mssql-dataframe 1.5.2

Update, Upsert, and Merge from Python dataframes to SQL Server and Azure SQL database.

mssql_dataframe

A data engineering package for Python pandas dataframes and Microsoft Transact-SQL. It provides more advanced methods for writting dataframes including update, merge, upsert.

1. Update: updates records in SQL table
2. Upsert: insert or update records in SQL table
3. Merge: update, insert, or delete records in SQL table

These more advanced methods are designed to provide more funcationality than is offered by the pandas.DataFrame.to_sql method. pandas.DataFrame.to_sql offers 3 options if the SQL table already exists with the parameter if_exists={'fail', 'replace', 'append'}.

See QUICKSTART for a full overview of functionality.

Initialization and Sample SQL Table

import env
import pandas as pd
from mssql_dataframe import SQLServer

# connect to database using pyodbc
sql = SQLServer(database=env.database, server=env.server)

# create a demonstration dataframe
df = pd.DataFrame({
    'ColumnA': ['1','2','3','4','5'],
    'ColumnB': ['a  .','b!','  c','d','e'],
}, index=pd.Index([0, 1, 2, 3, 4], name='PK'))

# create the table using a dataframe
df = sql.create.table_from_dataframe(
    table_name='##mssql_dataframe_readme',
    dataframe = df,
    primary_key = 'index'
)

Update

Records in an SQL table are updated by simply providing a dataframe. By default a match on the SQL table's primary key is required for a record to be updated.

# update records for index 0 & 1
update_df = pd.DataFrame({
        'ColumnA': ['11','22'],
        'ColumnB': ['A','B'],
}, index=pd.Index([0, 1], name='PK'))
# update data in the SQL table
update_df = sql.write.update('##mssql_dataframe_readme', update_df)

Merge

Records can be inserted/updated/deleted by providing a dataframe to the merge method. Again the primary key in the SQL table is used by default.

1. dataframe column value doesn't match SQL column value -> insert record into SQL
2. dataframe column value matches SQL column value -> update record in SQL
3. SQL column value not in dataframe column -> delete record in SQL

# update existing record for index 0
# insert new record for index 5
# delete missing records for index 1,2,3,4
merge_df = pd.DataFrame({
        'ColumnA': ['11','6'],
        'ColumnB': ['aa','f'],
}, index=pd.Index([0, 6], name='PK'))
# merge data in the SQL table
merged_df = sql.write.merge('##mssql_dataframe_readme', merge_df)

Upsert

The merge method can be restricted to not delete records in SQL by specifying the upsert flag. Records in SQL are then only inserted or updated.

# update existing record for index 0
# insert new record for index 7
# records not in the dataframe but in SQL won't be deleted
upsert_df = pd.DataFrame({
        'ColumnA': ['11','7'],
        'ColumnB': ['AA','g'],
}, index=pd.Index([0, 7], name='PK'))
sql.write.merge('##mssql_dataframe_readme', upsert_df, upsert=True)

Installation

pip install mssql-dataframe

Dependancies

pandas: Python DataFrames.

pyodbc: ODBC driver used for executing Transact-SQL statements.

Contributing

See CONTRIBUTING.