> For the complete documentation index, see [llms.txt](https://george-jen.gitbook.io/data-science-and-apache-spark/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://george-jen.gitbook.io/data-science-and-apache-spark/python-with-apache-spark-using-jupyter-notebook.md).

# Python with Apache Spark using Jupyter notebook

### Python with Apache Spark using Jupyter notebook

Now let’s run the Python version of pi program. Start Anaconda Navigator, select Virtual Environment spark

![](/files/-M1fiKSo5cmLjTh1wX99)

Click Jupyter Notebook

In the Jupyter Notebook, need to import findspark and run findspark.init(), which will find where the SPARK\_HOME points to.

Following is the Python script that runs pi.py, you can simply run:

python pi.py

```
#!/usr/bin/env python
# coding: utf-8
from __future__ import print_function
import findspark
findspark.init()
import sys
from random import random
from operator import add
from pyspark.sql import SparkSession
spark =SparkSession.builder.appName("PythonPi").getOrCreate()
partitions = 1
n = 100000 * partitions
def f(_):
    x = random() * 2 - 1
    y = random() * 2 - 1
    return 1 if x ** 2 + y ** 2 <= 1 else 0
count = spark.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add)
print("Pi is roughly %f" % (4.0 * count / n))
spark.stop()
```