Python in data science
Python the best choice over the many other programming languages available. Although it is a general-purpose programming language, it's becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. The ability to analyze data with Python is not an easy task, it's critical in data science. Python is a very powerful programming language used for many different applications. In recent years, the huge community around this open source language has created quite a few tools to effectively work with Python. A number of tools have been built specifically for data science. As a result, analyzing data with Python has been easier. The tools you choose depends on the requirements you need for coding. This language is synonymous with flexibility, powerful yet easy to use features. Python has its USP in the rich set of utilities and the libraries it offers for analytics and data processing tasks.
Python has different data structures that help in making the code. Some of the data structures are -
Tuples - Tuples are described by the elements or values separated by commas. The values in the tuple cannot be changed or modified. They work much quicker than lists.
Lists - Lists are flexible data structures of Python that have the features to change each element of the list. A list can be described by writing a list of elements or values separated by the comma within the square brackets.
Dictionary - Dictionary is an unordered set of keys. The keys need to be unique to make the set as the dictionary. A dictionary contains a set of unique values. An empty dictionary is made up of a pair of braces.
Strings - Strings in Python are defined by commas. It may be single, double or triple inverted comma. Triple comma quotes are used for docstrings for multiple lines. Once the value is added to the strings, it cannot be changed.
All the above data structures play an important role in Python whether it an addition of elements or values into the program or any other operations.
The important python tools used for data analytics are -
Libraries are very helpful for the ones who wish to learn Python. Before using any library, you need to import that library into your environment. Learn some important libraries used in Python for scientific calculations and data analysis.
Numerical Python, in-short NumPy, is the most dominant library in Python. Its most commanding characteristic is its n-dimensional array with the help of which n-dimensional quantities can be solved. Basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration are also present in NumPy just like the features of other languages such as C and C++.
Pandas are most commonly used libraries in Python for data munging and preparing data operations. Pandas are used for structured data procedures and planning. The usage of Python is increased after the addition of Pandas into it. Pandas help in enhancing Python among data scientists for further research and analysis.
Matplotlib is the library used for the purpose of plotting a large number of graphs whether they are from histograms or from heat plots. The important feature that iPython notebook include for plotting is Pylab feature to use inline plotting. If you don’t use an inline option in iPython environment, The Pylab will convert iPython environment to Matlab environment. In order to add math to your plot, you can use Latex commands.
Scientific Python is an important and useful library for you if you want to use various high-level engineering modules such as discrete Fourier transform, linear algebra, optimization and Sparse Matrices.
SymPy is used to do various symbolic calculations and has the efficiency to perform basic arithmetic calculations, calculus, algebra discrete mathematics and quantum physics. The result of the calculations is formatted into Latex code.
Blaze is used to accessing data from various sources such as Bclz, MongoDB, Apache Spark, PyTables etc and is an important library that creates interesting visualizations and dashboards for large amounts of data.
Why choose Python for big data analysis?
Ease to learn
Compared to other languages Python is easy to learn even for non-programmers. It makes an ideal first language due to three primary reasons - ample learning resources, readable code and large community. All these translate to a gradual learning curve with direct application of concepts in real-world programs.
Compatibility with Hadoop
Hadoop is the most popular open-source big data platform and the inherent compatibility of Python is yet another reason to prefer it over other languages. The PyDoop package offers access to the HDFS API for Hadoop and hence allows to write Hadoop MapReduce programs and applications. Using HDFS API you can connect your program to an HDFS installation thus, making it possible to read, write and get information on files, directories, and global file system properties.
Write less do more
Python is known for making programs work in the least lines of code. It automatically identifies and associates data types and follows an indentation based nesting structure. Overall the language is easy to use and takes less time in coding. There is also no limitation to the data processing. You can compute data in commodity machines, laptop, cloud, desktop, basically everywhere.
Python has a powerful set of packages for a wide range of data science and analytical needs.
With recent packages, Python has improved and now it has many cool APIs like Plotly and libraries like Matplotlib, ggplot, Pygal, NetworkX etc. that can create breathtaking data visualizations. You can even use TabPy to integrate Tableau and use win32com and Pythoncom to integrate Qlikview, both are popular big data visualization tools.
The day-to-day tasks of a data scientist involve many interrelated but different activities such as accessing and manipulating data, computing statistics and creating visual reports around that data. The tasks also include building predictive and explanatory models, evaluating these models on additional data, integrating models into production systems, among others. Python has a diverse range of open source libraries for just about everything that a Data Scientist does on an average day.
The other great thing about Python’s broad and diverse base is that there are millions of users who are happy to offer advice or suggestions when you get stuck on something. Chances are, someone else has been stuck there first. Open-source communities are known for their open discussion policies, but some of them have fierce reputations for not suffering newcomers lightly. Python, happily, is an exception. Both online and in local meetup groups, many Python experts are happy to help you stumble through the intricacies of learning a new language.