Manipulation/selection

Series.alias(name)	Rename the series.
Series.append(other)	Append a Series to this one.
Series.arg_sort(*[, descending, nulls_last])	Get the index values that would sort this Series.
Series.bottom_k([k])	Return the k smallest elements.
Series.cast(dtype, *[, strict])	Cast between data types.
Series.ceil()	Rounds up to the nearest integer value.
Series.clear([n])	Create an empty copy of the current Series, with zero to 'n' elements.
Series.clip([lower_bound, upper_bound])	Set values outside the given boundaries to the boundary value.
Series.clip_max(upper_bound)	Clip (limit) the values in an array to a max boundary.
Series.clip_min(lower_bound)	Clip (limit) the values in an array to a min boundary.
Series.clone()	Create a copy of this Series.
Series.cut(breaks, *[, labels, ...])	Bin continuous values into discrete categories.
Series.drop_nans()	Drop all floating point NaN values.
Series.drop_nulls()	Drop all null values.
Series.explode()	Explode a list Series.
Series.extend(other)	Extend the memory backed by this Series with the values from another.
Series.extend_constant(value, n)	Extremely fast method for extending the Series with 'n' copies of a value.
Series.fill_nan(value)	Fill floating point NaN value with a fill value.
Series.fill_null([value, strategy, limit])	Fill null values using the specified value or strategy.
Series.filter(predicate)	Filter elements by a boolean mask.
Series.floor()	Rounds down to the nearest integer value.
Series.gather(indices)	Take values by index.
Series.gather_every(n[, offset])	Take every nth value in the Series and return as new Series.
Series.head([n])	Get the first n elements.
Series.interpolate([method])	Fill null values using interpolation.
Series.interpolate_by(by)	Fill null values using interpolation based on another column.
Series.item([index])	Return the Series as a scalar, or return the element at the given index.
Series.limit([n])	Get the first n elements.
Series.new_from_index(index, length)	Create a new Series filled with values from the given index.
Series.qcut(quantiles, *[, labels, ...])	Bin continuous values into discrete categories based on their quantiles.
Series.rechunk(*[, in_place])	Create a single chunk of memory for this Series.
Series.rename(name)	Rename this Series.
Series.reshape(dimensions[, nested_type])	Reshape this Series to a flat Series or a Series of Lists.
Series.reverse()	Return Series in reverse order.
Series.rle()	Compress the Series data using run-length encoding.
Series.rle_id()	Get a distinct integer ID for each run of identical values.
Series.round([decimals])	Round underlying floating point data by decimals digits.
Series.round_sig_figs(digits)	Round to a number of significant figures.
Series.sample([n, fraction, ...])	Sample from this Series.
Series.scatter(indices, values)	Set values at the index locations.
Series.set(filter, value)	Set masked values.
Series.set_at_idx(indices, values)	Set values at the index locations.
Series.shift([n, fill_value])	Shift values by the given number of indices.
Series.shift_and_fill(fill_value, *[, n])	Shift values by the given number of places and fill the resulting null values.
Series.shrink_dtype()	Shrink numeric columns to the minimal required datatype.
Series.shrink_to_fit(*[, in_place])	Shrink Series memory usage.
Series.shuffle([seed])	Shuffle the contents of this Series.
Series.slice(offset[, length])	Get a slice of this Series.
Series.sort(*[, descending, nulls_last, ...])	Sort this Series.
Series.tail([n])	Get the last n elements.
Series.take(indices)	Take values by index.
Series.take_every(n[, offset])	Take every nth value in the Series and return as new Series.
Series.to_dummies(*[, separator, drop_first])	Get dummy/indicator variables.
Series.top_k([k])	Return the k largest elements.
Series.unique(*[, maintain_order])	Get unique elements in series.
Series.view(*[, ignore_nulls])	Get a view into this Series data with a numpy array.
Series.zip_with(mask, other)	Take values from self or other based on the given mask.