In this article, we will be looking at some methods to write Pandas dataframes to PostgreSQL tables in the Python.
Method 1: Using to_sql() function
to_sql function is used to write the given dataframe to a SQL database.
Syntax
df.to_sql(‘data’, con=conn, if_exists=’replace’, index=False)
Parameters :
- data: name of the table.
- con: connection to the database.
- if_exists: if table exists or not. “replace” or “append”.
- index: True or False.
Example:
In the example demonstrated below, we import the required packages and modules, establish a connection to the PostgreSQL database and convert the dataframe to PostgreSQL table by using the to_sql() method. Finally, all rows are fetched using fetchall() method.
To access the CSV file used click here.
Python3
# import packagesimport psycopg2import pandas as pdfrom sqlalchemy import create_engine # establish connections db = create_engine(conn_string)conn = db.connect()conn1 = psycopg2.connect( database="Airlines_Database", user='postgres', password='pass', host='127.0.0.1', port= '5432') conn1.autocommit = Truecursor = conn1.cursor() # drop table if it already existscursor.execute('drop table if exists airlines_final') sql = '''CREATE TABLE airlines_final(id int ,day int ,airline char(20),destination char(20));''' cursor.execute(sql) # import the csv file to create a dataframedata = pd.read_csv("airlines_final.csv") data = data[["id","day","airline","destination"]]# Create DataFrameprint(data) # converting data to sqldata.to_sql('airlines_final', conn, if_exists= 'replace') # fetching all rowssql1='''select * from airlines_final;'''cursor.execute(sql1)for i in cursor.fetchall(): print(i) conn1.commit()conn1.close() |
Output:
Method 2: Using execute_values() function
The execute_values() function from the psycopg2 library is used to get the postgres table of the given data frame.
Syntax:
psycopg2.extras.execute_values(cur, sql, argslist, template=None, page_size=100, fetch=False)
Parameters:
- cur – the cursor that will be used to run the query.
- sql – the query that will be run. A single percent s placeholder must be present, which will be substituted by a VALUES list.
- argslist — a list of series or dictionaries containing the query’s arguments. The template must be followed in terms of font and content.
- template – the snippet that will be merged into each item in the argslist to form the query
- page size – the maximum amount of argslist items that each statement can have.
- fetch – it’s similar to fetchall. the values can be “True” or “False”
Example:
In this example, the same CSV file is used in this method. code begins with importing packages, then we form a custom function execute_values, where the given dataframe, connection, and table name are given as arguments. The dataframe rows and values are updated into the PostgreSQL table using the execute_values() method. The defined method contains an exception handling block, if there’s no exception “execute_values() done” is printed.
Python3
# import packagesimport psycopg2import 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("execute_values() done") cursor.close() # establishing connectionconn = psycopg2.connect( database="Airlines_Database", user='postgres', password='sherlockedisi', host='127.0.0.1', port='5432')sql = '''CREATE TABLE airlines_final1(id int ,daychar(20) ,airline char(20),destination char(20));''' # creating a cursorcursor = conn.cursor()cursor.execute(sql)data = pd.read_csv("airlines_final.csv") data = data[["id", "day", "airline", "destination"]] # using the function definedexecute_values(conn, data, 'airlines_final1') |
Output:
execute_values() done
