Gist fa05e9e0684e37e537c08085e5900435

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Gist fa05e9e0684e37e537c08085e5900435

✓ Published0🌍 Public

Last edited Dec 27, 2021

Created on Dec 27, 2021

This example shows a data-cleaning pipeline for an Instagram influencer dataset, highlighting the number of unique users and missing values across key columns. The code uses pandas to inspect and transform the data, filling missing industry labels with "General," dropping rows without captions, removing duplicate entries, and converting the post date column to datetime format for time-series indexing. The visualization relies on pandas' built-in data manipulation functions, such as `fillna`, `drop_duplicates`, and `set_index`, rather than a plotting library. The final output would be a cleaned dataframe ready for trend analysis, though no chart is generated in the provided script.

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