DataFrame.bottom_k(k, *, by[, descending, ...])

Return the k smallest elements.

DataFrame.cast(dtypes, *[, strict])

Cast DataFrame column(s) to the specified dtype(s).


Create an empty (n=0) or n-row null-filled (n>0) copy of the DataFrame.


Cheap deepcopy/clone.

DataFrame.drop(columns, *more_columns)

Remove columns from the dataframe.


Drop a single column in-place and return the dropped column.


Drop all rows that contain null values.

DataFrame.explode(columns, *more_columns)

Explode the dataframe to long format by exploding the given columns.


Extend the memory backed by this DataFrame with the values from other.


Fill floating point NaN values by an Expression evaluation.

DataFrame.fill_null([value, strategy, ...])

Fill null values using the specified value or strategy.


Filter the rows in the DataFrame based on a predicate expression.


Find the index of a column by name.


Get a single column as Series by name.


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.


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, series)

Insert a Series at a certain column index.


Interpolate intermediate values.

DataFrame.item([row, column])

Return the dataframe as a scalar, or return the element at the given row/column.


Returns an iterator over the DataFrame of rows of python-native values.


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.


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.


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.


Rechunk the data in this DataFrame to a contiguous allocation.


Rename column names.

DataFrame.replace(column, new_column)

Replace a column by a new Series.

DataFrame.replace_at_idx(index, series)

Replace a column at an index location.


Reverse the DataFrame.


Get the values of a single row, either by index or by predicate.


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.*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.


Shift values by the given period.

DataFrame.shift_and_fill(fill_value, *[, ...])

Shift the values by a given period 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.


Get the last n rows.


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.


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, how])

Update the values in this DataFrame with the non-null 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.