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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

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   Hey Friends! In today's post, we will learn how to compare two arrays in Numpy. For comparison in Numpy, we have two methods. == comparison operator or NumPy comparison operator Let us understand both methods one by one. 1. Using == operator We can do element to element-wise comparison using == operator.  Syntax:-   array1 == array2 Let us undestand it by Code:- Explanation:- In the above code, we created two arrays and then compare both of them with == operator. imported NumPy as np arr1 and arr2 are arrays print the result of the comparison OutPut Explanation :- It will compare both arrays element-wise i.e one element from one array to one element of another element and print the output. In the above example, it compared both arrays and gives output element-wise. 2. Using comparison operator We can compare array using a comparison operator also. It will also compare arrays element-wise and provide output as True or False.  We have standard Python comparison operators to compar