I am using the following code to read a table from an access db as a pandas dataframe:
import pyodbc
import pandas as pd
connStr = (
r"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};"
r"DBQ=C:\Users\A\Documents\Database3.accdb;"
)
cnxn = pyodbc.connect(connStr)
sql = "Select * From Table1"
data = pd.read_sql(sql,cnxn) # without parameters [non-prepared statement]
# with a prepared statement, use list/tuple/dictionary of parameters depending on DB
#data = pd.read_sql(sql=sql, con=cnxn, params=query_params)
I plan to make some transformations and then write the dataframe back into the databsae in a similar way. Does anyone know how I can do this?.
Thank you
When working with pandas and a database other than SQLite we need to use SQLAlchemy. In this case, we would use the sqlalchemy-access dialect.
(I am currently the maintainer.)
Example:
import pandas as pd
import sqlalchemy as sa
connection_string = (
r"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};"
r"DBQ=C:\Users\Public\test\sqlalchemy-access\sqlalchemy_test.accdb;"
r"ExtendedAnsiSQL=1;" )
connection_url = sa.engine.URL.create(
"access+pyodbc",
query={"odbc_connect": connection_string}
)
engine = sa.create_engine(connection_url)
df = pd.DataFrame([(1, "foo"), (2, "bar")], columns=["id", "txt"])
df.to_sql("my_table", engine, index=False, if_exists="append")