Manipulation/selection

DataFrame.bottom_k(k, *, by[, descending, ...])	Return the k smallest elements.
DataFrame.cast(dtypes, *[, strict])	Cast DataFrame column(s) to the specified dtype(s).
DataFrame.clear([n])	Create an empty (n=0) or n-row null-filled (n>0) copy of the DataFrame.
DataFrame.clone()	Create a copy of this DataFrame.
DataFrame.drop(columns, *more_columns)	Remove columns from the dataframe.
DataFrame.drop_in_place(name)	Drop a single column in-place and return the dropped column.
DataFrame.drop_nulls([subset])	Drop all rows that contain null values.
DataFrame.explode(columns, *more_columns)	Explode the dataframe to long format by exploding the given columns.
DataFrame.extend(other)	Extend the memory backed by this DataFrame with the values from other.
DataFrame.fill_nan(value)	Fill floating point NaN values by an Expression evaluation.
DataFrame.fill_null([value, strategy, ...])	Fill null values using the specified value or strategy.
DataFrame.filter(*predicates, **constraints)	Filter the rows in the DataFrame based on a predicate expression.
DataFrame.find_idx_by_name(name)	Find the index of a column by name.
DataFrame.gather_every(n)	Take every nth row in the DataFrame and return as a new DataFrame.
DataFrame.get_column(name)	Get a single column by name.
DataFrame.get_column_index(name)	Find the index of a column by name.
DataFrame.get_columns()	Get the DataFrame as a List of Series.
DataFrame.group_by(by, *more_by[, ...])	Start a group by operation.
DataFrame.group_by_dynamic(index_column, *, ...)	Group based on a time value (or index value of type Int32, Int64).
DataFrame.group_by_rolling(index_column, *, ...)	Create rolling groups based on a time, Int32, or Int64 column.
DataFrame.groupby(by, *more_by[, maintain_order])	Start a group by operation.
DataFrame.groupby_dynamic(index_column, *, every)	Group based on a time value (or index value of type Int32, Int64).
DataFrame.groupby_rolling(index_column, *, ...)	Create rolling groups based on a time, Int32, or Int64 column.
DataFrame.head([n])	Get the first n rows.
DataFrame.hstack(columns, *[, in_place])	Return a new DataFrame grown horizontally by stacking multiple Series to it.
DataFrame.insert_at_idx(index, column)	Insert a Series at a certain column index.
DataFrame.insert_column(index, column)	Insert a Series at a certain column index.
DataFrame.interpolate()	Interpolate intermediate values.
DataFrame.item([row, column])	Return the DataFrame as a scalar, or return the element at the given row/column.
DataFrame.iter_columns()	Returns an iterator over the DataFrame's columns.
DataFrame.iter_rows()	Returns an iterator over the DataFrame of rows of python-native values.
DataFrame.iter_slices([n_rows])	Returns a non-copying iterator of slices over the underlying DataFrame.
DataFrame.join(other[, on, how, left_on, ...])	Join in SQL-like fashion.
DataFrame.join_asof(other, *[, left_on, ...])	Perform an asof join.
DataFrame.limit([n])	Get the first n rows.
DataFrame.melt([id_vars, value_vars, ...])	Unpivot a DataFrame from wide to long format.
DataFrame.merge_sorted(other, key)	Take two sorted DataFrames and merge them by the sorted key.
DataFrame.partition_by()	Group by the given columns and return the groups as separate dataframes.
DataFrame.pipe(function, *args, **kwargs)	Offers a structured way to apply a sequence of user-defined functions (UDFs).
DataFrame.pivot(values, index, columns[, ...])	Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.rechunk()	Rechunk the data in this DataFrame to a contiguous allocation.
DataFrame.rename(mapping)	Rename column names.
DataFrame.replace(column, new_column)	Replace a column by a new Series.
DataFrame.replace_at_idx(index, new_column)	Replace a column at an index location.
DataFrame.replace_column(index, column)	Replace a column at an index location.
DataFrame.reverse()	Reverse the DataFrame.
DataFrame.rolling(index_column, *, period[, ...])	Create rolling groups based on a time, Int32, or Int64 column.
DataFrame.row()	Get the values of a single row, either by index or by predicate.
DataFrame.rows()	Returns all data in the DataFrame as a list of rows of python-native values.
DataFrame.rows_by_key(key, *[, named, ...])	Returns DataFrame data as a keyed dictionary of python-native values.
DataFrame.sample([n, fraction, ...])	Sample from this DataFrame.
DataFrame.select(*exprs, **named_exprs)	Select columns from this DataFrame.
DataFrame.select_seq(*exprs, **named_exprs)	Select columns from this LazyFrame.
DataFrame.set_sorted(column, *more_columns)	Indicate that one or multiple columns are sorted.
DataFrame.shift([n, fill_value])	Shift values by the given number of indices.
DataFrame.shift_and_fill(fill_value, *[, n])	Shift values by the given number of places and fill the resulting null values.
DataFrame.shrink_to_fit(*[, in_place])	Shrink DataFrame memory usage.
DataFrame.slice(offset[, length])	Get a slice of this DataFrame.
DataFrame.sort(by, *more_by[, descending, ...])	Sort the dataframe by the given columns.
DataFrame.tail([n])	Get the last n rows.
DataFrame.take_every(n)	Take every nth row in the DataFrame and return as a new DataFrame.
DataFrame.top_k(k, *, by[, descending, ...])	Return the k largest elements.
DataFrame.to_dummies([columns, separator, ...])	Convert categorical variables into dummy/indicator variables.
DataFrame.to_series([index])	Select column as Series at index location.
DataFrame.transpose(*[, include_header, ...])	Transpose a DataFrame over the diagonal.
DataFrame.unique([subset, keep, maintain_order])	Drop duplicate rows from this dataframe.
DataFrame.unnest(columns, *more_columns)	Decompose struct columns into separate columns for each of their fields.
DataFrame.unstack(step[, how, columns, ...])	Unstack a long table to a wide form without doing an aggregation.
DataFrame.update(other[, on, left_on, ...])	Update the values in this DataFrame with the values in other.
DataFrame.upsample(time_column, *, every[, ...])	Upsample a DataFrame at a regular frequency.
DataFrame.vstack(other, *[, in_place])	Grow this DataFrame vertically by stacking a DataFrame to it.
DataFrame.with_columns(*exprs, **named_exprs)	Add columns to this DataFrame.
DataFrame.with_columns_seq(*exprs, **named_exprs)	Add columns to this DataFrame.
DataFrame.with_row_count([name, offset])	Add a column at index 0 that counts the rows.