How can Python’s pandas library be used to analyze time series data? Let’s find out. The pandas library is frequently used to import, manage, and analyze datasets in a variety of formats. In this article, we’ll use it to analyze Microsoft’s stock prices for previous years. We’ll also see how to perform basic tasks, such as time resampling and time shifting, with pandas. What is Time Series Data?
Regression analysis is one of the most fundamental tasks in data-oriented industries. In simple words, it involves finding a relationship between independent and dependent variables (attributes) in a given dataset. Consider the example of a house price prediction problem—given the size and number of bedrooms, we want to predict the price of a house. This is a simple regression problem where the size of the house and the number of bedrooms are the independent variables and the price of the house is the dependent variable.
An increasing number of fintech companies are using Python for data analysis. But what makes Python so special? And why is it a better language for data analysis compared to traditional software? Python is quickly becoming the most popular coding language in the world. Currently, it’s perching comfortably in the fourth spot after Java, C, and C++ on the Tiobe Index of Language Popularity. And the Popularity of Programming Language Index ranks Python as the most popular programming language in the world in October 2018.