)\n",
"0 2019-07-01 1:00 22.2 NaN 90 993.5 NaN 22.0\n",
"1 2019-07-01 2:00 22.4 NaN 89 993.4 NaN 22.2\n",
"2 2019-07-01 3:00 22.3 NaN 90 993.4 NaN 22.1\n",
"3 2019-07-01 4:00 21.9 NaN 91 993.4 NaN 21.4\n",
"4 2019-07-01 5:00 21.8 NaN 91 993.9 NaN 21.2"
]
},
"metadata": {
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"execution_count": 4
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"base_uri": "https://localhost:8080/",
"height": 235
}
},
"source": [
"variables=[\"기온(°C)\",\"강수량(mm)\",\"습도(%)\",\"현지기압(hPa)\",\"지면온도(°C)\"]\n",
"wtData=wt[variables]\n",
"wtData.index=wt['일시']\n",
"wtData.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>기온(°C)</th>\n",
" <th>강수량(mm)</th>\n",
" <th>습도(%)</th>\n",
" <th>현지기압(hPa)</th>\n",
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" <tr>\n",
" <th>2019-07-01 1:00</th>\n",
" <td>22.2</td>\n",
" <td>NaN</td>\n",
" <td>90</td>\n",
" <td>993.5</td>\n",
" <td>22.0</td>\n",
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" <tr>\n",
" <th>2019-07-01 2:00</th>\n",
" <td>22.4</td>\n",
" <td>NaN</td>\n",
" <td>89</td>\n",
" <td>993.4</td>\n",
" <td>22.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 3:00</th>\n",
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" <td>90</td>\n",
" <td>993.4</td>\n",
" <td>22.1</td>\n",
" </tr>\n",
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" <th>2019-07-01 4:00</th>\n",
" <td>21.9</td>\n",
" <td>NaN</td>\n",
" <td>91</td>\n",
" <td>993.4</td>\n",
" <td>21.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 5:00</th>\n",
" <td>21.8</td>\n",
" <td>NaN</td>\n",
" <td>91</td>\n",
" <td>993.9</td>\n",
" <td>21.2</td>\n",
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],
"text/plain": [
" 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 지면온도(°C)\n",
"일시 \n",
"2019-07-01 1:00 22.2 NaN 90 993.5 22.0\n",
"2019-07-01 2:00 22.4 NaN 89 993.4 22.2\n",
"2019-07-01 3:00 22.3 NaN 90 993.4 22.1\n",
"2019-07-01 4:00 21.9 NaN 91 993.4 21.4\n",
"2019-07-01 5:00 21.8 NaN 91 993.9 21.2"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
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"id": "4DGaculbN3PN",
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"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"print(wtData)"
],
"execution_count": 60,
"outputs": [
{
"output_type": "stream",
"text": [
" 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 지면온도(°C)\n",
"일시 \n",
"2019-07-01 1:00 22.2 NaN 90 993.5 22.0\n",
"2019-07-01 2:00 22.4 NaN 89 993.4 22.2\n",
"2019-07-01 3:00 22.3 NaN 90 993.4 22.1\n",
"2019-07-01 4:00 21.9 NaN 91 993.4 21.4\n",
"2019-07-01 5:00 21.8 NaN 91 993.9 21.2\n",
"... ... ... ... ... ...\n",
"2020-06-29 20:00 20.0 6.1 93 986.6 21.2\n",
"2020-06-29 21:00 19.9 11.1 94 984.7 20.9\n",
"2020-06-29 22:00 20.2 6.0 94 983.4 20.9\n",
"2020-06-29 23:00 20.2 5.1 94 981.2 20.7\n",
"2020-06-30 0:00 20.3 2.0 91 979.8 20.6\n",
"\n",
"[8760 rows x 5 columns]\n"
],
"name": "stdout"
}
]
},
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"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"gnt=pd.read_csv(generate,encoding='CP949')\n",
"gnt.head()"
],
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"outputs": [
{
"output_type": "execute_result",
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" 일자 1:00 2:00 3:00 4:00 ... 20:00 21:00 22:00 23:00 0:00\n",
"0 2019-07-01 0 0 0 0 ... 0 0 0 0 0\n",
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"3 2019-07-04 0 0 0 0 ... 2760 0 0 0 0\n",
"4 2019-07-05 0 0 0 0 ... 0 0 0 0 0\n",
"\n",
"[5 rows x 25 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
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"height": 235
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},
"source": [
"GNTvariables=['1:00','2:00','3:00','4:00','5:00','6:00','7:00','8:00','9:00','10:00','11:00','12:00','13:00','14:00','15:00','16:00','17:00','18:00','19:00','20:00','21:00','22:00','23:00','0:00']\n",
"tempGNT=gnt[GNTvariables]\n",
"tempGNT.index=gnt['일자']\n",
"tempGNT.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
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" <td>0</td>\n",
" <td>120</td>\n",
" <td>81720</td>\n",
" <td>313800</td>\n",
" <td>625680</td>\n",
" <td>831360</td>\n",
" <td>1062240</td>\n",
" <td>861240</td>\n",
" <td>849600</td>\n",
" <td>785280</td>\n",
" <td>915960</td>\n",
" <td>843240</td>\n",
" <td>565320</td>\n",
" <td>178080</td>\n",
" <td>115800</td>\n",
" <td>6720</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-03</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>54480</td>\n",
" <td>219720</td>\n",
" <td>320400</td>\n",
" <td>602400</td>\n",
" <td>805440</td>\n",
" <td>917280</td>\n",
" <td>815760</td>\n",
" <td>569520</td>\n",
" <td>613080</td>\n",
" <td>726480</td>\n",
" <td>510120</td>\n",
" <td>313440</td>\n",
" <td>68400</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-04</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120</td>\n",
" <td>65160</td>\n",
" <td>310560</td>\n",
" <td>623400</td>\n",
" <td>877440</td>\n",
" <td>1044240</td>\n",
" <td>1170240</td>\n",
" <td>1202400</td>\n",
" <td>1159920</td>\n",
" <td>1071600</td>\n",
" <td>680280</td>\n",
" <td>467640</td>\n",
" <td>267000</td>\n",
" <td>130560</td>\n",
" <td>2760</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-05</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>57120</td>\n",
" <td>310320</td>\n",
" <td>520320</td>\n",
" <td>847800</td>\n",
" <td>1031760</td>\n",
" <td>1145280</td>\n",
" <td>1186680</td>\n",
" <td>1146360</td>\n",
" <td>970200</td>\n",
" <td>664920</td>\n",
" <td>506520</td>\n",
" <td>304200</td>\n",
" <td>64560</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1:00 2:00 3:00 4:00 5:00 ... 20:00 21:00 22:00 23:00 0:00\n",
"일자 ... \n",
"2019-07-01 0 0 0 0 0 ... 0 0 0 0 0\n",
"2019-07-02 0 0 0 0 0 ... 6720 0 0 0 0\n",
"2019-07-03 0 0 0 0 0 ... 0 0 0 0 0\n",
"2019-07-04 0 0 0 0 0 ... 2760 0 0 0 0\n",
"2019-07-05 0 0 0 0 0 ... 0 0 0 0 0\n",
"\n",
"[5 rows x 24 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tCCfCxurIETg",
"outputId": "5be25bb4-050b-4fbe-cafa-615f489c1b9b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"print(tempGNT.columns)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Index(['1:00', '2:00', '3:00', '4:00', '5:00', '6:00', '7:00', '8:00', '9:00',\n",
" '10:00', '11:00', '12:00', '13:00', '14:00', '15:00', '16:00', '17:00',\n",
" '18:00', '19:00', '20:00', '21:00', '22:00', '23:00', '0:00'],\n",
" dtype='object')\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "i7DnCIAaM_lq"
},
"source": [
"temp=pd.DataFrame(columns=['발전량'])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "iCbGuA5oKX46"
},
"source": [
"t={}"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "vsPgEOdzL63t",
"outputId": "0a722ead-28ce-4540-baae-60d860a9b10b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"print(tempGNT['0:00']['2019-07-01'])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DY2K5OagIB8A"
},
"source": [
"for date in tempGNT.index:\n",
" for hour in tempGNT.columns:\n",
" t[date+' '+hour]=tempGNT[hour][date]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9jwM6hWaN-lu",
"outputId": "e90c4ab0-3a10-4989-8ddc-aef8f078f578",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"print(t.values())"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "akNtoFUpMUAR"
},
"source": [
"temp=pd.DataFrame(t.values(), index=t.keys())"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "p2a5g_jRMqwy",
"outputId": "dccec0e2-884a-43c9-b294-4933a6b5de1e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"print(temp)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" 0\n",
"2019-07-01 1:00 0\n",
"2019-07-01 2:00 0\n",
"2019-07-01 3:00 0\n",
"2019-07-01 4:00 0\n",
"2019-07-01 5:00 0\n",
"... ..\n",
"2020-06-30 20:00 0\n",
"2020-06-30 21:00 0\n",
"2020-06-30 22:00 0\n",
"2020-06-30 23:00 0\n",
"2020-06-30 0:00 0\n",
"\n",
"[8784 rows x 1 columns]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "yv-wtS5tNLNv",
"outputId": "351f0e0c-3e97-4e4a-a982-3571c0a64e07",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
}
},
"source": [
"temp.columns=['발전량']\n",
"temp.index.names=['일자']\n",
"temp.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>발전량</th>\n",
" </tr>\n",
" <tr>\n",
" <th>일자</th>\n",
" <th></th>\n",
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" <td>0</td>\n",
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"text/plain": [
" 발전량\n",
"일자 \n",
"2019-07-01 1:00 0\n",
"2019-07-01 2:00 0\n",
"2019-07-01 3:00 0\n",
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"metadata": {
"tags": []
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"execution_count": 57
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]
},
{
"cell_type": "code",
"metadata": {
"id": "sYP_krzqQ7OS"
},
"source": [
"def min_max_normalize(lst):\n",
" normalize = []\n",
"\n",
" for value in lst:\n",
" normalized_num=(value - min(lst))/(max(lst)-min(lst))\n",
" normalize.append(normalized_num)\n",
"\n",
" return normalize"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nRTowLdHWfHp",
"outputId": "15589397-2570-437f-e421-b1f5c9e8a8b1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 343
}
},
"source": [
"wtData['발전량']=0\n",
"wtData.head()"
],
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>기온(°C)</th>\n",
" <th>강수량(mm)</th>\n",
" <th>습도(%)</th>\n",
" <th>현지기압(hPa)</th>\n",
" <th>지면온도(°C)</th>\n",
" <th>발전량</th>\n",
" </tr>\n",
" <tr>\n",
" <th>일시</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-07-01 1:00</th>\n",
" <td>22.2</td>\n",
" <td>NaN</td>\n",
" <td>90</td>\n",
" <td>993.5</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 2:00</th>\n",
" <td>22.4</td>\n",
" <td>NaN</td>\n",
" <td>89</td>\n",
" <td>993.4</td>\n",
" <td>22.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 3:00</th>\n",
" <td>22.3</td>\n",
" <td>NaN</td>\n",
" <td>90</td>\n",
" <td>993.4</td>\n",
" <td>22.1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 4:00</th>\n",
" <td>21.9</td>\n",
" <td>NaN</td>\n",
" <td>91</td>\n",
" <td>993.4</td>\n",
" <td>21.4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 5:00</th>\n",
" <td>21.8</td>\n",
" <td>NaN</td>\n",
" <td>91</td>\n",
" <td>993.9</td>\n",
" <td>21.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 지면온도(°C) 발전량\n",
"일시 \n",
"2019-07-01 1:00 22.2 NaN 90 993.5 22.0 0\n",
"2019-07-01 2:00 22.4 NaN 89 993.4 22.2 0\n",
"2019-07-01 3:00 22.3 NaN 90 993.4 22.1 0\n",
"2019-07-01 4:00 21.9 NaN 91 993.4 21.4 0\n",
"2019-07-01 5:00 21.8 NaN 91 993.9 21.2 0"
]
},
"metadata": {
"tags": []
},
"execution_count": 61
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xAfRDfGsS5Gh",
"outputId": "d7433755-bf6e-4e49-d0b8-cf99339883bf",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"for date1 in wtData.index:\n",
" for date2 in temp.index:\n",
" if date1==date2:\n",
" wtData['발전량'][date1]=temp['발전량'][date2]"
],
"execution_count": 62,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" after removing the cwd from sys.path.\n",
"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2882: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QcgiXXo0XJfY",
"outputId": "3910dc06-297d-4caf-d18a-a0a0c9f30377",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
}
},
"source": [
"wtData.head()"
],
"execution_count": 63,
"outputs": [
{
"output_type": "execute_result",
"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>기온(°C)</th>\n",
" <th>강수량(mm)</th>\n",
" <th>습도(%)</th>\n",
" <th>현지기압(hPa)</th>\n",
" <th>지면온도(°C)</th>\n",
" <th>발전량</th>\n",
" </tr>\n",
" <tr>\n",
" <th>일시</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-07-01 1:00</th>\n",
" <td>22.2</td>\n",
" <td>NaN</td>\n",
" <td>90</td>\n",
" <td>993.5</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 2:00</th>\n",
" <td>22.4</td>\n",
" <td>NaN</td>\n",
" <td>89</td>\n",
" <td>993.4</td>\n",
" <td>22.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 3:00</th>\n",
" <td>22.3</td>\n",
" <td>NaN</td>\n",
" <td>90</td>\n",
" <td>993.4</td>\n",
" <td>22.1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 4:00</th>\n",
" <td>21.9</td>\n",
" <td>NaN</td>\n",
" <td>91</td>\n",
" <td>993.4</td>\n",
" <td>21.4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-01 5:00</th>\n",
" <td>21.8</td>\n",
" <td>NaN</td>\n",
" <td>91</td>\n",
" <td>993.9</td>\n",
" <td>21.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 기온(°C) 강수량(mm) 습도(%) 현지기압(hPa) 지면온도(°C) 발전량\n",
"일시 \n",
"2019-07-01 1:00 22.2 NaN 90 993.5 22.0 0\n",
"2019-07-01 2:00 22.4 NaN 89 993.4 22.2 0\n",
"2019-07-01 3:00 22.3 NaN 90 993.4 22.1 0\n",
"2019-07-01 4:00 21.9 NaN 91 993.4 21.4 0\n",
"2019-07-01 5:00 21.8 NaN 91 993.9 21.2 0"
]
},
"metadata": {
"tags": []
},
"execution_count": 63
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "p27JCDz8XRaA"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
\ No newline at end of file