How to execute a * .PY file from a * .IPYNB file on the Jupyter notebook?

I am working on a Python Notebook and I would like that large input code [input] pack into a [* .PY] files and call this files from the notebook.

The action of running a [.PY] file from the Notebook is known to me and the command varies between Linux or Windows. But when I do this action and execute the [.PY] file from the notebook, it does not recognize any existing library or variable loaded in the notebook (it’s like the [.PY] file start from zero…).

Is there any way to fix this?

A possible simplified example of the problem would be the following:

In[1]:
import numpy as np
import matplotlib.pyplot as plt

In[2]:
def f(x):
    return np.exp(-x ** 2)

In[3]:
x = np.linspace(-1, 3, 100)

In[4]:
%run script.py

Where “script.py” has the following content:

plt.plot(x, f(x))
plt.xlabel("Eje $x$",fontsize=16)
plt.ylabel("$f(x)$",fontsize=16)
plt.title("Funcion $f(x)$")

• In the real problem, the file [* .PY] does not have 4 lines of code, it has enough more.

Answers:

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Method 1

In the %run magic documentation you can find:

-i run the file in IPython’s namespace instead of an empty one. This is useful if you are experimenting with code written in a text editor which depends on variables defined interactively.

Therefore, supplying -i does the trick:

%run -i 'script.py'

The “correct” way to do it

Maybe the command above is just what you need, but with all the attention this question gets, I decided to add a few more cents to it for those who don’t know how a more pythonic way would look like.
The solution above is a little hacky, and makes the code in the other file confusing (Where does this x variable come from? and what is the f function?).

I’d like to show you how to do it without actually having to execute the other file over and over again.
Just turn it into a module with its own functions and classes and then import it from your Jupyter notebook or console. This also has the advantage of making it easily reusable and jupyters contextassistant can help you with autocompletion or show you the docstring if you wrote one.
If you’re constantly editing the other file, then autoreload comes to your help.

Your example would look like this:
script.py

import matplotlib.pyplot as plt

def myplot(f, x):
    """
    :param f: function to plot
    :type f: callable
    :param x: values for x
    :type x: list or ndarray

    Plots the function f(x).
    """
    # yes, you can pass functions around as if
    # they were ordinary variables (they are)
    plt.plot(x, f(x))
    plt.xlabel("Eje $x$",fontsize=16)
    plt.ylabel("$f(x)$",fontsize=16)
    plt.title("Funcion $f(x)$")

Jupyter console

In [1]: import numpy as np

In [2]: %load_ext autoreload

In [3]: %autoreload 1

In [4]: %aimport script

In [5]: def f(x):
      :     return np.exp(-x ** 2)
      :
      :

In [6]: x = np.linspace(-1, 3, 100)

In [7]: script.myplot(f, x)

In [8]: ?script.myplot
Signature: script.myplot(f, x)
Docstring:
:param f: function to plot
:type f: callable
:param x: x values
:type x: list or ndarray
File:      [...]script.py
Type:      function

Method 2

the below lines would also work

!python script.py

Method 3

Maybe not very elegant, but it does the job:

exec(open("script.py").read())

Method 4

!python ‘script.py’

replace script.py with your real file name, DON’T forget ”

Method 5

Also the same can be done by just calling the module using the %run magic as follows:

%run -m script

where -m is your module i.e. script.py but using this just supplying script would also do the job.

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0