Are Hadoop and Python SciPy used for the same? Right now

I've been doing a bit of research because I'm quite new to Big Data world. Among several other tools or frameworks, I've read about Apache Hadoop and Python for data analysis.

Specifically, I've read that:

Hadoop: allows you to perform any task you want, if it fits the Map-Reduce paradigm. It can add concurrence using a cluster, etc.

SciPy: python based ecosystem for (in fact, a lot of things)...

So my question is

If I've data collected from an environment, and want to correlate the data, calculate means, extract conclusions, etc...

Is SciPy as suitable as Hadoop (or more) to do so?

I'm basically trying to choose between them in order to build a little example, so I wonder which one fits better, or may I be misunderstanding the uses of each one.
I think you're quite confused.  

Hadoop is a collection of software that contains a a distributed file system called HDFS. Essentially HDFS is a way to store data cross a cluster.  You can access file stores as you would in a local file store (with some modification) and modify things via Java API.  Furthermore, ON TOP OF the file system there exist a MapReduce engine that allows for distributive workflow. 

Python on the other hand is a generic programming language that can be made to do a myriad of task such as build a web applciation, to generating reports and even peforming analytics.  

SciPy is a package that can be used in conjunction with Python (and often numpy) as a way to perform common scientific task.

Truthfully, they focus on different paradigms.  If you have LARGE DATA (ie terabytes worth of it), it might be worth wild to setup a hadoop cluster (ie multiple servers and racks) and use Java MapReduce, Hive, Pig or Spark (of which there is a python version) to do analytics.  

If your data is small or you only have one computer, then it probably makes sense to just use python instead of adding the overhead of setting up hadoop.

Edit:  Made correction via comment.