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decisionstats/postgres with sqlalchemy

Created March 9, 2017 15:48
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postgres with sqlalchemy
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import psycopg2\n",
"import pandas as pd\n",
"import sqlalchemy as sa\n",
"import time\n",
"import seaborn as sns\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: psycopg2 in c:\\users\\dell\\anaconda3\\lib\\site-packages\n"
]
}
],
"source": [
"! pip install psycopg2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"parameters = { \n",
" 'username': 'postgres', \n",
" 'password': 'root',\n",
" 'server': 'localhost',\n",
" 'database': 'ajay'\n",
" }\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"connection= 'postgresql://{username}:{password}@{server}:5432/{database}'.format(**parameters)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"postgresql://postgres:root@localhost:5432/ajay\n"
]
}
],
"source": [
"print (connection)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"engine = sa.create_engine(connection, encoding=\"utf-8\")\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"insp = sa.inspect(engine)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['information_schema', 'public']\n"
]
}
],
"source": [
"db_list = insp.get_schema_names()\n",
"print(db_list)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['sales', 'iris']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"engine.table_names()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data3= pd.read_sql_query('select * from \"sales\" limit 10',con=engine)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10 entries, 0 to 9\n",
"Data columns (total 4 columns):\n",
"customer_id 10 non-null int64\n",
"sales 10 non-null int64\n",
"date 10 non-null object\n",
"product_id 10 non-null int64\n",
"dtypes: int64(3), object(1)\n",
"memory usage: 400.0+ bytes\n"
]
}
],
"source": [
"data3.info()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>sales</th>\n",
" <th>date</th>\n",
" <th>product_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10001</td>\n",
" <td>5230</td>\n",
" <td>2017-02-07</td>\n",
" <td>524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10002</td>\n",
" <td>2781</td>\n",
" <td>2017-05-12</td>\n",
" <td>469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10003</td>\n",
" <td>2083</td>\n",
" <td>2016-12-18</td>\n",
" <td>917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10004</td>\n",
" <td>214</td>\n",
" <td>2015-01-19</td>\n",
" <td>354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10005</td>\n",
" <td>9407</td>\n",
" <td>2016-09-26</td>\n",
" <td>292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>10006</td>\n",
" <td>4705</td>\n",
" <td>2015-10-17</td>\n",
" <td>380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>10007</td>\n",
" <td>4729</td>\n",
" <td>2016-01-02</td>\n",
" <td>469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>10008</td>\n",
" <td>7715</td>\n",
" <td>2015-09-12</td>\n",
" <td>480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10009</td>\n",
" <td>9898</td>\n",
" <td>2015-04-05</td>\n",
" <td>611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10010</td>\n",
" <td>5797</td>\n",
" <td>2015-08-13</td>\n",
" <td>959</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id sales date product_id\n",
"0 10001 5230 2017-02-07 524\n",
"1 10002 2781 2017-05-12 469\n",
"2 10003 2083 2016-12-18 917\n",
"3 10004 214 2015-01-19 354\n",
"4 10005 9407 2016-09-26 292\n",
"5 10006 4705 2015-10-17 380\n",
"6 10007 4729 2016-01-02 469\n",
"7 10008 7715 2015-09-12 480\n",
"8 10009 9898 2015-04-05 611\n",
"9 10010 5797 2015-08-13 959"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data3"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data5= pd.read_sql_query('select * from \"iris\" limit 10',con=engine)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5.4</td>\n",
" <td>3.9</td>\n",
" <td>1.7</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4.6</td>\n",
" <td>3.4</td>\n",
" <td>1.4</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5.0</td>\n",
" <td>3.4</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4.4</td>\n",
" <td>2.9</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4.9</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa\n",
"5 5.4 3.9 1.7 0.4 setosa\n",
"6 4.6 3.4 1.4 0.3 setosa\n",
"7 5.0 3.4 1.5 0.2 setosa\n",
"8 4.4 2.9 1.4 0.2 setosa\n",
"9 4.9 3.1 1.5 0.1 setosa"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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"version": "3.5.2"
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"nbformat_minor": 1
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