Pandas Data Wrangling Data Science & Python

Prompts

• Messy CSV Cleaning Plan Before Any Code

  Inspect a messy file and plan the cleaning steps.

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• Pandas Merge and Join Troubleshooter

  Fix wrong row counts after merging tables.

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• Datetime and Time Zone Cleaning Guide

  Parse mixed timestamps without silent errors.

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• Groupby and Aggregation Recipe Builder

  Choose the right aggregation for a business question.

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• Missing Data Strategy Interview

  Decide how to handle nulls case by case.

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• Slow Pandas Code Performance Review

  Find and fix the slowest steps.

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• Reshaping Wide and Long Tables

  Pivot, melt, and stack without confusion.

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• Text Column Cleaning and Standardization

  Normalize names, categories, and free text.

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• Duplicate Detection and Record Linkage Plan

  Find duplicates safely without deleting good rows.

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• Data Quality Report From a DataFrame

  Produce a repeatable profile of a dataset.

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• Window Functions and Rolling Calculations

  Moving averages, ranks, and running totals.

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• Unit Tests for Data Transformations

  Test a cleaning function with small known cases.

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• Reading Many Files into One Clean Table

  Combine monthly exports with different layouts.

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• Explain This Pandas Code Line by Line

  Understand inherited code before changing it.

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• Choosing Data Types to Save Memory

  Right-size columns safely.

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• Pipeline from Raw Export to Analysis-Ready Table

  Design a reproducible cleaning pipeline.

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• Merge Validation With Indicator Columns

  Prove a join did what you intended.

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• Large CSV Processing in Chunks

  Handle files that do not fit in memory.

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• Nested JSON and API Data to Tidy Tables

  Flatten messy nested data reliably.

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• Time Series Resampling and Gap Handling

  Align irregular data to regular intervals.

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• Fuzzy Matching Names and Addresses

  Link records that are almost the same.

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• Cohort Table Construction in Pandas

  Build retention-style cohort tables.

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• Parsing Numbers, Currencies and Dates in Messy Columns

  Clean values stored as text.

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