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JIElite/EDA_future_price.ipynb

Created May 29, 2022 13:13
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EDA_future_price.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7f674ca2",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pandas.plotting import scatter_matrix\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9309b5b7",
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_columns', 120)\n",
"pd.set_option('display.max_rows', 120)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "90151ea2",
"metadata": {},
"outputs": [],
"source": [
"def clean_and_drop(df):\n",
" # 只篩選有包含 '住' 用途的交易案\n",
" df = df.loc[df['Main_Usage_Living'] == 1]\n",
" df = df.drop(columns=['Main_Usage_Living'])\n",
" \n",
" # 因為都是 0\n",
" df = df.drop(columns=['Non_City_Land_Usage', 'Main_Usage_Walk', \n",
" 'Main_Usage_Selling',\n",
" 'Main_Usage_SnE'])\n",
" \n",
" # 只有 344 筆是包含工廠用途，且都不具住宅用途，故剔除\n",
" df = df.loc[df['Main_Usage_Manufacturing'] == 0]\n",
" df = df.drop(columns=['Main_Usage_Manufacturing'])\n",
" \n",
" # 只有 76 筆是包含停車用途，且都不具住宅用途，故剔除\n",
" df = df.loc[df['Main_Usage_Parking'] == 0]\n",
" df = df.drop(columns=['Main_Usage_Parking'])\n",
" \n",
" # 只有 78 筆有農業用途，且都不具住宅用途，故剔除\n",
" df = df.loc[df['Main_Usage_Farm'] == 0]\n",
" df = df.drop(columns=['Main_Usage_Farm'])\n",
" \n",
" # NOTICE: 我沒有錢，所以我先只買 6 房以下的\n",
" df = df.loc[df['room'] < 6]\n",
" \n",
" df = df.loc[df['trading_floors_count'] == 1]\n",
" \n",
" # 雖然有 95 個樣本包含地下室，但是樣本太少，可能不足以推廣\n",
" # 所以先剔除，剔除完後，都是 0 所以直接 drop\n",
" df = df.loc[df['including_basement'] == 0]\n",
" df = df.drop(columns=['including_basement'])\n",
" \n",
" # 所有的樣本都不包含人行道，所以直接去除這個 feature\n",
" df = df.drop(columns=['including_arcade'])\n",
"\n",
" # 剔除交易樓層高度是 -1 (原本有一個樣本)\n",
" df = df.loc[df['min_floors_height'] != -1]\n",
"\n",
" # 剔除交易建物是 0 個樓層的情況\n",
" df = df.loc[df['building_total_floors'] != 0]\n",
" \n",
" # 因為車位交易 50 坪以上的資料只有 22 筆，所以先去除\n",
" # 因為浮點數在硬體儲存會有小數點，故不能直接用 == 50.0 去比較\n",
" df = df.loc[df['Parking_Area'] < 49.5]\n",
" \n",
" # 把農舍，廠辦踢掉\n",
" df = df.loc[df['Building_Types'] < 8]\n",
"\n",
" # 把超大轉移坪數刪掉\n",
" df = df.loc[df['Transfer_Total_Ping'] < 150]\n",
" \n",
" # 我先刪除 area_m2, 因為覺得跟 area_ping 的意義很類似，但是不確定會不會有些微差距。\n",
" # 因為在 future data 中，manager 都是 0，所以也把這個欄位刪除\n",
" # trading_floor_count 有 0 的情況，這樣應該不是房屋交易\n",
" df = df.drop(columns=['address', 'area_m2', 'manager', 'Building_Material_stone', \n",
" 'TDATE', 'Total_price', '編號'])\n",
" \n",
" # Convert the categorical features' dtype to 'category'\n",
" category_columns = ['Type', 'Month', 'Month_raw',\n",
" 'room', 'City_Land_Usage', 'Main_Usage_Business',\n",
" 'Building_Material_S', 'Building_Material_R', 'Building_Material_C',\n",
" 'Building_Material_steel', 'Building_Material_B', \n",
" 'Building_Material_W', 'Building_Material_iron',\n",
" 'Building_Material_tile', 'Building_Material_clay',\n",
" 'Building_Material_RC_reinforce',\n",
" 'Parking_Space_Types', 'Building_Types']\n",
" df.loc[:, category_columns] = df.loc[:, category_columns].astype('category')\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "66e77973",
"metadata": {},
"outputs": [],
"source": [
"df_future = pd.read_csv('../temp_future/output_feature/clean_data_future_train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9b7d596d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Place_id', 'Type', 'area_m2', 'area_ping', 'TDATE', 'Month', 'room',\n",
" 'hall', 'bathroom', 'compartment', 'manager', 'Total_price',\n",
" 'parking_price', '編號', 'address', 'trading_floors_count',\n",
" 'building_total_floors', 'min_floors_height', 'including_basement',\n",
" 'including_arcade', 'City_Land_Usage', 'Parking_Area',\n",
" 'Main_Usage_Walk', 'Main_Usage_Living', 'Main_Usage_Selling',\n",
" 'Main_Usage_Manufacturing', 'Main_Usage_Business', 'Main_Usage_Parking',\n",
" 'Main_Usage_SnE', 'Main_Usage_Farm', 'Building_Material_S',\n",
" 'Building_Material_R', 'Building_Material_C', 'Building_Material_steel',\n",
" 'Building_Material_stone', 'Building_Material_B', 'Building_Material_W',\n",
" 'Building_Material_iron', 'Building_Material_tile',\n",
" 'Building_Material_clay', 'Building_Material_RC_reinforce',\n",
" 'Non_City_Land_Usage', 'Parking_Space_Types', 'Building_Types',\n",
" 'Unit_Price_Ping', 'Transfer_Total_Ping', 'Month_raw', 'CPI',\n",
" 'CPI_rate', 'unemployment rate', 'Pain_index_3month', 'ppen_price',\n",
" 'high_price', 'low_price', 'close_price', 'qmatch', 'amt_millon',\n",
" 'return_rate_month', 'Turnover_rate_month',\n",
" 'outstanding_share_thousand', 'Capitalization_million',\n",
" 'excess total _ million_usdollars', 'import_price_index_usdollars',\n",
" 'export_price_index_usdollars', 'export_million_usdollars',\n",
" 'import_million_usdollars', 'survival_mobility_rate',\n",
" 'live_deposit_mobility_interest_rate', 'CCI_3month',\n",
" 'construction_engineering_index'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future.columns"
]
},
{
"cell_type": "markdown",
"id": "ec61a7f1",
"metadata": {},
"source": [
"以 Place_id(市政區) 來看, 是 heavy-tailed distribution, 而且很多市政區的資料 \\\n",
"剩下來的 data 有多少種不同的行政區，可以藉由下面的程式碼查看\n",
"```\n",
"df_future['Place_id'].unique().shape # original: (226,)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1e18cb1b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of distinct districts in the data: 226\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(df_future['Place_id'].value_counts(), bins=30)\n",
"plt.xlabel('number of cases(samples) in the specific district')\n",
"plt.ylabel('frequency')\n",
"print('Number of distinct districts in the data:', df_future['Place_id'].unique().shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a2da690a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.0 89166\n",
"2.0 77696\n",
"4.0 15650\n",
"1.0 9863\n",
"0.0 9342\n",
"5.0 1889\n",
"6.0 127\n",
"7.0 10\n",
"10.0 7\n",
"9.0 1\n",
"Name: room, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['room'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e1b9ce34",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 8618\n",
"0 499\n",
"2 1\n",
"Name: Type, dtype: int64\n"
]
}
],
"source": [
"# 查看賣出 0 樓層的交易類型是什麼? {'車位': 0, '房地(土地+建物)': 1, '房地(土地+建物)+車位': 2, '建物': 3}\n",
"print(df_future.loc[df_future['trading_floors_count'] == 0, 'Type'].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b447c416",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" 5 14972\n",
" 4 14955\n",
" 3 14085\n",
" 6 13905\n",
" 7 13324\n",
" 1 13281\n",
" 8 12367\n",
" 9 12248\n",
" 10 11572\n",
" 11 11159\n",
" 2 11158\n",
" 12 10487\n",
" 13 9845\n",
" 14 8726\n",
" 15 6985\n",
" 16 3097\n",
" 17 3040\n",
" 18 2828\n",
" 19 2639\n",
" 20 2238\n",
" 21 2031\n",
" 22 1781\n",
" 23 1511\n",
" 24 1295\n",
" 25 728\n",
" 26 684\n",
" 27 633\n",
" 28 593\n",
" 29 428\n",
" 30 235\n",
" 31 208\n",
" 32 174\n",
" 33 173\n",
" 34 112\n",
" 35 91\n",
" 36 52\n",
" 38 18\n",
" 37 17\n",
" 40 7\n",
" 43 6\n",
" 48 6\n",
" 54 5\n",
" 41 5\n",
" 44 5\n",
" 55 5\n",
" 52 5\n",
" 42 4\n",
" 51 4\n",
" 49 4\n",
" 46 4\n",
" 39 4\n",
" 53 3\n",
" 47 3\n",
" 56 2\n",
" 77 2\n",
" 45 1\n",
"-1 1\n",
"Name: min_floors_height, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['min_floors_height'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5d922b65",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['including_arcade'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6550c636",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 203751\n",
"Name: including_arcade, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['including_arcade'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "59fe316e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parking Area > 50: 22\n",
"0.0 34428\n",
"9.0 31147\n",
"8.0 27541\n",
"10.0 25689\n",
"7.0 19023\n",
"11.0 17825\n",
"12.0 7774\n",
"6.0 6960\n",
"5.0 5817\n",
"4.0 4415\n",
"13.0 3269\n",
"3.0 2856\n",
"14.0 2377\n",
"2.0 1727\n",
"18.0 1622\n",
"17.0 1605\n",
"15.0 1600\n",
"19.0 1484\n",
"16.0 1427\n",
"20.0 1178\n",
"22.0 883\n",
"21.0 808\n",
"23.0 738\n",
"24.0 354\n",
"25.0 244\n",
"26.0 167\n",
"27.0 147\n",
"1.0 109\n",
"28.0 82\n",
"29.0 75\n",
"31.0 66\n",
"30.0 60\n",
"32.0 41\n",
"36.0 39\n",
"34.0 27\n",
"35.0 20\n",
"41.0 19\n",
"38.0 18\n",
"37.0 14\n",
"40.0 13\n",
"33.0 12\n",
"39.0 8\n",
"45.0 5\n",
"47.0 5\n",
"53.0 4\n",
"43.0 3\n",
"46.0 3\n",
"48.0 3\n",
"51.0 2\n",
"72.0 2\n",
"89.0 1\n",
"155.0 1\n",
"153.0 1\n",
"143.0 1\n",
"138.0 1\n",
"122.0 1\n",
"96.0 1\n",
"60.0 1\n",
"88.0 1\n",
"86.0 1\n",
"76.0 1\n",
"42.0 1\n",
"52.0 1\n",
"50.0 1\n",
"49.0 1\n",
"191.0 1\n",
"Name: Parking_Area, dtype: int64\n"
]
},
{
"data": {
"text/plain": [
"Text(0, 0.5, 'frequency')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('Parking Area > 50:', df_future.loc[df_future['Parking_Area'] > 49.5].shape[0])\n",
"print(df_future['Parking_Area'].sort_values().value_counts())\n",
"plt.hist(df_future['Parking_Area'], bins=50, log=True)\n",
"plt.xlabel('parking area')\n",
"plt.ylabel('frequency')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9506e3b3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 130024\n",
"4 54333\n",
"0 10977\n",
"7 7320\n",
"2 699\n",
"3 398\n",
"Name: City_Land_Usage, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['City_Land_Usage'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "8566dc6b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['Non_City_Land_Usage'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "d2effaa1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 203407\n",
"1 344\n",
"Name: Main_Usage_Manufacturing, dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['Main_Usage_Manufacturing'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "0040a367",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 344\n",
"Name: Main_Usage_Living, dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future.loc[df_future['Main_Usage_Manufacturing'] == 1, 'Main_Usage_Living'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "4b7eb2d7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 203675\n",
"1 76\n",
"Name: Main_Usage_Parking, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['Main_Usage_Parking'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "342ae9d2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 76\n",
"Name: Main_Usage_Living, dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future.loc[df_future['Main_Usage_Parking'] == 1, 'Main_Usage_Living'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "abc498e8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 193293\n",
"1 10458\n",
"Name: Main_Usage_Business, dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['Main_Usage_Business'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3a6f7879",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['Main_Usage_SnE'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "5f628066",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 203673\n",
"1 78\n",
"Name: Main_Usage_Farm, dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['Main_Usage_Farm'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d7347d51",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 78\n",
"Name: Main_Usage_Living, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future.loc[df_future['Main_Usage_Farm'] == 1, 'Main_Usage_Living'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "78453073",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 153632\n",
"1 31285\n",
"2 10873\n",
"3 3493\n",
"4 1932\n",
"5 1248\n",
"6 703\n",
"7 585\n",
"Name: Parking_Space_Types, dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future['Parking_Space_Types'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "95fd5808",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 169537\n",
"1 19747\n",
"2 8947\n",
"3 2134\n",
"4 1619\n",
"5 904\n",
"6 342\n",
"7 311\n",
"8 168\n",
"9 42\n",
"Name: Building_Types, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 住宅大樓(11層含以上有電梯): 0\n",
"# 其他: 1\n",
"# 透天厝: 2\n",
"# 華廈(10層含以下有電梯): 3\n",
"# 公寓(5樓含以下無電梯): 4\n",
"# 套房(1房1廳1衛): 5\n",
"# 店面(店鋪): 6\n",
"# 辦公商業大樓: 7\n",
"# 廠辦: 8\n",
"# 農舍: 9\n",
"# 工廠: 10\n",
"# 倉庫: 11\n",
"df_future['Building_Types'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "6abf9a32",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2021-09-18 636\n",
"2021-09-11 609\n",
"2021-11-05 585\n",
"2021-09-26 575\n",
"2021-11-06 563\n",
" ... \n",
"2017-01-30 2\n",
"2018-02-15 2\n",
"2017-01-27 2\n",
"2017-02-01 1\n",
"2017-01-29 1\n",
"Name: TDATE, Length: 1825, dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Redudant (compared to 'Month')\n",
"df_future['TDATE'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "fa9863ba",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'frequency')"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(df_future['Transfer_Total_Ping'].value_counts(), bins=100)\n",
"plt.xlabel('transfer number of ping')\n",
"plt.ylabel('frequency')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "2f1cfded",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2126.0 178\n",
"177.0 38\n",
"207.0 19\n",
"151.0 13\n",
"159.0 7\n",
"174.0 7\n",
"171.0 7\n",
"173.0 7\n",
"175.0 6\n",
"189.0 6\n",
"161.0 6\n",
"183.0 5\n",
"163.0 5\n",
"219.0 5\n",
"204.0 5\n",
"176.0 5\n",
"162.0 5\n",
"193.0 4\n",
"155.0 4\n",
"165.0 4\n",
"153.0 4\n",
"202.0 3\n",
"190.0 3\n",
"160.0 3\n",
"359.0 3\n",
"209.0 3\n",
"156.0 3\n",
"197.0 2\n",
"186.0 2\n",
"168.0 2\n",
"187.0 2\n",
"181.0 2\n",
"182.0 2\n",
"213.0 2\n",
"201.0 2\n",
"172.0 2\n",
"169.0 2\n",
"299.0 2\n",
"194.0 1\n",
"214.0 1\n",
"195.0 1\n",
"627.0 1\n",
"301.0 1\n",
"457.0 1\n",
"342.0 1\n",
"272.0 1\n",
"211.0 1\n",
"293.0 1\n",
"320895.0 1\n",
"198.0 1\n",
"320.0 1\n",
"205.0 1\n",
"291.0 1\n",
"529.0 1\n",
"273.0 1\n",
"289.0 1\n",
"221.0 1\n",
"154.0 1\n",
"252.0 1\n",
"167.0 1\n",
"223.0 1\n",
"170.0 1\n",
"152.0 1\n",
"265.0 1\n",
"349.0 1\n",
"232.0 1\n",
"315.0 1\n",
"210.0 1\n",
"220.0 1\n",
"200.0 1\n",
"229.0 1\n",
"208.0 1\n",
"466.0 1\n",
"Name: Transfer_Total_Ping, dtype: int64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future.loc[df_future['Transfer_Total_Ping'] > 150, 'Transfer_Total_Ping'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "5a33a411",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Month_raw</th>\n",
" <th>Month</th>\n",
" <th>TDATE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>201612</td>\n",
" <td>201701</td>\n",
" <td>2017-01-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>201612</td>\n",
" <td>201701</td>\n",
" <td>2017-01-21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>201701</td>\n",
" <td>201702</td>\n",
" <td>2017-02-09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>201701</td>\n",
" <td>201702</td>\n",
" <td>2017-02-19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>201701</td>\n",
" <td>201702</td>\n",
" <td>2017-02-12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203746</th>\n",
" <td>202111</td>\n",
" <td>202112</td>\n",
" <td>2021-12-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203747</th>\n",
" <td>202111</td>\n",
" <td>202112</td>\n",
" <td>2021-12-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203748</th>\n",
" <td>202111</td>\n",
" <td>202112</td>\n",
" <td>2021-12-17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203749</th>\n",
" <td>202111</td>\n",
" <td>202112</td>\n",
" <td>2021-12-20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203750</th>\n",
" <td>202111</td>\n",
" <td>202112</td>\n",
" <td>2021-12-20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>203751 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Month_raw Month TDATE\n",
"0 201612 201701 2017-01-02\n",
"1 201612 201701 2017-01-21\n",
"2 201701 201702 2017-02-09\n",
"3 201701 201702 2017-02-19\n",
"4 201701 201702 2017-02-12\n",
"... ... ... ...\n",
"203746 202111 202112 2021-12-29\n",
"203747 202111 202112 2021-12-16\n",
"203748 202111 202112 2021-12-17\n",
"203749 202111 202112 2021-12-20\n",
"203750 202111 202112 2021-12-20\n",
"\n",
"[203751 rows x 3 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future[['Month_raw', 'Month', 'TDATE']]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "2dda4730",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 1254., 1540., 808., 1693., 2096., 3089., 14927., 8536.,\n",
" 14494., 33541., 30269., 19907., 2941., 9297., 8534., 6483.,\n",
" 0., 11833., 11117., 21392.]),\n",
" array([ 99.59 , 99.8915, 100.193 , 100.4945, 100.796 , 101.0975,\n",
" 101.399 , 101.7005, 102.002 , 102.3035, 102.605 , 102.9065,\n",
" 103.208 , 103.5095, 103.811 , 104.1125, 104.414 , 104.7155,\n",
" 105.017 , 105.3185, 105.62 ]),\n",
" <BarContainer object of 20 artists>)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD7CAYAAACIYvgKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUXUlEQVR4nO3df4xd5X3n8fcnBlLUbtYmnrqs7azZxVLkpItDpsbdRlpKVGPIHyYSG0FXwUpR3G1gt5W6K5z+UVIIEkjbZMUuQXKLa7Nq4yCSFCsxdV2XVRq1/JgkDmAI6ykQYcvBXmygKFqysN/94z5ubpw7M3d+3vH4/ZKO7rnf85xzn0fXns+c85x7J1WFJOns9o5Bd0CSNHiGgSTJMJAkGQaSJAwDSRKGgSSJPsIgyc8keTzJd5McTPIHrb4jyQtJDrRlbasnyd1JRpM8meTSrmNtTnKoLZu76h9M8lTb5+4kmYWxSpLGcE4fbd4ErqiqN5KcC3wzycNt23+uqgdPa38VsLotlwH3ApcluQC4FRgGCvhWkt1VdbK1+STwGLAH2Ag8jCRpTkwYBtX5VNob7em5bRnvk2qbgPvbfo8mWZzkQuByYF9VnQBIsg/YmOR/Au+qqkdb/X7gGiYIg6VLl9aqVasm6r4kqVm6dCl79+7dW1UbT9/Wz5kBSRYB3wIuBu6pqseS/BZwR5LfB/YDW6vqTWA58FLX7odbbbz64R71ca1atYqRkZF+ui9JapIs7VXvawK5qt6uqrXACmBdkvcDnwbeC/wScAFwy8x0dWxJtiQZSTJy/Pjx2X45STprTOpuoqp6FXgE2FhVR6vjTeBPgHWt2RFgZdduK1ptvPqKHvVer7+tqoaranhoaGgyXZckjaOfu4mGkixu6+cDvwZ8r80D0O78uQZ4uu2yG7ih3VW0Hnitqo4Ce4ENSZYkWQJsAPa2ba8nWd+OdQPw0EwOUpI0vn7mDC4EdrZ5g3cAD1TV15L8dZIhIMAB4N+39nuAq4FR4IfAJwCq6kSS24EnWrvbTk0mA58CdgDn05k49k4iSZpDOVO/wnp4eLicQJakyUnyraoaPr3uJ5AlSYaBJMkwkCRhGEiS6PMTyNLZZtXWr0953xfv/MgM9kSaG54ZSJIMA0mSYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkiT7CIMnPJHk8yXeTHEzyB61+UZLHkowm+VKS81r9ne35aNu+qutYn27155Jc2VXf2GqjSbbOwjglSePo58zgTeCKqroEWAtsTLIeuAv4fFVdDJwEbmztbwROtvrnWzuSrAGuA94HbAS+kGRRkkXAPcBVwBrg+tZWkjRHJvwbyFVVwBvt6bltKeAK4NdbfSfwGeBeYFNbB3gQ+O9J0uq7qupN4IUko8C61m60qp4HSLKrtX1mOgOTBsW/n6wzUV9zBu03+APAMWAf8PfAq1X1VmtyGFje1pcDLwG07a8B7+6un7bPWHVJ0hzpKwyq6u2qWgusoPPb/Htns1NjSbIlyUiSkePHjw+iC5K0IE3qbqKqehV4BPhlYHGSU5eZVgBH2voRYCVA2/5PgVe666ftM1a91+tvq6rhqhoeGhqaTNclSePo526ioSSL2/r5wK8Bz9IJhWtbs83AQ219d3tO2/7Xbd5hN3Bdu9voImA18DjwBLC63Z10Hp1J5t0zMDZJUp8mnEAGLgR2trt+3gE8UFVfS/IMsCvJZ4HvAPe19vcB/6NNEJ+g88OdqjqY5AE6E8NvATdV1dsASW4G9gKLgO1VdXDGRihJmlA/dxM9CXygR/15fnw3UHf9/wD/doxj3QHc0aO+B9jTR38lSbPATyBLkgwDSZJhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSfQRBklWJnkkyTNJDib57Vb/TJIjSQ605equfT6dZDTJc0mu7KpvbLXRJFu76hcleazVv5TkvJkeqCRpbP2cGbwF/G5VrQHWAzclWdO2fb6q1rZlD0Dbdh3wPmAj8IUki5IsAu4BrgLWANd3HeeudqyLgZPAjTM0PklSHyYMg6o6WlXfbuv/ADwLLB9nl03Arqp6s6peAEaBdW0Zrarnq+pHwC5gU5IAVwAPtv13AtdMcTySpCmY1JxBklXAB4DHWunmJE8m2Z5kSastB17q2u1wq41VfzfwalW9dVq91+tvSTKSZOT48eOT6bokaRx9h0GSnwO+DPxOVb0O3Av8S2AtcBT4w9noYLeq2lZVw1U1PDQ0NNsvJ0lnjXP6aZTkXDpB8KdV9RWAqnq5a/sfAV9rT48AK7t2X9FqjFF/BVic5Jx2dtDdXpI0B/q5myjAfcCzVfW5rvqFXc0+Cjzd1ncD1yV5Z5KLgNXA48ATwOp259B5dCaZd1dVAY8A17b9NwMPTW9YkqTJ6OfM4FeAjwNPJTnQar9H526gtUABLwK/CVBVB5M8ADxD506km6rqbYAkNwN7gUXA9qo62I53C7AryWeB79AJH0nSHJkwDKrqm0B6bNozzj53AHf0qO/ptV9VPU/nbiNJ0gD4CWRJkmEgSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCTR35+9lCTNoFVbvz7lfV+88yMz2JMf88xAkmQYSJIMA0kShoEkiT7CIMnKJI8keSbJwSS/3eoXJNmX5FB7XNLqSXJ3ktEkTya5tOtYm1v7Q0k2d9U/mOSpts/dSTIbg5Uk9dbPmcFbwO9W1RpgPXBTkjXAVmB/Va0G9rfnAFcBq9uyBbgXOuEB3ApcBqwDbj0VIK3NJ7v22zj9oUmS+jVhGFTV0ar6dlv/B+BZYDmwCdjZmu0Ermnrm4D7q+NRYHGSC4ErgX1VdaKqTgL7gI1t27uq6tGqKuD+rmNJkubApOYMkqwCPgA8BiyrqqNt0w+AZW19OfBS126HW228+uEe9V6vvyXJSJKR48ePT6brkqRx9P2hsyQ/B3wZ+J2qer37sn5VVZKahf79hKraBmwDGB4envXXk+badD6MBLP3gSQtfH2dGSQ5l04Q/GlVfaWVX26XeGiPx1r9CLCya/cVrTZefUWPuiRpjvRzN1GA+4Bnq+pzXZt2A6fuCNoMPNRVv6HdVbQeeK1dTtoLbEiypE0cbwD2tm2vJ1nfXuuGrmNJkuZAP5eJfgX4OPBUkgOt9nvAncADSW4Evg98rG3bA1wNjAI/BD4BUFUnktwOPNHa3VZVJ9r6p4AdwPnAw22RJM2RCcOgqr4JjHXf/4d7tC/gpjGOtR3Y3qM+Arx/or5IkmaHn0CWJBkGkiTDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkugjDJJsT3IsydNdtc8kOZLkQFuu7tr26SSjSZ5LcmVXfWOrjSbZ2lW/KMljrf6lJOfN5AAlSRPr58xgB7CxR/3zVbW2LXsAkqwBrgPe1/b5QpJFSRYB9wBXAWuA61tbgLvasS4GTgI3TmdAkqTJmzAMquobwIk+j7cJ2FVVb1bVC8AosK4to1X1fFX9CNgFbEoS4Argwbb/TuCayQ1BkjRd05kzuDnJk+0y0pJWWw681NXmcKuNVX838GpVvXVavackW5KMJBk5fvz4NLouSep2zhT3uxe4Haj2+IfAb8xUp8ZSVduAbQDDw8M126+n6Vu19etT3vfFOz8ygz2RNJ4phUFVvXxqPckfAV9rT48AK7uarmg1xqi/AixOck47O+huL0maI1MKgyQXVtXR9vSjwKk7jXYDf5bkc8A/A1YDjwMBVie5iM4P++uAX6+qSvIIcC2deYTNwENTHYzUbTpnJdLZZsIwSPJF4HJgaZLDwK3A5UnW0rlM9CLwmwBVdTDJA8AzwFvATVX1djvOzcBeYBGwvaoOtpe4BdiV5LPAd4D7ZmpwkqT+TBgGVXV9j/KYP7Cr6g7gjh71PcCeHvXn6dxtJEkaED+BLEkyDCRJhoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJDH1P3spSWc8/yzrj3lmIEkyDCRJhoEkCecMJGlKpjPfMB95ZiBJMgwkSYaBJIk+wiDJ9iTHkjzdVbsgyb4kh9rjklZPkruTjCZ5MsmlXftsbu0PJdncVf9gkqfaPncnyUwPUpI0vn7ODHYAG0+rbQX2V9VqYH97DnAVsLotW4B7oRMewK3AZcA64NZTAdLafLJrv9NfS5I0yyYMg6r6BnDitPImYGdb3wlc01W/vzoeBRYnuRC4EthXVSeq6iSwD9jYtr2rqh6tqgLu7zqWJGmOTHXOYFlVHW3rPwCWtfXlwEtd7Q632nj1wz3qPSXZkmQkycjx48en2HVJ0ummPYHcfqOvGehLP6+1raqGq2p4aGhoLl5Sks4KUw2Dl9slHtrjsVY/Aqzsarei1carr+hRlyTNoamGwW7g1B1Bm4GHuuo3tLuK1gOvtctJe4ENSZa0ieMNwN627fUk69tdRDd0HUuSNEcm/DqKJF8ELgeWJjlM566gO4EHktwIfB/4WGu+B7gaGAV+CHwCoKpOJLkdeKK1u62qTk1Kf4rOHUvnAw+3RdIc8+ucz24ThkFVXT/Gpg/3aFvATWMcZzuwvUd9BHj/RP2QJM0eP4EsSTIMJEmGgSQJ/56B5rGF9n3xC5mTz2c+zwwkSYaBJMkwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkvCL6iQNmF9yNz94ZiBJMgwkSYaBJAnDQJLENMMgyYtJnkpyIMlIq12QZF+SQ+1xSasnyd1JRpM8meTSruNsbu0PJdk8vSFJkiZrJs4MfrWq1lbVcHu+FdhfVauB/e05wFXA6rZsAe6FTngAtwKXAeuAW08FiCRpbszGZaJNwM62vhO4pqt+f3U8CixOciFwJbCvqk5U1UlgH7BxFvolSRrDdMOggL9M8q0kW1ptWVUdbes/AJa19eXAS137Hm61seqSpDky3Q+dfaiqjiT5eWBfku91b6yqSlLTfI1/1AJnC8B73vOemTqsJJ31pnVmUFVH2uMx4Kt0rvm/3C7/0B6PteZHgJVdu69otbHqvV5vW1UNV9Xw0NDQdLouSeoy5TBI8rNJ/smpdWAD8DSwGzh1R9Bm4KG2vhu4od1VtB54rV1O2gtsSLKkTRxvaDVJ0hyZzmWiZcBXk5w6zp9V1V8keQJ4IMmNwPeBj7X2e4CrgVHgh8AnAKrqRJLbgSdau9uq6sQ0+iVJmqQph0FVPQ9c0qP+CvDhHvUCbhrjWNuB7VPtiyRpevwEsiTJMJAkGQaSJAwDSRKGgSQJw0CShGEgSWL6302ks8B0/mC5pDODYSAtIAa3psrLRJIkw0CSZBhIkjAMJEk4gXzWcGJR0ng8M5AkeWZwJvG3e0mzxTCYpOn8QH7xzo/MYE8kaeZ4mUiSdHaeGXi5RZJ+kmcGkiTDQJJ0ll4mGhQvT0mar+bNmUGSjUmeSzKaZOug+yNJZ5N5EQZJFgH3AFcBa4Drk6wZbK8k6ewxL8IAWAeMVtXzVfUjYBewacB9kqSzxnwJg+XAS13PD7eaJGkOnFETyEm2AFva0zeSPDfI/pxmKfC/B92JGbBQxgELZywLZRwww2PJXTN1pEkb2HsyzTGP2ef5EgZHgJVdz1e02k+oqm3Atrnq1GQkGamq4UH3Y7oWyjhg4YxloYwDFs5YFso4us2Xy0RPAKuTXJTkPOA6YPeA+yRJZ415cWZQVW8luRnYCywCtlfVwQF3S5LOGvMiDACqag+wZ9D9mIZ5eflqChbKOGDhjGWhjAMWzlgWyjj+Uapq0H2QJA3YfJkzkCQNkGHQhyTbkxxL8nRX7YIk+5Icao9LWj1J7m5fq/FkkksH1/OfNsmxvDfJ3yV5M8l/Glyvf9okx/Hv2nvxVJK/TXLJ4Hr+0yY5lk1tLAeSjCT50OB6/pMmM46u7b+U5K0k1859j8c2yffk8iSvtffkQJLfH1zPp84w6M8OYONpta3A/qpaDexvz6HzlRqr27IFuHeO+tivHfQ/lhPAfwT+y5z1rn876H8cLwD/pqp+Ebid+Xe9dwf9j2U/cElVrQV+A/jjOepjP3bQ/zhOfQ3NXcBfzlUHJ2EHkxgL8DdVtbYtt81RH2eUYdCHqvoGnR+M3TYBO9v6TuCarvr91fEosDjJhXPS0T5MZixVdayqngD+75x1sE+THMffVtXJVn+UzudY5o1JjuWN+vFE388C82bSb5L/TwD+A/Bl4Nisd26SpjCWM55hMHXLqupoW/8BsKytn4lfrTHWWM40/YzjRuDhuevSlI05liQfTfI94Ot0zg7ms57jSLIc+Cjz78x5POP9+/rlJN9N8nCS9w2gb9NmGMyA9pvavPkNbToWylh6jSPJr9IJg1sG0qkpOn0sVfXVqnovnd9Mbx9UvybrtHH8V+CWqvp/g+vR1J02lm8D/7yqLgH+G/Dng+rXdBgGU/fyqcs/7fHUqW5fX60xz4w1ljPNmONI8q/oXF/fVFWvDKh/kzHhe9IuZfyLJEvnunOTMNY4hoFdSV4ErgW+kOSagfSwfz3HUlWvV9UbbX0PcO48f096Mgymbjewua1vBh7qqt/Q7ipaD7zWdWo5X401ljNNz3EkeQ/wFeDjVfW/BtS3yRprLBcnSVu/FHgnMJ/Drec4quqiqlpVVauAB4FPVdWfD6SH/RvrPfmFrvdkHZ2fq/P5PemtqlwmWIAvAkfpTKQepnOp4d107ig4BPwVcEFrGzp/qOfvgaeA4UH3fxpj+YXW5nXg1bb+rkGPYQrj+GPgJHCgLSOD7v80xnILcLCN4++ADw26/1MZx2n77QCuHXT/p/Ge3Nzek+/SuUHhXw+6/1NZ/ASyJMnLRJIkw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kS8P8BSZsuxkybrQEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(df_future['CPI'], bins=20)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "d914add9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 5024., 4081., 6565., 13207., 20084., 5367., 12179., 12416.,\n",
" 16556., 3569., 9111., 5237., 12027., 3142., 11350., 11024.,\n",
" 5573., 14730., 21511., 10998.]),\n",
" array([-1.2081 , -1.005035, -0.80197 , -0.598905, -0.39584 , -0.192775,\n",
" 0.01029 , 0.213355, 0.41642 , 0.619485, 0.82255 , 1.025615,\n",
" 1.22868 , 1.431745, 1.63481 , 1.837875, 2.04094 , 2.244005,\n",
" 2.44707 , 2.650135, 2.8532 ]),\n",
" <BarContainer object of 20 artists>)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(df_future['CPI_rate'], bins=20)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "41bbb4ea",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([49965., 44335., 37027., 6652., 29817., 9105., 2897., 0.,\n",
" 11833., 0., 0., 6483., 0., 0., 5637.]),\n",
" array([4.82 , 4.90933333, 4.99866667, 5.088 , 5.17733333,\n",
" 5.26666667, 5.356 , 5.44533333, 5.53466667, 5.624 ,\n",
" 5.71333333, 5.80266667, 5.892 , 5.98133333, 6.07066667,\n",
" 6.16 ]),\n",
" <BarContainer object of 15 artists>)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(df_future['unemployment rate'], bins=15)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "5360abfa",
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'D_Year_Month_Day'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/anaconda3/envs/py310/lib/python3.10/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
"File \u001b[0;32m~/anaconda3/envs/py310/lib/python3.10/site-packages/pandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m~/anaconda3/envs/py310/lib/python3.10/site-packages/pandas/_libs/index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'D_Year_Month_Day'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [32]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf_future\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mD_Year_Month_Day\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/py310/lib/python3.10/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3507\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
"File \u001b[0;32m~/anaconda3/envs/py310/lib/python3.10/site-packages/pandas/core/indexes/base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3623\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3625\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3626\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3627\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3628\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
"\u001b[0;31mKeyError\u001b[0m: 'D_Year_Month_Day'"
]
}
],
"source": [
"df_future['D_Year_Month_Day']"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "33a6f5f4",
"metadata": {},
"outputs": [],
"source": [
"df_future = clean_and_drop(df_future)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "628f7b28",
"metadata": {},
"outputs": [
{
"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>Place_id</th>\n",
" <th>Type</th>\n",
" <th>area_ping</th>\n",
" <th>Month</th>\n",
" <th>room</th>\n",
" <th>hall</th>\n",
" <th>bathroom</th>\n",
" <th>compartment</th>\n",
" <th>parking_price</th>\n",
" <th>trading_floors_count</th>\n",
" <th>building_total_floors</th>\n",
" <th>min_floors_height</th>\n",
" <th>City_Land_Usage</th>\n",
" <th>Parking_Area</th>\n",
" <th>Main_Usage_Business</th>\n",
" <th>Building_Material_S</th>\n",
" <th>Building_Material_R</th>\n",
" <th>Building_Material_C</th>\n",
" <th>Building_Material_steel</th>\n",
" <th>Building_Material_B</th>\n",
" <th>Building_Material_W</th>\n",
" <th>Building_Material_iron</th>\n",
" <th>Building_Material_tile</th>\n",
" <th>Building_Material_clay</th>\n",
" <th>Building_Material_RC_reinforce</th>\n",
" <th>Parking_Space_Types</th>\n",
" <th>Building_Types</th>\n",
" <th>Unit_Price_Ping</th>\n",
" <th>Transfer_Total_Ping</th>\n",
" <th>Month_raw</th>\n",
" <th>CPI</th>\n",
" <th>CPI_rate</th>\n",
" <th>unemployment rate</th>\n",
" <th>Pain_index_3month</th>\n",
" <th>ppen_price</th>\n",
" <th>high_price</th>\n",
" <th>low_price</th>\n",
" <th>close_price</th>\n",
" <th>qmatch</th>\n",
" <th>amt_millon</th>\n",
" <th>return_rate_month</th>\n",
" <th>Turnover_rate_month</th>\n",
" <th>outstanding_share_thousand</th>\n",
" <th>Capitalization_million</th>\n",
" <th>excess total _ million_usdollars</th>\n",
" <th>import_price_index_usdollars</th>\n",
" <th>export_price_index_usdollars</th>\n",
" <th>export_million_usdollars</th>\n",
" <th>import_million_usdollars</th>\n",
" <th>survival_mobility_rate</th>\n",
" <th>live_deposit_mobility_interest_rate</th>\n",
" <th>CCI_3month</th>\n",
" <th>construction_engineering_index</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>242</td>\n",
" <td>0</td>\n",
" <td>3.0</td>\n",
" <td>201701</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>8.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>309908.8326</td>\n",
" <td>33.0</td>\n",
" <td>201612</td>\n",
" <td>100.34</td>\n",
" <td>1.6925</td>\n",
" <td>4.99</td>\n",
" <td>5.4825</td>\n",
" <td>9245.55</td>\n",
" <td>9430.34</td>\n",
" <td>9078.64</td>\n",
" <td>9253.50</td>\n",
" <td>35298</td>\n",
" <td>1235892</td>\n",
" <td>0.1384</td>\n",
" <td>5.1209</td>\n",
" <td>689283423</td>\n",
" <td>27201163</td>\n",
" <td>82482</td>\n",
" <td>103.70</td>\n",
" <td>102.09</td>\n",
" <td>23247.9</td>\n",
" <td>20664.4</td>\n",
" <td>0.08</td>\n",
" <td>0.2</td>\n",
" <td>77.22</td>\n",
" <td>101.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>242</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>201702</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>8.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>322579.9640</td>\n",
" <td>27.0</td>\n",
" <td>201701</td>\n",
" <td>100.32</td>\n",
" <td>2.2422</td>\n",
" <td>4.98</td>\n",
" <td>6.0222</td>\n",
" <td>9252.56</td>\n",
" <td>9468.34</td>\n",
" <td>9235.95</td>\n",
" <td>9447.95</td>\n",
" <td>28103</td>\n",
" <td>1024124</td>\n",
" <td>2.1014</td>\n",
" <td>4.0707</td>\n",
" <td>690360680</td>\n",
" <td>27807016</td>\n",
" <td>58813</td>\n",
" <td>105.56</td>\n",
" <td>103.26</td>\n",
" <td>21816.5</td>\n",
" <td>19971.2</td>\n",
" <td>0.08</td>\n",
" <td>0.2</td>\n",
" <td>74.35</td>\n",
" <td>101.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>242</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>201702</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>8.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>316923.7402</td>\n",
" <td>27.0</td>\n",
" <td>201701</td>\n",
" <td>100.32</td>\n",
" <td>2.2422</td>\n",
" <td>4.98</td>\n",
" <td>6.0222</td>\n",
" <td>9252.56</td>\n",
" <td>9468.34</td>\n",
" <td>9235.95</td>\n",
" <td>9447.95</td>\n",
" <td>28103</td>\n",
" <td>1024124</td>\n",
" <td>2.1014</td>\n",
" <td>4.0707</td>\n",
" <td>690360680</td>\n",
" <td>27807016</td>\n",
" <td>58813</td>\n",
" <td>105.56</td>\n",
" <td>103.26</td>\n",
" <td>21816.5</td>\n",
" <td>19971.2</td>\n",
" <td>0.08</td>\n",
" <td>0.2</td>\n",
" <td>74.35</td>\n",
" <td>101.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>242</td>\n",
" <td>0</td>\n",
" <td>4.0</td>\n",
" <td>201702</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>14</td>\n",
" <td>4</td>\n",
" <td>8.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>304698.8918</td>\n",
" <td>39.0</td>\n",
" <td>201701</td>\n",
" <td>100.32</td>\n",
" <td>2.2422</td>\n",
" <td>4.98</td>\n",
" <td>6.0222</td>\n",
" <td>9252.56</td>\n",
" <td>9468.34</td>\n",
" <td>9235.95</td>\n",
" <td>9447.95</td>\n",
" <td>28103</td>\n",
" <td>1024124</td>\n",
" <td>2.1014</td>\n",
" <td>4.0707</td>\n",
" <td>690360680</td>\n",
" <td>27807016</td>\n",
" <td>58813</td>\n",
" <td>105.56</td>\n",
" <td>103.26</td>\n",
" <td>21816.5</td>\n",
" <td>19971.2</td>\n",
" <td>0.08</td>\n",
" <td>0.2</td>\n",
" <td>74.35</td>\n",
" <td>101.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>242</td>\n",
" <td>0</td>\n",
" <td>4.0</td>\n",
" <td>201702</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>15</td>\n",
" <td>4</td>\n",
" <td>8.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>299601.3482</td>\n",
" <td>39.0</td>\n",
" <td>201701</td>\n",
" <td>100.32</td>\n",
" <td>2.2422</td>\n",
" <td>4.98</td>\n",
" <td>6.0222</td>\n",
" <td>9252.56</td>\n",
" <td>9468.34</td>\n",
" <td>9235.95</td>\n",
" <td>9447.95</td>\n",
" <td>28103</td>\n",
" <td>1024124</td>\n",
" <td>2.1014</td>\n",
" <td>4.0707</td>\n",
" <td>690360680</td>\n",
" <td>27807016</td>\n",
" <td>58813</td>\n",
" <td>105.56</td>\n",
" <td>103.26</td>\n",
" <td>21816.5</td>\n",
" <td>19971.2</td>\n",
" <td>0.08</td>\n",
" <td>0.2</td>\n",
" <td>74.35</td>\n",
" <td>101.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203745</th>\n",
" <td>892</td>\n",
" <td>1</td>\n",
" <td>11.0</td>\n",
" <td>202112</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>194040.5426</td>\n",
" <td>27.0</td>\n",
" <td>202111</td>\n",
" <td>105.62</td>\n",
" <td>2.8532</td>\n",
" <td>4.87</td>\n",
" <td>6.5132</td>\n",
" <td>17021.80</td>\n",
" <td>17986.20</td>\n",
" <td>17021.80</td>\n",
" <td>17427.80</td>\n",
" <td>120589</td>\n",
" <td>7668790</td>\n",
" <td>2.5920</td>\n",
" <td>16.3983</td>\n",
" <td>735375176</td>\n",
" <td>53502315</td>\n",
" <td>74619</td>\n",
" <td>127.23</td>\n",
" <td>115.75</td>\n",
" <td>38330.6</td>\n",
" <td>35648.9</td>\n",
" <td>0.04</td>\n",
" <td>0.1</td>\n",
" <td>73.33</td>\n",
" <td>125.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203746</th>\n",
" <td>892</td>\n",
" <td>1</td>\n",
" <td>12.0</td>\n",
" <td>202112</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>224754.7304</td>\n",
" <td>29.0</td>\n",
" <td>202111</td>\n",
" <td>105.62</td>\n",
" <td>2.8532</td>\n",
" <td>4.87</td>\n",
" <td>6.5132</td>\n",
" <td>17021.80</td>\n",
" <td>17986.20</td>\n",
" <td>17021.80</td>\n",
" <td>17427.80</td>\n",
" <td>120589</td>\n",
" <td>7668790</td>\n",
" <td>2.5920</td>\n",
" <td>16.3983</td>\n",
" <td>735375176</td>\n",
" <td>53502315</td>\n",
" <td>74619</td>\n",
" <td>127.23</td>\n",
" <td>115.75</td>\n",
" <td>38330.6</td>\n",
" <td>35648.9</td>\n",
" <td>0.04</td>\n",
" <td>0.1</td>\n",
" <td>73.33</td>\n",
" <td>125.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203748</th>\n",
" <td>880</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
" <td>202112</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>192430.6180</td>\n",
" <td>20.0</td>\n",
" <td>202111</td>\n",
" <td>105.62</td>\n",
" <td>2.8532</td>\n",
" <td>4.87</td>\n",
" <td>6.5132</td>\n",
" <td>17021.80</td>\n",
" <td>17986.20</td>\n",
" <td>17021.80</td>\n",
" <td>17427.80</td>\n",
" <td>120589</td>\n",
" <td>7668790</td>\n",
" <td>2.5920</td>\n",
" <td>16.3983</td>\n",
" <td>735375176</td>\n",
" <td>53502315</td>\n",
" <td>74619</td>\n",
" <td>127.23</td>\n",
" <td>115.75</td>\n",
" <td>38330.6</td>\n",
" <td>35648.9</td>\n",
" <td>0.04</td>\n",
" <td>0.1</td>\n",
" <td>73.33</td>\n",
" <td>125.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203749</th>\n",
" <td>880</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
" <td>202112</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>119818.7210</td>\n",
" <td>35.0</td>\n",
" <td>202111</td>\n",
" <td>105.62</td>\n",
" <td>2.8532</td>\n",
" <td>4.87</td>\n",
" <td>6.5132</td>\n",
" <td>17021.80</td>\n",
" <td>17986.20</td>\n",
" <td>17021.80</td>\n",
" <td>17427.80</td>\n",
" <td>120589</td>\n",
" <td>7668790</td>\n",
" <td>2.5920</td>\n",
" <td>16.3983</td>\n",
" <td>735375176</td>\n",
" <td>53502315</td>\n",
" <td>74619</td>\n",
" <td>127.23</td>\n",
" <td>115.75</td>\n",
" <td>38330.6</td>\n",
" <td>35648.9</td>\n",
" <td>0.04</td>\n",
" <td>0.1</td>\n",
" <td>73.33</td>\n",
" <td>125.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203750</th>\n",
" <td>880</td>\n",
" <td>1</td>\n",
" <td>6.0</td>\n",
" <td>202112</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\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>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>112423.6464</td>\n",
" <td>38.0</td>\n",
" <td>202111</td>\n",
" <td>105.62</td>\n",
" <td>2.8532</td>\n",
" <td>4.87</td>\n",
" <td>6.5132</td>\n",
" <td>17021.80</td>\n",
" <td>17986.20</td>\n",
" <td>17021.80</td>\n",
" <td>17427.80</td>\n",
" <td>120589</td>\n",
" <td>7668790</td>\n",
" <td>2.5920</td>\n",
" <td>16.3983</td>\n",
" <td>735375176</td>\n",
" <td>53502315</td>\n",
" <td>74619</td>\n",
" <td>127.23</td>\n",
" <td>115.75</td>\n",
" <td>38330.6</td>\n",
" <td>35648.9</td>\n",
" <td>0.04</td>\n",
" <td>0.1</td>\n",
" <td>73.33</td>\n",
" <td>125.81</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>182738 rows × 53 columns</p>\n",
"</div>"
],
"text/plain": [
" Place_id Type area_ping Month room hall bathroom compartment \\\n",
"1 242 0 3.0 201701 2.0 2.0 1.0 1 \n",
"2 242 0 2.0 201702 1.0 1.0 1.0 1 \n",
"3 242 0 2.0 201702 1.0 1.0 1.0 1 \n",
"4 242 0 4.0 201702 3.0 2.0 1.0 1 \n",
"5 242 0 4.0 201702 3.0 2.0 1.0 1 \n",
"... ... ... ... ... ... ... ... ... \n",
"203745 892 1 11.0 202112 3.0 2.0 2.0 1 \n",
"203746 892 1 12.0 202112 3.0 2.0 2.0 1 \n",
"203748 880 1 5.0 202112 2.0 1.0 2.0 1 \n",
"203749 880 1 5.0 202112 2.0 1.0 2.0 1 \n",
"203750 880 1 6.0 202112 2.0 1.0 2.0 1 \n",
"\n",
" parking_price trading_floors_count building_total_floors \\\n",
"1 0.0 1 18 \n",
"2 0.0 1 18 \n",
"3 0.0 1 18 \n",
"4 0.0 1 18 \n",
"5 0.0 1 18 \n",
"... ... ... ... \n",
"203745 0.0 1 5 \n",
"203746 0.0 1 5 \n",
"203748 0.0 1 9 \n",
"203749 0.0 1 9 \n",
"203750 0.0 1 9 \n",
"\n",
" min_floors_height City_Land_Usage Parking_Area Main_Usage_Business \\\n",
"1 11 4 8.0 1 \n",
"2 10 4 8.0 1 \n",
"3 11 4 8.0 1 \n",
"4 14 4 8.0 1 \n",
"5 15 4 8.0 1 \n",
"... ... ... ... ... \n",
"203745 3 1 0.0 1 \n",
"203746 1 1 0.0 1 \n",
"203748 5 0 0.0 1 \n",
"203749 8 0 0.0 1 \n",
"203750 4 0 0.0 1 \n",
"\n",
" Building_Material_S Building_Material_R Building_Material_C \\\n",
"1 0 1 1 \n",
"2 0 1 1 \n",
"3 0 1 1 \n",
"4 0 1 1 \n",
"5 0 1 1 \n",
"... ... ... ... \n",
"203745 0 1 1 \n",
"203746 0 1 1 \n",
"203748 0 1 1 \n",
"203749 0 1 1 \n",
"203750 0 1 1 \n",
"\n",
" Building_Material_steel Building_Material_B Building_Material_W \\\n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"5 0 0 0 \n",
"... ... ... ... \n",
"203745 0 0 0 \n",
"203746 0 0 0 \n",
"203748 0 0 0 \n",
"203749 0 0 0 \n",
"203750 0 0 0 \n",
"\n",
" Building_Material_iron Building_Material_tile Building_Material_clay \\\n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"5 0 0 0 \n",
"... ... ... ... \n",
"203745 0 0 0 \n",
"203746 0 0 0 \n",
"203748 0 0 0 \n",
"203749 0 0 0 \n",
"203750 0 0 0 \n",
"\n",
" Building_Material_RC_reinforce Parking_Space_Types Building_Types \\\n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"5 0 0 0 \n",
"... ... ... ... \n",
"203745 0 1 1 \n",
"203746 0 1 1 \n",
"203748 0 1 1 \n",
"203749 0 1 1 \n",
"203750 0 1 1 \n",
"\n",
" Unit_Price_Ping Transfer_Total_Ping Month_raw CPI CPI_rate \\\n",
"1 309908.8326 33.0 201612 100.34 1.6925 \n",
"2 322579.9640 27.0 201701 100.32 2.2422 \n",
"3 316923.7402 27.0 201701 100.32 2.2422 \n",
"4 304698.8918 39.0 201701 100.32 2.2422 \n",
"5 299601.3482 39.0 201701 100.32 2.2422 \n",
"... ... ... ... ... ... \n",
"203745 194040.5426 27.0 202111 105.62 2.8532 \n",
"203746 224754.7304 29.0 202111 105.62 2.8532 \n",
"203748 192430.6180 20.0 202111 105.62 2.8532 \n",
"203749 119818.7210 35.0 202111 105.62 2.8532 \n",
"203750 112423.6464 38.0 202111 105.62 2.8532 \n",
"\n",
" unemployment rate Pain_index_3month ppen_price high_price \\\n",
"1 4.99 5.4825 9245.55 9430.34 \n",
"2 4.98 6.0222 9252.56 9468.34 \n",
"3 4.98 6.0222 9252.56 9468.34 \n",
"4 4.98 6.0222 9252.56 9468.34 \n",
"5 4.98 6.0222 9252.56 9468.34 \n",
"... ... ... ... ... \n",
"203745 4.87 6.5132 17021.80 17986.20 \n",
"203746 4.87 6.5132 17021.80 17986.20 \n",
"203748 4.87 6.5132 17021.80 17986.20 \n",
"203749 4.87 6.5132 17021.80 17986.20 \n",
"203750 4.87 6.5132 17021.80 17986.20 \n",
"\n",
" low_price close_price qmatch amt_millon return_rate_month \\\n",
"1 9078.64 9253.50 35298 1235892 0.1384 \n",
"2 9235.95 9447.95 28103 1024124 2.1014 \n",
"3 9235.95 9447.95 28103 1024124 2.1014 \n",
"4 9235.95 9447.95 28103 1024124 2.1014 \n",
"5 9235.95 9447.95 28103 1024124 2.1014 \n",
"... ... ... ... ... ... \n",
"203745 17021.80 17427.80 120589 7668790 2.5920 \n",
"203746 17021.80 17427.80 120589 7668790 2.5920 \n",
"203748 17021.80 17427.80 120589 7668790 2.5920 \n",
"203749 17021.80 17427.80 120589 7668790 2.5920 \n",
"203750 17021.80 17427.80 120589 7668790 2.5920 \n",
"\n",
" Turnover_rate_month outstanding_share_thousand \\\n",
"1 5.1209 689283423 \n",
"2 4.0707 690360680 \n",
"3 4.0707 690360680 \n",
"4 4.0707 690360680 \n",
"5 4.0707 690360680 \n",
"... ... ... \n",
"203745 16.3983 735375176 \n",
"203746 16.3983 735375176 \n",
"203748 16.3983 735375176 \n",
"203749 16.3983 735375176 \n",
"203750 16.3983 735375176 \n",
"\n",
" Capitalization_million excess total _ million_usdollars \\\n",
"1 27201163 82482 \n",
"2 27807016 58813 \n",
"3 27807016 58813 \n",
"4 27807016 58813 \n",
"5 27807016 58813 \n",
"... ... ... \n",
"203745 53502315 74619 \n",
"203746 53502315 74619 \n",
"203748 53502315 74619 \n",
"203749 53502315 74619 \n",
"203750 53502315 74619 \n",
"\n",
" import_price_index_usdollars export_price_index_usdollars \\\n",
"1 103.70 102.09 \n",
"2 105.56 103.26 \n",
"3 105.56 103.26 \n",
"4 105.56 103.26 \n",
"5 105.56 103.26 \n",
"... ... ... \n",
"203745 127.23 115.75 \n",
"203746 127.23 115.75 \n",
"203748 127.23 115.75 \n",
"203749 127.23 115.75 \n",
"203750 127.23 115.75 \n",
"\n",
" export_million_usdollars import_million_usdollars \\\n",
"1 23247.9 20664.4 \n",
"2 21816.5 19971.2 \n",
"3 21816.5 19971.2 \n",
"4 21816.5 19971.2 \n",
"5 21816.5 19971.2 \n",
"... ... ... \n",
"203745 38330.6 35648.9 \n",
"203746 38330.6 35648.9 \n",
"203748 38330.6 35648.9 \n",
"203749 38330.6 35648.9 \n",
"203750 38330.6 35648.9 \n",
"\n",
" survival_mobility_rate live_deposit_mobility_interest_rate \\\n",
"1 0.08 0.2 \n",
"2 0.08 0.2 \n",
"3 0.08 0.2 \n",
"4 0.08 0.2 \n",
"5 0.08 0.2 \n",
"... ... ... \n",
"203745 0.04 0.1 \n",
"203746 0.04 0.1 \n",
"203748 0.04 0.1 \n",
"203749 0.04 0.1 \n",
"203750 0.04 0.1 \n",
"\n",
" CCI_3month construction_engineering_index \n",
"1 77.22 101.05 \n",
"2 74.35 101.67 \n",
"3 74.35 101.67 \n",
"4 74.35 101.67 \n",
"5 74.35 101.67 \n",
"... ... ... \n",
"203745 73.33 125.81 \n",
"203746 73.33 125.81 \n",
"203748 73.33 125.81 \n",
"203749 73.33 125.81 \n",
"203750 73.33 125.81 \n",
"\n",
"[182738 rows x 53 columns]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_future"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6681498b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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