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)

__init__(data=None, index=None, dtype=None, name=None, copy=False, chunk_size=None, gpu=None, sparse=None, num_partitions=None)

Methods

__init__([data, index, dtype, name, copy, ...])
abs()
add(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operator add).
add_prefix(prefix)	Prefix labels with string prefix.
add_suffix(suffix)	Suffix labels with string suffix.
agg([func, axis])
aggregate([func, axis])
align(other[, join, axis, level, copy, ...])	Align two objects on their axes with the specified join method.
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'.
between(left, right[, inclusive])	Return boolean Series equivalent to left <= series <= right.
bfill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='bfill'.
cartesian_chunk(right, func[, skip_infer, 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, kwargs, skip_infer])	Apply function to each chunk.
mask(cond[, other, inplace, axis, level, ...])	Replace values where the condition is True.
max([axis, skipna, level, combine_size, method])
mean([axis, skipna, level, combine_size, method])
median([axis, skipna, out, overwrite_input, ...])	Return the median of the values over the requested axis.
memory_usage([index, deep])	Return the memory usage of the Series.
min([axis, skipna, level, combine_size, method])
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[, reassign_worker])
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, ...])
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, ...])
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, ...])
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, ...])	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, ...])
where(cond[, other, inplace, axis, level, ...])	Replace values where the condition is False.

Attributes

T	Return the transpose, which is by definition self.
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