Jupyter - Hotkeys

Question 1

Esc

Answer 1

Esc will take you into command mode where you can navigate around your notebook with arrow keys.

Question 2

A

Answer 2

A to insert a new cell above the current cell

Question 3

B

Answer 3

B to insert a new cell below

Question 4

M

Answer 4

M to change the current cell to Markdown

Question 5

Y

Answer 5

Y to change it back to code

Question 6

D + D

Answer 6

D + D (press the key twice) to delete the current cell

Question 7

Enter

Answer 7

Enter will take you from command mode back into edit mode for the given cell.

Question 8

Shift + Tab

Answer 8

Shift + Tab will show you the Docstring (documentation) for the the object you have just typed in a code cell - you can keep pressing this short cut to cycle through a few modes of documentation.

Question 9

Ctrl + Shift + -

Answer 9

Ctrl + Shift + - will split the current cell into two from where your cursor is

Question 10

Esc + F

Answer 10

Esc + F Find and replace on your code but not the outputs.

Question 11

Esc + O Toggle cell output.

Answer 11

Toggle cell output.

Question 12

Shift + Down

Shift + Up

Answer 12

Shift + J or Shift + Down selects the next sell in a downwards direction. You can also select sells in an upwards direction by using Shift + K or Shift + Up.

(Once cells are selected, you can then delete / copy / cut / paste / run them as a batch. This is helpful when you need to move parts of a notebook.)

Question 13

Shift + M

Answer 13

You can also use Shift + M to merge multiple cells.

Question 14

?[insert library, method, or variable here]

Answer 14

Don’t forget also that by prepending a library, method or variable with ?, you can access the Docstring for quick reference on syntax.

Question 15

%run [insert .py or .ipynb file here]

Answer 15

%run can execute python code from .py files - this is well-documented behavior. Lesser known is the fact that it can also execute other jupyter notebooks, which can quite useful.

Question 16

%load [insert script file or URL here]

Answer 16

This will replace the contents of the cell with an external script. You can either use a file on your computer as a source, or alternatively a URL.

Question 17

%%time

Answer 17

%%time will give you information about a single run of the code in your cell.

Question 18

%%timeit

Answer 18

%%timeit uses the Python timeit module which runs a statement 100,000 times (by default) and then provides the mean of the fastest three times.

Question 19

%%writefile

%pycat

Answer 19

Using the %%writefile magic saves the contents of that cell to an external file. %pycat does the opposite, and shows you (in a popup) the syntax highlighted contents of an external file.

Question 20

%pdb

Answer 20

Jupyter has own interface for The Python Debugger (pdb). This makes it possible to go inside the function and investigate what happens there.

Question 21

adding a ; to end of line

Answer 21

Sometimes it’s handy to suppress the output of the function on a final line, for instance when plotting. To do this, you just add a semicolon at the end.

Question 22

It’s easy to execute a shell command from inside your notebook. You can use this to check what datasets are in available in your working folder…

Answer 22

!ls *.csv

Question 23

It’s easy to execute a shell command from inside your notebook…like to check and manage packages

Answer 23

!pip install numpy

!pip list | grep pandas

Question 24

Just use IPython Magics with the name of your kernel at the start of each cell that you want to use that Kernel for…

Answer 24

%%bash

%%HTML

%%python2

%%python3

%%ruby

%%perl

Question 25

Running R and Python in the same notebook.

Answer 25

The best solution to this is to install rpy2 (requires a working version of R as well), which can be easily done with pip.

pip install rpy2

You can then use the two languages together, and even pass variables inbetween:

%load_ext rpy2.ipython

%R require(ggplot2)

array([1], dtype=int32)

import pandas as pd
df = pd.DataFrame({
‘Letter’: [‘a’, ‘a’, ‘a’, ‘b’, ‘b’, ‘b’, ‘c’, ‘c’, ‘c’],
‘X’: [4, 3, 5, 2, 1, 7, 7, 5, 9],
‘Y’: [0, 4, 3, 6, 7, 10, 11, 9, 13],
‘Z’: [1, 2, 3, 1, 2, 3, 1, 2, 3]
})

%%R -i df
ggplot(data = df) + geom_point(aes(x = X, y= Y, color = Letter, size = Z))