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‎Create table in python to sql db (4).ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "8a21c3b6",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"ename": "ImportError",
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"evalue": "cannot import name 'config'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-2-ac36e1922f39>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msqlio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpsycopg2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mImportError\u001b[0m: cannot import name 'config'"
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]
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}
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],
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"source": [
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"#Import libraries \n",
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"import numpy as np\n",
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"import numpy as np\n",
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"import pandas as pd \n",
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"import numpy as np\n",
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"from sklearn.preprocessing import QuantileTransformer\n",
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"from sklearn.metrics import roc_auc_score\n",
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"from sklearn.metrics import roc_curve\n",
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"from sklearn.datasets import load_breast_cancer\n",
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"from numpy import int64\n",
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"from sklearn import svm\n",
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"from sklearn.svm import SVC \n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib import rcParams\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.model_selection import train_test_split\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from xgboost import XGBClassifier\n",
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"import xgboost\n",
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"from sklearn.naive_bayes import GaussianNB\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.datasets import load_iris\n",
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"from sklearn.metrics import classification_report, accuracy_score \n",
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"from sklearn.metrics import precision_score, recall_score \n",
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"from sklearn.metrics import f1_score, matthews_corrcoef \n",
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"from sklearn.metrics import confusion_matrix \n",
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"\n",
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"import pandas as pd\n",
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"from scipy import stats\n",
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"from sklearn.utils import resample\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from mpl_toolkits.mplot3d import Axes3D\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"import matplotlib.pyplot as plt # plotting\n",
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"import numpy as np # linear algebra\n",
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"import os # accessing directory structure\n",
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"import pandas as pd # data processing\n",
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"from pandas.plotting import scatter_matrix\n",
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"#import library psycopyg2\n",
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"import psycopg2\n",
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"#import library pandas\n",
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"import pandas as pd\n",
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"#import library sqlio\n",
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"import pandas.io.sql as sqlio\n",
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"import psycopg2\n",
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"\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0b9bcdfb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# get the final results\n",
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"RandomForest_Model_Prediction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "28700cbc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Delete the existing table in SQL DB\n",
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"import psycopg2\n",
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"from psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT\n",
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"psqlCon = psycopg2.connect(\"dbname=rawData user=data_user password=kgtopg8932\");\n",
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"psqlCon.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT);\n",
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"psqlCursor = psqlCon.cursor();\n",
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"tableName = \"ml_prediction\";\n",
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"dropTableStmt = \"DROP TABLE %s;\"%tableName;\n",
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"psqlCursor.execute(dropTableStmt);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e0a7bff7",
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"metadata": {},
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"outputs": [],
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"source": [
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"#upload new table in to SQL DB\n",
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"from sqlalchemy import create_engine\n",
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"engine = create_engine('postgresql://data_user:kgtopg8932@localhost:5432/rawData')\n",
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"RandomForest_Model_Prediction.to_sql('ml_prediction', engine)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2e57cd92",
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"metadata": {},
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"outputs": [],
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"source": [
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"# CHECK THE TABLE \n",
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"query1 = \"select * from ml_prediction\" \n",
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"dataset = sqlio.read_sql_query(query1,conn)\n",
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"dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9e21a631",
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"metadata": {},
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"outputs": [],
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"source": [
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"# CHECK THE TABLE \n",
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"query1 = \"select * from ml_prediction\" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "232768fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = sqlio.read_sql_query(query1,conn)\n",
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"dataset\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Create table in python to sql db.py

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#!/usr/bin/env python
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# coding: utf-8
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# In[2]:
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#Import libraries
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import numpy as np
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import numpy as np
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import QuantileTransformer
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from sklearn.metrics import roc_auc_score
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from sklearn.metrics import roc_curve
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from sklearn.datasets import load_breast_cancer
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from numpy import int64
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from sklearn import svm
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from sklearn.svm import SVC
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import matplotlib.pyplot as plt
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from matplotlib import rcParams
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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import seaborn as sns
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import matplotlib.pyplot as plt
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from xgboost import XGBClassifier
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import xgboost
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from sklearn.naive_bayes import GaussianNB
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_iris
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from sklearn.metrics import classification_report, accuracy_score
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from sklearn.metrics import precision_score, recall_score
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from sklearn.metrics import f1_score, matthews_corrcoef
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from sklearn.metrics import confusion_matrix
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import pandas as pd
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from scipy import stats
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from sklearn.utils import resample
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import numpy as np
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import pandas as pd
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from mpl_toolkits.mplot3d import Axes3D
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from sklearn.preprocessing import StandardScaler
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import matplotlib.pyplot as plt # plotting
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import numpy as np # linear algebra
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import os # accessing directory structure
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import pandas as pd # data processing
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from pandas.plotting import scatter_matrix
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#import library psycopyg2
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import psycopg2
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#import library pandas
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import pandas as pd
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#import library sqlio
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import pandas.io.sql as sqlio
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import psycopg2
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# In[ ]:
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# get the final results
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RandomForest_Model_Prediction
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# In[ ]:
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# Delete the existing table in SQL DB
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import psycopg2
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from psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT
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psqlCon = psycopg2.connect("dbname=rawData user=data_user password=kgtopg8932");
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psqlCon.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT);
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psqlCursor = psqlCon.cursor();
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tableName = "ml_prediction";
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dropTableStmt = "DROP TABLE %s;"%tableName;
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psqlCursor.execute(dropTableStmt);
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# In[ ]:
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#upload new table in to SQL DB
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from sqlalchemy import create_engine
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engine = create_engine('postgresql://data_user:kgtopg8932@localhost:5432/rawData')
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RandomForest_Model_Prediction.to_sql('ml_prediction', engine)
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# In[ ]:
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# CHECK THE TABLE
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query1 = "select * from ml_prediction"
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dataset = sqlio.read_sql_query(query1,conn)
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dataset
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# In[ ]:
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# CHECK THE TABLE
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query1 = "select * from ml_prediction"
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# In[ ]:
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dataset = sqlio.read_sql_query(query1,conn)
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dataset
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