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  Series is one-dimensional labeled array in Python Pandas which is capable of holding data of any type i.e. integer, float, characters, etc. The axis labels are collectively called index. How Series can be Created? It can be created using list, numpy arrays, from dictionaries, from scaler objects, etc. Let us explore it one by one. Syntax:- Import pandas as pd Pd.Series(data, index, dtype) Where pd is pandas alias, Series is Function through which we create it Series, data is the data we are creating, index is index of data and dtype is type of the data.       1. Create Series from  Python List:- Pandas Series can be created by Python List. We can create python list and there after call Python’s Pandas Series function to convert List into Series. Let us understand it by Code:-  Explanation:- First we imported modules and created a Python's list (list1). After that we called Pandas Series Function to convert Python List into Series. Here we have not passed any index, It will take a

   In the Introduction to   Data Science  post, we discussed about   Roadmap for Data Science . In which we learned about Pandas in Python. So, lets jump into Pandas introduction. What is Pandas in Python? Introduction to Pandas in Python are given below:- Pandas is a fast, powerful, flexible and easy to use open source Data Analysis and manipulation tool, built with Python Programing. Pandas is Python package that is widely used for Data Science/ Data Analysis and machine learning tasks. It is used for manipulation of 1-D Arrays as well as 2-D arrays.  It is one of the most popular Data Wrangling packages of Python Programming. Key features of Pandas in Python and Why should we learn Pandas? We should learn Pandas because it provides us magnificent Data operations in easy and advanced way. There are some key features of Pandas are given below:- Pandas has Fast and efficient DataFrame with default and customized indexing. Pandas provides us tools for Data Loading from momory and differ

  Arrays   store elements in contiguous memory locations, resulting in easily calculable addresses for the elements stored and this allows a faster access to an element at a specific index.   Linked lists   are less rigid in their storage structure and elements are usually not stored in contiguous locations, hence they need to be stored with additional tags giving a reference to the next element. This difference in the data storage scheme decides which data structure would be more suitable for a given situation.  Data storage scheme of an array Data storage scheme of a linked list   Major differences are listed below:  Size:   Since data can only be stored in contiguous blocks of memory in an array, its size cannot be altered at runtime due to risk of overwriting over other data. However in a linked list, each node points to the next one such that data can exist at scattered (non-contiguous) addresses; this allows for a dynamic size which can change at runtime. Memory allocation:  For

  A linked list is a sequence of data structures, which are connected together via links. Linked List is a sequence of links which contains items. Each link contains a connection to another link. Linked list is the second most-used data structure after array. Following are the important terms to understand the concept of Linked List. Link  − Each link of a linked list can store a data called an element. Next  − Each link of a linked list contains a link to the next link called Next. LinkedList  − A Linked List contains the connection link to the first link called First. Linked List Representation Linked list can be visualized as a chain of nodes, where every node points to the next node. As per the above illustration, following are the important points to be considered. Linked List contains a link element called first. Each link carries a data field(s) and a link field called next. Each link is linked with its next link using its next link. Last link carries a link as null to mark the