General functions

melt(frame[, id_vars, value_vars, var_name, ...])

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

pivot_table(data[, values, index, columns, ...])

Create a spreadsheet-style pivot table as a DataFrame.

cut(x, bins[, right, labels, retbins, ...])

Bin values into discrete intervals.

merge(left, right[, how, on, left_on, ...])

Merge DataFrame or named Series objects with a database-style join.

merge_asof(left, right[, on, left_on, ...])

Perform an asof merge.

get_dummies(data[, prefix, prefix_sep, ...])

Convert categorical variable into dummy/indicator variables.

unique(values)

Hash table-based unique.

isna(obj)

Detect missing values for an array-like object.

notna(obj)

Detect non-missing values for an array-like object.

to_numeric(arg[, errors, downcast])

Convert argument to a numeric type.

to_datetime(arg[, errors, dayfirst, ...])

Convert argument to datetime.

date_range([start, end, periods, freq, tz, ...])

Return a fixed frequency DatetimeIndex.

period_range([start, end, periods, freq, name])

Return a fixed frequency PeriodIndex.

infer_freq(index[, warn])

Infer the most likely frequency given the input index.

interval_range([start, end, periods, freq, ...])

Return a fixed frequency IntervalIndex.

eval(expr[, parser, engine, truediv, ...])

Evaluate a Python expression as a string using various backends.

util.hash_array(vals[, encoding, hash_key, ...])

Given a 1d array, return an array of deterministic integers.

test([extra_args])