mars.dataframe.Series

class mars.dataframe.Series(data=None, index=None, dtype=None, name=None, copy=False, chunk_size=None, gpu=None, sparse=None, num_partitions=None)[source]
__init__(data=None, index=None, dtype=None, name=None, copy=False, chunk_size=None, gpu=None, sparse=None, num_partitions=None)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([data, index, dtype, name, copy, …])

Initialize self.

abs()

add(other[, level, fill_value, axis])

Return Addition of series and other, element-wise (binary operator add).

agg([func, axis])

aggregate([func, axis])

all([axis, bool_only, skipna, level, …])

any([axis, bool_only, skipna, level, …])

append(other[, ignore_index, …])

apply(func[, convert_dtype, output_type, …])

Invoke function on values of Series.

astype(dtype[, copy, errors])

Cast a pandas object to a specified dtype dtype.

autocorr([lag])

Compute the lag-N autocorrelation.

backfill([axis, inplace, limit, downcast])

Synonym for DataFrame.fillna() with method='bfill'.

bfill([axis, inplace, limit, downcast])

Synonym for DataFrame.fillna() with method='bfill'.

cartesian_chunk(right, func[, args])

check_monotonic([decreasing, strict])

Check if values in the object are monotonic increasing or decreasing.

copy([deep])

Make a copy of this object’s indices and data.

copy_from(obj)

copy_to(target)

corr(other[, method, min_periods])

Compute correlation with other Series, excluding missing values.

count([level, combine_size])

cummax([axis, skipna])

cummin([axis, skipna])

cumprod([axis, skipna])

cumsum([axis, skipna])

describe([percentiles, include, exclude])

diff([periods])

First discrete difference of element.

div(other[, level, fill_value, axis])

Return Floating division of series and other, element-wise (binary operator truediv).

dot(other)

Compute the dot product between the Series and the columns of other.

drop([labels, axis, index, columns, level, …])

Return Series with specified index labels removed.

drop_duplicates([keep, inplace, method])

Return Series with duplicate values removed.

dropna([axis, inplace, how])

Return a new Series with missing values removed.

duplicated([keep, method])

Indicate duplicate Series values.

eq(other[, level, axis])

Return Equal to of series and other, element-wise (binary operator eq).

ewm([com, span, halflife, alpha, …])

Provide exponential weighted functions.

execute([session])

expanding([min_periods, center, axis])

Provide expanding transformations.

explode([ignore_index])

Transform each element of a list-like to a row.

ffill([axis, inplace, limit, downcast])

Synonym for DataFrame.fillna() with method='ffill'.

fillna([value, method, axis, inplace, …])

Fill NA/NaN values using the specified method.

floordiv(other[, level, fill_value, axis])

Return Integer division of series and other, element-wise (binary operator floordiv).

from_tensor(in_tensor[, index, name])

ge(other[, level, axis])

Return Greater than or equal to of series and other, element-wise (binary operator ge).

groupby([by, level, as_index, sort, group_keys])

gt(other[, level, axis])

Return Greater than of series and other, element-wise (binary operator gt).

head([n])

Return the first n rows.

isin(values)

Whether elements in Series are contained in values.

isna()

Detect missing values.

isnull()

Detect missing values.

items([batch_size, session])

Lazily iterate over (index, value) tuples.

iteritems([batch_size, session])

Lazily iterate over (index, value) tuples.

keys()

Return alias for index.

kurt([axis, skipna, level, combine_size, …])

kurtosis([axis, skipna, level, …])

le(other[, level, axis])

Return Less than or equal to of series and other, element-wise (binary operator le).

lt(other[, level, axis])

Return Less than of series and other, element-wise (binary operator lt).

map(arg[, na_action, dtype, memory_scale])

Map values of Series according to input correspondence.

map_chunk(func[, args])

Apply function to each chunk.

mask(cond[, other, inplace, axis, level, …])

Replace values where the condition is True.

max([axis, skipna, level, combine_size])

mean([axis, skipna, level, combine_size])

memory_usage([index, deep])

Return the memory usage of the Series.

min([axis, skipna, level, combine_size])

mod(other[, level, fill_value, axis])

Return Modulo of series and other, element-wise (binary operator mod).

mul(other[, level, fill_value, axis])

Return Multiplication of series and other, element-wise (binary operator mul).

multiply(other[, level, fill_value, axis])

Return Multiplication of series and other, element-wise (binary operator mul).

ne(other[, level, axis])

Return Not equal to of series and other, element-wise (binary operator ne).

notna()

Detect existing (non-missing) values.

notnull()

Detect existing (non-missing) values.

nunique([dropna, combine_size])

Return number of unique elements in the object.

pad([axis, inplace, limit, downcast])

Synonym for DataFrame.fillna() with method='ffill'.

pct_change([periods, fill_method, limit, freq])

Percentage change between the current and a prior element.

pow(other[, level, fill_value, axis])

Return Exponential power of series and other, element-wise (binary operator pow).

prod([axis, skipna, level, min_count, …])

product([axis, skipna, level, min_count, …])

quantile([q, interpolation])

Return value at the given quantile.

radd(other[, level, fill_value, axis])

Return Addition of series and other, element-wise (binary operator radd).

rdiv(other[, level, fill_value, axis])

Return Floating division of series and other, element-wise (binary operator rtruediv).

rebalance([factor, axis, num_partitions, …])

Make Data more balanced across entire cluster.

rechunk(chunk_size[, threshold, …])

reindex(*args, **kwargs)

Conform Series/DataFrame to new index with optional filling logic.

reindex_like(other[, method, copy, limit, …])

Return an object with matching indices as other object.

rename([index, axis, copy, inplace, level, …])

Alter Series index labels or name.

rename_axis([mapper, index, columns, axis, …])

Set the name of the axis for the index or columns.

replace([to_replace, value, inplace, limit, …])

Replace values given in to_replace with value.

reset_index([level, drop, name, inplace, …])

Generate a new DataFrame or Series with the index reset.

rfloordiv(other[, level, fill_value, axis])

Return Integer division of series and other, element-wise (binary operator rfloordiv).

rmod(other[, level, fill_value, axis])

Return Modulo of series and other, element-wise (binary operator rmod).

rmul(other[, level, fill_value, axis])

Return Multiplication of series and other, element-wise (binary operator rmul).

rolling(window[, min_periods, center, …])

Provide rolling window calculations.

round([decimals])

Round each value in a Series to the given number of decimals.

rpow(other[, level, fill_value, axis])

Return Exponential power of series and other, element-wise (binary operator rpow).

rsub(other[, level, fill_value, axis])

Return Subtraction of series and other, element-wise (binary operator rsubtract).

rtruediv(other[, level, fill_value, axis])

Return Floating division of series and other, element-wise (binary operator rtruediv).

sample([n, frac, replace, weights, …])

Return a random sample of items from an axis of object.

sem([axis, skipna, level, ddof, combine_size])

set_axis(labels[, axis, inplace])

Assign desired index to given axis.

shift([periods, freq, axis, fill_value])

Shift index by desired number of periods with an optional time freq.

skew([axis, skipna, level, combine_size, bias])

sort_index([axis, level, ascending, …])

Sort object by labels (along an axis).

sort_values([axis, ascending, inplace, …])

Sort by the values.

std([axis, skipna, level, ddof, combine_size])

sub(other[, level, fill_value, axis])

Return Subtraction of series and other, element-wise (binary operator subtract).

sum([axis, skipna, level, min_count, …])

tail([n])

Return the last n rows.

tiles()

to_cpu()

to_csv(path[, sep, na_rep, float_format, …])

Write object to a comma-separated values (csv) file.

to_dict([into, batch_size, session])

Convert Series to {label -> value} dict or dict-like object.

to_frame([name])

Convert Series to DataFrame.

to_gpu()

to_pandas([session])

to_sql(name, con[, schema, if_exists, …])

Write records stored in a DataFrame to a SQL database.

to_tensor([dtype])

transform(func[, convert_dtype, axis, dtype])

Call func on self producing a Series with transformed values.

truediv(other[, level, fill_value, axis])

Return Floating division of series and other, element-wise (binary operator truediv).

tshift([periods, freq, axis])

Shift the time index, using the index’s frequency if available.

unique([method])

Uniques are returned in order of appearance.

value_counts([normalize, sort, ascending, …])

Return a Series containing counts of unique values.

var([axis, skipna, level, ddof, combine_size])

where(cond[, other, inplace, axis, level, …])

Replace values where the condition is False.

Attributes

at

Access a single value for a row/column label pair.

data

dtype

Return the dtype object of the underlying data.

iat

iloc

index

The index (axis labels) of the Series.

is_monotonic

Return boolean scalar if values in the object are monotonic_increasing.

is_monotonic_decreasing

Return boolean scalar if values in the object are monotonic_decreasing.

is_monotonic_increasing

Return boolean scalar if values in the object are monotonic_increasing.

loc

name

ndim

Return an int representing the number of axes / array dimensions.

shape

size

type_name

values