Python Pandas -1
Introduction:
- Python library for data analysis.
- Has derived its name from “Panel data system” (Its an echometric term for multidimensional, structured data sets)
As you must be aware of that data analysis refers to process of evaluating big data sets using analytical and statistical tools so as to discover useful information and conclusions to supports business decision - making.
The Main author of pandas is “Wes McKinney”
Python pandas Library
Data Structure
1. Series
2. DataFrame
Using Pandas:
- Pandas is an open source
- BSD(Berkeley Software Distribution) library built for python programming language.
- Pandas offers high-performance, easy-to-use data structures and data analysis tools.
- Step 1: you need to import pandas library in your python environment.
- You can do this in either on the shell prompt or in your script file (.py) by writing
Why pandas?
- Pandas is the most popular library in the Scientific python ecosystem for doing data analysis.
- Pandas is capable of many tasks including:
- It can read and write in many different data formats (integer,float,double,..etc)
- It can calculate in all the possible ways data is organized I.e., across rows and down columns.
- It can easily select subsets of data from bulky data sets and even combine multiple datasets together.it has functionality to find and fill missing data.
- It allows you to apply operations to independent groups within the data
- It supports reshaping of data into different forms.
- It supports advanced time-series functionality ( Time series forecasting is the use of a model to predict future values based on previously observed values)
- It Supports visualization by integrating matplotlib and seaborn etc., libraries.
- Pandas is best at handling huge tabular data sets comprising different data formats.
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