Data scientists use APIs to request data from web-based servers. To do this, the data scientist would make a request using a specific URL and a set of parameters that specify the type of data being requested and any other relevant information. The server would then respond to the request by providing the data in a specified format, such as JSON. The data scientist can then use this data in their analysis, either by directly working with the data in its raw format, or by importing it into a data science tool or framework for further processing.
Pandas DataReader is a library that allows data scientists to easily read data from a variety of sources into a Pandas DataFrame. This is especially useful for accessing data that resides outside of their local development environment and needs to be accessed via APIs. The Pandas DataReader provides functions for loading data from various online … Read more
Twitter is a rich source of data that can be used to understand current and future trends. Because tweets often include hashtags, they can be easily linked to specific contexts such as political discussions or financial instruments. This makes Twitter a valuable tool for collecting and analyzing data. In this article, we’ll demonstrate how to … Read more
REST APIs provide straightforward access to remote data sources. Data scientists should learn about REST APIs because APIs (Application Programming Interfaces) are an important way for data scientists to access data from other sources. By using REST APIs, data scientists can access data from a wide range of sources, including databases, web services, and other … Read more
