General functions

Data manipulations

melt(frame[, id_vars, value_vars, var_name, …])	Unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set.
pivot(data[, index, columns, values])	Return reshaped DataFrame organized by given index / column values.
pivot_table(data[, values, index, columns, …])	Create a spreadsheet-style pivot table as a DataFrame.
crosstab(index, columns[, values, rownames, …])	Compute a simple cross-tabulation of two (or more) factors.
cut(x, bins[, right, labels, retbins, …])	Bin values into discrete intervals.
qcut(x, q[, labels, retbins, precision, …])	Quantile-based discretization function.
merge(left, right[, how, on, left_on, …])	Merge DataFrame or named Series objects with a database-style join.
merge_ordered(left, right[, on, left_on, …])	Perform merge with optional filling/interpolation designed for ordered data like time series data.
merge_asof(left, right[, on, left_on, …])	Perform an asof merge.
concat(objs[, axis, join, join_axes, …])	Concatenate pandas objects along a particular axis with optional set logic along the other axes.
get_dummies(data[, prefix, prefix_sep, …])	Convert categorical variable into dummy/indicator variables
factorize(values[, sort, order, …])	Encode the object as an enumerated type or categorical variable.
unique(values)	Hash table-based unique.
wide_to_long(df, stubnames, i, j[, sep, suffix])	Wide panel to long format.

Top-level missing data

isna(obj)	Detect missing values for an array-like object.
isnull(obj)	Detect missing values for an array-like object.
notna(obj)	Detect non-missing values for an array-like object.
notnull(obj)	Detect non-missing values for an array-like object.

Top-level conversions

to_numeric(arg[, errors, downcast])

Convert argument to a numeric type.

Top-level dealing with datetimelike

to_datetime(arg[, errors, dayfirst, …])	Convert argument to datetime.
to_timedelta(arg[, unit, box, errors])	Convert argument to timedelta.
date_range([start, end, periods, freq, tz, …])	Return a fixed frequency DatetimeIndex.
bdate_range([start, end, periods, freq, tz, …])	Return a fixed frequency DatetimeIndex, with business day as the default frequency
period_range([start, end, periods, freq, name])	Return a fixed frequency PeriodIndex, with day (calendar) as the default frequency
timedelta_range([start, end, periods, freq, …])	Return a fixed frequency TimedeltaIndex, with day as the default frequency
infer_freq(index[, warn])	Infer the most likely frequency given the input index.

Top-level dealing with intervals

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

Return a fixed frequency IntervalIndex

Top-level evaluation

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

Evaluate a Python expression as a string using various backends.

Hashing

util.hash_array(vals[, encoding, hash_key, …])	Given a 1d array, return an array of deterministic integers.
util.hash_pandas_object(obj[, index, …])	Return a data hash of the Index/Series/DataFrame

Testing

test([extra_args])