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PySpark SQL Date and Timestamp Functions
🔧 Setup First (Optional for Reference) from pyspark.sql import functions as F from pyspark.sql import types as T data = df = spark.createDataFrame(data, )…
Advanced Pandas: Performance, Time Series, ML Pipelines & Interview Questions (Part 3)
Master advanced Pandas — MultiIndex, time series resampling, rolling windows, memory optimization, Pandas 2.x features, ML pipelines, and 30+ interview Q&A.
PySpark Built-in Functions
These functions are commonly used with groupBy() , agg() , or select() to compute things like sum, average, max, min, count, etc. PySpark functions come fr…
The Complete NumPy Guide — Part 3: Advanced Patterns, Real-World Pipelines & Complete Interview Guide
Master NumPy's advanced internals — memory optimization, vectorization, Numba JIT, real-world data science pipelines, and the ultimate NumPy interview questi...
Pandas Data Manipulation: The Complete Guide (Part 2 — Indexing, GroupBy, Merge & Reshape)
Master Pandas data manipulation — loc/iloc, boolean filtering, GroupBy, merge/join, pivot tables, melt, string ops, and apply functions with real examples.
JSON in Python: The Complete Guide from Basics to Advanced
Master JSON in Python — from parsing and serialization to advanced techniques, real-world use cases, and best practices for production systems.