A zero-dependency python package that prints basic charts to a Jupyter output
Charts supported:
- Bar graphs
- Scatter plots
- Histograms
-
🍑 📊 👏
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: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇
mummify: ▇▇▇▇▇▇▇
chart: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇
sausagelink: ▇▇▇▇▇▇▇▇▇▇▇▇▇
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='🔊')
Tame Impala: 🔊🔊🔊🔊🔊🔊🔊🔊🔊
Childish Ga: 🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊
The Knocks: 🔊🔊🔊
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)
▇
▇
▇
▇
▇ ▇
▇ ▇
▇ ▇
▇ ▇ ▇
▇ ▇ ▇
▇ ▇ ▇ ▇
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='🍑')
🍑
🍑 🍑
🍑 🍑 🍑
🍑 🍑 🍑
🍑 🍑 🍑 🍑
🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑
🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑
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)
•
• •
•
• •
• •
•
• •
•
• •
• •
•
• •
•
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
0 May 04, 2022
4 Apr 14, 2022
2 Nov 15, 2021
670 Jan 09, 2023
185 Jan 06, 2023
2 Dec 13, 2021
3 May 14, 2022
12.7k Jan 05, 2023
312 Jan 07, 2023
7 Sep 20, 2022
138 Dec 06, 2022
27 Sep 03, 2022
12 Oct 20, 2022
15 Nov 22, 2022
2.9k Dec 28, 2022
3.3k Dec 27, 2022
249 Jan 06, 2023
11 Dec 25, 2022
1.6k Jan 08, 2023
3.3k Dec 31, 2022