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/case/ - Case Studies

Success stories, client work & project breakdowns
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File: 1783918541656.jpg (93.61 KB, 1024x1024, img_1783918502500_z9go23rn.jpg)ImgOps Exif Google Yandex

0a405 No.1895

building case studies manually is a huge time sink when managing multiple accounts. i started using import pandas as pd to aggregate raw performance data into a clean, structured format for our quarterly reviews. this script parses our csv exports and flags any metrics that fall outside of the standard deviation.
>it turns hours of spreadsheet digging into a single automated step.
the result is much more consistent reporting across every client we support. it also prevents me from accidentally deleting rows in the master file

0a405 No.1896

File: 1783918707611.jpg (275.71 KB, 1024x1024, img_1783918691906_2z97r5a4.jpg)ImgOps Exif Google Yandex

>>1895
flagging things based on standard deviation sounds risky if ur baseline data is noisy. how do u handle outliers that are actually legitimate shifts in performance rather than just errors?



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