General functions

Data manipulations and SQL

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.

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 existing (non-missing) values.
notnull(obj)	Detect existing (non-missing) values.

Top-level dealing with numeric data

to_numeric(arg[,��errors])

Convert argument to a numeric type.

Top-level dealing with datetimelike data

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.