maxframe.dataframe.Series

class maxframe.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 using one or more operations over the specified axis.
aggregate([func, axis])	Aggregate using one or more operations over the specified axis.
align(other[, join, axis, level, copy, ...])	Align two objects on their axes with the specified join method.
all([axis, bool_only, skipna, level, method])
any([axis, bool_only, skipna, level, method])
append(other[, ignore_index, ...])	Append rows of other to the end of caller, returning a new object.
apply(func[, convert_dtype, output_type, ...])	Invoke function on values of Series.
argmax([axis, skipna])	Return int position of the smallest value in the Series.
argmin([axis, skipna])	Return int position of the smallest value in the Series.
argsort([axis, kind, order, stable])	Return the integer indices that would sort the Series values.
around([decimals])	Round each value in a Series to the given number of decimals.
astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
at_time(time[, axis])	Select values at particular time of day (e.g., 9:30AM).
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.
between_time(start_time, end_time[, ...])	Select values between particular times of the day (e.g., 9:00-9:30 AM).
bfill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='bfill'.
case_when(caselist)	Replace values where the conditions are True.
check_monotonic([decreasing, strict])	Check if values in the object are monotonic increasing or decreasing.
clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
combine(other, func[, fill_value])	Combine the Series with a Series or scalar according to func.
combine_first(other)	Update null elements with value in the same location in 'other'.
compare(other[, align_axis, keep_shape, ...])	Compare to another Series and show the differences.
convert_dtypes([infer_objects, ...])	Convert columns to best possible dtypes using dtypes supporting pd.NA.
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])
cov(other[, min_periods, ddof])	Compute covariance with Series, excluding missing values.
cummax([axis, skipna])
cummin([axis, skipna])
cumprod([axis, skipna])
cumsum([axis, skipna])
describe([percentiles, include, exclude])	Generate descriptive statistics.
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, ...])	Return Series with duplicate values removed.
droplevel(level[, axis])	Return Series/DataFrame with requested index / column level(s) removed.
dropna([axis, inplace, how, ignore_index])	Return a new Series with missing values removed.
duplicated([keep, method])	Indicate duplicate Series values.
eq(other[, level, fill_value, 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, shift, reverse_range])	Provide expanding transformations.
explode([ignore_index, default_index_type])	Transform each element of a list-like to a row.
factorize([sort, use_na_sentinel])	Encode the object as an enumerated type or categorical variable.
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.
filter([items, like, regex, axis])	Subset the dataframe rows or columns according to the specified index labels.
first_valid_index()	Return index for first non-NA value or None, if no non-NA value is found.
floordiv(other[, level, fill_value, axis])	Return Integer division of series and other, element-wise (binary operator floordiv).
from_tensor(tensor[, index, name, dtype, ...])
ge(other[, level, fill_value, axis])	Return Greater than or equal to of series and other, element-wise (binary operator ge).
groupby([by, level, as_index, sort, group_keys])	Group DataFrame using a mapper or by a Series of columns.
gt(other[, level, fill_value, axis])	Return Greater than of series and other, element-wise (binary operator gt).
head([n])	Return the first n rows.
idxmax([axis, skipna])	Return the row label of the maximum value.
idxmin([axis, skipna])	Return the row label of the minimum value.
infer_objects([copy])	Attempt to infer better dtypes for object columns.
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, bias, fisher, method])
kurtosis([axis, skipna, level, bias, ...])
last_valid_index()	Return index for last non-NA value or None, if no non-NA value is found.
le(other[, level, fill_value, axis])	Return Less than or equal to of series and other, element-wise (binary operator le).
lt(other[, level, fill_value, 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.
mask(cond[, other, inplace, axis, level, ...])	Replace values where the condition is True.
max([axis, skipna, level, method])
mean([axis, skipna, level, method])
median([axis, skipna, level, method])
memory_usage([index, deep])	Return the memory usage of the Series.
min([axis, skipna, level, method])
mod(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operator mod).
mode([dropna, combine_size])	Return the mode(s) of the Series.
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, fill_value, axis])	Return Not equal to of series and other, element-wise (binary operator ne).
nlargest(n[, keep])	Return the largest n elements.
notna()	Detect existing (non-missing) values.
notnull()	Detect existing (non-missing) values.
nsmallest(n[, keep])	Return the smallest n elements.
nunique([dropna])	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.
pop(item)	Return item and drops from series.
pow(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operator pow).
prod([axis, skipna, level, min_count, method])
product([axis, skipna, level, min_count, method])
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).
rank([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
rdiv(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator rtruediv).
rechunk(chunk_size[, reassign_worker])	Rechunk DataFrame, Series or Index data.
reindex([labels, index, columns, axis, ...])	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.
reorder_levels(order)	Rearrange index levels using input order.
repeat(repeats[, axis])	Repeat elements of a Series.
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, method])
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, bias, method])
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, method])
sub(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator subtract).
sum([axis, skipna, level, min_count, method])
swaplevel([i, j])	Swap levels i and j in a MultiIndex.
tail([n])	Return the last n rows.
take(indices[, axis])	Return the elements in the given positional indices along an axis.
to_clipboard(*[, excel, sep, batch_size, ...])	Copy object to the system clipboard.
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_json([path, orient, date_format, ...])	Convert the object to a JSON string.
to_list([batch_size, session])	Return a list of the values.
to_pandas([session])
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).
truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.
tshift([periods, freq, axis])	Shift the time index, using the index's frequency if available.
unique([method])	Uniques are returned in order of appearance.
unstack([level, fill_value])	Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
update(other)	Modify Series in place using values from passed Series.
value_counts([normalize, sort, ascending, ...])	Return a Series containing counts of unique values.
var([axis, skipna, level, ddof, method])
where(cond[, other, inplace, axis, level, ...])	Replace values where the condition is False.
xs(key[, axis, level, drop_level])	Return cross-section from the Series/DataFrame.

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	Access a single value for a row/column pair by integer position.
iloc	Purely integer-location based indexing for selection by position.
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.
is_unique	Return boolean if values in the object are unique.
loc	Access a group of rows and columns by label(s) or a boolean array.
name
ndim	Return an int representing the number of axes / array dimensions.
shape
size
type_name
values