Pandas for Data Analysis and their Benefits

Pandas, an open source Python programming library for data analysis provides high performance and easy to use data structures for data analysis. The project development here is done by NUMFocus -- this makes Pandas  the best open source library for data analysis. At Suneratech, we use various applications and technologies that work best for our clients. Our team works to seamlessly deliver any project deployment that includes a thorough data analysis. One of the techniques we use for data analysis include through Pandas.

Problems Solved by Pandas:

Seamless Data Analysis Workflow

Python has been used for data munging for a long time now, but it was not well identified for data analysis, and that Pandas will help to connect the gap. Pandas enable you to work on the complete path of data analysis work flow. They give a chance to work on or choose other languages for data analysis.

Easy Collaboration with Other Tools

Pandas can be combined with other powerful libraries and Ipython toolkit. This combination of environment will support in doing data analysis, it excels productivity and performance -- maximizes collaboration with other tools.

Addresses Panel Regression

In addition to collaborating with other tools like statsmodels and scikit-learn, Pandas also solve linear and panel regression.

Strengths of Pandas:

Data structure

Pandas has a fast and efficient Data Structure i.e. DataFrame for data manipulation. A DataFrame is a 2-dimentional data structure with rows and columns. It’s a table like structure in SQL or like a spread sheet. Pandas object replicated like a dictionary from a Python perspective.

Tools:

Pandas has very powerful tools for reading and writing data between computer memory and inbuilt data structures. Tools for supporting different formats include plain text, Comma Separated Values (CSV), Relational Databases and HDF5 for fast access. The following include strengths of Pandas.

·        Pandas support high performance of data sets merging and joining all types of data sets such as small, medium and large

·        Performs intelligent label based slicing, performance quick indexing and fast sub setting of large data sets

·        Pandas have the capability of handling missing values from data, and data alignment

·        They provide flexibility to users in reshaping and setting pivot values to data sets

·        Best consideration of pandas is performance -- there are some areas whose code written using Cython and C language to speed up access and generally, code written in c almost highly optimized

·        Time series: Date range generation and frequency conversion -- moving window statistics, moving window linear regressions, date shifting and lagging can be possible at very easy way

·        Create domain oriented time offsets and join time series data sets without losing singe bit of data

·        Pandas data structures allow insertion and deletion of columns of any size with very simple and user-friendly operations

·        Python Pandas have a powerful tool for aggregation and transforming of data with a group by engine that allows split, apply and combine operations

·        In combination with python there are multiple domains using Pandas, a few of them include Academic, Finance, Analytics, Statistics and Advertising