Pandas Num Py Study Notes

Aggregation Functions

Explore the built-in and custom aggregation functions available after groupby, including agg() for applying multiple statistics at once.

Aggregations and Statistics

NumPy provides fast, axis-aware aggregation functions like sum, mean, std, and var, plus NaN-safe variants for handling missing values in numerical data.

apply, map, and applymap

A practical comparison of pandas' three element-wise transformation tools — Series.map, DataFrame.apply, and the deprecated DataFrame.applymap — and when to re…

Array Creation and Data Types

NumPy offers many ways to construct arrays — from literals to specialized constructors — and choosing the right dtype affects both correctness and memory effic…

Array Indexing and Slicing

NumPy supports basic slicing, fancy (array) indexing, and boolean masking, each with distinct rules about whether the result is a view or a copy.

Array Reshaping and Stacking

Reshaping changes an array's shape without altering its data, while stacking and splitting functions combine or divide arrays along chosen axes.

Binning and Discretization

Turning continuous numeric data into discrete categorical buckets using pandas' cut and qcut functions, and why bin edges and labels matter for downstream anal…

Boolean Filtering

Learn how to filter DataFrame rows using boolean masks built from comparison and logical operators, including combining conditions and using isin and query for…

Common Pandas & NumPy Pitfalls

A field guide to the mistakes that trip up even experienced users of pandas and NumPy — chained assignment, dtype surprises, mutable defaults, and silent misal…

Concatenating DataFrames

Using pandas' concat function to stack DataFrames or Series along rows or columns, and how it differs from merge in purpose and alignment behavior.

Crosstabs and Pivot Analysis

Use pd.crosstab() to build frequency and cross-tabulation tables between categorical variables, and compare it to pivot_table for richer aggregation.

Data Type Conversion

Master how to inspect and convert pandas column dtypes with astype, to_numeric, to_datetime, and category types to fix incorrect or inefficient typing.

DataFrame Basics

Understand the structure of a pandas DataFrame — rows, columns, index, and dtypes — and the core operations for creating, inspecting, and selecting from tabula…

Exporting and Reporting Results

Learn how to write pandas DataFrames back out to CSV, Excel, JSON, and other formats, and how to structure exports for downstream tools and stakeholder-facing…

GroupBy Basics

Understand pandas' split-apply-combine model via groupby(), including grouping keys, selecting columns, and iterating over groups.

Handling Missing Data in Pandas

Learn how pandas represents missing values with NaN and NA, and the core toolkit — isna, dropna, fillna, interpolate — for detecting and handling them.

Handling Outliers

Learn common statistical techniques — z-score, IQR, and percentile capping — for detecting and treating outliers in pandas and NumPy data.

Indexing with loc and iloc

Master the two primary pandas selection accessors — .loc for label-based indexing and .iloc for integer-position-based indexing — and the pitfalls of chained i…

Linear Algebra with NumPy

Learn how NumPy represents vectors and matrices and how to perform matrix multiplication, transposition, inversion, determinants, and eigen-decomposition using…

melt and Reshaping Data

Learn how pd.melt() converts wide-format DataFrames into long (tidy) format, and how it pairs with pivot to move data between shapes for analysis.

Merging and Joining DataFrames

How pandas' merge function combines two DataFrames on shared keys using inner, left, right, and outer join logic, and how it compares to the index-based join m…

MultiIndex Basics

Learn how pandas' hierarchical MultiIndex lets you represent and query higher-dimensional data using multiple index levels on a single axis.

The ndarray: NumPy Basics

The ndarray is NumPy's core data structure — understanding its shape, dtype, strides, and axis conventions is essential to using arrays correctly and efficient…

Pandas & NumPy Interview Questions

A curated set of frequently asked pandas and NumPy interview questions with precise answers, covering indexing, performance, missing data, and common gotchas.