databricks.koalas.Series

class databricks.koalas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)

Koalas Series that corresponds to pandas Series logically. This holds Spark Column internally.

Variables

• _internal – an internal immutable Frame to manage metadata.

• _kdf – Parent’s Koalas DataFrame

Parameters

dataarray-like, dict, or scalar value, pandas Series

Contains data stored in Series If data is a dict, argument order is maintained for Python 3.6 and later. Note that if data is a pandas Series, other arguments should not be used.

indexarray-like or Index (1d)

Values must be hashable and have the same length as data. Non-unique index values are allowed. Will default to RangeIndex (0, 1, 2, …, n) if not provided. If both a dict and index sequence are used, the index will override the keys found in the dict.

dtypenumpy.dtype or None

If None, dtype will be inferred

copyboolean, default False

Copy input data

__init__(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)

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

Methods

__init__([data, index, dtype, name, copy, …])	Initialize self.
abs()	Return a Series/DataFrame with absolute numeric value of each element.
add(other)	Return Addition of series and other, element-wise (binary operator +).
add_prefix(prefix)	Prefix labels with string prefix.
add_suffix(suffix)	Suffix labels with string suffix.
agg(func)	Aggregate using one or more operations over the specified axis.
aggregate(func)	Aggregate using one or more operations over the specified axis.
alias(name)	An alias for Series.rename().
all([axis])	Return whether all elements are True.
any([axis])	Return whether any element is True.
append(to_append[, ignore_index, …])	Concatenate two or more Series.
apply(func[, args])	Invoke function on values of Series.
asof(where)	Return the last row(s) without any NaNs before where.
astype(dtype)	Cast a Koalas object to a specified dtype dtype.
between(left, right[, inclusive])	Return boolean Series equivalent to left <= series <= right.
bfill([axis, inplace, limit])	Synonym for DataFrame.fillna() or Series.fillna() with method=`bfill`.
bool()	Return the bool of a single element in the current object.
clip([lower, upper])	Trim values at input threshold(s).
combine_first(other)	Combine Series values, choosing the calling Series’s values first.
copy([deep])	Make a copy of this object’s indices and data.
corr(other[, method])	Compute correlation with other Series, excluding missing values.
count()	Return number of non-NA/null observations in the Series.
cummax([skipna])	Return cumulative maximum over a DataFrame or Series axis.
cummin([skipna])	Return cumulative minimum over a DataFrame or Series axis.
cumprod([skipna])	Return cumulative product over a DataFrame or Series axis.
cumsum([skipna])	Return cumulative sum over a DataFrame or Series axis.
describe([percentiles])	Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
diff([periods])	First discrete difference of element.
div(other)	Return Floating division of series and other, element-wise (binary operator /).
divide(other)	Return Floating division of series and other, element-wise (binary operator /).
divmod(other)	Return Integer division and modulo of series and other, element-wise (binary operator divmod).
dot(other)	Compute the dot product between the Series and the columns of other.
drop([labels, index, level])	Return Series with specified index labels removed.
drop_duplicates([keep, inplace])	Return Series with duplicate values removed.
dropna([axis, inplace])	Return a new Series with missing values removed.
eq(other)	Compare if the current value is equal to the other.
equals(other)	Compare if the current value is equal to the other.
expanding([min_periods])	Provide expanding transformations.
ffill([axis, inplace, limit])	Synonym for DataFrame.fillna() or Series.fillna() with method=`ffill`.
fillna([value, method, axis, inplace, limit])	Fill NA/NaN values.
filter([items, like, regex, axis])	Subset rows or columns of dataframe according to labels in the specified index.
first_valid_index()	Retrieves the index of the first valid value.
floordiv(other)	Return Integer division of series and other, element-wise (binary operator //).
ge(other)	Compare if the current value is greater than or equal to the other.
get(key[, default])	Get item from object for given key (DataFrame column, Panel slice, etc.).
get_dtype_counts()	Return counts of unique dtypes in this object.
groupby(by[, axis, as_index])	Group DataFrame or Series using a Series of columns.
gt(other)	Compare if the current value is greater than the other.
head([n])	Return the first n rows.
hist([bins])	Draw one histogram of the DataFrame’s columns.
idxmax([skipna])	Return the row label of the maximum value.
idxmin([skipna])	Return the row label of the minimum value.
isin(values)	Check whether values are contained in Series.
isna()	Detect existing (non-missing) values.
isnull()	Detect existing (non-missing) values.
item()	Return the first element of the underlying data as a Python scalar.
keys()	Return alias for index.
kurt([axis, numeric_only])	Return unbiased kurtosis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
kurtosis([axis, numeric_only])	Return unbiased kurtosis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
le(other)	Compare if the current value is less than or equal to the other.
lt(other)	Compare if the current value is less than the other.
mad()	Return the mean absolute deviation of values.
map(arg)	Map values of Series according to input correspondence.
mask(cond[, other])	Replace values where the condition is True.
max([axis, numeric_only])	Return the maximum of the values.
mean([axis, numeric_only])	Return the mean of the values.
median([axis, numeric_only, accuracy])	Return the median of the values for the requested axis.
min([axis, numeric_only])	Return the minimum of the values.
mod(other)	Return Modulo of series and other, element-wise (binary operator %).
mode([dropna])	Return the mode(s) of the dataset.
mul(other)	Return Multiplication of series and other, element-wise (binary operator *).
multiply(other)	Return Multiplication of series and other, element-wise (binary operator *).
ne(other)	Compare if the current value is not equal to the other.
nlargest([n])	Return the largest n elements.
notna()	Detect existing (non-missing) values.
notnull()	Detect existing (non-missing) values.
nsmallest([n])	Return the smallest n elements.
nunique([dropna, approx, rsd])	Return number of unique elements in the object.
pct_change([periods])	Percentage change between the current and a prior element.
pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs).
pop(item)	Return item and drop from sereis.
pow(other)	Return Exponential power of series of series and other, element-wise (binary operator **).
quantile([q, accuracy])	Return value at the given quantile.
radd(other)	Return Reverse Addition of series and other, element-wise (binary operator +).
rank([method, ascending])	Compute numerical data ranks (1 through n) along axis.
rdiv(other)	Return Reverse Floating division of series and other, element-wise (binary operator /).
rdivmod(other)	Return Integer division and modulo of series and other, element-wise (binary operator rdivmod).
rename([index])	Alter Series name.
repeat(repeats)	Repeat elements of a Series.
replace([to_replace, value, regex])	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)	Return Reverse Integer division of series and other, element-wise (binary operator //).
rmod(other)	Return Reverse Modulo of series and other, element-wise (binary operator %).
rmul(other)	Return Reverse Multiplication of series and other, element-wise (binary operator *).
rolling(window[, min_periods])	Provide rolling transformations.
round([decimals])	Round each value in a Series to the given number of decimals.
rpow(other)	Return Reverse Exponential power of series and other, element-wise (binary operator **).
rsub(other)	Return Reverse Subtraction of series and other, element-wise (binary operator -).
rtruediv(other)	Return Reverse Floating division of series and other, element-wise (binary operator /).
sample([n, frac, replace, random_state])	Return a random sample of items from an axis of object.
shift([periods, fill_value])	Shift Series/Index by desired number of periods.
skew([axis, numeric_only])	Return unbiased skew normalized by N-1.
sort_index([axis, level, ascending, …])	Sort object by labels (along an axis)
sort_values([ascending, inplace, na_position])	Sort by the values.
squeeze([axis])	Squeeze 1 dimensional axis objects into scalars.
std([axis, numeric_only])	Return sample standard deviation.
sub(other)	Return Subtraction of series and other, element-wise (binary operator -).
subtract(other)	Return Subtraction of series and other, element-wise (binary operator -).
sum([axis, numeric_only])	Return the sum of the values.
take(indices)	Return the elements in the given positional indices along an axis.
toPandas()	Return a pandas Series.
to_clipboard([excel, sep])	Copy object to the system clipboard.
to_csv([path, sep, na_rep, columns, header, …])	Write object to a comma-separated values (csv) file.
to_dataframe([name])	Convert Series to DataFrame.
to_dict([into])	Convert Series to {label -> value} dict or dict-like object.
to_excel(excel_writer[, sheet_name, na_rep, …])	Write object to an Excel sheet.
to_frame([name])	Convert Series to DataFrame.
to_json([path, compression, num_files, …])	Convert the object to a JSON string.
to_latex([buf, columns, col_space, header, …])	Render an object to a LaTeX tabular environment table.
to_list()	Return a list of the values.
to_markdown([buf, mode])	Print Series or DataFrame in Markdown-friendly format.
to_numpy()	A NumPy ndarray representing the values in this DataFrame or Series.
to_pandas()	Return a pandas Series.
to_string([buf, na_rep, float_format, …])	Render a string representation of the Series.
tolist()	Return a list of the values.
transform(func[, axis])	Call func producing the same type as self with transformed values and that has the same axis length as input.
transform_batch(func, *args, **kwargs)	Transform the data with the function that takes pandas Series and outputs pandas Series.
transpose(*args, **kwargs)	Return the transpose, which is by definition self.
truediv(other)	Return Floating division of series and other, element-wise (binary operator /).
truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.
unique()	Return unique values of Series object.
unstack([level])	Unstack, a.k.a.
update(other)	Modify Series in place using non-NA values from passed Series.
value_counts([normalize, sort, ascending, …])	Return a Series containing counts of unique values.
var([axis, numeric_only])	Return unbiased variance.
where(cond[, other])	Replace values where the condition is False.
xs(key[, level])	Return cross-section from the Series.

Attributes

T	Return the transpose, which is by definition self.
at	Access a single value for a row/column label pair.
axes	Return a list of the row axis labels.
dtype	Return the dtype object of the underlying data.
dtypes	Return the dtype object of the underlying data.
empty	Returns true if the current object is empty.
hasnans	Return True if it has any missing values.
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) Column of the Series.
is_monotonic	Return boolean if values in the object are monotonically increasing.
is_monotonic_decreasing	Return boolean if values in the object are monotonically decreasing.
is_monotonic_increasing	Return boolean if values in the object are monotonically 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 Series.
name	Return name of the Series.
ndim	Return an int representing the number of array dimensions.
shape	Return a tuple of the shape of the underlying data.
size	Return an int representing the number of elements in this object.
spark_column	Spark Column object representing the Series/Index.
spark_type	Returns the data type as defined by Spark, as a Spark DataType object.
values	Return a Numpy representation of the DataFrame or the Series.

Series databricks.koalas.Series.index