In this article, we are going to see how to insert a pandas DataFrame to an existing PostgreSQL table.
Modules needed
- pandas: Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns.
- psycopg2: PostgreSQL is a powerful, open source object-relational database system. PostgreSQL runs on all major operating systems. PostgreSQL follows ACID property of DataBase system and has the support of triggers, updatable views and materialized views, foreign keys.
- sqlalchemy: SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL
we start the code by importing packages and creating a connection string of the format:
‘postgres://user:password@host/database’
The create_engine() function takes the connection string as an argument and forms a connection to the PostgreSQL database, after connecting we create a dictionary, and further convert it into a dataframe using the method pandas.DataFrame() method.
The to_sql() method is used to insert a pandas data frame into the Postgresql table. Finally, we execute commands using the execute() method to execute our SQL commands and fetchall() method to fetch the records.
df.to_sql(‘data’, con=conn, if_exists=’replace’, index=False)
arguments are:
- name of the table
- connection
- if_exists : if the table already exists the function we want to apply . ex: ‘append’ help us add data instead of replacing the data.
- index : True or False
Example 1:
Insert a pandas DataFrame to an existing PostgreSQL table using sqlalchemy. The create table command used to create a table in the PostgreSQL database in the following example is:
create table data( Name varchar, Age bigint);
Code:
Python3
import psycopg2import pandas as pdfrom sqlalchemy import create_engine db = create_engine(conn_string)conn = db.connect() # our dataframedata = {'Name': ['Tom', 'dick', 'harry'], 'Age': [22, 21, 24]} # Create DataFramedf = pd.DataFrame(data)df.to_sql('data', con=conn, if_exists='replace', index=False)conn = psycopg2.connect(conn_string )conn.autocommit = Truecursor = conn.cursor() sql1 = '''select * from data;'''cursor.execute(sql1)for i in cursor.fetchall(): print(i) # conn.commit()conn.close() |
Output:
('Tom', 22)
('dick', 21)
('harry', 24)
Output in PostgreSQL:
output table in PostgreSQL
Example 2:
Insert a pandas DataFrame to an existing PostgreSQL table without using sqlalchemy. As usual, we form a connection to PostgreSQL using the connect() command and execute the execute_values() method, where there’s the ‘insert’ SQL command is executed. a try-except clause is included to make sure the errors are caught if any.
To view or download the CSV file used in the below program: click here.
The create table command used to create a table in the PostgreSQL database in the following example is :
create table fossil_fuels_c02(year int, country varchar,total int,solidfuel int, liquidfuel int,gasfuel int,cement int,gasflaring int,percapita int,bunkerfuels int);
Code:
Python3
import psycopg2import numpy as npimport psycopg2.extras as extrasimport pandas as pd def execute_values(conn, df, table): tuples = [tuple(x) for x in df.to_numpy()] cols = ','.join(list(df.columns)) # SQL query to execute query = "INSERT INTO %s(%s) VALUES %%s" % (table, cols) cursor = conn.cursor() try: extras.execute_values(cursor, query, tuples) conn.commit() except (Exception, psycopg2.DatabaseError) as error: print("Error: %s" % error) conn.rollback() cursor.close() return 1 print("the dataframe is inserted") cursor.close() conn = psycopg2.connect( database="ENVIRONMENT_DATABASE", user='postgres', password='pass', host='127.0.0.1', port='5432') df = pd.read_csv('fossilfuels.csv') execute_values(conn, df, 'fossil_fuels_c02') |
Output:
the dataframe is inserted
after inserting the dataFrame
