streamlit-controllerDF 0.1.3

A solid base for controlling your data frame, getting quick metrics, and data visualizations using streamlit, pandas, numpy and matplotlib.

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Controller DF

A python library which creates a simple and easy to use data frame controller. Using this library, along with streamlit and minimal (included) code, anyone can spin up a web app which allows you to control, manipulate and display a data set quickly and easily.

Demo

Quick column metrics Rapid column filter Instant type based column widgets

Installation

$ pip install streamlit-controllerDF

Getting started

After you pip install the module

Batteries included method:

Quick start

• Copy the included test_code.py contents
• test_code here click me
• Create a new python file and paste the contents of test_code.py into it
• Name the file something you like and then:

$ streamlit run your_project.py 

• Drag and drop csv file
• Enjoy!

Batteries excluded method:

Module only

import streamlit_controllerDF as sc

• see documentation for usage

Documentation

class streamlit_controllerDF.Widgets(dataframe, omit_columns=list()) Parameters: dataframe: A pandas data frame Two-dimensional, size-mutable, potentially heterogeneous tabular data. omit_columns: A list of column names to be excluded The column names must be exact Example import streamlit_controllerDF as sc import pandas as pd mydf = pd.read_csv('mycsv.csv') ctrldf = sc.Widgets(mydf,omit_columns=['Engine_Size', 'Year']) method streamlit_controllerDF.Widgets.metrics() Parameters: None Example import streamlit_controllerDF as sc import pandas as pd mydf = pd.read_csv('mycsv.csv') ctrldf = sc.Widgets(mydf,omit_columns=['Engine_Size', 'Year']) ctrldf.metrics()

Limitations

• This library is currently limited to support only files under 20MB
• Due to browser limitations only 12000 rows of data can be viewed at a time

To Do

This library is the base of a much larger project.

• Create a chart method which will populate various charts automatically
• Create a model method which will populate various ML models automatically
• Add support for automated api data import
• Add support for relational and non relational data bases
• Add support for automated queries
• Add support for big data
• Create large file size detection and implement chunking automatically
• Migrate from Pandas to Dask
• After Dask migration remove file size limitation

Thank you for viewing my project sincerely