Issue: column encoding is altered when saving a DataFrame from Python to Oracle
When dealing with databases, it's important to pay attention to data types to ensure that the table structure is preserved when saving a DataFrame from Python. The following is a basic code snippet to save a DataFrame to an Oracle database using SQLAlchemy and pandas:
import pandas as pd
from sqlalchemy import create_engine
# Define the table structure
data = {
'id': [1, 2, 3],
'name': ['Alice', 'Bob', 'Charlie'],
'hire_date': ['2020-01-15', '2019-05-20', '2021-02-10'],
'insert_datetime': ['2021-09-15 10:00:00', '2021-09-16 11:30:00', '2021-09-17 09:45:00']
}
df = pd.DataFrame(data)
# Display the DataFrame and Data Types
print(df)
print(df.dtypes)
# Create SQLAlchemy engine
engine = create_engine('oracle://username:password@hostname:port/service_name')
# Use pd.to_sql() to replace/update the table in Oracle
df.to_sql('employee', con=engine, if_exists='replace', index=False)
id name hire_date insert_datetime
0 1 Alice 2020-01-15 2021-09-15 10:00:00
1 2 Bob 2019-05-20 2021-09-16 11:30:00
2 3 Charlie 2021-02-10 2021-09-17 09:45:00
id int64
name object
hire_date object
insert_datetime object
dtype: object
However, running this code may reveal a bug where the column encoding is altered in the Oracle database table. Upon inspection, it becomes apparent that only the first column id retains its integer data format, while the remaining columns name, hire_date, and insert_datetime are changed to CLOB (Character Large Object) encoding in Oracle.
Solution: using the dtype parameter when invoking to_sql()
To ensure that the correct data types are preserved when writing a DataFrame to an Oracle database, one approach is to explicitly define the data types for each column when using to_sql(). This can be achieved by creating an SQLAlchemy Table object with specified data types and then using the dtype parameter when invoking to_sql().
import pandas as pd
from sqlalchemy import create_engine, types
# Define the table structure
data = {
'id': [1, 2, 3],
'name': ['Alice', 'Bob', 'Charlie'],
'hire_date': ['2020-01-15', '2019-05-20', '2021-02-10'],
'insert_datetime': ['2021-09-15 10:00:00', '2021-09-16 11:30:00', '2021-09-17 09:45:00']
}
df = pd.DataFrame(data)
# convert data type to datetime
df['hire_date'] = pd.to_datetime(df['hire_date'], format='%Y-%m-%d')
df['insert_datetime'] = pd.to_datetime(df['insert_datetime'], format='%Y-%m-%d %H:%M:%S')
# Create SQLAlchemy engine
engine = create_engine('oracle://username:password@hostname:port/service_name')
# Set data types
dtype_dic = {'id': types.INTEGER(), 'name': types.NVARCHAR(length=50),'hire_date': types.DATE(),'insert_datetime': types.DateTime()}
# Use pd.to_sql() to replace/update the table in Oracle
df.to_sql('employee', con=engine, schema='TMP01', if_exists='replace', index=False, dtype=dtype_dic)
Ensure that the data type has been converted to datetime before using types.DATE().
Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on LinkedIn or X(@Luca_DataTeam). You can also catch glimpses of my personal life on Instagram, Happy exploring!👋