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

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

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

Merge DataFrame 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, also known as one hot encoding.

concat(objs[, axis, join, ignore_index, sort])

Concatenate pandas-on-Spark objects along a particular axis with optional set logic along the other axes.

sql(query[, index_col, args])

Execute a SQL query and return the result as a pandas-on-Spark DataFrame.

broadcast(obj)

Marks a DataFrame as small enough for use in broadcast joins.

isna(obj)

Detect missing values for an array-like object.

isnull(obj)

Detect missing values for an array-like object.

notna(obj)

Detect existing (non-missing) values.

notnull(obj)

Detect existing (non-missing) values.

to_numeric(arg[, errors])

Convert argument to a numeric type.

to_datetime(arg[, errors, format, unit, ...])

Convert argument to datetime.

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

Return a fixed frequency DatetimeIndex.

to_timedelta(arg[, unit, errors])

Convert argument to timedelta.

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

Return a fixed frequency TimedeltaIndex, with day as the default frequency.