simpledt

This is a third party party package for the framework Flet.

It enables Flet users to serialize

• Pandas DataFrame
• SQL
• CSV
• JSON
• Excel

Into Flet's DataTable.

simpledt is mainly based on pandas, SQLAlchemy and Flet's DataTable, DataRow, DataColumn and DataCell.

Note: Every class instance of simpledt package, has the following attributes

• datatable
• datarows
• datacolumns

simpledt package does not have any hardcoded values and any change can be done to the DataTable, list of DataRow and DataColumn according to Flet docs

Installation

pip install simpledatatable

Quickstart

Display pandas dataframe

import pandas as pd
import flet as ft
from simpledt import DataFrame


def main(page: ft.Page):
    df = pd.read_excel("dataset/Excel_MOCK_DATA.xlsx")
    simpledt_df = DataFrame(df)  # Initialize simpledt DataFrame object
    simpledt_dt = simpledt_df.datatable  # Extract DataTable instance from simpledt

    page.add(simpledt_dt)


ft.app(target=main)

So far, we've generated Flet's DataTable from pandas dataframe using simpledt package.

Take in consideration that every class instance from simpledt package has datatable, datarows and datacolumns attributes.

• datatable returns a Flet's DataTable instance, which already consists of DataColumn, DataRow and DataCell

• datarows returns a list of Flet's DataRow instance

• datacolumns returns a list of Flet's DataColumn instance

Note: any change of these attributes can be done according to Flet docs. There is no limitations, or hardcoded values.

Modify datarows

To see how to modify datarows, lets move and change color of rows whose rownum is even.

import pandas as pd
import flet as ft
from simpledt import DataFrame


def main(page: ft.Page):
    df = pd.read_excel("dataset/Excel_MOCK_DATA.xlsx")
    simpledt_df = DataFrame(df)  # Initialize simpledt DataFrame object
    simpledt_dt = simpledt_df.datatable  # Extract DataTable instance from simpledt
    
    simpledt_dt.bgcolor = ft.colors.RED # Change background color of generated DataTable
    simpledt_dt.border = ft.border.all(10, ft.colors.PINK_600) # Add ping border to DataTable

    dr = simpledt_df.datarows

    for i in dr:
        rownum = i.cells[0].content.value # Getting the first instance of DataCell, which consists of `id` and getting its `content` and `value`
        if int(rownum) % 2 == 0: # If rownum is even, change row color to green
            i.color = ft.colors.GREEN
        
    page.add(simpledt_dt)


ft.app(target=main)

Modify datacolumns

To see how to modify datacolumns, add snowflakes at the end of every column name

import pandas as pd
import flet as ft
from simpledt import DataFrame


def main(page: ft.Page):
    df = pd.read_excel("dataset/Excel_MOCK_DATA.xlsx")
    simpledt_df = DataFrame(df)  # Initialize simpledt DataFrame object
    simpledt_dt = simpledt_df.datatable  # Extract DataTable instance from simpledt
    
    simpledt_dt.bgcolor = ft.colors.RED # Change background color of generated DataTable
    simpledt_dt.border = ft.border.all(10, ft.colors.PINK_600) # Add ping border to DataTable

    dr = simpledt_df.datarows

    for i in dr:
        rownum = i.cells[0].content.value
        if int(rownum) % 2 == 0:
            i.color = ft.colors.GREEN
        
    dc = simpledt_df.datacolumns

    for i in dc:
        i.label=ft.Row([i.label, ft.Icon(ft.icons.AC_UNIT)])
    

    page.add(simpledt_dt)


ft.app(target=main)

Use shortcut classes

CSVDataTable

import flet as ft
from simpledt import CSVDataTable


def main(page: ft.Page):
    csv = CSVDataTable("https://raw.githubusercontent.com/kb22/Heart-Disease-Prediction/master/dataset.csv")
    dt = csv.datatable

    page.add(dt)


ft.app(target=main)

ExcelDataTable

import flet as ft
from simpledt import ExcelDataTable


def main(page: ft.Page):
    excel = ExcelDataTable('dataset/Excel_MOCK_DATA.xlsx')
    dt = excel.datatable

    page.add(dt)


ft.app(target=main)

SQLDataTable

Serialize everything from users table

import flet as ft
from simpledt import SQLDataTable


def main(page: ft.Page):
    sql = SQLDataTable('sqlite', 'data.db', 'users') # Serialize everything from `users` table
    dt = sql.datatable

    page.add(dt)


ft.app(target=main)

Write a custom query statement

import flet as ft
from simpledt import SQLDataTable


def main(page: ft.Page):
    sql = SQLDataTable('sqlite', 'dataset/data.db', statement="SELECT name, surname, LENGTH(name || surname) as len_name_surname FROM users") # Write a custom query statement
    dt = sql.datatable

    page.add(dt)


ft.app(target=main)

Docs

Classes

Note: you can pass IO to DataFrame constructor as a source argument, but it's recommended to have your pandas dataframe and pass it as a source argument

• DataFrame(source: Union[pd.DataFrame, IO], **kwargs)
  • source: You can pass your own pandas DataFrame as an argument, or any file like 'json', 'excel', 'csv'...
  • **kwargs: you can pass keyword arguments if only IO is provided insted of DataFrame

Shortcut Classes

If you don't want to create your own pandas dataframe, these classes does it for you. You just have to pass source

• CSVDataTable(csv_file: str)

• ExcelDataTable(excel_file: str)

• JsonDataTable(json_file: str)

• SQLDataTable(sql_engine: str, database: str, table: str = None, statement: str = None, user: str = None, password: str = None, host: str = None, port: str = None)

  • Required arguments

    • sql_engine is a literal, you can choose sqlite, mysql, postgresql
    • database is a database binary file, like "data.sqlite3"
    • table or statement, you can choose one of these, but not both. If table is provided, it'll serialize everything from that table. Else, you can write your own custom sql statement

  • Optional arguments

    • user is a username for your choice of sql_engine (sqlite does not need user)
    • password is a password for your choice of sql_engine (sqlite does not need password)
    • host is a host for your choice of sql_engine
    • port is a port for your choice of sql_engine