mars.dataframe.
Series
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([data, index, dtype, name, copy, …])
Initialize self.
abs()
abs
add(other[, level, fill_value, axis])
add
Return Addition of series and other, element-wise (binary operator add).
agg([func, axis])
agg
aggregate([func, axis])
aggregate
all([axis, bool_only, skipna, level, …])
all
any([axis, bool_only, skipna, level, …])
any
append(other[, ignore_index, …])
append
apply(func[, convert_dtype, output_type, …])
apply
Invoke function on values of Series.
astype(dtype[, copy, errors])
astype
Cast a pandas object to a specified dtype dtype.
dtype
autocorr([lag])
autocorr
Compute the lag-N autocorrelation.
backfill([axis, inplace, limit, downcast])
backfill
Synonym for DataFrame.fillna() with method='bfill'.
DataFrame.fillna()
method='bfill'
bfill([axis, inplace, limit, downcast])
bfill
cartesian_chunk(right, func[, args])
cartesian_chunk
check_monotonic([decreasing, strict])
check_monotonic
Check if values in the object are monotonic increasing or decreasing.
copy([deep])
copy
Make a copy of this object’s indices and data.
copy_from(obj)
copy_from
copy_to(target)
copy_to
corr(other[, method, min_periods])
corr
Compute correlation with other Series, excluding missing values.
count([level, combine_size])
count
cummax([axis, skipna])
cummax
cummin([axis, skipna])
cummin
cumprod([axis, skipna])
cumprod
cumsum([axis, skipna])
cumsum
describe([percentiles, include, exclude])
describe
diff([periods])
diff
First discrete difference of element.
div(other[, level, fill_value, axis])
div
Return Floating division of series and other, element-wise (binary operator truediv).
dot(other)
dot
Compute the dot product between the Series and the columns of other.
drop([labels, axis, index, columns, level, …])
drop
Return Series with specified index labels removed.
drop_duplicates([keep, inplace, method])
drop_duplicates
Return Series with duplicate values removed.
dropna([axis, inplace, how])
dropna
Return a new Series with missing values removed.
duplicated([keep, method])
duplicated
Indicate duplicate Series values.
eq(other[, level, axis])
eq
Return Equal to of series and other, element-wise (binary operator eq).
ewm([com, span, halflife, alpha, …])
ewm
Provide exponential weighted functions.
execute([session])
execute
expanding([min_periods, center, axis])
expanding
Provide expanding transformations.
explode([ignore_index])
explode
Transform each element of a list-like to a row.
ffill([axis, inplace, limit, downcast])
ffill
Synonym for DataFrame.fillna() with method='ffill'.
method='ffill'
fillna([value, method, axis, inplace, …])
fillna
Fill NA/NaN values using the specified method.
floordiv(other[, level, fill_value, axis])
floordiv
Return Integer division of series and other, element-wise (binary operator floordiv).
from_tensor(in_tensor[, index, name])
from_tensor
ge(other[, level, axis])
ge
Return Greater than or equal to of series and other, element-wise (binary operator ge).
groupby([by, level, as_index, sort, group_keys])
groupby
gt(other[, level, axis])
gt
Return Greater than of series and other, element-wise (binary operator gt).
head([n])
head
Return the first n rows.
isin(values)
isin
Whether elements in Series are contained in values.
isna()
isna
Detect missing values.
isnull()
isnull
items([batch_size, session])
items
Lazily iterate over (index, value) tuples.
iteritems([batch_size, session])
iteritems
keys()
keys
Return alias for index.
kurt([axis, skipna, level, combine_size, …])
kurt
kurtosis([axis, skipna, level, …])
kurtosis
le(other[, level, axis])
le
Return Less than or equal to of series and other, element-wise (binary operator le).
lt(other[, level, axis])
lt
Return Less than of series and other, element-wise (binary operator lt).
map(arg[, na_action, dtype, memory_scale])
map
Map values of Series according to input correspondence.
map_chunk(func[, args])
map_chunk
Apply function to each chunk.
mask(cond[, other, inplace, axis, level, …])
mask
Replace values where the condition is True.
max([axis, skipna, level, combine_size])
max
mean([axis, skipna, level, combine_size])
mean
memory_usage([index, deep])
memory_usage
Return the memory usage of the Series.
min([axis, skipna, level, combine_size])
min
mod(other[, level, fill_value, axis])
mod
Return Modulo of series and other, element-wise (binary operator mod).
mul(other[, level, fill_value, axis])
mul
Return Multiplication of series and other, element-wise (binary operator mul).
multiply(other[, level, fill_value, axis])
multiply
ne(other[, level, axis])
ne
Return Not equal to of series and other, element-wise (binary operator ne).
notna()
notna
Detect existing (non-missing) values.
notnull()
notnull
nunique([dropna, combine_size])
nunique
Return number of unique elements in the object.
pad([axis, inplace, limit, downcast])
pad
pow(other[, level, fill_value, axis])
pow
Return Exponential power of series and other, element-wise (binary operator pow).
prod([axis, skipna, level, min_count, …])
prod
product([axis, skipna, level, min_count, …])
product
quantile([q, interpolation])
quantile
Return value at the given quantile.
radd(other[, level, fill_value, axis])
radd
Return Addition of series and other, element-wise (binary operator radd).
rdiv(other[, level, fill_value, axis])
rdiv
Return Floating division of series and other, element-wise (binary operator rtruediv).
rebalance([factor, axis, num_partitions, …])
rebalance
Make Data more balanced across entire cluster.
rechunk(chunk_size[, threshold, …])
rechunk
reindex(*args, **kwargs)
reindex
Conform Series/DataFrame to new index with optional filling logic.
rename([index, axis, copy, inplace, level, …])
rename
Alter Series index labels or name.
rename_axis([mapper, index, columns, axis, …])
rename_axis
Set the name of the axis for the index or columns.
replace([to_replace, value, inplace, limit, …])
replace
Replace values given in to_replace with value.
reset_index([level, drop, name, inplace, …])
reset_index
Generate a new DataFrame or Series with the index reset.
rfloordiv(other[, level, fill_value, axis])
rfloordiv
Return Integer division of series and other, element-wise (binary operator rfloordiv).
rmod(other[, level, fill_value, axis])
rmod
Return Modulo of series and other, element-wise (binary operator rmod).
rmul(other[, level, fill_value, axis])
rmul
Return Multiplication of series and other, element-wise (binary operator rmul).
rolling(window[, min_periods, center, …])
rolling
Provide rolling window calculations.
round([decimals])
round
Round each value in a Series to the given number of decimals.
rpow(other[, level, fill_value, axis])
rpow
Return Exponential power of series and other, element-wise (binary operator rpow).
rsub(other[, level, fill_value, axis])
rsub
Return Subtraction of series and other, element-wise (binary operator rsubtract).
rtruediv(other[, level, fill_value, axis])
rtruediv
sample([n, frac, replace, weights, …])
sample
Return a random sample of items from an axis of object.
sem([axis, skipna, level, ddof, combine_size])
sem
set_axis(labels[, axis, inplace])
set_axis
Assign desired index to given axis.
shift([periods, freq, axis, fill_value])
shift
Shift index by desired number of periods with an optional time freq.
skew([axis, skipna, level, combine_size, bias])
skew
sort_index([axis, level, ascending, …])
sort_index
Sort object by labels (along an axis).
sort_values([axis, ascending, inplace, …])
sort_values
Sort by the values.
std([axis, skipna, level, ddof, combine_size])
std
sub(other[, level, fill_value, axis])
sub
Return Subtraction of series and other, element-wise (binary operator subtract).
sum([axis, skipna, level, min_count, …])
sum
tail([n])
tail
Return the last n rows.
tiles()
tiles
to_cpu()
to_cpu
to_csv(path[, sep, na_rep, float_format, …])
to_csv
Write object to a comma-separated values (csv) file.
to_dict([into, batch_size, session])
to_dict
Convert Series to {label -> value} dict or dict-like object.
to_frame([name])
to_frame
Convert Series to DataFrame.
to_gpu()
to_gpu
to_pandas([session])
to_pandas
to_sql(name, con[, schema, if_exists, …])
to_sql
Write records stored in a DataFrame to a SQL database.
to_tensor([dtype])
to_tensor
transform(func[, convert_dtype, axis, dtype])
transform
Call func on self producing a Series with transformed values.
func
truediv(other[, level, fill_value, axis])
truediv
tshift([periods, freq, axis])
tshift
Shift the time index, using the index’s frequency if available.
unique([method])
unique
Uniques are returned in order of appearance.
value_counts([normalize, sort, ascending, …])
value_counts
Return a Series containing counts of unique values.
var([axis, skipna, level, ddof, combine_size])
var
where(cond[, other, inplace, axis, level, …])
where
Replace values where the condition is False.
Attributes
at
Access a single value for a row/column label pair.
data
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
loc
name
ndim
Return an int representing the number of axes / array dimensions.
shape
size
values