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)

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, …])
astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
autocorr([lag])	Compute the lag-N autocorrelation.
bfill([axis, inplace, limit, downcast])
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.
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])
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.
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])
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.
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.
rename([index, axis, copy, inplace, level, …])	Alter Series index labels or name.
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).
sem([axis, skipna, level, ddof, combine_size])
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_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])
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.
loc
name
ndim	Return an int representing the number of axes / array dimensions.
shape
size

Series mars.dataframe.Series.index