A Powerful Python Library for Data Analysis BY BADRI PRUDHVI BADRI PRUDHVI.

Contents Contents:  Introduction  Key Features  Core Operations  Sample Dataset Analysis in Pandas

Introduction Why Learn Pandas?  If you like Python…It’s a better Python.  It’s a smoother path than raw numpy  Very easy to do Data Analysis Why do you need pandas?  When working with tabular or structured data (like R data frame, SQL table, Excel spreadsheet,...):  Import data  Clean up messy data  Explore data, gain insight into data  Process and prepare your data for analysis  Analyze your data (together with statsmodels,...)

Introduction   Software library written for the Python programming language.  Mainly used for data manipulation and analysis.  Core Data Structures:  Series  Data Frames.  Offers data structures and operations for manipulating :  Numerical tables  Time series.

Key features  Data Frame object for data manipulation with integrated indexing  Fast, easy and flexible input/output for a lot of different data formats  Merging and joining (concat, join)  Powerful time series manipulation (resampling, timezones,..)  Easy plotting  Data alignment  integrated handling of missing data  Reshaping and pivoting of data sets  Label-based slicing, fancy indexing, and sub setting of large data sets

Key features (Contd..)  Data structure column insertion and deletion  Grouping : groupby functionality  Hierarchical axis indexing to work with high-dimensional data in a lower-dimensional data structure  Time series-functionality :  Date range generation,  Frequency conversion,  Moving window statistics,  Moving window linear regressions,  Date shifting and lagging

Core Operations  Create  Select  Insert  Map  Join  Sort  Clean  Bin  View  Update  Filter  Append  Group  Summarize  Conform  Rotate

Let’s see some Cool stuff !!! Analyzing Sample Data using Pandas in Python

Thanks for listening…. Any Questions?