Package: bintang
A tiny and temporary db for quick data cleansing and transformation.
It is a high-level Python coding and would help anyone up to speed with ETL work.OverviewHow to get startedRequirementsInstallationExamples of UsageCommon FunctionsBintang.read_excel(path, sheetnames=None)Bintang.read_json(json_str, tablepaths=None)Bintang.Table.blookup(lkp_table, on, ret_columns)Bintang.Table.groupby(columns, drop_none=True, group_count=False, counts=None, sums=None, mins=None, maxs=None, means=None, group_concat=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_csv(path, delimiter=’,’, quotechar=’”’, header_row=1)Bintang.Table.read_excel(path, sheetname, header_row=1)Bintang.Table.read_sql(conn, sql_str=None, params=None)Bintang.table.to_csv(path, index=False, delimiter=’,’, quotechar=’”’, quoting=0)Bintang.Table.to_excel(path, index=False)Bintang.Table.to_json()Bintang.Table.to_sql(conn, schema, table, columns, method=’prep’, max_rows = 1)Other Functions/attributesBintang.add_table(table_object)Bintang.create_table(name, columns=None)Bintang.drop_table(name)Bintang.nameBintang.Table.add_column(name)Bintang.Table.drop_column(name)Bintang.Table.get_columns()Bintang.Table.nameBintang.Table.print(columns=None, show_data_type=False)Bintang.Table.update(column, value, where=None)Bintang.Table.update_column(name)How to get startedRequirementsPython 3.7 or higherrapidfuzzopenpyxlInstallationpipinstallbintangExamples of UsagefrombintangimportBintang# import the packagebt=Bintang()# bintang object createdbt.create_table('Person')print(bt)#{#   "name": null,#   "tables": [#   "Person"#   ]#}Use insert function to populate a table. 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.foridx,rowinbt['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'}If the table is small, you can use print() function to output the records to terminal.Person.print()#                           Table: Person# --------------+---------+-----------+----------------+-----------#       id      |   name  |  surname  |    address     |   hobby# --------------+---------+-----------+----------------+-----------#             1 | John    | Smith     | 1 Station St   | None#             2 | Jane    | Brown     | 8 Parade Rd    | Digging#             3 | Okie    | Dokie     | 7 Ocean Rd     | None#             4 | Maria   | None      | 7 Heaven Ave   | Digging#             5 | Bing    | None      | None           | Digging# --------------+---------+-----------+----------------+-----------# (5 rows)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=lambdarow: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 FunctionsWe are going to provide some functions that may be needed most when working with Bintang objects.Bintang.read_excel(path, sheetnames=None)Read an Excel file and create Bintang tables. The first row will be assumed as column header.
Go to Bintang.Table.read_excel() to read a single sheet and populate the data into created table.path:an excel file path to read from.sheetname:a list of sheets that only needed to be read. If not specified all available sheets will be read (default).bt.read_excel('/path/to/file.xlsx')Bintang.read_json(json_str, tablepaths=None)Read JSON string and create a table or more according to hierarchy paths contained in json ‘object’.json_str:a json stringtablepaths:a list of paths which contain a list of objects (equivalent to records).# other module import# ...importbintangimportjson# example json datajson_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": "From JSON",#    "tables": [#       "/",#       "/Person",#       "/Person/Address",#       "/PersonDetails",#       "/PersonDetails/LuckyDays"#    ]# }# loop through root table ('/')foridx,rowinbt['/'].iterrows():print(idx,row)0{'Page:':100,'Time':'2033-09-05T00:00:00Z'}# loop through  /Person table.foridx,rowinbt['/Person'].iterrows():print(idx,row)# 0 {'Person': 0, 'id': 1, 'name': 'John', 'surname': 'Smith'}# 1 {'Person': 1, 'id': 2, 'name': 'Jane', 'surname': 'Brown'}# print /Person/Address table. Because this table under /Person, then each record will have their own# reference to /Person table.bt['/Person/Address'].print()#                      Table: /Person/Address# -----------+--------------+--------------+-----------+---------------#   Address  |    Person    |    number    |   street  |  street_type# -----------+--------------+--------------+-----------+---------------#  Address   |            0 |            1 | Station   | Street#  Address   |            1 |            8 | Parade    | Road# -----------+--------------+--------------+-----------+---------------# (2 rows)Please note that since json can contain complex hierarchy paths and still valid (eg. system configuration), then this function may not be in your favour. It might be better to manually extract/locate a certain path manually (hard coded).Bintang.Table.blookup(lkp_table, on, ret_columns)Return one or more columns from lookup table.lkp_table:lookup tableon:lookup key tuplesret_columns:lookup columns to be returned# using tables from Example of Usage section above.bt['Person'].blookup('FishingClub')],\[('name','FirstName')],\['Membership'])# check resultsforidx,rowinbt['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 their names exists in both tables.Bintang.Table.groupby(columns, drop_none=True, group_count=False, counts=None, sums=None, mins=None, maxs=None, means=None, group_concat=None)Return grouped rows based upon the value of columns.columns:a list of columns that is used to group the data.drop_none:if True, to drop/exclude the group if all columns hold None.group_count:if True, create row count from group columns.group_concat:a column to create group_concat like mysql.counts:a list of columns create count column(s)sums:a list of columns create sum column(s)mins:a list of columns create min column(s)maxs:a list of columns create max column(s)bt.create_table('Product')# this will be our basis table for groupingp=bt['Product']# assign p as the tablep.insert({'id':1,'brand':'Shimano','class':'rod','name':'Extraction','price':299})p.insert({'id':2,'brand':'Shimano','class':'rod','name':'Zodias Travel','price':399})p.insert({'id':3,'brand':'Ugly Stik','class':'rod','name':'Balance II','price':63.99})p.insert({'id':4,'brand':'Shimano','class':'rod','name':'Zodias Travel','price':399})p.insert({'id':5,'brand':'Shimano','class':'reel','sub class':'spinning','name':'Sedona F1','price':99.00})p.insert({'id':6,'brand':'Shimano','class':'reel','sub class':'spinning','name':'FX Series 4000','price':54.99})grouped=p.groupby(['brand','class'],group_count=True)grouped.print()#              Table: grouped# -------------+---------+---------------#     brand    |  class  |  group_count# -------------+---------+---------------#  Shimano     | rod     |             3#  Ugly Stik   | rod     |             1#  Shimano     | reel    |             2# -------------+---------+---------------# (3 rows)grouped=p.groupby(['brand'],group_concat='id',sums=['price'])# another examplegrouped.print()#                     Table: grouped# -------------+-------------------+-------------------#     brand    |    group_concat   |     sum_price# -------------+-------------------+-------------------#  Shimano     |   [1, 2, 4, 5, 6] |           1250.99#  Ugly Stik   |               [3] |             63.99# -------------+-------------------+-------------------# (2 rows)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.foridx,rowinbt['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=recordcolumns: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 to output. If None, will output all columns.row_type:either ‘dict’ (default) or ‘list’.foridx,rowinbt['tablename'].iterrows():# do something with idx or rowprint(idx,row)Bintang.Table.read_csv(path, delimiter=’,’, quotechar=’”’, header_row=1)Read csv file and populate its records to table.path:a csv file path to read from.delimiter:field seperator, by default it’ll accept a comma character.header_row:the row number that contains column name or label.## data example in  csv file# "id","name","surname","address","hobby"# "1","John","Smith","1 Station St",""# "2","Jane","Brown","8 Parade Rd","Digging"bt.create_table('Person')bt['Person'].read('/path/to/file.csv')bt['Person'].print()#                          Table: Person# ------+---------+-----------+----------------+-----------#   id  |   name  |  surname  |    address     |   hobby# ------+---------+-----------+----------------+-----------#  1    | John    | Smith     | 1 Station St   |#  2    | Jane    | Brown     | 8 Parade Rd    | Digging# ------+---------+-----------+----------------+-----------# (2 rows)Bintang.Table.read_excel(path, sheetname, header_row=1)Read an Excel file into Bintang table.path:an excel file path to read from.sheetname:the sheetname to read from.header_row:the row number that contains column name or label.bt.create_table('Person')bt['Person'].read_excel('/path/to/file.xlsx','Sheet1')Bintang.Table.read_sql(conn, sql_str=None, params=None)Read sql table and populate the data to Bintang table.conn:pyodbc database connectionsql_str:sql query, if none it will select * from a same sql table name.params:sql parametersimportbintangimportpyodbc# connect to sql serverconn_str="DRIVER={ODBC Driver 17 for SQL Server};SERVER=localhost;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)foridx,rowinbt['Person'].iterrows():print(idx,row)# would print {'ID': 3, 'FirstName': 'Okie', 'LastName': 'Dokey', 'address': '7 Ocean Rd'}conn.close()Bintang.table.to_csv(path, index=False, delimiter=’,’, quotechar=’”’, quoting=0)Write bintang table to a csv file.path:a csv file path to write to.index:write row index if it sets True.delimiter:field seperatorquotechar:a character to quote the dataquoting:the csv enum for quoting, csv.QUOTE_MINIMAL or  0, csv.QUOTE_ALL or 1, csv.QUOTE_NONNUMERIC or 2, csv.QUOTE_NONE or 3Notes: setting quoting parameter properly will provide correct value to be presented in csv even if the value containing a delimiter character.bt['tablename'].to_csv('/path/to/file.csv')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# ...importjson# other codes here# ...dict_obj=bt['table_name'].to_dict()# example to serialise dict_obj to json stringjson_str=json.dumps(dict_obj)# use json_str here!# ...# example to write dict_obj to a json filewithopen('myfile.json','w')asfp:json.dump(dict_obj,fp)# this would serialise dict_obj into myfile.jsonBintang.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 connectionschema:the schema name the sql table belong to.table:the table name in the sql databasecolumns: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 string method when the datasource is external or not known.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.importbintangimportpyodbcbt=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 databaseconn=pyodbc.connect("DRIVER={ODBC Driver 17 for SQL Server};SERVER=localhost;PORT=1443;DATABASE=test;Trusted_Connection=yes;")# send data to sqlret=person.to_sql(conn,'dbo','Person',columns)print(f'{ret}record(s) affected.')conn.commit()conn.close()Other Functions/attributesBintang.add_table(table_object)Add a table to Bintang object. Think Bintang is a container of tables.table_object:table object is a table created from create_table function.Bintang.create_table(name, columns=None)Create a table inside Bintang objectname:string to name the tablecolumns:a list of columns. Bintang can create columns during record insertion (dynamic schema effect).Bintang.drop_table(name)Drop table from tables container.name:table namebt.drop_table(name)Bintang.nameTo rename Bintang object name can be done through normal assignment.importbintangbt=bintang.Bintang('my db')bt.name='your db'# change name from 'my db' to 'your db'Bintang.Table.add_column(name)Add a column to table. Bintang can create columns during record insertion (dynamic schema effect).column:a string of column nameBintang.Table.drop_column(name)Drop a column from a table.column:a string of column nameBintang.Table.get_columns()Return a list of columnsBintang.Table.nameTo rename table name can be done through normal assignment.bt['my table'].name='your table'# change name from 'my table' to 'your table'Bintang.Table.print(columns=None, show_data_type=False)Print rows to terminal in table format. This would be handy if the table can fit into terminal.columns:a list of columns to output. If None, will output all columns (default).show_data_type:if True, will output data type.# assume Person table object exists and has dataPerson.print()Bintang.Table.update(column, value, where=None)To update row(s) in the tablecolumn:column to be updated. If column does not exist then it will be created (dynamic schema effect).value:the value to be set for the row(s). Can also use lambda.where:condition in lambda so which row(s) that needs update.# assume Person table object exists and has records.Person.update('job','fisherman')# this will create job column and set value of the rows to 'fisherman'# update only row that has name 'John'Person.update('job','fisherman',where=lambdarow:row['name']=='John')# create 'full name' column and populate value with name and surname combined.bt['Person'].update('full name',lambdarow:row['name']+' '+row['surname'])# one liner conditional expression is common with lambda.# for instance, if you know there is None in surname then you want to resolve the full name as name.bt['Person'].update('full name',lambdarow:row['name']ifrow['surname']isNoneelserow['name']+' '+row['surname'])Bintang.Table.update_column(name)To update column’s name.name:the string of the column name.