General Functions

Data manipulations

cudf.concat(objs[, axis, join, ...])	Concatenate DataFrames, Series, or Indices row-wise.
cudf.crosstab(index, columns[, values, ...])	Compute a simple cross tabulation of two (or more) factors.
cudf.cut(x, bins[, right, labels, retbins, ...])	Bin values into discrete intervals.
cudf.factorize(values[, sort, ...])	Encode the input values as integer labels
cudf.get_dummies(df[, prefix, prefix_sep, ...])	Returns a dataframe whose columns are the one hot encodings of all columns in df
cudf.melt(frame[, id_vars, value_vars, ...])	Unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set.
cudf.merge(left, right, *args, **kwargs)	Merge GPU DataFrame objects by performing a database-style join operation by columns or indexes.
cudf.pivot(data[, columns, index, values])	Return reshaped DataFrame organized by the given index and column values.
cudf.pivot_table(data[, values, index, ...])	Create a spreadsheet-style pivot table as a DataFrame.
cudf.unstack(df, level[, fill_value, sort])	Pivot one or more levels of the (necessarily hierarchical) index labels.

Top-level conversions

cudf.to_numeric(arg[, errors, downcast])	Convert argument into numerical types.
cudf.from_dataframe(df[, allow_copy])	Build a DataFrame from an object supporting the dataframe interchange protocol.
cudf.from_dlpack(pycapsule_obj)	Converts from a DLPack tensor to a cuDF object.
cudf.from_pandas(obj[, nan_as_null])	Convert certain Pandas objects into the cudf equivalent.

Top-level dealing with datetimelike data

cudf.to_datetime(arg[, errors, dayfirst, ...])	Convert argument to datetime.
cudf.date_range([start, end, periods, freq, ...])	Return a fixed frequency DatetimeIndex.

Top-level dealing with Interval data

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

Returns a fixed frequency IntervalIndex.