Manipulation/selection#

DataFrame.cleared()

Create an empty copy of the current DataFrame.

DataFrame.clone()

Cheap deepcopy/clone.

DataFrame.drop(columns)

Remove column from DataFrame and return as new.

DataFrame.drop_in_place(name)

Drop in place.

DataFrame.drop_nulls([subset])

Return a new DataFrame where the null values are dropped.

DataFrame.explode(columns)

Explode DataFrame to long format by exploding a column with Lists.

DataFrame.extend(other)

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

DataFrame.fill_nan(fill_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(predicate)

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.get_column(name)

Get a single column as Series by name.

DataFrame.get_columns()

Get the DataFrame as a List of Series.

DataFrame.groupby(by[, maintain_order])

Start a groupby 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 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, series)

Insert a Series at a certain column index.

DataFrame.interpolate()

Interpolate intermediate values.

DataFrame.join(other[, left_on, right_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.partition_by()

Split into multiple DataFrames partitioned by groups.

DataFrame.pipe(func, *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_col)

Replace a column by a new Series.

DataFrame.replace_at_idx(index, series)

Replace a column at an index location.

DataFrame.reverse()

Reverse the DataFrame.

DataFrame.row([index, by_predicate])

Get a row as tuple, either by index or by predicate.

DataFrame.rows()

Convert columnar data to rows as python tuples.

DataFrame.sample([n, frac, ...])

Sample from this DataFrame.

DataFrame.select(exprs)

Select columns from this DataFrame.

DataFrame.shift(periods)

Shift values by the given period.

DataFrame.shift_and_fill(periods, 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[, reverse, nulls_last])

Sort the DataFrame by column.

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.to_dummies(*[, columns])

Get one hot encoded dummy variables.

DataFrame.to_series([index])

Select column as Series at index location.

DataFrame.transpose([include_header, ...])

Transpose a DataFrame over the diagonal.

DataFrame.unique([maintain_order, subset, keep])

Drop duplicate rows from this DataFrame.

DataFrame.unnest(names)

Decompose a struct into its fields.

DataFrame.unstack(step[, how, columns, ...])

Unstack a long table to a wide form without doing an aggregation.

DataFrame.upsample(time_column, *, every[, ...])

Upsample a DataFrame at a regular frequency.

DataFrame.vstack(df[, in_place])

Grow this DataFrame vertically by stacking a DataFrame to it.

DataFrame.with_column(column)

Return a new DataFrame with the column added or replaced.

DataFrame.with_columns([exprs])

Add or overwrite multiple columns in a DataFrame.

DataFrame.with_row_count([name, offset])

Add a column at index 0 that counts the rows.