In [4]:

    

import psycopg2
import pandas as pd
import sqlalchemy as sa
import time
import seaborn as sns
import re


    

In [2]:

    

! pip install psycopg2


    

    




Requirement already satisfied: psycopg2 in c:\users\dell\anaconda3\lib\site-packages

In [5]:

    

parameters = { 
               'username': 'postgres', 
               'password': 'root',
               'server':   'localhost',
               'database': 'ajay'
             }


    

In [6]:

    

connection= 'postgresql://{username}:{password}@{server}:5432/{database}'.format(**parameters)


    

In [7]:

    

print (connection)


    

    




postgresql://postgres:root@localhost:5432/ajay

In [9]:

    

engine = sa.create_engine(connection, encoding="utf-8")


    

In [10]:

    

insp = sa.inspect(engine)


    

In [11]:

    

db_list = insp.get_schema_names()
print(db_list)


    

    




['information_schema', 'public']

In [12]:

    

engine.table_names()


    

    
Out[12]:





['sales', 'iris']

In [13]:

    

data3= pd.read_sql_query('select * from "sales" limit 10',con=engine)


    

In [14]:

    

data3.info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 4 columns):
customer_id    10 non-null int64
sales          10 non-null int64
date           10 non-null object
product_id     10 non-null int64
dtypes: int64(3), object(1)
memory usage: 400.0+ bytes

In [15]:

    

data3


    

    
Out[15]:






  

    

      

      
customer_id
      
sales
      
date
      
product_id
    
  
  

    

      
0
      
10001
      
5230
      
2017-02-07
      
524
    
    

      
1
      
10002
      
2781
      
2017-05-12
      
469
    
    

      
2
      
10003
      
2083
      
2016-12-18
      
917
    
    

      
3
      
10004
      
214
      
2015-01-19
      
354
    
    

      
4
      
10005
      
9407
      
2016-09-26
      
292
    
    

      
5
      
10006
      
4705
      
2015-10-17
      
380
    
    

      
6
      
10007
      
4729
      
2016-01-02
      
469
    
    

      
7
      
10008
      
7715
      
2015-09-12
      
480
    
    

      
8
      
10009
      
9898
      
2015-04-05
      
611
    
    

      
9
      
10010
      
5797
      
2015-08-13
      
959
    
  

In [16]:

    

data5= pd.read_sql_query('select * from "iris" limit 10',con=engine)


    

In [17]:

    

data5


    

    
Out[17]:






  

    

      

      
sepal_length
      
sepal_width
      
petal_length
      
petal_width
      
species
    
  
  

    

      
0
      
5.1
      
3.5
      
1.4
      
0.2
      
setosa
    
    

      
1
      
4.9
      
3.0
      
1.4
      
0.2
      
setosa
    
    

      
2
      
4.7
      
3.2
      
1.3
      
0.2
      
setosa
    
    

      
3
      
4.6
      
3.1
      
1.5
      
0.2
      
setosa
    
    

      
4
      
5.0
      
3.6
      
1.4
      
0.2
      
setosa
    
    

      
5
      
5.4
      
3.9
      
1.7
      
0.4
      
setosa
    
    

      
6
      
4.6
      
3.4
      
1.4
      
0.3
      
setosa
    
    

      
7
      
5.0
      
3.4
      
1.5
      
0.2
      
setosa
    
    

      
8
      
4.4
      
2.9
      
1.4
      
0.2
      
setosa
    
    

      
9
      
4.9
      
3.1
      
1.5
      
0.1
      
setosa
    
  

In [ ]: