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⇱ Pandas DataFrame apply(): Methods, Examples, and Uses | DigitalOcean


πŸ‘ Pandas DataFrame apply(): Methods, Examples, and Uses

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πŸ‘ Anish Singh Walia
Anish Singh Walia
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Sr Technical Content Strategist and Team Lead
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I help Businesses scale with AI x SEO x (authentic) Content that revives traffic and keeps leads flowing | 3,000,000+ Average monthly readers on Medium | Sr Technical Writer(Team Lead) @ DigitalOcean | Ex-Cloud Consultant @ AMEX | Ex-Site Reliability Engineer(DevOps)@Nutanix

Java and Python Developer for 20+ years, Open Source Enthusiast, Founder of https://www.askpython.com/, https://www.linuxfordevices.com/, and JournalDev.com (acquired by DigitalOcean). Passionate about writing technical articles and sharing knowledge with others. Love Java, Python, Unix and related technologies. Follow my X @PankajWebDev

πŸ‘ Vinayak Baranwal
Vinayak Baranwal
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Technical Writer II
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Building future-ready infrastructure with Linux, Cloud, and DevOps. Full Stack Developer & System Administrator. Technical Writer @ DigitalOcean | GitHub Contributor | Passionate about Docker, PostgreSQL, and Open Source | Exploring NLP & AI-TensorFlow | Nailed over 50+ deployments across production environments.

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Thank you as I have been searching for ways to apply functions to pandas df as the current data is in insufficient!

- Carolyn

Hi, I have one problem in which two columns have 10 values and all are same assume 890 in one column and 689 in another and i have 3rd column where values are like this =>value = [23, 45, 67, 89, 90, 234, 1098, 4567] i want another column in which i have to add the value of third column and first compare it to 2nd column if it equals i have to stop adding for that column and then take next column i have to add values of 3rd column till its value equal to other column and collect its corresponding date where the sum has stopped since i will have one more column which contains a different date. 3980 0 2021-04-12 00:00:00 9.4 3980 0 2021-04-13 00:00:00 9.4 3980 0 2021-04-12 00:00:00 9.8 3980 0 2021-04-13 00:00:00 9.8 3980 0 2021-03-01 00:00:00 760 3980 0 2021-03-02 00:00:00 1630 3980 0 2021-03-03 00:00:00 1150 3980 0 2021-03-04 00:00:00 1000 3980 0 2021-03-05 00:00:00 20 3980 0 2021-03-08 00:00:00 210 3980 0 2021-03-09 00:00:00 340 3980 0 2021-03-10 00:00:00 150 3980 0 2021-03-11 00:00:00 160 3980 0 2021-03-12 00:00:00 50 3980 0 2021-03-15 00:00:00 10 3980 0 2021-03-16 00:00:00 350 3980 0 2021-03-17 00:00:00 200 3980 0 2021-03-18 00:00:00 50 If you find any solution please mail me

- swetha

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