Series([data, index, dtype, name, copy, ...])

pandas-on-Spark Series that corresponds to pandas Series logically.

Series.index

The index (axis labels) Column of the Series.

Series.dtype

Return the dtype object of the underlying data.

Series.dtypes

Return the dtype object of the underlying data.

Series.ndim

Return an int representing the number of array dimensions.

Series.name

Return name of the Series.

Series.shape

Return a tuple of the shape of the underlying data.

Series.axes

Return a list of the row axis labels.

Series.size

Return an int representing the number of elements in this object.

Series.empty

Returns true if the current object is empty.

Series.T

Return the transpose, which is self.

Series.hasnans

Return True if it has any missing values.

Series.values

Return a Numpy representation of the DataFrame or the Series.

Series.astype(dtype)

Cast a pandas-on-Spark object to a specified dtypedtype.

Series.copy([deep])

Make a copy of this object's indices and data.

Series.bool()

Return the bool of a single element in the current object.

Series.at

Access a single value for a row/column label pair.

Series.iat

Access a single value for a row/column pair by integer position.

Series.loc

Access a group of rows and columns by label(s) or a boolean Series.

Series.iloc

Purely integer-location based indexing for selection by position.

Series.keys()

Return alias for index.

Series.pop(item)

Return item and drop from series.

Series.items()

Lazily iterate over (index, value) tuples.

Series.item()

Return the first element of the underlying data as a Python scalar.

Series.xs(key[, level])

Return cross-section from the Series.

Series.get(key[, default])

Get item from object for given key (DataFrame column, Panel slice, etc.).

Series.add(other[, fill_value])

Return Addition of series and other, element-wise (binary operator+).

Series.div(other)

Return Floating division of series and other, element-wise (binary operator/).

Series.mul(other)

Return Multiplication of series and other, element-wise (binary operator*).

Series.radd(other[, fill_value])

Return Reverse Addition of series and other, element-wise (binary operator+).

Series.rdiv(other)

Return Reverse Floating division of series and other, element-wise (binary operator/).

Series.rmul(other)

Return Reverse Multiplication of series and other, element-wise (binary operator*).

Series.rsub(other)

Return Reverse Subtraction of series and other, element-wise (binary operator-).

Series.rtruediv(other)

Return Reverse Floating division of series and other, element-wise (binary operator/).

Series.sub(other)

Return Subtraction of series and other, element-wise (binary operator-).

Series.truediv(other)

Return Floating division of series and other, element-wise (binary operator/).

Series.pow(other)

Return Exponential power of series of series and other, element-wise (binary operator**).

Series.rpow(other)

Return Reverse Exponential power of series and other, element-wise (binary operator**).

Series.mod(other)

Return Modulo of series and other, element-wise (binary operator%).

Series.rmod(other)

Return Reverse Modulo of series and other, element-wise (binary operator%).

Series.floordiv(other)

Return Integer division of series and other, element-wise (binary operator//).

Series.rfloordiv(other)

Return Reverse Integer division of series and other, element-wise (binary operator//).

Series.divmod(other)

Return Integer division and modulo of series and other, element-wise (binary operatordivmod).

Series.rdivmod(other)

Return Integer division and modulo of series and other, element-wise (binary operatorrdivmod).

Series.combine_first(other)

Combine Series values, choosing the calling Series's values first.

Series.lt(other)

Compare if the current value is less than the other.

Series.gt(other)

Compare if the current value is greater than the other.

Series.le(other)

Compare if the current value is less than or equal to the other.

Series.ge(other)

Compare if the current value is greater than or equal to the other.

Series.ne(other)

Compare if the current value is not equal to the other.

Series.eq(other)

Compare if the current value is equal to the other.

Series.product([axis, skipna, numeric_only, ...])

Return the product of the values.

Series.dot(other)

Compute the dot product between the Series and the columns of other.

Series.apply(func[, args])

Invoke function on values of Series.

Series.agg(func)

Aggregate using one or more operations over the specified axis.

Series.aggregate(func)

Aggregate using one or more operations over the specified axis.

Series.transform(func[, axis])

Callfunc producing the same type asself with transformed values and that has the same axis length as input.

Series.map(arg[, na_action])

Map values of Series according to input correspondence.

Series.groupby(by[, axis, as_index, dropna])

Group DataFrame or Series using one or more columns.

Series.rolling(window[, min_periods])

Provide rolling transformations.

Series.expanding([min_periods])

Provide expanding transformations.

Series.pipe(func, *args, **kwargs)

Apply func(self, *args, **kwargs).

Series.abs()

Return a Series/DataFrame with absolute numeric value of each element.

Series.all([axis, skipna])

Return whether all elements are True.

Series.any([axis])

Return whether any element is True.

Series.autocorr([lag])

Compute the lag-N autocorrelation.

Series.between(left, right[, inclusive])

Return boolean Series equivalent to left <= series <= right.

Series.clip([lower, upper, inplace])

Trim values at input threshold(s).

Series.corr(other[, method, min_periods])

Compute correlation withother Series, excluding missing values.

Series.count([axis, numeric_only])

Count non-NA cells for each column.

Series.cov(other[, min_periods, ddof])

Compute covariance with Series, excluding missing values.

Series.cummax([skipna])

Return cumulative maximum over a DataFrame or Series axis.

Series.cummin([skipna])

Return cumulative minimum over a DataFrame or Series axis.

Series.cumsum([skipna])

Return cumulative sum over a DataFrame or Series axis.

Series.cumprod([skipna])

Return cumulative product over a DataFrame or Series axis.

Series.describe([percentiles])

Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset's distribution, excludingNaN values.

Series.ewm([com, span, halflife, alpha, ...])

Provide exponentially weighted window transformations.

Series.filter([items, like, regex, axis])

Subset rows or columns of dataframe according to labels in the specified index.

Series.kurt([axis, skipna, numeric_only])

Return unbiased kurtosis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).

Series.max([axis, skipna, numeric_only])

Return the maximum of the values.

Series.mean([axis, skipna, numeric_only])

Return the mean of the values.

Series.min([axis, skipna, numeric_only])

Return the minimum of the values.

Series.mode([dropna])

Return the mode(s) of the dataset.

Series.nlargest([n])

Return the largestn elements.

Series.nsmallest([n])

Return the smallestn elements.

Series.pct_change([periods])

Percentage change between the current and a prior element.

Series.prod([axis, skipna, numeric_only, ...])

Return the product of the values.

Series.nunique([dropna, approx, rsd])

Return number of unique elements in the object.

Series.is_unique

Return boolean if values in the object are unique

Series.quantile([q, accuracy])

Return value at the given quantile.

Series.rank([method, ascending, numeric_only])

Compute numerical data ranks (1 through n) along axis.

Series.sem([axis, skipna, ddof, numeric_only])

Return unbiased standard error of the mean over requested axis.

Series.skew([axis, skipna, numeric_only])

Return unbiased skew normalized by N-1.

Series.std([axis, skipna, ddof, numeric_only])

Return sample standard deviation.

Series.sum([axis, skipna, numeric_only, ...])

Return the sum of the values.

Series.median([axis, skipna, numeric_only, ...])

Return the median of the values for the requested axis.

Series.var([axis, ddof, numeric_only])

Return unbiased variance.

Series.kurtosis([axis, skipna, numeric_only])

Return unbiased kurtosis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).

Series.unique()

Return unique values of Series object.

Series.value_counts([normalize, sort, ...])

Return a Series containing counts of unique values.

Series.round([decimals])

Round each value in a Series to the given number of decimals.

Series.diff([periods])

First discrete difference of element.

Series.is_monotonic_increasing

Return boolean if values in the object are monotonically increasing.

Series.is_monotonic_decreasing

Return boolean if values in the object are monotonically decreasing.

Series.align(other[, join, axis, copy])

Align two objects on their axes with the specified join method.

Series.drop([labels, index, columns, level, ...])

Return Series with specified index labels removed.

Series.droplevel(level)

Return Series with requested index level(s) removed.

Series.drop_duplicates([keep, inplace])

Return Series with duplicate values removed.

Series.duplicated([keep])

Indicate duplicate Series values.

Series.equals(other)

Compare if the current value is equal to the other.

Series.add_prefix(prefix)

Prefix labels with stringprefix.

Series.add_suffix(suffix)

Suffix labels with string suffix.

Series.first(offset)

Select first periods of time series data based on a date offset.

Series.head([n])

Return the first n rows.

Series.idxmax([skipna])

Return the row label of the maximum value.

Series.idxmin([skipna])

Return the row label of the minimum value.

Series.isin(values)

Check whethervalues are contained in Series or Index.

Series.last(offset)

Select final periods of time series data based on a date offset.

Series.rename([index])

Alter Series index labels or name.

Series.rename_axis([mapper, index, inplace])

Set the name of the axis for the index or columns.

Series.reindex([index, fill_value])

Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.

Series.reindex_like(other)

Return a Series with matching indices as other object.

Series.reset_index([level, drop, name, inplace])

Generate a new DataFrame or Series with the index reset.

Series.sample([n, frac, replace, ...])

Return a random sample of items from an axis of object.

Series.searchsorted(value[, side])

Find indices where elements should be inserted to maintain order.

Series.swaplevel([i, j, copy])

Swap levels i and j in a MultiIndex.

Series.swapaxes(i, j[, copy])

Interchange axes and swap values axes appropriately.

Series.take(indices)

Return the elements in the givenpositional indices along an axis.

Series.tail([n])

Return the lastn rows.

Series.where(cond[, other])

Replace values where the condition is False.

Series.mask(cond[, other])

Replace values where the condition is True.

Series.truncate([before, after, axis, copy])

Truncate a Series or DataFrame before and after some index value.

Series.backfill([axis, inplace, limit])

Synonym forDataFrame.fillna() orSeries.fillna() withmethod=`bfill`.

Series.bfill([axis, inplace, limit])

Synonym forDataFrame.fillna() orSeries.fillna() withmethod=`bfill`.

Series.ffill([axis, inplace, limit])

Synonym forDataFrame.fillna() orSeries.fillna() withmethod=`ffill`.

Series.isna()

Detect existing (non-missing) values.

Series.isnull()

Detect existing (non-missing) values.

Series.notna()

Detect existing (non-missing) values.

Series.notnull()

Detect existing (non-missing) values.

Series.pad([axis, inplace, limit])

Synonym forDataFrame.fillna() orSeries.fillna() withmethod=`ffill`.

Series.dropna([axis, inplace])

Return a new Series with missing values removed.

Series.fillna([value, method, axis, ...])

Fill NA/NaN values.

Series.interpolate([method, limit, ...])

Fill NaN values using an interpolation method.

Series.argsort()

Return the integer indices that would sort the Series values.

Series.argmin([axis, skipna])

Return int position of the smallest value in the Series.

Series.argmax([axis, skipna])

Return int position of the largest value in the Series.

Series.sort_index([axis, level, ascending, ...])

Sort object by labels (along an axis)

Series.sort_values([ascending, inplace, ...])

Sort by the values.

Series.unstack([level])

Unstack, a.k.a.

Series.explode()

Transform each element of a list-like to a row.

Series.repeat(repeats)

Repeat elements of a Series.

Series.squeeze([axis])

Squeeze 1 dimensional axis objects into scalars.

Series.factorize([sort, use_na_sentinel])

Encode the object as an enumerated type or categorical variable.

Series.compare(other[, keep_shape, keep_equal])

Compare to another Series and show the differences.

Series.replace([to_replace, value, regex])

Replace values given in to_replace with value.

Series.update(other)

Modify Series in place using non-NA values from passed Series.

Series.asof(where)

Return the last row(s) without any NaNs beforewhere.

Series.resample(rule[, closed, label, on])

Resample time-series data.

Series.shift([periods, fill_value])

Shift Series/Index by desired number of periods.

Series.first_valid_index()

Retrieves the index of the first valid value.

Series.last_valid_index()

Return index for last non-NA/null value.

Series.at_time(time[, asof, axis])

Select values at particular time of day (example: 9:30AM).

Series.between_time(start_time, end_time[, ...])

Select values between particular times of the day (example: 9:00-9:30 AM).

Series.spark.column

Spark Column object representing the Series/Index.

Series.spark.transform(func)

Applies a function that takes and returns a Spark column.

Series.spark.apply(func)

Applies a function that takes and returns a Spark column.

Datetime

dt

String

str

Categorical

cat

Series.str.capitalize()

Convert Strings in the series to be capitalized.

Series.str.cat([others, sep, na_rep, join])

Not supported.

Series.str.center(width[, fillchar])

Filling left and right side of strings in the Series/Index with an additional character.

Series.str.contains(pat[, case, flags, na, ...])

Test if pattern or regex is contained within a string of a Series.

Series.str.count(pat[, flags])

Count occurrences of pattern in each string of the Series.

Series.str.decode(encoding[, errors])

Not supported.

Series.str.encode(encoding[, errors])

Not supported.

Series.str.endswith(pattern[, na])

Test if the end of each string element matches a pattern.

Series.str.extract(pat[, flags, expand])

Not supported.

Series.str.extractall(pat[, flags])

Not supported.

Series.str.find(sub[, start, end])

Return lowest indexes in each string in the Series where the substring is fully contained between [start:end].

Series.str.findall(pat[, flags])

Find all occurrences of pattern or regular expression in the Series.

Series.str.get(i)

Extract element from each string or string list/tuple in the Series at the specified position.

Series.str.get_dummies([sep])

Not supported.

Series.str.index(sub[, start, end])

Return lowest indexes in each string where the substring is fully contained between [start:end].

Series.str.isalnum()

Check whether all characters in each string are alphanumeric.

Series.str.isalpha()

Check whether all characters in each string are alphabetic.

Series.str.isdigit()

Check whether all characters in each string are digits.

Series.str.isspace()

Check whether all characters in each string are whitespaces.

Series.str.islower()

Check whether all characters in each string are lowercase.

Series.str.isupper()

Check whether all characters in each string are uppercase.

Series.str.istitle()

Check whether all characters in each string are title case.

Series.str.isnumeric()

Check whether all characters in each string are numeric.

Series.str.isdecimal()

Check whether all characters in each string are decimals.

Series.str.join(sep)

Join lists contained as elements in the Series with passed delimiter.

Series.str.len()

Computes the length of each element in the Series.

Series.str.ljust(width[, fillchar])

Filling right side of strings in the Series with an additional character.

Series.str.lower()

Convert strings in the Series/Index to all lowercase.

Series.str.lstrip([to_strip])

Remove leading characters.

Series.str.match(pat[, case, flags, na])

Determine if each string matches a regular expression.

Series.str.normalize(form)

Return the Unicode normal form for the strings in the Series.

Series.str.pad(width[, side, fillchar])

Pad strings in the Series up to width.

Series.str.partition([sep, expand])

Not supported.

Series.str.repeat(repeats)

Duplicate each string in the Series.

Series.str.replace(pat, repl[, n, case, ...])

Replace occurrences of pattern/regex in the Series with some other string.

Series.str.rfind(sub[, start, end])

Return highest indexes in each string in the Series where the substring is fully contained between [start:end].

Series.str.rindex(sub[, start, end])

Return highest indexes in each string where the substring is fully contained between [start:end].

Series.str.rjust(width[, fillchar])

Filling left side of strings in the Series with an additional character.

Series.str.rpartition([sep, expand])

Not supported.

Series.str.rsplit([pat, n, expand])

Split strings around given separator/delimiter.

Series.str.rstrip([to_strip])

Remove trailing characters.

Series.str.slice([start, stop, step])

Slice substrings from each element in the Series.

Series.str.slice_replace([start, stop, repl])

Slice substrings from each element in the Series.

Series.str.split([pat, n, expand])

Split strings around given separator/delimiter.

Series.str.startswith(pattern[, na])

Test if the start of each string element matches a pattern.

Series.str.strip([to_strip])

Remove leading and trailing characters.

Series.str.swapcase()

Convert strings in the Series/Index to be swap cased.

Series.str.title()

Convert Strings in the series to be title case.

Series.str.translate(table)

Map all characters in the string through the given mapping table.

Series.str.upper()

Convert strings in the Series/Index to all uppercase.

Series.str.wrap(width, **kwargs)

Wrap long strings in the Series to be formatted in paragraphs with length less than a given width.

Series.str.zfill(width)

Pad strings in the Series by prepending ‘0’ characters.

Series.cat.categories

The categories of this categorical.

Series.cat.ordered

Whether the categories have an ordered relationship.

Series.cat.codes

Return Series of codes as well as the index.

Series.cat.rename_categories(new_categories)

Rename categories.

Series.cat.reorder_categories(new_categories)

Reorder categories as specified in new_categories.

Series.cat.add_categories(new_categories)

Add new categories.

Series.cat.remove_categories(removals)

Remove the specified categories.

Series.cat.remove_unused_categories()

Remove categories which are not used.

Series.cat.set_categories(new_categories[, ...])

Set the categories to the specified new_categories.

Series.cat.as_ordered()

Set the Categorical to be ordered.

Series.cat.as_unordered()

Set the Categorical to be unordered.

Series.plot.area([x, y])

Draw a stacked area plot.

Series.plot.bar([x, y])

Vertical bar plot.

Series.plot.barh([x, y])

Make a horizontal bar plot.

Series.plot.box(**kwds)

Make a box plot of the DataFrame columns.

Series.plot.density([bw_method, ind])

Generate Kernel Density Estimate plot using Gaussian kernels.

Series.plot.hist([bins])

Draw one histogram of the DataFrame’s columns.

Series.plot.kde([bw_method, ind])

Generate Kernel Density Estimate plot using Gaussian kernels.

Series.plot.line([x, y])

Plot DataFrame/Series as lines.

Series.plot.pie(**kwds)

Generate a pie plot.

Series.hist([bins])

Draw one histogram of the DataFrame’s columns.

Series.to_pandas()

Return a pandas Series.

Series.to_numpy()

A NumPy ndarray representing the values in this DataFrame or Series.

Series.to_list()

Return a list of the values.

Series.to_string([buf, na_rep, ...])

Render a string representation of the Series.

Series.to_dict([into])

Convert Series to {label -> value} dict or dict-like object.

Series.to_clipboard([excel, sep])

Copy object to the system clipboard.

Series.to_latex([buf, columns, header, ...])

Render an object to a LaTeX tabular environment table.

Series.to_markdown([buf, mode])

Print Series or DataFrame in Markdown-friendly format.

Series.to_json([path, compression, ...])

Convert the object to a JSON string.

Series.to_csv([path, sep, na_rep, columns, ...])

Write object to a comma-separated values (csv) file.

Series.to_excel(excel_writer[, sheet_name, ...])

Write object to an Excel sheet.

Series.to_hdf(path_or_buf, key[, mode, ...])

Write the contained data to an HDF5 file using HDFStore.

Series.to_frame([name])

Convert Series to DataFrame.

Series.pandas_on_spark.transform_batch(func, ...)

Transform the data with the function that takes pandas Series and outputs pandas Series.