recent_test_data-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"recent_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# 데이터 역순으로 변경\n",
"recent_data = recent_data.loc[::-1]\n",
"recent_data = recent_data.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20201029</td>\n",
" <td>309.94</td>\n",
" <td>308.99</td>\n",
" <td>310.56</td>\n",
" <td>306.63</td>\n",
" <td>1.3904e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20201030</td>\n",
" <td>301.60</td>\n",
" <td>308.91</td>\n",
" <td>309.08</td>\n",
" <td>301.58</td>\n",
" <td>1.7891e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20201102</td>\n",
" <td>305.95</td>\n",
" <td>302.67</td>\n",
" <td>306.43</td>\n",
" <td>301.37</td>\n",
" <td>1.1103e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20201029 309.94 308.99 310.56 306.63 1.3904e+08\n",
"1 20201030 301.60 308.91 309.08 301.58 1.7891e+08\n",
"2 20201102 305.95 302.67 306.43 301.37 1.1103e+08"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recent_data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def vol_char_to_num(data):\n",
" for i in tqdm(range(len(data))):\n",
" unit = data['vol'][i][-1]\n",
" data['vol'][i] = data['vol'][i][:-1]\n",
" if unit == 'K':\n",
" data['vol'][i] = float(data['vol'][i]) * 1000\n",
" elif unit == 'M':\n",
" data['vol'][i] = float(data['vol'][i]) * 1000000"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/7 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
" 57%|█████▋ | 4/7 [00:00<00:00, 4.51it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 7/7 [00:01<00:00, 4.77it/s]\n"
]
}
],
"source": [
"vol_char_to_num(recent_data)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"scale_cols = ['close','open','high','low','vol']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"recent_data_scaled = scaler.fit_transform(recent_data[scale_cols]) \n",
"recent_data_scaled = pd.DataFrame(recent_data_scaled)\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" }\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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.406829</td>\n",
" <td>0.315212</td>\n",
" <td>0.231373</td>\n",
" <td>0.273389</td>\n",
" <td>0.746880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.000000</td>\n",
" <td>0.311222</td>\n",
" <td>0.148459</td>\n",
" <td>0.010915</td>\n",
" <td>0.961373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.212195</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.596191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.510732</td>\n",
" <td>0.272818</td>\n",
" <td>0.331653</td>\n",
" <td>0.345114</td>\n",
" <td>0.692866</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.596585</td>\n",
" <td>0.580549</td>\n",
" <td>0.514846</td>\n",
" <td>0.519751</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.971220</td>\n",
" <td>0.651870</td>\n",
" <td>0.844818</td>\n",
" <td>0.722453</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.773348</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4\n",
"0 0.406829 0.315212 0.231373 0.273389 0.746880\n",
"1 0.000000 0.311222 0.148459 0.010915 0.961373\n",
"2 0.212195 0.000000 0.000000 0.000000 0.596191\n",
"3 0.510732 0.272818 0.331653 0.345114 0.692866\n",
"4 0.596585 0.580549 0.514846 0.519751 0.000000\n",
"5 0.971220 0.651870 0.844818 0.722453 1.000000\n",
"6 1.000000 1.000000 1.000000 1.000000 0.773348"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recent_data_scaled"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"recent_data_scaled.to_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data_scaled.csv')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checking if any null values are present in train\n",
" 0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(\"checking if any null values are present in train\\n\", recent_data_scaled.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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",
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"version": "3.7.3"
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