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.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)

Reshape this Series to a flat Series or a Series of Lists.

Series.reverse()

Return Series in reverse order.

Series.rle()

Get the lengths and values of runs of identical values.

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])

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.