πŸ“Š Charts with pure python

Overview

chart

MIT Travis PyPI Downloads

A zero-dependency python package that prints basic charts to a Jupyter output

Charts supported:

  • Bar graphs
  • Scatter plots
  • Histograms
  • πŸ‘ πŸ“Š πŸ‘

Examples

Bar graphs can be drawn quickly with the bar function:

from chart import bar

x = [500, 200, 900, 400]
y = ['marc', 'mummify', 'chart', 'sausagelink']

bar(x, y)
       marc: β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡             
    mummify: β–‡β–‡β–‡β–‡β–‡β–‡β–‡                       
      chart: β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡
sausagelink: β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡β–‡                              

And the bar function can accept columns from a pd.DataFrame:

from chart import bar
import pandas as pd

df = pd.DataFrame({
    'artist': ['Tame Impala', 'Childish Gambino', 'The Knocks'],
    'listens': [8_456_831, 18_185_245, 2_556_448]
})
bar(df.listens, df.artist, width=20, label_width=11, mark='πŸ”Š')
Tame Impala: πŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”Š           
Childish Ga: πŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”ŠπŸ”Š
 The Knocks: πŸ”ŠπŸ”ŠπŸ”Š                                

Histograms are just as easy:

from chart import histogram

x = [1, 2, 4, 3, 3, 1, 7, 9, 9, 1, 3, 2, 1, 2]

histogram(x)
β–‡        
β–‡        
β–‡        
β–‡        
β–‡ β–‡      
β–‡ β–‡      
β–‡ β–‡      
β–‡ β–‡     β–‡
β–‡ β–‡     β–‡
β–‡ β–‡   β–‡ β–‡

And they can accept objects created by scipy:

from chart import histogram
import scipy.stats as stats
import numpy as np

np.random.seed(14)
n = stats.norm(loc=0, scale=10)

histogram(n.rvs(100), bins=14, height=7, mark='πŸ‘')
            πŸ‘              
            πŸ‘   πŸ‘          
            πŸ‘ πŸ‘ πŸ‘          
            πŸ‘ πŸ‘ πŸ‘          
        πŸ‘   πŸ‘ πŸ‘ πŸ‘          
      πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘    
      πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘ πŸ‘   πŸ‘

Scatter plots can be drawn with a simple scatter call:

from chart import scatter

x = range(0, 20)
y = range(0, 20)

scatter(x, y)
                                       β€’
                                   β€’ β€’  
                                 β€’      
                             β€’ β€’        
                         β€’ β€’            
                       β€’                
                  β€’  β€’                  
                β€’                       
            β€’ β€’                         
        β€’ β€’                             
      β€’                                 
  β€’ β€’                                   
β€’                                       

And at this point you gotta know it works with any np.array:

from chart import scatter
import numpy as np

np.random.seed(1)
N = 100
x = np.random.normal(100, 50, size=N)
y = x * -2 + 25 + np.random.normal(0, 25, size=N)

scatter(x, y, width=20, height=9, mark='^')
^^                  
 ^                  
    ^^^             
    ^^^^^^^         
       ^^^^^^       
        ^^^^^^^     
            ^^^^    
             ^^^^^ ^
                ^^ ^

In fact, all chart functions work with pandas, numpy, scipy and regular python objects.

Preprocessors

In order to create the simple outputs generated by bar, histogram, and scatter I had to create a couple of preprocessors, namely: NumberBinarizer and RangeScaler.

I tried to adhere to the scikit-learn API in their construction. Although you won't need them to use chart here they are for your tinkering:

from chart.preprocessing import NumberBinarizer

nb = NumberBinarizer(bins=4)
x = range(10)
nb.fit(x)
nb.transform(x)
[0, 0, 0, 1, 1, 2, 2, 3, 3, 3]
from chart.preprocessing import RangeScaler

rs = RangeScaler(out_range=(0, 10), round=False)
x = range(50, 59)
rs.fit_transform(x)
[0.0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75, 10.0]

Installation

pip install chart

Contribute

For feature requests or bug reports, please use Github Issues

Inspiration

I wanted a super-light-weight library that would allow me to quickly grok data. Matplotlib had too many dependencies, and Altair seemed overkill. Though I really like the idea of termgraph, it didn't really fit well or integrate with my Jupyter workflow. Here's to chart πŸ₯‚ (still can't believe I got it on PyPI)

Owner
Max Humber
Human
Max Humber
Compute and visualise incidence (reworking of the original incidence package)

incidence2 incidence2 is an R package that implements functions and classes to compute, handle and visualise incidence from linelist data. It refocuss

15 Nov 22, 2022
Simple addon for snapping active object to mesh ground

Snap to Ground Simple addon for snapping active object to mesh ground How to install: install the Python file as an addon use shortcut "D" in 3D view

Iyad Ahmed 12 Nov 07, 2022
Data-FX is an addon for Blender (2.9) that allows for the visualization of data with different charts

Data-FX Data-FX is an addon for Blender (2.9) that allows for the visualization of data with different charts Currently, there are only 2 chart option

Landon Ferguson 20 Nov 21, 2022
An intuitive library to add plotting functionality to scikit-learn objects.

Welcome to Scikit-plot Single line functions for detailed visualizations The quickest and easiest way to go from analysis... ...to this. Scikit-plot i

Reiichiro Nakano 2.3k Dec 31, 2022
Simple, realtime visualization of neural network training performance.

pastalog Simple, realtime visualization server for training neural networks. Use with Lasagne, Keras, Tensorflow, Torch, Theano, and basically everyth

Rewon Child 416 Dec 29, 2022
Python Package for CanvasXpress JS Visualization Tools

CanvasXpress Python Library About CanvasXpress for Python CanvasXpress was developed as the core visualization component for bioinformatics and system

Dr. Todd C. Brett 5 Nov 07, 2022
This package creates clean and beautiful matplotlib plots that work on light and dark backgrounds

This package creates clean and beautiful matplotlib plots that work on light and dark backgrounds. Inspired by the work of Edward Tufte.

Nico SchlΓΆmer 205 Jan 07, 2023
This is a learning tool and exploration app made using the Dash interactive Python framework developed by Plotly

Support Vector Machine (SVM) Explorer This app has been moved here. This repo is likely outdated and will not be updated. This is a learning tool and

Plotly 150 Nov 03, 2022
A simple interpreted language for creating basic mathematical graphs.

graphr Introduction graphr is a small language written to create basic mathematical graphs. It is an interpreted language written in python and essent

2 Dec 26, 2021
Datapane is the easiest way to create data science reports from Python.

Datapane Teams | Documentation | API Docs | Changelog | Twitter | Blog Share interactive plots and data in 3 lines of Python. Datapane is a Python lib

Datapane 744 Jan 06, 2023
Schema validation just got Pythonic

Schema validation just got Pythonic schema is a library for validating Python data structures, such as those obtained from config-files, forms, extern

Vladimir Keleshev 2.7k Jan 06, 2023
GUI for visualization and interactive editing of SMPL-family body models ie. SMPL, SMPL-X, MANO, FLAME.

Body Model Visualizer Introduction This is a simple Open3D-based GUI for SMPL-family body models. This GUI lets you play with the shape, expression, a

Muhammed Kocabas 207 Jan 01, 2023
An animation engine for explanatory math videos

Powered By: An animation engine for explanatory math videos Hi there, I'm Zheer πŸ‘‹ I'm a Software Engineer and student!! 🌱 I’m currently learning eve

Zaheer ud Din Faiz 2 Nov 04, 2021
Homework 2: Matplotlib and Data Visualization

Homework 2: Matplotlib and Data Visualization Overview These data visualizations were created for my introductory computer science course using Python

Sophia Huang 12 Oct 20, 2022
Render tokei's output to interactive sunburst chart.

Render tokei's output to interactive sunburst chart.

134 Dec 15, 2022
In-memory Graph Database and Knowledge Graph with Natural Language Interface, compatible with Pandas

CogniPy for Pandas - In-memory Graph Database and Knowledge Graph with Natural Language Interface Whats in the box Reasoning, exploration of RDF/OWL,

Cognitum Octopus 34 Dec 13, 2022
GitHub Stats Visualizations : Transparent

GitHub Stats Visualizations : Transparent Generate visualizations of GitHub user and repository statistics using GitHub Actions. ⚠️ Disclaimer The pro

YuanYap 7 Apr 05, 2022
A Graph Learning library for Humans

A Graph Learning library for Humans These novel algorithms include but are not limited to: A graph construction and graph searching class can be found

Richard TjΓΆrnhammar 1 Feb 08, 2022
Tools for exploratory data analysis in Python

Dora Exploratory data analysis toolkit for Python. Contents Summary Setup Usage Reading Data & Configuration Cleaning Feature Selection & Extraction V

Nathan Epstein 599 Dec 25, 2022
metedraw is a project mainly for data visualization projects of Atmospheric Science, Marine Science, Environmental Science or other majors

It is mainly for data visualization projects of Atmospheric Science, Marine Science, Environmental Science or other majors.

Nephele 11 Jul 05, 2022