Pandas is one of the most versatile and powerful open-source data analysis libraries in Python. It enables users to make sense of data quickly and easily. Series and DataFrame are two core data structures in pandas used to store and manipulate data. Series stores data in a one-dimensional array while DataFrame stores data in a two-dimensional data structure. In this blog post, we’ll explore what Series and DataFrame are in pandas and the various useful operations they provide. We’ll also take a look at some examples to demonstrate how to use these data structures. With a better understanding of these data structures, you’ll be able to work with data more efficiently and effectively.
Python Pandas Tutorial (Part 2): DataFrame and Series Basics – Selecting Rows and Columns
What is series and DataFrame in pandas Python?
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Pandas Series
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Pandas DataFrame
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One-dimensional
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Two-dimensional
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Homogenous – Series elements must be of the same data type.
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Heterogenous – DataFrame elements can have different data types.
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What are pandas series?
A Pandas Series is like a column in a table. It is a one-dimensional array holding data of any type.
What is a pandas DataFrame?
DataFrame. A 2-dimensional labeled data structure called a “DataFrame” has columns that could be of various types. It can be compared to a table in SQL, a spreadsheet, or a dictionary of Series objects. It is generally the most commonly used pandas object.
What is series in Python?
One-dimensional labeled array Series can store any type of data (integer, string, float, Python objects, etc.). ). The axis labels are collectively called index.
What is a pandas series vs DataFrame?
While dataframes can be made of multiple series or we can say that a dataframe is a collection of series that can be used to analyze the data, series can only contain a single list with an index.
Why we use series in pandas?
Pandas Series is a one-dimensional labeled array that can hold any type of data technically. The single column in memory that is either independent of or a part of a Pandas DataFrame is represented by A Series in terms of Pandas DataStructure.