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chart

Project description

chart

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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)

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