This package is disigned to be a light wight wrapper for pymssql used for Miscrosoft SQL Server. This
was developed as I was working with Sql Servers to make it easy and quick to do a lot of the functionality that would duplicate code for in Data Science my projects. This is not a comprehensive list of functionality but the most popular ones I used. This version does not support Azure at the moment. That will come in a later version.
pip install SqlMethods
Connect to the SQL Server:
Each class instance can only connect to one database. To connect to multiple databases you will need multple instances of the class.
from SqlMethods import SqlMethods
list_args = [r'login name', r'server name', r'passwrod', r'database name']
sql_srvr = SqlMethods(list_args)
print(sql_svr.bool_is_connected)Connect to the Sql Server ouput:
True # if connection is established
# or
False # if connection is not establishedQuery database:
Assuming a connection (sql_srvr) is established.
Assuming a connection (sql_srvr) is established.
# simple query
string_query = 'select top 10 * from dbo.table'
list_query_results = sql_srvr.query_select(string_query)The list returned is a list of length 2.
list_query_results[0] -> boolean; True or False
list_query_results[1] -> potentially a multidimensional list of values from the query; in this case the first 10 records
if list_query_results[0] is True list_query_results[1] will have the data
if list_query_results[0] is False list_query_results[1] will have the error message from the Sql Server
Note: if you are pulling only one column or your results results in a sinlge column of data. You will
need to use a list comprehension to be able to add it to a pandas Series.
example:
list_query_results[0] -> boolean; True or False
list_query_results[1] -> potentially a multidimensional list of values from the query; in this case the first 10 records
if list_query_results[0] is True list_query_results[1] will have the data
if list_query_results[0] is False list_query_results[1] will have the error message from the Sql Server
Note: if you are pulling only one column or your results results in a sinlge column of data. You will
need to use a list comprehension to be able to add it to a pandas Series.
example:
list_results_for_series = [x[0] for x in list_query_results[1]]
series_one_column = pandas.Series(data = list_results_for_series, name = 'column_name')If your query reults in more than one column of data you can use the following code to add it to
a pandas datafarme.
example:
a pandas datafarme.
example:
list_table_columns = sql_srvr.get_table_columns('table_name')
if list_table_columns[0] and list_query_results[0]:
df_sql_query = pandas.DataFrame(data = list_query_results[1], columns = list_table_columns[1])Insert into database:
Before you insert it is a good idea to check if the table is created. The below example illistrates how
to insert into the table and check if the table is created.
example (check table or create):
Before you insert it is a good idea to check if the table is created. The below example illistrates how
to insert into the table and check if the table is created.
example (check table or create):
bool_table_exists = sql_srvr.table_exists(string_table_name)
if not bool_table_exists:
list_create_table = sql_srvr.create_table(string_table_name,
['int_id int', 'date_record datetime', 'string_customer varchar(100)'])
bool_table_exists = list_create_table[0]The columns are in a list with the column name and data type. The when the table is created
the data compression is enable and the table is a narrow table (1,024 columns or less).
When inserting into the table the method will determine if you are inerting one value or more than
by the lenght of the list of values. This needs to be a list of lists. An easy way to convert a pandas
dataframe is to use the following line of code:
the data compression is enable and the table is a narrow table (1,024 columns or less).
When inserting into the table the method will determine if you are inerting one value or more than
by the lenght of the list of values. This needs to be a list of lists. An easy way to convert a pandas
dataframe is to use the following line of code:
list_insert = df_sql_query.values.tolist()The following example will insert into the table created above:
if bool_table_exists:
list_insert_results = sql_srvr.insert(string_table_name, list_table_columns,
df_sql_query.values.tolist())The method will insert 100,000 records at a time. In
as
errors. The second values will be an empy list of the first value is
Other methods:
Other functions in the package are:
delete_table() -> deletes a table in the database
truncate_table() -> clears the table in the database
update() -> changes values in the table based on specific criteria
delete_records() -> removes records from the directory
get_wide_columns() -> finds the columns of a wide / sparse table
get_num_columns() -> returns the number of columns in a table
alter_table() -> changes the columns (add, delete, modify) in a table
Close connection:
The last and also important aspect of the table is to close the connection.
example:
list_insert_results the format is the sameas
list_query_results. The first value is a boolean determining if the insert completed with noerrors. The second values will be an empy list of the first value is
True. If the first value isFalse then the error message will be in the second postion of the list.Other methods:
Other functions in the package are:
delete_table() -> deletes a table in the database
truncate_table() -> clears the table in the database
update() -> changes values in the table based on specific criteria
delete_records() -> removes records from the directory
get_wide_columns() -> finds the columns of a wide / sparse table
get_num_columns() -> returns the number of columns in a table
alter_table() -> changes the columns (add, delete, modify) in a table
Close connection:
The last and also important aspect of the table is to close the connection.
example:
sql_srvr.close()