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Welcome to Kite's JupyterLab extension tutorial¶

Kite gives you ML-powered autocompletions and rich documentation inside JupyterLab. This guide will teach you everything you need to know about Kite in 5 minutes or less.

💡 Tip: You can open this file at any time with the command Kite: Open Tutorial in JupyterLab's command palette.

Before we start...¶

Make sure that the Kite icon at the bottom of the window reads Kite: ready.

Kite icon

If it says Kite: not running, please start the Kite Engine first.
If it says Kite: not installed, please download and install Kite first.

Part 1: Autocompletions¶

Step 1a
Run the code cell below with all the necessary imports 👇

In [1]:

# Run me!
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

Matplotlib is building the font cache; this may take a moment.

Step 1b
Let's try typing out some code to plot a sine graph. As you type, Kite will automatically show you completions for what you're going to type next.

Autocompletions

💡 Tip: You can turn completions docs on and off in JupyterLab's command palette with the command Kite: Toggle Docs Panel.

💡 Tip: The starred completions ★ are from Kite Pro. You can start your free Kite Pro trial anytime. Afterwards, if you choose not to upgrade, you can still use Kite 100% for free.

Try typing out the code yourself to see Kite's autocompletions in action.

x = np.linspace(-np.pi, np.pi, 50)
y = np.sin(x)
plt.plot(x, y)

Type this code in the cell below 👇

In [3]:

# Put code in me
x = np.linspace(-np.pi, np.pi, 50)
y = np.sin(x)
plt.plot(x, y)

Out[3]:

[<matplotlib.lines.Line2D at 0x7fa4200e4b90>]

Part 2: Manual completions¶

You can still use JupyterLab's builtin kernel completions. These are particularly useful when you need to access a DataFrame's column names.

Step 2a
First, run the code cell below to get some sample data to store in a DataFrame 👇

In [4]:

# Run me!
url = 'https://kite.com/kite-public/iris.csv'
df = pd.read_csv(url)
df.head()

Out[4]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

Step 2b
Let's plot a scatter graph of sepal length vs. sepal width. When you are accessing a DataFrame's columns, you'll still need to hit tab to request completions from the kernel.

Manual completions

Try requesting kernel completions yourself.

plt.scatter(df['sepal_length'], df['sepal_width'])

Type this code in the cell below, making sure to hit tab when you are filling in the column names 👇

In [1]:

# Put code in me
plt.scatter(df['sepal_length'], df['sepal_width'])

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-81be8620ec67> in <module>
      1 # Put code in me
----> 2 plt.scatter(df['sepal_length'], df['sepal_width'])

NameError: name 'plt' is not defined

Part 3: Copilot Documentation¶

If you've enabled "docs following cursor" in the Copilot, the Copilot will automatically update with the documentation of the identifier underneath your cursor.

Autosearch

Step 3a
Try it yourself! Just click around in the code cells of this notebook and see the Copilot update automatically.

The End¶

Now you know everything you need to know about Kite's JupyterLab plugin. Kite is under active development and we expect to ship improvements and more features in the near future.

In the meantime, if you experience bugs or have feature requests, feel free to open an issue in our public GitHub repo.

Happy coding!