bangbingy / bintang

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

Bintang

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

Overview

• How to get started
  • Requirements
  • Installation
  • Examples of Usage
• Common Functions
  • Bintang.read_excel(path, sheetname)
  • Table.iterrows(columnnames=None, result_as='dict')
  • Table.to_excel(path, index=False)

How to get started

Requirements

1. Python 3.6 or higher
2. openpyxl

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"
   ]
}

Use insert function to insert data. The parameter is a pair of column names and values and must be a list or tuple.

bt['Person'].insert(['id','name','age','address'], [1,'John',35,'1 Station St'])
bt['Person'].insert(['id','name','age','hobby','address'], [2,'Jane',17,'Reading','8 Parade Rd'])
bt['Person'].insert(['id','name','hobby','address'], [3,'Nutmeg','Fishing','7 Ocean Rd'])
bt['Person'].insert(['id','name','hobby','address'], [4,'Maria','Digging',None])
bt['Person'].insert(['id','name','hobby'], [5,'Bing','Digging'])

Loop your data using iterows 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', 'age': 35, 'address': '1 Station St', 'hobby': None}
1 {'id': 2, 'name': 'Jane', 'age': 17, 'address': '8 Parade Rd', 'hobby': 'Reading'}
2 {'id': 3, 'name': 'Nutmeg', 'age': None, 'address': '7 Ocean Rd', 'hobby': 'Fishing'}
3 {'id': 4, 'name': 'Maria', 'age': None, 'address': None, 'hobby': 'Digging'}
4 {'id': 5, 'name': 'Bing', 'age': None, 'address': None, 'hobby': 'Digging'}

You should notice that all column names now have been normalised for each row, even though all records have not used all column names during insertion.

Inspect Person table column list. You can also use function table.get_columnnames() to list all column names.

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(columnname, value, where=None). The value and where arg can use lambda function 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': None, 'hobby': 'Digging'}

Common Functions

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

Bintang.read_excel(path, sheetname)

Read an Excel file into Bintang table.

path: an excel file path to read from.

sheetname: the sheetname and will be used as tablename.

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

Table.iterrows(columnnames=None, result_as='dict')

Loop through Bintang table's rows and yield index and row. By default row will be in python dictionary.

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

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 set True.

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