Data is an incredibly broad topic but it can be broken down into many subsections, including (in no particular order):
The Python community has built and continues to create open source libraries and tutorials for all of the above topics.
Python has a wide array of open source code libraries available and a diverse community of people with different backgrounds who contribute to make those libraries better each day.
In addition, Python data manipulation code can be combined with web frameworks and web APIs to build software that would be difficult to create with a single other language. For example, Ruby is a fantastic language for building web applications but its data analysis and visualization libraries are very limited compared to what is currently available in the Python ecosystem.
Python is a general purpose programming language and can be applied to many problem areas. Over the past couple of decades, Python has become increasingly popular in the scientific and financial communities. Projects such as pandas grew out of a hedge-fund while NumPy and SciPy were created in academic environments then improved by the broader open source community.
The question is: why Python was used to created these projects? The answer is a mix of luck, the growth of the open source community as Python was maturing and wide adoption by people not formally trained as computer scientists. The pragmatic syntax and explicit style helped very intelligent people without programming backgrounds to pick up the language and get their work done with less fuss than other programming languages. Over time the code used in the financial world and scientific community was shared at the same time global open source communities were developing, further spreading their usage among a broader base of software developers.
There's no doubt some of the momentum behind Python's wide adoption for all types of data manipulation was that it happened to be the right language in the right place at the right time. Nevertheless, it was ultimately the hard work of a massive number of engineers and scientists around the world who created the incredible mix of data code libraries available today.
PyData is a community for developer and users of Python data tools. They put on fantastic conferences around the world and fund the continued development of open source data-related libraries.
Continuum Analytics is one of the leading Python companies that pours a tremendous amount of time and funding into the data community.
A crash course in Python for scientists provides an overview of the Python language with iPython Notebook for those in scientific fields.
The videos of Travis Oliphant on Python's Role in Big Data Analytics: Past, Present, and Future and Building the PyData Community give historical perspective on how the Python data tools have evolved over the past 20ish years based on his first-hand experience as a leader and member in that community.
Secure and manage identities in your Python web apps with Okta.
Easily build maps, search and navigation into your Python applications with Mapbox.
Scout monitors the performance of your Python apps, identifying slow queries, memory bloat, and more. Free during Tech Preview.