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#MONGODB PYTHON EXAMPLE CODE#
I read each line of code and then I humbly as well as calmly adapted it to my code and BAM!! it worked. Mind you, I came across some solutions and scripts and then tried to adapt it to my code but none of it worked.or so I thought.Īfter searching for hours, I decided to take a look at one of the online solution script again. But still, i couldn't find a solution for my problem.or so I thought. To solve my dilemma, I turned to Google, medium blogs, StackOverflow and even YouTube.
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The difficulty was due to the fact that Flask doesn't natively support Scrapy. Integrating Scrapy with Flask proved to be difficult for a while.a long while?. Before now, I've only used BeautifulSoup and Selenium with Flask but I've never tried it with Scrapy. The scraping script had already been created using Scrapy library. I planned to create a Flask RestFul API for my scraping script today. #datascience #webdeveloper #git #machinelearning #softwareengineer Git can be so powerful and helpful if utilized well. Git merge newbranch: This will merge your new branch with the master branch in case your new edits worked so well. Git branch master: This will take you to your previous branch that your file was before any edit. Git checkout -b newbranch: This will create and put you on a new branch so you can do your edits. You can just go back to your old branch and your file will be as it was. If the new feature engineering techniques works, then you can just merge your new branch with old branch and voila, you have an updated script.
![mongodb python example mongodb python example](https://i.gyazo.com/863952f6098445b3a0269c23d12d7893.jpg)
Then you can work on that script while on the new branch. You can commit your current script and then create a new branch on git. You want to do this without tampering with your current script that works well enough. If the new feature engineering works well, then you want to use that as your final result. You are working on a machine learning model and you have created some models but you would like to do some more work on the feature engineering part so as to check if you can generate better results. You can also do this to go back to the current code file if you want to. Git checkout SSH - filename.py : This will revert your python file back to the SSH commit irrespective of how many days old the commit has been.
![mongodb python example mongodb python example](https://www.simplifiedpython.net/wp-content/uploads/2019/04/Python-MongoDB-Connection-2.png)
It displays only 7 characters of the unique SSH for each commit as well as the commit message that you wrote while commiting each code file. Git log -oneline: This will show you all your log history in a concise manner. Solution to Scenario 1 with Git: If you had been committing every changes into git with a clear commit message, then getting your code cell will require only two lines of git code. You might not even get to that point again because.well,it's code. Normally, the only way to go back would be to start editing and deleting some code lines whle trying to remember how day 1 code looked like. Then you realized that the code you wrote at a particular point on day 1 actually worked best for your use case. Let's say you have been writing a code script for about 3 days and making changes as you go while saving the file. Some scenario in which git can be helpful for you
#MONGODB PYTHON EXAMPLE HOW TO#
Git can be so amazing and helpful if you know how to utilize it well. #naaslife #opensource #buildinpublic #datascience #jupyternotebook #66daysofdata #100daysofcode #linkedin #network #connections #naas_drivers #analytics #csv #html #image #content #plotly PS: If you check the GIF below, you can see my number of contacts on LinkedIn doubled since I started posting regularly, crazy right? ? → Join our club of data science templates creators here: + Why not follow my brother ? Florent Ravenel, the creator of this amazing template? ✅ A chart is automatically created to show the contact number evolution.ĭon't forget to star the GitHub repository! ✅ The list of all direct contacts is exported in a CSV in seconds So, we created a naas.ai template for that! ❌ I would not be able to see how my network is growing. ❌ Most of my contacts are on LinkedIn, but the export is not so easy to get. ❌ It would take a lot of time to build it if I wanted to :) "we don't have that anymore, we have everything online know" Where you have all your contacts ranked in by alphabetical order, written by hand. You know, this little book people had back in the days, ? Do you want to access your LinkedIn "contact book" and analyze its evolution?Ī few months ago, my dad asked me where is my "address book". ⚡️ Building - open source data platform | Stanford LEAD alumni | Data strategy & mentoring