Best of Pandas — July 2025

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   Article

   freeCodeCamp·46w

   How to Transform JSON Data to Match Any Schema

   Learn how to transform JSON data to match specific schemas using two approaches: pure Python and pandas. The tutorial covers loading JSON files, defining target schemas, cleaning and renaming fields, and validating the output. It demonstrates transforming customer records by removing unwanted fields and renaming others, while comparing performance between pure Python (faster for simple tasks) and pandas (better for complex datasets with built-in data cleaning methods).

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2. 2
   Article

   Towards Data Science·44w

   I Analysed 25,000 Hotel Names and Found Four Surprising Truths

   A data scientist analyzed 25,000 hotel names worldwide using the Hotel Data API to uncover why hotels are named after cities they're not located in. The study revealed that Paris is the most borrowed city name (1,100+ hotels), followed by Vienna and Rome. Three main reasons emerged: proximity for search visibility, branding to evoke luxury and sophistication, and historical tradition dating back to 18th-century aristocratic travel patterns. The analysis used Python, pandas, and geographic distance calculations to map naming patterns across countries.

   72

   5
3. 3
   Article

   Machine Learning Mastery·45w

   7 Pandas Tricks That Cut Your Data Prep Time in Half

   Seven practical pandas techniques to accelerate data preparation workflows: chaining transformations with assign(), filling missing values using dictionaries in fillna(), flattening list columns with explode(), readable filtering with query(), named aggregations with groupby().agg(), date parsing with pd.to_datetime(), and building modular workflows with pipe(). These methods help reduce boilerplate code, improve readability, and streamline the data cleaning process.

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