Python is a powerful, object-oriented programming language. With a simple and easy to use syntax, python programming is a perfect language for learning computer programming. It allows to focus on the solution rather than syntax. If we are talking in computer programming language then pandas is a software library written in Python language for data manipulation and analysis. Through it, the data analysis can be done in a simpler way. In a wide variety of fields that includes finance, economics, statistics and analytics etc. python pandas can be used in daily practice.
Here, we have shared some of the benefits of using Python for data analysis over other languages such as Java, C, C++ and others:
1. Less Coding: In the least number of lines the programming can be done. On the anaconda platform, the speed for development and execution is very good. It is an easy language to learn and use. Even less time is required for doing coding. The data can be easily computed into commodity machines, desktop, cloud, laptop etc and there is no limitation over data processing.
2. Powerful Packages: For a wide range of data science and analytical needs python offers a powerful set of packages. We are going to discuss some of he popular packages:
I. NumPy: For all the operations related to linear algebra, Fourier transforms and random number crunching python can be used for scientific computing. Being a multi-dimensional generic data container, python can be integrated with distinct databases.
II. Pandas: For dealing with data structures and operations of manipulating numerical tables the python data analysis library offers a range of functions.
III. SciPy: For common data science and engineering tasks of interpolation, linear algebra, image processing and ODE solvers SciPy contains modules for scientific and technical computing.
IV. Tensorflow: For research in deep neural networks Tensorflow machine learning library is developed by Google’s team. With the leverage of flexible architecture and data flow graphs through a single API operation and computation of data can be done.
V. PyBrain: Using a wide variety of simple and powerful algorithms for machine learning tasks, Pybrain offers python based reinforcement learning, artificial intelligence and neural network library.
3. Compatibility with Hadoop: Hadoop is the most popular open-source big-data platform. It has a very great inherent compatibility with Python. The PyDoop package offers access to HDFS API for Hadoop. Therefore, you can easily write Hadoop MapReduce programs and applications.
4. Ease of Learning: Due to ample learning resources, readable coding and large community support are the three reasons that make python language easy to learn for non-programmers. With the assistance of a large community, if in case you stuck in a problem then there will be a number of fellows to resolve it.
Apart from it, there are a few things in which the pandas provides exceptional features:
> All the floating and non-floating points can be easily handled.
> From the python dataframe, the columns can be inserted and deleted. Python drop function allows to drop/remove one or more column from the dataframe.
> Intelligently slicing, fancy indexing and sub-setting of large data sets can be labeled.
> Merging and joining datasets intuitively.
> Hierarchical labeling of axes.