A Powerful Python Library for Data Analysis

BYBADRI PRUDHVI.

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

http://pandas.pydata.org 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?