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: Python 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) 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 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. id name hire_date insert_datetime CLOB Solution: using the dtype parameter when invoking to_sql() dtype 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(). to_sql() dtype 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) 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(). types.DATE()

Walkthroughs, tutorials, guides, and tips. This story will teach you how to do something new or how to do something better.

Python: Setting Data Types When Using 'to_sql'

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

A Quick Guide to Listing All Measures in Power BI via DAX Studio

4 Data Analytics Certifications That Boost Your Career

Common MS Excel Questions to Help you Excel in a Data Analyst Job Interview

Data Scientist Careers at Amazon: What You'll Earn, Learn, and Work On

How Much Can You Make as a Data Scientist?

A Quick Guide to Listing All Measures in Power BI via DAX Studio

4 Data Analytics Certifications That Boost Your Career

Common MS Excel Questions to Help you Excel in a Data Analyst Job Interview

Data Scientist Careers at Amazon: What You'll Earn, Learn, and Work On

How Much Can You Make as a Data Scientist?

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps