#How to install pyspark with pip how to#

If you installed Python any other way (from source, using pyenv, virtualenv, etc.), then use pip to install Python packagesДЇinally, because it often comes up, I should mention that you should never use sudo pip install. If conda tells you the package you want doesn't exist, then use pip (or try conda-forge, which has more packages available than the default conda channel). If you installed Python using Anaconda or Miniconda, then use conda to install Python packages. If you already have a Python installation that you're using, then the choice of which to use is easy:

conda installs any package in conda environments.

pip installs python packages in any environment.

I wrote way more than you ever want to know about these in a post last year, but the essential difference between the two is this:

Other package managers exist (including platform-specific tools like yum, apt, homebrew, etc., as well as cross-platform tools like enstaller), but I'm less familiar with them and won't be remarking on them further.ДЇor many users, the choice between pip and conda can be a confusing one. This post will focus on two approaches to installing Python packages: pip and conda. Third, I'll talk about some ideas the community might consider to help smooth-over these issues, including some changes that the Jupyter, Pip, and Conda developers might consider to ease the cognitive load on users. Second, I'll dive into some of the background of exactly what the Jupyter notebook abstraction is doing, how it interacts with the complexities of the operating system, and how you can think about where the 'leaks' are, and thus better understand what's happening when things stop working. In the wake of several discussions on this topic with colleagues, some online ( exhibit A, exhibit B) and some off, I decided to treat this issue in depth here.ДЇirst, I'll provide a quick, bare-bones answer to the general question, how can I install a Python package so it works with my jupyter notebook, using pip and/or conda?. In other words, the Jupyter notebook, like all abstractions, is leaky. In the simplest contexts this issue does not arise, but when it does, debugging the problem requires knowledge of the intricacies of the operating system, the intricacies of Python package installation, and the intricacies of Jupyter itself. etc.).ДЇundamentally the problem is usually rooted in the fact that the Jupyter kernels are disconnected from Jupyter's shell in other words, the installer points to a different Python version than is being used in the notebook. this, that, here, there, another, this one, that one, and this.

This issue is a perrennial source of StackOverflow questions (e.g. I installed package X and now I can't import it in the notebook. I most often see this manifest itself with the following issue:

#How to install pyspark with pip how to#

How to access the spark process in ps commands?Д®.g.,root 13770 1 0 14:18 pts/0 00:00:10 /opt/java/jdk1.8.0_201/bin/java -cp /opt/spark/conf/:/opt/spark/jars/* -Xmx1g .master.Master -host test.In software, it's said that all abstractions are leaky, and this is true for the Jupyter notebook as it is for any other software.

opt/java/jdk1.8.0_201/bin/java -cp /opt/spark/conf/:/opt/spark/jars/* -Xmx1g .SparkSubmit -name PySparkShell pyspark-shell Spark Command: /opt/java/jdk1.8.0_201/bin/java -cp /opt/spark/conf/:/opt/spark/jars/* -Xmx1g .master.Master -host -port 7077 -webui-port 8080ДЇYI, This internally runs command like below: Getting 'Unsupported major.minor version 52.0' exception while usingД¬heck Java version of jar files (/opt/spark/jars) and your installed java Export export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.10.4-src.zip:$SPARK_HOME/python/lib/pyspark.zip:$PYTHONPATH