Reasons to Choose Python for Big data?
More and
more data science professionals are using Python for big data. Your choice of
this appropriate programming language for large projects is a crucial decision,
since it is difficult to migrate a project once development begins.
When they
bet on Python for big data they make it aware that, although there are other
popular options, such as R, Python, Java or SAS, they prefer this alternative
for its benefits for the analysis of large data.
Although the
choice of programming language depends on the case of individual use, there are
many reasons that support Python as an ideal option for Big Data.
5 reasons to choose Python for Big
Data projects
If you doubt
between using R, Java or Python
for big data, after reading the following arguments you may decide on the
last of these languages. In favor of Python reasons such as:
1.
Simplicity Python is known for making
programs work in the least amount of lines of code .
Automatically
identifies and associates data types and, in general, is an easy-to-use
language and takes less time to encode There is also no limitation for data
processing. You can calculate data on any type of equipment and environment,
basically everywhere. Previously it was argued that Python was slower than some
of its counterparts such as Java and Scala, but with the python platform it has
been updated demonstrating that it is fast in both development and execution.
2.Compatibility.
Hadoop is
the most popular open source big data platform and Python's inherent
compatibility is yet another reason to prefer it to other languages .
3.Ease of learning Compared to other languages, Python is easy to learn
even for less experienced programmers.
It is an ideal first language due to three
main reasons: it has extensive learning resources, guarantees a readable code
and is surrounded by a large community. All this translates into a gradual
learning curve with the direct application of concepts in real-world programs.
The large Python community offers the security of knowing that, if there are
problems in development, there will be others who can lend a hand to help solve
them.
4.Powerful packages Python has a powerful set of packages for a wide
range of analysis and data science needs.
Some of the
popular packages that give this language an advantage are NumPy, Pandas, Scipy,
Scikit-learn, PyBrain, Tensorflow,
Cython, PyMySQL, BeautifulSoup or iPython.
5.Data visualization Although R is better when it comes to data
visualization, with recent packages, Python for big data has improved its offer
in this space. Now
there are APIs that can offer good results.
Python is a
very popular language as can be seen in any team of data scientists. It is
always easy to find some people in each department such as marketing,
development, maintenance, customer service with a practical knowledge of
Python, which is the best insurance for companies. It is not always easy to
establish a communication between the different departments and, with Python
and big data, this type of inconvenience does not exist.
It is most widely used language, including by a number of big
top companies like Google, Pinterest, Instagram, Disney, Yahoo!, Nokia, IBM,
and so many others. The Raspberry Pi - which is a mini computer and DIY lover's
dream - relies on this as it's must programming language too. Even If you also
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