Stop Using Print and Start Using Logging – Python logger #pandas #code #technology #shorts #python

Logging is a handy tool in a programmer’s toolbox. It can help develop a better understanding of the flow of a program and discover scenarios that you might not even have thought of while developing. This is doubly true for non-interactive programs, such as batch processing, servers, and calculations running in a cluster.

Logs provide developers with the ability to monitor in detail the progress and errors and flow of information in an application. They often store information, for example, usernames or IP addresses accessed by the application. If an error occurs, logs can provide more insights than a stack trace by telling you what the state of the program was before it arrived at the line of code where the error occurred.

By logging useful data from the right places, you can find and fix errors easily but also use the data to analyze the performance of the application to plan for scaling or look at usage patterns to plan for marketing.

The logging module in Python is a ready-to-use and powerful module that is designed to meet the needs of beginners as well as enterprise teams. It is used by most third-party Python libraries, so you can integrate your log messages with the ones from those libraries to produce a homogeneous log for your application.

With the logging module imported, you can use something called a “logger” to log messages that you want to see. By default, there are 5 standard levels indicating the severity of events. Each has a corresponding method that can be used to log events at that level of severity. The defined levels, in order of increasing severity, are the following, DEBUG, INFO, WARNING, ERROR, CRITICAL.

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#log #logger #plot #tech #dataanalysis #dataanalytics #matplotlib #datascience #google #machinelearning #ai #pandastutorial #pythonprogramming #learncode
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