diff --git a/机器学习竞赛实战_优胜解决方案/特征工程建模/特征工程建模.ipynb b/机器学习竞赛实战_优胜解决方案/特征工程建模/特征工程建模.ipynb index 7d7b3c0..7ec97bf 100644 --- a/机器学习竞赛实战_优胜解决方案/特征工程建模/特征工程建模.ipynb +++ b/机器学习竞赛实战_优胜解决方案/特征工程建模/特征工程建模.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -54,106 +54,7 @@ "
5 rows × 122 columns
\n", + "" + ], + "text/plain": [ + " SK_ID_CURR TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR \\\n", + "0 100002 1 Cash loans M N \n", + "1 100003 0 Cash loans F N \n", + "2 100004 0 Revolving loans M Y \n", + "3 100006 0 Cash loans F N \n", + "4 100007 0 Cash loans M N \n", + "\n", + " FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY \\\n", + "0 Y 0 202500.0 406597.5 24700.5 \n", + "1 N 0 270000.0 1293502.5 35698.5 \n", + "2 Y 0 67500.0 135000.0 6750.0 \n", + "3 Y 0 135000.0 312682.5 29686.5 \n", + "4 Y 0 121500.0 513000.0 21865.5 \n", + "\n", + " ... FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 \\\n", + "0 ... 0 0 0 0 \n", + "1 ... 0 0 0 0 \n", + "2 ... 0 0 0 0 \n", + "3 ... 0 0 0 0 \n", + "4 ... 0 0 0 0 \n", + "\n", + " AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 NaN NaN \n", + "4 0.0 0.0 \n", + "\n", + " AMT_REQ_CREDIT_BUREAU_WEEK AMT_REQ_CREDIT_BUREAU_MON \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 NaN NaN \n", + "4 0.0 0.0 \n", + "\n", + " AMT_REQ_CREDIT_BUREAU_QRT AMT_REQ_CREDIT_BUREAU_YEAR \n", + "0 0.0 1.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 NaN NaN \n", + "4 0.0 0.0 \n", + "\n", + "[5 rows x 122 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# https://www.kaggle.com/c/home-credit-default-risk/data 数据集地址\n", + "application_raw = pd.read_csv('data/application_train.csv') # 银行贷款数据,预测违约可能性0/1\n", + "application_raw.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# 数据预处理\n", + "def get_nonull_dummy_data(application_train_raw, dummy_drop=['ORGANIZATION_TYPE']):\n", + " # 缺失值填充\n", + " nulls = pd.isnull(application_train_raw).sum()\n", + " less_nulls = nulls[(nulls<3075)&(nulls!=0)].index\n", + " less_nulls_float = []\n", + " for i in range(len(less_nulls)):\n", + " if application_train_raw[less_nulls[i]].dtype != 'O':\n", + " less_nulls_float.append(less_nulls[i])\n", + " \n", + " application_train_raw[less_nulls_float] = application_train_raw[less_nulls_float].fillna(application_train_raw[less_nulls_float].mean())\n", + " \n", + " # 缺失值填充\n", + " more_nulls = nulls[(nulls >= 3075)].index\n", + " more_nulls_float = []\n", + " for i in range(len(more_nulls)):\n", + " if application_train_raw[more_nulls[i]].dtype != 'O':\n", + " more_nulls_float.append(more_nulls[i])\n", + " \n", + " application_train_raw[more_nulls_float] = application_train_raw[more_nulls_float].fillna(application_train_raw[more_nulls_float].mean())\n", + " \n", + " # 特征编码\n", + " application_train_raw.drop(columns=dummy_drop, axis=1, inplace=True)\n", + " \n", + " all_cols = application_train_raw.columns\n", + " cat_cols = []\n", + " for col in all_cols:\n", + " if application_train_raw[col].dtype == 'O':\n", + " cat_cols.append(col)\n", + " \n", + " application_train_raw = pd.get_dummies(application_train_raw,columns=cat_cols, dummy_na=True)\n", + " \n", + " return application_train_raw\n", + "\n", + "\n", + "def import_and_create_train_test_data(test_size=0.33, random_state=42):\n", + " # 训练和验证集制作\n", + " application_raw = pd.read_csv('data/application_train.csv') \n", + " application = get_nonull_dummy_data(application_raw, dummy_drop=['ORGANIZATION_TYPE'])\n", + " \n", + " X = application.drop(['TARGET'],axis=1)\n", + " y = application['TARGET']\n", + " X_train, X_test,y_train,y_test = train_test_split(X, y, test_size=test_size,random_state=random_state)\n", + " \n", + " train_users = X_train[['SK_ID_CURR']]\n", + " train_users['TARGET'] = y_train\n", + " test_users = X_test[['SK_ID_CURR']]\n", + " test_users['TARGET'] = y_test\n", + " train_users.reset_index(drop=True, inplace=True)\n", + " test_users.reset_index(drop=True, inplace=True)\n", + " \n", + " return(X_train, X_test,y_train,y_test,train_users,test_users)\n", + "\n", + "\n", + "def import_and_create_TEST_data():\n", + " # 线上测试集\n", + " application_raw = pd.read_csv('data/application_test.csv') \n", + " application = get_nonull_dummy_data(application_raw, dummy_drop=['ORGANIZATION_TYPE'])\n", + " \n", + " X = application\n", + " users = X[['SK_ID_CURR']]\n", + " users.reset_index(drop=True, inplace=True)\n", + " \n", + " return(X, users)\n", + "\n", + "\n", + "def get_imp_df(xgb_model):\n", + "# 获取特征重要性\n", + " imp = pd.DataFrame(np.asarray(list(xgb_model.get_fscore().keys())))\n", + " imp.columns = ['Feature']\n", + " imp['importance'] = np.asarray(list(xgb_model.get_fscore().keys()))\n", + " imp = imp.sort_values(by=['importance'], ascending=False)\n", + " imp = imp.reset_index(drop=True)\n", + " \n", + " return (imp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 训练集和验证集" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:46: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:48: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" + ] + } + ], + "source": [ + "X_train, X_test, y_train,y_test,train_users,test_users = import_and_create_train_test_data()\n", + "X_TEST,TEST_users = import_and_create_TEST_data()\n", + "\n", + "drop=['CODE_GENDER_XNA','NAME_INCOME_TYPE_Maternity leave',\n", + " 'NAME_FAMILY_STATUS_Unknown','SK_ID_CURR']\n", + "X_train = X_train.drop(drop, axis=1)\n", + "X_test = X_test.drop(drop, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data_train = X_train.reset_index(drop=True)\n", + "data_train['target'] = y_train.reset_index(drop=True)\n", + "data_test = X_test.reset_index(drop=True)\n", + "data_test['target'] = y_test.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Plots for CNT_CHILDREN \n", + " Train data plots \n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\n", + " | AMT_INCOME_TOTAL | \n", + "Samples_in_bin | \n", + "target_mean | \n", + "AMT_INCOME_TOTAL_mean | \n", + "
---|---|---|---|---|
0 | \n", + "[25650.0, 81000.0] | \n", + "22334 | \n", + "0.082699 | \n", + "66257.130366 | \n", + "
1 | \n", + "(81000.0, 99000.0] | \n", + "20424 | \n", + "0.081032 | \n", + "91076.626816 | \n", + "
2 | \n", + "(99000.0, 112500.0] | \n", + "24746 | \n", + "0.086842 | \n", + "111311.671092 | \n", + "
3 | \n", + "(112500.0, 135000.0] | \n", + "32725 | \n", + "0.085623 | \n", + "131844.069914 | \n", + "
4 | \n", + "(135000.0, 144000.0] | \n", + "2846 | \n", + "0.087491 | \n", + "142867.813598 | \n", + "
5 | \n", + "(144000.0, 162000.0] | \n", + "20804 | \n", + "0.087531 | \n", + "157082.746126 | \n", + "
6 | \n", + "(162000.0, 180000.0] | \n", + "20550 | \n", + "0.086326 | \n", + "178073.976044 | \n", + "
7 | \n", + "(180000.0, 225000.0] | \n", + "29937 | \n", + "0.077997 | \n", + "212795.095082 | \n", + "
8 | \n", + "(225000.0, 270000.0] | \n", + "13392 | \n", + "0.070266 | \n", + "259515.009845 | \n", + "
9 | \n", + "(270000.0, 9000000.0] | \n", + "18274 | \n", + "0.062110 | \n", + "389590.875377 | \n", + "
\n", + " | Feature | \n", + "Trend_changes | \n", + "Trend_changes_test | \n", + "Trend_correlation | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", + "CNT_CHILDREN | \n", + "2 | \n", + "2 | \n", + "0.975688 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
1 | \n", + "AMT_INCOME_TOTAL | \n", + "4 | \n", + "3 | \n", + "0.921382 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
2 | \n", + "AMT_CREDIT | \n", + "3 | \n", + "3 | \n", + "0.988779 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
3 | \n", + "AMT_ANNUITY | \n", + "4 | \n", + "4 | \n", + "0.972325 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
4 | \n", + "AMT_GOODS_PRICE | \n", + "7 | \n", + "7 | \n", + "0.994683 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
5 | \n", + "REGION_POPULATION_RELATIVE | \n", + "5 | \n", + "5 | \n", + "0.989683 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
6 | \n", + "DAYS_BIRTH | \n", + "0 | \n", + "0 | \n", + "0.992783 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
7 | \n", + "DAYS_EMPLOYED | \n", + "1 | \n", + "1 | \n", + "0.995426 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
8 | \n", + "DAYS_REGISTRATION | \n", + "2 | \n", + "2 | \n", + "0.976891 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
9 | \n", + "DAYS_ID_PUBLISH | \n", + "0 | \n", + "2 | \n", + "0.985101 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
10 | \n", + "OWN_CAR_AGE | \n", + "2 | \n", + "1 | \n", + "0.966213 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
11 | \n", + "FLAG_MOBIL | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
12 | \n", + "FLAG_EMP_PHONE | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
13 | \n", + "FLAG_WORK_PHONE | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
14 | \n", + "FLAG_CONT_MOBILE | \n", + "0 | \n", + "0 | \n", + "0.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
15 | \n", + "FLAG_PHONE | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
16 | \n", + "FLAG_EMAIL | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
17 | \n", + "CNT_FAM_MEMBERS | \n", + "1 | \n", + "1 | \n", + "0.996885 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
18 | \n", + "REGION_RATING_CLIENT | \n", + "0 | \n", + "0 | \n", + "0.999736 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
19 | \n", + "REGION_RATING_CLIENT_W_CITY | \n", + "0 | \n", + "0 | \n", + "0.999113 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
20 | \n", + "HOUR_APPR_PROCESS_START | \n", + "2 | \n", + "2 | \n", + "0.945565 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
21 | \n", + "REG_REGION_NOT_LIVE_REGION | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
22 | \n", + "REG_REGION_NOT_WORK_REGION | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
23 | \n", + "LIVE_REGION_NOT_WORK_REGION | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
24 | \n", + "REG_CITY_NOT_LIVE_CITY | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
25 | \n", + "REG_CITY_NOT_WORK_CITY | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
26 | \n", + "LIVE_CITY_NOT_WORK_CITY | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
27 | \n", + "EXT_SOURCE_1 | \n", + "2 | \n", + "0 | \n", + "0.998696 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
28 | \n", + "EXT_SOURCE_2 | \n", + "0 | \n", + "0 | \n", + "0.998221 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
29 | \n", + "EXT_SOURCE_3 | \n", + "0 | \n", + "0 | \n", + "0.998048 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
168 | \n", + "OCCUPATION_TYPE_Waiters/barmen staff | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
169 | \n", + "OCCUPATION_TYPE_nan | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
170 | \n", + "WEEKDAY_APPR_PROCESS_START_FRIDAY | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
171 | \n", + "WEEKDAY_APPR_PROCESS_START_MONDAY | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
172 | \n", + "WEEKDAY_APPR_PROCESS_START_SATURDAY | \n", + "0 | \n", + "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
173 | \n", + "WEEKDAY_APPR_PROCESS_START_SUNDAY | \n", "0 | \n", - "1 | \n", + "0 | \n", + "-1.000000 | \n", + "|||||||||||||||||||||||||||||||||||||||||||||||||||||
174 | \n", + "WEEKDAY_APPR_PROCESS_START_THURSDAY | \n", "0 | \n", "0 | \n", + "-1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
175 | \n", + "WEEKDAY_APPR_PROCESS_START_TUESDAY | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
176 | \n", + "WEEKDAY_APPR_PROCESS_START_WEDNESDAY | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
177 | \n", + "WEEKDAY_APPR_PROCESS_START_nan | \n", "0 | \n", "0 | \n", + "0.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
178 | \n", + "FONDKAPREMONT_MODE_not specified | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
179 | \n", + "FONDKAPREMONT_MODE_org spec account | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
180 | \n", + "FONDKAPREMONT_MODE_reg oper account | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
181 | \n", + "FONDKAPREMONT_MODE_reg oper spec account | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
182 | \n", + "FONDKAPREMONT_MODE_nan | \n", "0 | \n", "0 | \n", - "0.0 | \n", - "0.0 | \n", - "0.0 | \n", - "1.0 | \n", - "0.0 | \n", - "1.0 | \n", - "2018-04 | \n", + "1.000000 | \n", "|||||||||||||||||||||||||||||||||||||||||||||||
4 | \n", - "216609 | \n", + "183 | \n", + "HOUSETYPE_MODE_block of flats | \n", "0 | \n", - "Revolving loans | \n", - "M | \n", - "Y | \n", - "Y | \n", - "1 | \n", - "112500.0 | \n", - "180000.0 | \n", - "9000.0 | \n", - "180000.0 | \n", - "Unaccompanied | \n", - "State servant | \n", - "Higher education | \n", - "Married | \n", - "House / apartment | \n", - "0.007020 | \n", - "-10234 | \n", - "-1993 | \n", - "-4040.0 | \n", - "-2913 | \n", - "6.0 | \n", - "1 | \n", - "1 | \n", "0 | \n", - "1 | \n", + "1.000000 | \n", + "|||||||||||||||||||||||||||||
184 | \n", + "HOUSETYPE_MODE_specific housing | \n", "0 | \n", "0 | \n", - "NaN | \n", - "3.0 | \n", - "2 | \n", - "2 | \n", - "WEDNESDAY | \n", - "11 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||
185 | \n", + "HOUSETYPE_MODE_terraced house | \n", "0 | \n", "0 | \n", + "-1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
186 | \n", + "HOUSETYPE_MODE_nan | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
187 | \n", + "WALLSMATERIAL_MODE_Block | \n", "0 | \n", "0 | \n", - "Emergency | \n", - "0.405051 | \n", - "0.528879 | \n", - "0.604113 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "... | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "1.0 | \n", - "0.0 | \n", - "1.0 | \n", - "0.0 | \n", - "-429.0 | \n", + "1.000000 | \n", + "
188 | \n", + "WALLSMATERIAL_MODE_Mixed | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
189 | \n", + "WALLSMATERIAL_MODE_Monolithic | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
190 | \n", + "WALLSMATERIAL_MODE_Others | \n", "0 | \n", "0 | \n", + "-1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
191 | \n", + "WALLSMATERIAL_MODE_Panel | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
192 | \n", + "WALLSMATERIAL_MODE_Stone, brick | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
193 | \n", + "WALLSMATERIAL_MODE_Wooden | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
194 | \n", + "WALLSMATERIAL_MODE_nan | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
195 | \n", + "EMERGENCYSTATE_MODE_No | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
196 | \n", + "EMERGENCYSTATE_MODE_Yes | \n", "0 | \n", "0 | \n", + "1.000000 | \n", + "||||||||||||||||||||||||||||||||||||||||||||||||||||||
197 | \n", + "EMERGENCYSTATE_MODE_nan | \n", "0 | \n", "0 | \n", - "0.0 | \n", - "0.0 | \n", - "1.0 | \n", - "0.0 | \n", - "0.0 | \n", - "0.0 | \n", - "2018-03 | \n", + "1.000000 | \n", "
5 rows × 123 columns
\n", + "198 rows × 4 columns
\n", "