Popularity
1.2
Stable
Activity
0.1
Declining
49
2
5

Description

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

Programming language: Python
License: MIT License

chart alternatives and similar packages

Based on the "Data Visualization" category.
Alternatively, view chart alternatives based on common mentions on social networks and blogs.

Do you think we are missing an alternative of chart or a related project?

Add another 'Data Visualization' Package

README

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

Charts supported:

  • Bar graphs
  • Scatter plots
  • Histograms
  • ๐Ÿ‘๐Ÿ“Š๐Ÿ‘

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: โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡             
    mummify: โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡                       
      chart: โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡
sausagelink: โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡โ–‡                              

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='๐Ÿ”Š')
Tame Impala: ๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š           
Childish Ga: ๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š๐Ÿ”Š
 The Knocks: ๐Ÿ”Š๐Ÿ”Š๐Ÿ”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)
โ–‡        
โ–‡        
โ–‡        
โ–‡        
โ–‡ โ–‡      
โ–‡ โ–‡      
โ–‡ โ–‡      
โ–‡ โ–‡     โ–‡
โ–‡ โ–‡     โ–‡
โ–‡ โ–‡   โ–‡ โ–‡

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='๐Ÿ‘')
            ๐Ÿ‘              
            ๐Ÿ‘   ๐Ÿ‘          
            ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘          
            ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘          
        ๐Ÿ‘   ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘          
      ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘    
      ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘ ๐Ÿ‘   ๐Ÿ‘

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)
                                       โ€ข
                                   โ€ข โ€ข  
                                 โ€ข      
                             โ€ข โ€ข        
                         โ€ข โ€ข            
                       โ€ข                
                  โ€ข  โ€ข                  
                โ€ข                       
            โ€ข โ€ข                         
        โ€ข โ€ข                             
      โ€ข                                 
  โ€ข โ€ข                                   
โ€ข                                       

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 ๐Ÿฅ‚ (still can't believe I got it on PyPI)


*Note that all licence references and agreements mentioned in the chart README section above are relevant to that project's source code only.