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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Pandas Guide: DataFrames, Cleaning and Aggregation
One messy sales export, cleaned and summarised step by step with pandas: reading it with the right types, the values pandas silently turns into missing data, fixing text and number columns, removing duplicates, named aggregations with groupby, pivot tables that can hide gaps, and merges that check themselves.
Pandas Interview Questions Every Analyst Should Know
Pandas interview questions are rarely about remembering method names. They check whether you notice what the data and the library do quietly. Eight questions answered on 41,188 real bank marketing calls with pandas 3: disguised missing values, agg vs transform, groupby and NaN, copy-on-write, merges, pivots, bins.
PySpark vs Pandas: When to Use Which
When to use PySpark and when to use pandas: the same hourly revenue query written in both and checked to give identical answers on three million taxi trips, the eightfold memory cost of loading the data into pandas, eager versus lazy execution, Arrow and toPandas, pandas API on Spark, and a decision guide.
Pandas Performance: Making Your Code 10x Faster
Where pandas code loses its time and the fixes that recover it, each timed on 200,000 rows: replacing iterrows and row-wise apply with column arithmetic, np.select for if-else logic, built-in groupby aggregations instead of lambdas, category columns for memory, and loading only what you need.
Pandas vs SQL for Data Analysis: When to Use Which
Pandas versus SQL for data analysis, compared on the same real dataset: identical answers from a GROUP BY and a groupby, a moving average as a window function and as rolling, a pivot that is one line in pandas and many in SQL, and why filtering in the database first matters when data is large.