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Unit I: Data Handling using Pandas-I
1 DATA HANDLING USING
PANDAS
Chapter Outline
1.1 Series 1.2 Indexing and Slicing
1.3 Typecasting 1.4 Using Dictionary to Create Series Object
1.5 Operations on Series Objects
Introduction
A lot of data is being generated around us all day. For example, satellites launched by India keep sending data to the
weather, space, and defence departments. Huge amounts of data are being exchanged daily via social media, E-commerce
websites and financial transactions. Day-to-day operations in educational institutions, hospitals, Governments, and
other organizations also produce massive amount of data. These data must be analyzed to make rational decisions.
There are several data analysis tools, such as Pandas, R, SPSS, Excel, Tableau, Power BI, and QlikView/Qlik. In this
chapter, we will learn data analysis using the open-source Python package Pandas, developed by Wes McKinney. It
provides easy-to-use data structures and data analysis tools, making it a popular choice for working with tabular data.
1.1 Series
Pandas introduces a data structure called a "Series." Similar to a Python list, a series can accommodate objects of
various types. This versatility proves particularly valuable in numerous applications that demand a mix of object types
within a series. In this section, we will explore various functionalities of series that contribute to its significance in data
analysis.
To begin working with series, we need to import the pandas module. It is customary to use the widely adopted alias
"pd" to refer to the Pandas library, as demonstrated below:
>>> import pandas as pd
Suppose, we already have a list of four names of animals. Using this list, we create a Pandas series animalSeries. A
series object is created using the Pandas class series.
>>> import pandas as pd
>>> animals = ['Elephant', 'Tiger', 'Bear', 'Lion'] #Creating Pandas Series using list
>>> animalSeries = pd.Series(animals)
>>> print(animalSeries)
output:
0 Elephant
1 Tiger
Data Handling using Pandas 1

