11 docs tagged with "Pandas"

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

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

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

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

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

Defensive data loading in pandas — handling type inference traps, hidden missingness sentinels, automated pre-flight audits, and memory-efficient array conversions.

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

Why a value is missing decides what you may do about it — MCAR, MAR and MNAR, what mean imputation costs a distribution, when a smarter imputer pays, and the fit/transform contract that keeps it honest.

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.

The five Python libraries that do the work, how they stack, scikit-learn's fit/predict/transform contract, and the seven-step pipeline every project follows.