Python charting libraries¶

This workbook details example of interfacing PyKX with Python charting libraries.

PyKX supports rich datatype mapping meaning you can convert data from PyKX objects to:

• Python objects using .py()
• NumPy objects using .np()
• Pandas objects using .pd()
• PyArrow objects using .pa()

The full breakdown of how these map is documented here.

The resulting objects behave as expected with all Python libraries.

For efficiency and exactness the examples below aim to use PyKX objects directly, minimising conversions when possible.

In [2]:

import pykx as kx
tab = kx.Table(data={
    'sym':kx.random.random(1000, ['a', 'b', 'c']), 
    'price':kx.random.random(1000, 1.0), 
    'size':kx.random.random(1000, 10),
    'quantity':kx.random.random(1000,100),
    'in_stock':kx.random.random(1000, [True, False])})
tab.head()

import pykx as kx tab = kx.Table(data={ 'sym':kx.random.random(1000, ['a', 'b', 'c']), 'price':kx.random.random(1000, 1.0), 'size':kx.random.random(1000, 10), 'quantity':kx.random.random(1000,100), 'in_stock':kx.random.random(1000, [True, False])}) tab.head()

Out[2]:

	sym	price	size	quantity	in_stock
0	a	0.9094126	4	5	1b
1	a	0.2988477	5	18	1b
2	c	0.454063	8	11	0b
3	b	0.156942	1	36	1b
4	c	0.04699265	4	43	1b

Matplotlib¶

Let's generate a scatter plot using the price and size columns of our table.

Use the scatter(tab['price'], tab['quantity']) notation to access PyKX objects directly.

The x= and y= syntax requires conversion to a dataframe using .pd() .i.e scatter(tab.pd(), x='price' ,y='quantity')

scatter uses a series of 1D arrays and one of the only charts where you don't need to start by converting the column values in NumPy objects using .np().

In [3]:

import matplotlib.pyplot as plt

plt.scatter(tab['price'], tab['quantity'])
plt.show()

import matplotlib.pyplot as plt plt.scatter(tab['price'], tab['quantity']) plt.show()

To make the column values compatible with most Matplotlib charts, first convert them to NumPy objects using the .np() function.

In [4]:

plt.bar(tab['size'].np(), tab['price'].np())
plt.show()

plt.bar(tab['size'].np(), tab['price'].np()) plt.show()

Plotly¶

Plotly allows you to pass vector objects as the color argument. Set this parameter using the sym column to obtain the scatter chart below.

In [5]:

import plotly.express as px

fig = px.scatter(
    x=tab['quantity'],
    y=tab['price'],
    size=tab['size'],
    color=tab['sym'])
fig.show(renderer="png")

import plotly.express as px fig = px.scatter( x=tab['quantity'], y=tab['price'], size=tab['size'], color=tab['sym']) fig.show(renderer="png")