bintang 0.1.13

A tiny and temporary db for quick data cleansing and transformation.

A tiny and temporary db for quick data cleansing and transformation. It is a high-level Python coding and would help any beginners up to speed with ETL work.

Overview

• How to get started

  • Requirements

  • Installation

  • Examples of Usage

• Common Functions

  • Bintang.blookup(lkp_table, on, ret_columns)

  • Bintang.read_dict(dict_obj, tablepaths=None)

  • Bintang.read_excel(path, sheetname, table=None)

  • Bintang.read_json(json_str, tablepaths=None)

  • Bintang.Table.innerjoin(right_table, on, into, out_leftcolumns, out_rightcolumns)

  • Bintang.Table.insert(record, columns=None)

  • Bintang.Table.iterrows(columns=None, row_type=’dict’)

  • Bintang.Table.read_sql(conn, sql_str=None, params=None)

  • Bintang.Table.to_dict(columns=None)

  • Bintang.Table.to_excel(path, index=False)

  • Bintang.Table.to_json()

  • Bintang.Table.to_sql(conn, schema, table, columns, method=’prep’, max_rows = 1)

How to get started

Requirements

1. Python 3.7 or higher

2. openpyxl

3. pyodbc

4. thefuzz

Installation

pip install bintang

Examples of Usage

import bintang          # import the package
bt = bintang.Bintang()  # bintang object created

bt.create_table('Person')
bt
{
   "name": null,
   "tables": [
   "Person"
   ]
}

The parameters are record and columns.

# Create a couple of tables and use insert function to insert data.
bt.create_table('Person')

bt['Person'].insert([1,'John','Smith','1 Station St'], ['id','name','surname','address'])
bt['Person'].insert([2,'Jane','Brown','Digging','8 Parade Rd'], ['id','name','surname','hobby','address'])
bt['Person'].insert([3,'Okie','Dokie','7 Ocean Rd'], ['id','name','surname','Address'])
bt['Person'].insert((4,'Maria','Digging','7 Heaven Ave'), ('id','name','hobby','Address'))
bt['Person'].insert((5,'Bing','Digging',None), ('id','name','hobby','Address'))

bt.create_table("FishingClub")
bt['FishingClub'].insert(['Ajes','Freeman','Active'], ['FirstName','LastName','Membership'])
bt['FishingClub'].insert(['John','Smith','Active'], ['FirstName','LastName','Membership'])
bt['FishingClub'].insert(['Jane','Brown','Active'], ['FirstName','LastName','Membership'])
bt['FishingClub'].insert(['Nutmeg','Spaniel','Active'], ['FirstName','LastName','Membership'])
bt['FishingClub'].insert(['Zekey','Pokey','Active'], ['FirstName','LastName','Membership'])

Loop your data using iterrows function. This will loop through all the rows one by one in a Python dict.

for idx, row in bt['Person'].iterrows():
    print(idx, row)

#0 {'id': 1, 'name': 'John', 'surname': 'Smith', 'address': '1 Station St', 'hobby': None}
#1 {'id': 2, 'name': 'Jane', 'surname': 'Brown', 'address': '8 Parade Rd', 'hobby': 'Digging'}
#2 {'id': 3, 'name': 'Okie', 'surname': 'Dokie', 'address': '7 Ocean Rd', 'hobby': None}
#3 {'id': 4, 'name': 'Maria', 'surname': None, 'address': '7 Heaven Ave', 'hobby': 'Digging'}

You should notice that all columns now have been normalised for each row, even though all records have not used all column names during insertion.When the 1st record (idx 0) is inserted, four columns created. id, name, age and address. When insert 4th record is inserted (idx 3), a hobby column added.

Inspect Person table column list. You can also use function table.get_columns() to list all columns.

print(bt['Person'])
# {
#   "table name": "Person",
#   "columns": [
#       {"id": 0,"name": "id"},
#       {"id": 1,"name": "name"},
#       {"id": 2,"name": "age"},
#       etc...
# }

Use update function to change the data. The function signature is table.update(column, value, where=None). The value and where parameters can use lambda function argument for smarter expression.

bt['Person'].update('age', 10, where=lambda row: row['name']=='Maria')

Retrieve the row stored at the 3rd index by passing 3 inside the list next to table object.

bt['Person'][3]
{'id': 4, 'name': 'Maria', 'age': 10, 'address': '7 Heaven Ave', 'hobby': 'Digging'}

Common Functions

We are going to provide some functions that may be needed most when working with Bintang objects.

Bintang.blookup(lkp_table, on, ret_columns)

Return one or more columns from lookup table.

lkp_table:

lookup table

on:

lookup key tuples

ret_columns:

lookup columns to be returned

# using tables from Example of Usage section above.
bt['Person'].blookup('FishingClub')], \
    [('name','FirstName')], \
    ['Membership'])

# check results
for idx, row in bt['Person'].iterrows(['name','Membership']):
    print(idx, row)

# 0 {'name': 'John', 'Membership': 'Active'}
# 1 {'name': 'Jane', 'Membership': 'Active'}
# 2 {'name': 'Okie', 'Membership': None}
# 3 {'name': 'Maria', 'Membership': None}

We can see only John and Jane got the membership because only John and Jane exist in both tables.

Bintang.read_dict(dict_obj, tablepaths=None)

Read a dictionary object and create one or more table according to different hierarchy paths contained in object.

dict_obj:

a Python dictionary object.

tablepaths:

a list of paths which contain a list of objects (equivalent to records).

# example data
dict_obj = {
     'Person': [
         {'id': 1,'name': 'John','surname': 'Smith',
             'Address': {'number': 1, 'street': 'Station','street_type': 'Street'}
         },
         {'id': 2,'name': 'Jane','surname': 'Brown',
             'Address': {'number': 8,'street': 'Parade','street_type': 'Road'}
         }
     ],
     'PersonDetails': [
         {'person_id': '1', 'hobby': 'Blogging','is_meat_eater': True
         },
         {'person_id': '2','hobby': 'Reading','is_meat_eater': None,
             'LuckyDays': [13,17,19]
         }
     ]
}

bt = bintang.Bintang('From Dict')   # create bintang object.
bt.read_dict(dict_obj)              # call this function
print(bt) # show bt tables
# {
#    "name": "From Dict",
#    "tables": [
#       "/Person",
#       "/Person/Address",
#       "/PersonDetails",
#       "/PersonDetails/LuckyDays"
#    ]
# }

Bintang.read_excel(path, sheetname, table=None)

Read an Excel file into Bintang table.

path:

an excel file path to read from.

sheetname:

the sheetname to read from.

table:

table name to hold the data. If not given, table name will be the sheetname.

bt.read_excel('/path/to/file.xlsx', 'Sheet1')

Bintang.read_json(json_str, tablepaths=None)

Read JSON string and create a table or more according to hierarchy paths contained in json ‘object’. This function wraps built-in json.load() then pass the result to read_dict() to generate table(s).

json_str:

a json string

tablepaths:

a list of paths which contain a list of objects (equivalent to records).

# other module import
# ...
import bintang
import json

# example json data
json_str = '{"Page:": 100, "Time": "2033-09-05T00:00:00Z", \
            "Person": [{"id": 1, "name": "John", "surname": "Smith", \
                         "Address": {"number": 1, "street": "Station", "street_type": "Street"}}, \
                       {"id": 2, "name": "Jane", "surname": "Brown", \
                         "Address": {"number": 8, "street": "Parade", "street_type": "Road"}}], \
            "PersonDetails": [{"person_id": "1", "hobby": "Blogging", "is_meat_eater": true}, \
                              {"person_id": "2", "hobby": "Reading", "is_meat_eater": null, \
                                "LuckyDays": [13, 17, 19]}]}'

bt = bintang.Bintang('From JSON')
bt.read_json(json_str)

print(bt) # show bt tables
# {
#    "name": "some tables",
#    "tables": [
#       "/",
#       "/Person",
#       "/Person/Address",
#       "/PersonDetails",
#       "/PersonDetails/LuckyDays"
#    ]
# }

# loop through root table ('/')
for idx, row in bt['/'].iterrows():
    print(idx, row)
# 0 {'Page:': 100, 'Time': '2033-09-05T00:00:00Z'}

# loop through  /Person table.
for idx, row in bt['/Person'].iterrows():
    print(idx, row)
# 0 {'Person': 0, 'id': 1, 'name': 'John', 'surname': 'Smith'}
# 1 {'Person': 1, 'id': 2, 'name': 'Jane', 'surname': 'Brown'}

# loop through /Person/Address table. Because this table under /Person,
# then each record will have their own reference to /Person table.
for idx, row in bt['/Person'].iterrows():
    print(idx, row)
# 0 {'Address': 'Address', 'Person': 0, 'number': 1, 'street': 'Station', 'street_type': 'Street'}
# 1 {'Address': 'Address', 'Person': 1, 'number': 8, 'street': 'Parade', 'street_type': 'Road'}

# loop through /PersonDetails table.
for idx, row in bt['/PersonDetails'].iterrows():
     print(idx, row)
# 0 {'PersonDetails': 0, 'person_id': '1', 'hobby': 'Blogging', 'is_meat_eater': True}
# 1 {'PersonDetails': 1, 'person_id': '2', 'hobby': 'Reading', 'is_meat_eater': None}

# loop through /PersonDetails/LuckyDays table.
for idx, row in bt['/PersonDetails/LuckyDays'].iterrows():
     print(idx, row)
# 0 {'PersonDetails': 1, 'LuckyDays': 13}
# 1 {'PersonDetails': 1, 'LuckyDays': 17}
# 2 {'PersonDetails': 1, 'LuckyDays': 19}

Please note that since json can contain complex hierarchy paths and still valid (eg. system configuration), then this function may not in your favour. It might be better to manually extract/locate a certain path manually (hard coded).

Bintang.Table.innerjoin(right_table, on, into, out_leftcolumns, out_rightcolumns)

return a new table from an inner join operation.

right_table:

name of right table or the second table.

on:

a list of pair columns used for the join.

into:

a new table name to hold the result.

out_leftcolumns:

columns output from left table.

out_rightcolumns:

columns outpout from right table.

bt.create_table('Person') # This will be a left table
# insert some record here. See insert below for an example.
# ...

bt.create_table('FishingClub') # this will be a right table
# insert some records here. See insert below for an example.
# ...

# let's match the two tables for their firt name and last name.
res = bt.innerjoin('Person'                                       # left table
                  ,'FishingClub'                                  # right table
                  ,[('name','FirstName'), ('surname','LastName')] # on
                  ,'Fisherman'                                    # into
                  ,out_lcolumns=['name','address']
                  ,out_rcolumns=['Membership']
                  )

# check the result. you can loop through 'Fisherman' or res.
for idx, row in bt['Fisherman'].iterrows():
   print(idx, row)

Bintang.Table.insert(record, columns=None)

Insert a record into a table.

record:

a list/tuple of data. Or a dict where key=column, value=record

columns:

a list/tuple of columns (in the same order as in the record)

bt.create_table('Person')
p = bt.get_table('Person') # get table object for Person
# insert data directly from table object instead throug bt object.
p.insert([1,'John','Smith','1 Station St'], ['id','name','surname','address'])
p.insert([2,'Jane','Brown','Digging','8 Parade Rd'], ['id','name','surname','hobby','address'])
p.insert([3,'Okie','Dokie','7 Ocean Rd'], ['id','name','surname','Address'])
p.insert((4,'Maria','Digging','7 Heaven Ave'), ('id','name','hobby','Address'))
p.insert((5,'Bing','Digging',None), ('id','name','hobby','Address'))

bt.create_table('FishingClub')
# lets make a list of columns so we can pass it to insert.
columns = ['FirstName','LastName','Membership']
bt['FishingClub'].insert(['Ajes','Freeman','Active'], columns)
bt['FishingClub'].insert(['John','Smith','Active'], columns)
bt['FishingClub'].insert(['John','Brown','Active'], columns)
bt['FishingClub'].insert(['Okie','Dokie','Active'], columns)
bt['FishingClub'].insert(['Zekey','Pokey','Active'], columns)


bt.create_table("Product")
prod = bt['Product']
# example of assigning a dictionary argument for record parameter.
prod.insert({'id':1, 'name':'Hook','price':1.60})
prod.insert({'id':2, 'name':'Sinker','price':1.20})
prod.insert({'id':3, 'name':'Reels','price':75})

Bintang.Table.iterrows(columns=None, row_type=’dict’)

Loop through Bintang table’s rows and yield index and row. Row can be called out as dict (default) or list.

columns:

a list of columns for each row will output. If None, output all columns.

row_type:

either ‘dict’ (default) or ‘list’.

for idx, row in bt['tablename'].iterrows():
    # do something with idx or row
    print(idx, row)

Bintang.Table.read_sql(conn, sql_str=None, params=None)

Read sql table and populate the data to Bintang table.

conn:

pyodbc database connection

sql_str:

sql query, if none it will select * from a same sql table name.

params:

sql parameters

# connect to sql server
conn_str = "DRIVER={ODBC Driver 17 for SQL Server};SERVER=EHL5CD8434KLM;PORT=1443;DATABASE=test;Trusted_Connection=yes;"
conn = pyodbc.connect(conn_str)
sql_str = "SELECT * FROM Person WHERE LastName=?"
params = ('Dokey')

bt = bintang.Bintang()
bt.create_table('Person')
bt['Person'].read_sql(conn, sql_str, params)

for idx, row in bt['Person'].iterrows():
    print(idx, row)
    # would print {'ID': 3, 'FirstName': 'Okie', 'LastName': 'Dokey', 'address': '7 Ocean Rd'}

conn.close()

Bintang.Table.to_dict(columns=None)

Return bintang table object as a simple dictionary.

columns:

a list of columns for each row will output. If None, output all columns.

res = bt['tablename'].to_dict(columns=None)

Bintang.Table.to_excel(path, index=False)

Write Bintang table to an Excel file.

path:

an excel file path to write to.

index:

write row index if it sets True.

bt['tablename'].to_excel('/path/to/file.xlsx')

Bintang.Table.to_json()

This is just a placeholder. Python make it easy when serializing a dict object to JSON. Conversion would be done by built-in json.JSONEncoder(). Here an example of using our to_dict() function then use build-in module json to convert/export dict to JSON.

# other modules here
# ...
import json

# other codes here
# ...

dict_obj = bt['table_name'].to_dict()

# example to serialise dict_obj to json string
json_str = json.dumps(dict_obj)
# use json_str here!
# ...


# example to write dict_obj to a json file
with open ('myfile.json', 'w') as fp:
    json.dump(dict_obj, fp) # this would serialise dict_obj into myfile.json

Bintang.Table.to_sql(conn, schema, table, columns, method=’prep’, max_rows = 1)

Insert records into sql table. Notes: Currently tested for SQL Server 2019. However this function should work with other dbms supported by pyodbc.

conn:

pyodbc database connection

schema:

the schema name the sql table belong to.

table:

the table name in the sql database

columns:

a dictionary of column mappings where the key is sql column (destination) and the value is bintang columns (source). If columns is a list, column mapping will be created automatically assuming source columns and destination columns are the same.

method:

‘prep’ to use prepared statement (default) or ‘string’ to use sql string. To avoid sql injection, never use method string when the datasource is not known or from they are from external.

max_rows:

maximum rows per insert. Insert more then 1 record when using prep require all data in a column to use the same type, otherwise will raise error.

bt = bintang.Bintang('my bintang')
bt.create_table('Person')
person = bt.get_table('Person')
person.insert([1,'John','Smith','1 Station St'], ['id','name','surname','address'])
person.insert([2,'Jane','Brown','Digging','8 Parade Rd'], ['id','name','surname','address'])
person.insert([3,'Okie','Dokey','7 Ocean Rd'], ['id','name','surname','address'])
person.insert((4,'Maria','Digging','7 Heaven Ave'), ('id','name','hobby','Address'))
person.insert((5,'Bing','Digging',None), ('id','name','hobby','Address'))

# let's map column ID, FirstName, LastName, Address in database to bintang's Person table.
columns = {'ID':'id', 'FirstName':'name', 'LastName':'surname', 'Address':'address'}
# connect to database
conn = pyodbc.connect("DRIVER={ODBC Driver 17 for SQL Server};SERVER=localhost;PORT=1443;DATABASE=test;Trusted_Connection=yes;")
# send data to sql
ret = person.to_sql(conn, 'dbo', 'Person', columns)
print(f'{ret} record(s) affected.')
conn.commit()
conn.close()