3/1/2023 0 Comments Pycharm inotebook shortcutsI would recommend that anyone looking into data science become familiar with Jupyter Notebooks. Jupyter Notebook is the language of many data science projects It gets hanged and takes time to get stable. There should be more features and flexibility to target them as well. One can easily understand the codes written by others.įor beginners, Its interface is good but for moderate and advanced programmers, it is not their cup of tea. The presentation tool of Jupyter Notebook is very attractive. Just type jupyter notebook on the windows command prompt and there you go. One can even use it without downloading it separately. An easy and time saving platform for coding. Using Juypter Notebook, one can run or debug lengthy python codes line by line. However, for advanced programmers, It could be improved by adding more features like integration with other software tools etc. I would recommend beginners to start with Jupyter Notebook to fully understand how each line of the code works. Mostly I run small scripts of code for testing and debugging. I have been using Jupyter Notebook for more than 1 year. Jupyter Noterbook - a hassle free python IDE There is no flexibility to interchange between the two. Some notebooks aren't marked so one isn't sure if they are compatible with python 2 or 3. On some rare occasions, it doesn't execute properly or gives weird errors. The best part is I can run it easily from any browser or use it on the cloud.įile management is terrible at times. Google Collab is one platform where I can upload the notebooks and run it on the cloud for my data analysis and Machine Learning needs. Writing the notebooks is easy and I can save it to share with others so that we can work on the same thing. Even though it is not exclusively for Python, it still makes the job a lot easier for me so I avoid making mistakes. What's good is that the output is executed within the cells and stays there till you refresh it to do something new. If there are any errors, it's easy to rectify it on the spot. It is easy to simply write a script and execute it line by line. I prefer using JN instead of editing code in an IDE as it allows for more flexibility. Intuitive and easy to use interface Simple debugging Web-based interface - can be easily set up with minima installation Excellent application for creating and sharing notebooks with graphs, scientific plots, statistical analysis, intelligent algorithms and advanced analytics to uncover insights from data Easy sharing with the team for better collaboration and communication Multiple modes to save the notebook hence good for demos purposes, training, and storing in a repository for reference at a later date Open source - good community support Pricing - Free to useīetter memory allocation for huge datasets Some standard libraries could be installed beforehand to reduce the time to get started All these improved the efficiency of operations, reduced latency, and added business value. Furthermore, with the Jupyter notebook, we plotted scientific plots to extract insights from the data and next built a model to predict disruptions in the business processes. All of these were performed using Jupyter notebooks. Next, we cleaned the data, transformed it and standardised it according to pre-defined requirements. Using libraries of python, we ingested large volumes of log files from multiple data sources. We used the Jupyter notebook for python code development to analyse incoming log files from business processes. Excellent open source interactive development environment
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