30 docs tagged with "Python"

sum, mean, median, std and var; the axis argument; why NumPy and pandas disagree on std; and handling nan.

Creating ndarrays, the six shape attributes, dtypes, and why an array is not a list.

Every chart type answers one specific question — pick the chart by starting from the question.

Filling and dropping missing values, removing duplicates, and converting types back after the fix.

if, if/else, if/elif/else, nested conditions, and the logical operators that combine them.

Measuring whether two variables move together — the correlation scale, why covariance can't be compared, and what correlation misses.

Python's four scalar and four collection types—int, float, str, bool, list, tuple, dict, set—mutability, ordering, and hashability.

Mean, median, mode, range, variance, standard deviation and quartiles — what each one tells you and when it misleads.

Key/value lookup with dict, uniqueness and set algebra with set, and why keys must be immutable.

The EDA workflow — inspect, check for gaps, describe, count categories, group, and correlate.

Arithmetic operators, strict float division, right-to-left exponentiation, PEMDAS precedence, and structuring computations.

Formatted string literals, precision specifiers, alignment padding, conversion flags, and debug mode.

Foundations of Artificial Intelligence — programming basics, numerical computing with NumPy, data manipulation, and visualization.

1-D and 2-D access, why NumPy slices are views rather than copies, boolean masking, and reshape.

User input streams via input(), why input always returns a string, and custom print formatting with sep and end parameters.

head, tail, shape, dtypes, info and describe — the first six things to run on any new DataFrame.

The mutable sequence — methods that change it in place, sort vs sorted, the reference trap, and comprehensions.

read_csv, read_excel and read_json; writing back out; and why index=False matters.

for and while loops, range(), break and continue, the loop else clause, and nested loops.

Line, bar, histogram, scatter and pie charts with pyplot — plus figure, show and savefig.

Element-wise arithmetic, the broadcasting rules, universal functions, matrix multiplication, sorting and stacking.

Interactive charts with plotly.express — hover, zoom and pan, plus bubble charts and how to export.

Complete Python code examples using scikit-learn for all ML algorithms and techniques

Statistical charts in one line — bar, count, histogram, scatter, box, violin, heatmap and pair plot.

Column selection, loc vs iloc, filtering, sorting, adding columns, groupby, merge and concat.

The two pandas structures — a labelled 1-D Series and a 2-D DataFrame — and how the index changes everything.

Indexing, the three-part slice, immutability, and the string methods worth knowing.

The immutable sequence — the trailing-comma rule, shallow immutability, unpacking, and when a tuple beats a list.

Every Python value carries its type. How to inspect types, understand memory references, and navigate casting and truthiness traps.

What makes a Python identifier legal, the 35 reserved keywords, the danger of shadowing built-ins, and PEP 8 conventions.