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March 16, 2017 15:51
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"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": 36, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Requirement already satisfied: pandasql in c:\\users\\dell\\anaconda3\\lib\\site-packages\n", | |
"Requirement already satisfied: pandas in c:\\users\\dell\\anaconda3\\lib\\site-packages (from pandasql)\n", | |
"Requirement already satisfied: sqlalchemy in c:\\users\\dell\\anaconda3\\lib\\site-packages (from pandasql)\n", | |
"Requirement already satisfied: numpy in c:\\users\\dell\\anaconda3\\lib\\site-packages (from pandasql)\n", | |
"Requirement already satisfied: python-dateutil>=2 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from pandas->pandasql)\n", | |
"Requirement already satisfied: pytz>=2011k in c:\\users\\dell\\anaconda3\\lib\\site-packages (from pandas->pandasql)\n", | |
"Requirement already satisfied: six>=1.5 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from python-dateutil>=2->pandas->pandasql)\n" | |
] | |
} | |
], | |
"source": [ | |
"! pip install pandasql" | |
] | |
}, | |
{ | |
"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": 4, | |
"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": 17, | |
"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": 18, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"engine = sa.create_engine(connection, encoding=\"utf-8\")\n", | |
"\n", | |
"\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"insp = sa.inspect(engine)\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"<sqlalchemy.dialects.postgresql.base.PGInspector object at 0x000000000B2E5278>\n" | |
] | |
} | |
], | |
"source": [ | |
"print(insp)" | |
] | |
}, | |
{ | |
"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": [ | |
"['iris', 'temp', 'sales']" | |
] | |
}, | |
"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": 14, | |
"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": 14, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data3" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 29, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"data5= pd.read_sql_query('select * from \"sales\" limit 20',con=engine)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 30, | |
"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", | |
" <tr>\n", | |
" <th>10</th>\n", | |
" <td>10011</td>\n", | |
" <td>1283</td>\n", | |
" <td>2016-04-22</td>\n", | |
" <td>950</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>11</th>\n", | |
" <td>10012</td>\n", | |
" <td>2751</td>\n", | |
" <td>2015-01-01</td>\n", | |
" <td>322</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>12</th>\n", | |
" <td>10013</td>\n", | |
" <td>4422</td>\n", | |
" <td>2017-07-11</td>\n", | |
" <td>965</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>13</th>\n", | |
" <td>10014</td>\n", | |
" <td>6235</td>\n", | |
" <td>2015-03-07</td>\n", | |
" <td>783</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>14</th>\n", | |
" <td>10015</td>\n", | |
" <td>7302</td>\n", | |
" <td>2016-04-06</td>\n", | |
" <td>792</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>15</th>\n", | |
" <td>10016</td>\n", | |
" <td>6408</td>\n", | |
" <td>2016-10-21</td>\n", | |
" <td>347</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>16</th>\n", | |
" <td>10017</td>\n", | |
" <td>1880</td>\n", | |
" <td>2015-08-23</td>\n", | |
" <td>187</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>17</th>\n", | |
" <td>10018</td>\n", | |
" <td>3738</td>\n", | |
" <td>2017-03-12</td>\n", | |
" <td>222</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>18</th>\n", | |
" <td>10019</td>\n", | |
" <td>900</td>\n", | |
" <td>2015-07-20</td>\n", | |
" <td>236</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>19</th>\n", | |
" <td>10020</td>\n", | |
" <td>5516</td>\n", | |
" <td>2017-05-10</td>\n", | |
" <td>828</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\n", | |
"10 10011 1283 2016-04-22 950\n", | |
"11 10012 2751 2015-01-01 322\n", | |
"12 10013 4422 2017-07-11 965\n", | |
"13 10014 6235 2015-03-07 783\n", | |
"14 10015 7302 2016-04-06 792\n", | |
"15 10016 6408 2016-10-21 347\n", | |
"16 10017 1880 2015-08-23 187\n", | |
"17 10018 3738 2017-03-12 222\n", | |
"18 10019 900 2015-07-20 236\n", | |
"19 10020 5516 2017-05-10 828" | |
] | |
}, | |
"execution_count": 30, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data5" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 23, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import pandasql as pdsql" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 32, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"str1=\"select * from data5 limit 5;\"" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 33, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"df11=pdsql.sqldf(str1)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 34, | |
"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", | |
" </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" | |
] | |
}, | |
"execution_count": 34, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df11" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 35, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"pandas.core.frame.DataFrame" | |
] | |
}, | |
"execution_count": 35, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"type(data5)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 43, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"data5= pd.read_sql_query('select * from \"sales\" limit 250',con=engine)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 47, | |
"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", | |
" </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" | |
] | |
}, | |
"execution_count": 47, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data5.head()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 44, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"str2=\"select avg(sales) from data5 ;\"" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 45, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"df111=pdsql.sqldf(str2)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 46, | |
"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>avg(sales)</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>5226.868</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" avg(sales)\n", | |
"0 5226.868" | |
] | |
}, | |
"execution_count": 46, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df111" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"anaconda-cloud": {}, | |
"kernelspec": { | |
"display_name": "Python [conda root]", | |
"language": "python", | |
"name": "conda-root-py" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
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}, | |
"file_extension": ".py", | |
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"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.5.2" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 1 | |
} |
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