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
Lumen provides a framework for visual analytics, which allows users to build data-driven dashboards from a simple yaml specification

Lumen project provides a framework for visual analytics, which allows users to build data-driven dashboards from a simple yaml specification

HoloViz 120 Jan 04, 2023
This is a small program that prints a user friendly, visual representation, of your current bsp tree

bspcq, q for query A bspc analyzer (utility for bspwm) This is a small program that prints a user friendly, visual representation, of your current bsp

nedia 9 Apr 24, 2022
ecoglib: visualization and statistics for high density microecog signals

ecoglib: visualization and statistics for high density microecog signals This library contains high-level analysis tools for "topos" and "chronos" asp

1 Nov 17, 2021
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
An interactive UMAP visualization of the MNIST data set.

Code for an interactive UMAP visualization of the MNIST data set. Demo at https://grantcuster.github.io/umap-explorer/. You can read more about the de

grant 70 Dec 27, 2022
Fast 1D and 2D histogram functions in Python

About Sometimes you just want to compute simple 1D or 2D histograms with regular bins. Fast. No nonsense. Numpy's histogram functions are versatile, a

Thomas Robitaille 237 Dec 18, 2022
Bokeh Plotting Backend for Pandas and GeoPandas

Pandas-Bokeh provides a Bokeh plotting backend for Pandas, GeoPandas and Pyspark DataFrames, similar to the already existing Visualization feature of

Patrik Hlobil 822 Jan 07, 2023
WhatsApp Chat Analyzer is a WebApp and it can be used by anyone to analyze their chat. πŸ˜„

WhatsApp-Chat-Analyzer You can view the working project here. WhatsApp chat Analyzer is a WebApp where anyone either tech or non-tech person can analy

Prem Chandra Singh 26 Nov 02, 2022
A small script written in Python3 that generates a visual representation of the Mandelbrot set.

Mandelbrot Set Generator A small script written in Python3 that generates a visual representation of the Mandelbrot set. Abstract The colors in the ou

1 Dec 28, 2021
Visualize the bitcoin blockchain from your local node

Project Overview A new feature in Bitcoin Core 0.20 allows users to dump the state of the blockchain (the UTXO set) using the command dumptxoutset. I'

18 Sep 11, 2022
Dipto Chakrabarty 7 Sep 06, 2022
Interactive plotting for Pandas using Vega-Lite

pdvega: Vega-Lite plotting for Pandas Dataframes pdvega is a library that allows you to quickly create interactive Vega-Lite plots from Pandas datafra

Altair 342 Oct 26, 2022
Tandem Mass Spectrum Prediction with Graph Transformers

MassFormer This is the original implementation of MassFormer, a graph transformer for small molecule MS/MS prediction. Check out the preprint on arxiv

RΓΆst Lab 13 Oct 27, 2022
Some problems of SSLC ( High School ) before outputs and after outputs

Some problems of SSLC ( High School ) before outputs and after outputs 1] A Python program and its output (output1) while running the program is given

Fayas Noushad 3 Dec 01, 2021
Geospatial Data Visualization using PyGMT

Example script to visualize topographic data, earthquake data, and tomographic data on a map

Utpal Kumar 2 Jul 30, 2022
Data Visualizer for Super Mario Kart (SNES)

Data Visualizer for Super Mario Kart (SNES)

MrL314 21 Nov 20, 2022
Render tokei's output to interactive sunburst chart.

Render tokei's output to interactive sunburst chart.

134 Dec 15, 2022
The implementation of the paper "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information".

The HIST framework for stock trend forecasting The implementation of the paper "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining C

Wentao Xu 111 Jan 03, 2023
This project is created to visualize the system statistics such as memory usage, CPU usage, memory accessible by process and much more using Kibana Dashboard with Elasticsearch.

System Stats Visualizer This project is created to visualize the system statistics such as memory usage, CPU usage, memory accessible by process and m

Vishal Teotia 5 Feb 06, 2022
A set of useful perceptually uniform colormaps for plotting scientific data

Colorcet: Collection of perceptually uniform colormaps Build Status Coverage Latest dev release Latest release Docs What is it? Colorcet is a collecti

HoloViz 590 Dec 31, 2022