Install Jupyter notebook

$ pip3 install jupyter

Install PySpark

Make sure you have Java 8 or higher installed on your computer and visit the Spark download page

Select the latest Spark release, a prebuilt package for Hadoop, and download it directly.

Unzip it and move it to your /opt folder:

$ tar -xzf spark-2.4.0-bin-hadoop2.7.tgz
$ sudo mv spark-2.4.0-bin-hadoop2.7 /opt/spark-2.4.0

A symbolic link is like a shortcut from one file to another. The contents of a symbolic link are the address of the actual file or folder that is being linked to.

Create a symbolic link (this will let you have multiple spark versions):

$ sudo ln -s /opt/spark-2.4.0 /opt/spark̀

Check that the link was indeed created

$ ls -l /opt/spark̀

lrwxr-xr-x  1 root  wheel  16 Dec 26 15:08 /opt/spark̀ -> /opt/spark-2.4.0

Finally, tell your bash where to find Spark. To find what shell you are using, type:

$ echo $SHELL
/bin/bash

To do so, edit your bash file:

$ nano ~/.bash_profile

configure your $PATH variables by adding the following lines to your ~/.bash_profile file:

export SPARK_HOME=/opt/spark
export PATH=$SPARK_HOME/bin:$PATH
# For python 3, You have to add the line below or you will get an error
export PYSPARK_PYTHON=python3

Now to run PySpark in Jupyter you’ll need to update the PySpark driver environment variables. Just add these lines to your ~/.bash_profile file:

export PYSPARK_DRIVER_PYTHON=jupyter
export PYSPARK_DRIVER_PYTHON_OPTS='notebook'

Your ~/.bash_profile file may look like this:

Restart (our just source) your terminal and launch PySpark:

$ pyspark

This command should start a Jupyter Notebook in your web browser. Create a new notebook by clicking on ‘New’ > ‘Notebooks Python [default]’.

Running PySpark in Jupyter Notebook

The PySpark context can be

sc = SparkContext.getOrCreate()

To check if your notebook is initialized with SparkContext, you could try the following codes in your notebook:

sc = SparkContext.getOrCreate()
import numpy as np
TOTAL = 10000
dots = sc.parallelize([2.0 * np.random.random(2) - 1.0 for i in range(TOTAL)]).cache()
print("Number of random points:", dots.count())
stats = dots.stats()
print('Mean:', stats.mean())
print('stdev:', stats.stdev())

The result:

Running PySpark in your favorite IDE

Sometimes you need a full IDE to create more complex code, and PySpark isn’t on sys.path by default, but that doesn’t mean it can’t be used as a regular library. You can address this by adding PySpark to sys.path at runtime. The package findspark does that for you.

To install findspark just type:

$ pip3 install findspark

And then on your IDE (I use Eclipse and Pydev) to initialize PySpark, just call:

import findspark
findspark.init()
import pyspark
sc = pyspark.SparkContext(appName="myAppName")

Here is a full example of a standalone application to test PySpark locally 

import findspark
findspark.init()
import random
from pyspark import SparkContext
sc = SparkContext(appName="EstimatePi")
def inside(p):
x, y = random.random(), random.random()
return x<em>x + y</em>y &lt; 1
NUM_SAMPLES = 1000000
count = sc.parallelize(range(0, NUM_SAMPLES)) \
.filter(inside).count()
print("Pi is roughly %f" % (4.0 * count / NUM_SAMPLES))
sc.stop()

The result:

Enjoy!

Based on this article and on this article