Develop Reference

Error psycopg2.ProgrammingError: can't adapt type 'dict' from PokeJSON

Can't insert into SQL table the first 100 Pokemon from PokeApi
import psycopg2
import json
import myutils
import requests
conn=psycopg2.connect(
host='host',database='database',
user='user',password='password',
connect_timeout=3)
cur=conn.cursor()
sql='DROP TABLE IF EXISTS pokeapi;'
cur.execute(sql)
conn.commit()
print(sql)
sql = 'CREATE TABLE IF NOT EXISTS pokeapi (id SERIAL,body JSONB);'
cur.execute(sql)
conn.commit()
print(sql)
for i in range(1,101):
url='https://pokeapi.co/api/v2/pokemon/%s' % (str(i))
response = requests.get(url)
text = response.text
js = json.loads(text)
stuff = js.get('forms', None)
sql='INSERT INTO pokeapi VALUES (%s)'
cur.execute(sql,(stuff,))
conn.commit()
print(sql)
This is the error I'm getting
psycopg2.ProgrammingError: can't adapt type 'dict'
Notice the JSON structure
"species":{"name":"bulbasaur","url":"https://pokeapi.co/api/v2/pokemon-species/1/"}

You must adapt the python dict to Json when passing it as a query parameter.
from psycopg2.extras import Json
#...
cur.execute(sql,(Json(stuff),))

Not able to insert csv data into mysql table

I have one csv file that have so many fields but interested in only 5. We picked them up using pandas and now want to insert into mysql table.
This is the logic for same.
Table strucure:
order_execution_time,text,YES,"",,""
symbol,text,YES,"",,""
trade_type,text,YES,"",,""
price,double,YES,"",,""
quantity,int,YES,"",,""
Code:
import pandas as pd
from mysql.connector import Error
from src.config.python_mysql_config import get_connection
df = pd.read_csv("C:\\Users\\91720\\Downloads\\tradebook-VT0986.csv", usecols = ['order_execution_time', 'symbol', 'trade_type', 'price', 'quantity'])
try:
conn = get_connection()
if conn.is_connected():
cursor = conn.cursor()
cursor.execute("select database();")
record = cursor.fetchone()
print("You're connected to database: ", record)
#loop through the data frame
for i,row in df.iterrows():
#here %S means string values
sql = "INSERT INTO trades (symbol, order_execution_time, trade_type, price, quantity) VALUES (%s,%s,%s,%d,%f)"
cursor.execute(sql, tuple(row))
print("Record inserted")
# the connection is not auto committed by default, so we must commit to save our changes
conn.commit()
except Error as e:
print("Error while connecting to MySQL", e)
It is throwing me this error:
Error while connecting to MySQL Not all parameters were used in the SQL statement
How to get it resolve?

JSON response into database

Ok, I have tried several kinds of solutions recommended by others on this site and other sited. However, I can't get it work as I would like it to do.
I get a XML-response which I normalize and then save to a CSV. This first part works fine.
Instead of saving it to CSV I would like to save it into an existing table in an access database. The second part below:
Would like to use an existing table instead of creating a new one
The result is not separated with ";" into different columns. Everything ends up in the same column not separated, see image below
response = requests.get(u,headers=h).json()
dp = pd.json_normalize(response,'Units')
response_list.append(dp)
export = pd.concat(response_list)
export.to_csv(r'C:\Users\username\Documents\Python Scripts\Test\Test2_'+str(now)+'.csv', index=False, sep=';',encoding='utf-8')
access_path = r"C:\Users\username\Documents\Python Scripts\Test\Test_db.accdb"
conn = pyodbc.connect("DRIVER={{Microsoft Access Driver (*.mdb, *.accdb)}};DBQ={};" \
.format(access_path))
strSQL = "SELECT * INTO projects2 FROM [text;HDR=Yes;FMT=sep(;);" + \
"Database=C:\\Users\\username\\Documents\\Python Scripts\\Test].Testdata.csv;"
cur = conn.cursor()
cur.execute(strSQL)
conn.commit()
conn.close()

If you already have the data in a well-formed pandas DataFrame then you don't really need to dump it to a CSV file; you can use the sqlalchemy-access dialect to push the data directly into an Access table using pandas' to_sql() method:
from pprint import pprint
import urllib
import pandas as pd
import sqlalchemy as sa
connection_string = (
r"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};"
r"DBQ=C:\Users\Public\Database1.accdb;"
r"ExtendedAnsiSQL=1;"
)
connection_uri = f"access+pyodbc:///?odbc_connect={urllib.parse.quote_plus(connection_string)}"
engine = sa.create_engine(connection_uri)
with engine.begin() as conn:
# existing data in table
pprint(
conn.execute(sa.text("SELECT * FROM user_table")).fetchall(), width=30
)
"""
[('gord', 'gord#example.com'),
('jennifer', 'jennifer#example.com')]
"""
# DataFrame to insert
df = pd.DataFrame(
[
("newdev", "newdev#example.com"),
("newerdev", "newerdev#example.com"),
],
columns=["username", "email"],
)
df.to_sql("user_table", engine, index=False, if_exists="append")
with engine.begin() as conn:
# updated table
pprint(
conn.execute(sa.text("SELECT * FROM user_table")).fetchall(), width=30
)
"""
[('gord', 'gord#example.com'),
('jennifer', 'jennifer#example.com'),
('newdev', 'newdev#example.com'),
('newerdev', 'newerdev#example.com')]
"""
(Disclosure: I am currently the maintainer of the sqlalchemy-access dialect.)

Solved with the following code
SE_export_Tuple = list(zip(SE_export.Name,SE_export.URL,SE_export.ImageUrl,......,SE_export.ID))
print(SE_export_Tuple)
access_path = r"C:\Users\username\Documents\Python Scripts\Test\Test_db.accdb"
conn = pyodbc.connect("DRIVER={{Microsoft Access Driver (*.mdb, *.accdb)}};DBQ={};" \
.format(access_path))
cursor = conn.cursor()
mySql_insert_query="INSERT INTO Temp_table (UnitName,URL,ImageUrl,.......,ID) VALUES (?,?,?,......,?)"
cursor.executemany(mySql_insert_query,SE_export_Tuple)
conn.commit()
conn.close()
However, when I add many fields I get an error at "executemany", saying:
cursor.executemany(mySql_insert_query,SE_export_Tuple)
Error: ('HY004', '[HY004] [Microsoft][ODBC Microsoft Access Driver]Invalid SQL data type (67) (SQLBindParameter)')

Inserting Data to SQL Server from a Python Dataframe Quickly

I have been trying to insert data from a dataframe in Python to a table already created in SQL Server. The data frame has 90K rows and wanted the best possible way to quickly insert data in the table. I only have read,write and delete permissions for the server and I cannot create any table on the server.
Below is the code which is inserting the data but it is very slow. Please advise.
import pandas as pd
import xlsxwriter
import pyodbc
df = pd.read_excel(r"Url path\abc.xlsx")
conn = pyodbc.connect('Driver={ODBC Driver 11 for SQL Server};'
'SERVER=Server Name;'
'Database=Database Name;'
'UID=User ID;'
'PWD=Password;'
'Trusted_Connection=no;')
cursor= conn.cursor()
#Deleting existing data in SQL Table:-
cursor.execute("DELETE FROM datbase.schema.TableName")
conn.commit()
#Inserting data in SQL Table:-
for index,row in df.iterrows():
cursor.execute("INSERT INTO Table Name([A],[B],[C],) values (?,?,?)", row['A'],row['B'],row['C'])
conn.commit()
cursor.close()
conn.close()

To insert data much faster, try using sqlalchemy and df.to_sql. This requires you to create an engine using sqlalchemy, and to make things faster use the option fast_executemany=True
connect_string = urllib.parse.quote_plus(f'DRIVER={{ODBC Driver 11 for SQL Server}};Server=<Server Name>,<port>;Database=<Database name>')
engine = sqlalchemy.create_engine(f'mssql+pyodbc:///?odbc_connect={connect_string}', fast_executemany=True)
with engine.connect() as connection:
df.to_sql(<table name>, connection, index=False)

Here is the script and hope this works for you.
import pandas as pd
import pyodbc as pc
connection_string = "Driver=SQL Server;Server=localhost;Database={0};Trusted_Connection=Yes;"
cnxn = pc.connect(connection_string.format("DataBaseNameHere"), autocommit=True)
cur=cnxn.cursor()
df= pd.read_csv("your_filepath_and_filename_here.csv").fillna('')
query = 'insert into TableName({0}) values ({1})'
query = query.format(','.join(df.columns), ','.join('?' * len(df1.columns)))
cur.fast_executemany = True
cur.executemany(query, df.values.tolist())
cnxn.close()

This should do what you want...very generic example...
# Insert from dataframe to table in SQL Server
import time
import pandas as pd
import pyodbc
# create timer
start_time = time.time()
from sqlalchemy import create_engine
df = pd.read_csv("C:\\your_path\\CSV1.csv")
conn_str = (
r'DRIVER={SQL Server Native Client 11.0};'
r'SERVER=Excel-PC\SQLEXPRESS;'
r'DATABASE=NORTHWND;'
r'Trusted_Connection=yes;'
)
cnxn = pyodbc.connect(conn_str)
cursor = cnxn.cursor()
for index,row in df.iterrows():
cursor.execute('INSERT INTO dbo.Table_1([Name],[Address],[Age],[Work]) values (?,?,?,?)',
row['Name'],
row['Address'],
row['Age'],
row['Work'])
cnxn.commit()
cursor.close()
cnxn.close()
# see total time to do insert
print("%s seconds ---" % (time.time() - start_time))
Try that and post back if you have additional questions/issues/concerns.

Replace df.iterrows() with df.apply() for one thing. Remove the loop for something much more efficient.

Try to populate a temp table with 1 or none indexes then insert it into your good table all at once.
Might speed things up due to not having to update the indexes after each insert??

Teradata - Pandas TypeError: 'NoneType' object is not iterable

I am trying to run the following code to create a Teradata table using the teradata python library:
import teradata
import pandas as pd
udaExec = teradata.UdaExec (appName="Hello", version="1.0",
logConsole=False)
session = udaExec.connect(method="odbc", system="tdprod",
username="xxx", password="xxx");
sqlStr = "CREATE SET TABLE \"TEST123\" \
(col1 INTEGER) PRIMARY INDEX (col1);"
result = pd.read_sql(sqlStr, self.session)
I am receiving the following error:
File "..\pandas\io\sql.py", line 1436, in read_query
columns = [col_desc[0] for col_desc in cursor.description]
TypeError: 'NoneType' object is not iterable
Any idea on how to solve this?

Your SQL (sqlStr) is a DDL (CREATE Table) -> it will NOT deliver any Resultset that can be placed into the Pandas Dataframe (pd.read_sql).
If you just want to create the table, you don't need pandas:
session.execute(sqlStr);
If you want to read from the table "TEST123":
sqlStr = "SELECT col1 FROM Test123;";
result = pd.read_sql(sqlStr, self.session);
or alternatively:
result = pd.read_sql_table("Test123", self.session);

To addon hhoeck answer, it is a good practice to use Context Manager. Otherwise you are risking having unclosed sessions.
import teradata
import pandas as pd
udaExec = teradata.UdaExec (appName="Hello", version="1.0",
logConsole=False)
with udaExec.connect(method="odbc", system="tdprod",
username="xxx", password="xxx") as session:
sqlStr = "CREATE SET TABLE \"TEST123\" \
(col1 INTEGER) PRIMARY INDEX (col1);"
# Create Table
session.execute(sqlStr)
# Read table to result
result = pd.read_sql(sqlStr,session)