Series

Constructor

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

One-dimensional ndarray with axis labels (including time series).

Attributes

Axes

Series.index

The index (axis labels) of the Series.

Series.array	The ExtensionArray of the data backing this Series or Index.
Series.values	Return Series as ndarray or ndarray-like depending on the dtype.
Series.dtype	Return the dtype object of the underlying data.
Series.ftype	Return if the data is sparse|dense.
Series.shape	Return a tuple of the shape of the underlying data.
Series.nbytes	Return the number of bytes in the underlying data.
Series.ndim	Number of dimensions of the underlying data, by definition 1.
Series.size	Return the number of elements in the underlying data.
Series.strides	Return the strides of the underlying data.
Series.itemsize	Return the size of the dtype of the item of the underlying data.
Series.base	Return the base object if the memory of the underlying data is shared.
Series.T	Return the transpose, which is by definition self.
Series.memory_usage([index, deep])	Return the memory usage of the Series.
Series.hasnans	Return if I have any nans; enables various perf speedups.
Series.flags
Series.empty
Series.dtypes	Return the dtype object of the underlying data.
Series.ftypes	Return if the data is sparse|dense.
Series.data	Return the data pointer of the underlying data.
Series.is_copy	Return the copy.
Series.name	Return name of the Series.
Series.put(*args, **kwargs)	Applies the put method to its values attribute if it has one.

Conversion

Series.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
Series.infer_objects()	Attempt to infer better dtypes for object columns.
Series.convert_objects([convert_dates, …])	(DEPRECATED) Attempt to infer better dtype for object columns.
Series.copy([deep])	Make a copy of this object’s indices and data.
Series.bool()	Return the bool of a single element PandasObject.
Series.to_numpy([dtype, copy])	A NumPy ndarray representing the values in this Series or Index.
Series.to_period([freq, copy])	Convert Series from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).
Series.to_timestamp([freq, how, copy])	Cast to datetimeindex of timestamps, at beginning of period.
Series.to_list()	Return a list of the values.
Series.get_values()	Same as values (but handles sparseness conversions); is a view.
Series.__array__([dtype])	Return the values as a NumPy array.

Indexing, iteration

Series.get(key[, default])	Get item from object for given key (DataFrame column, Panel slice, etc.).
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 array.
Series.iloc	Purely integer-location based indexing for selection by position.
Series.__iter__()	Return an iterator of the values.
Series.iteritems()	Lazily iterate over (index, value) tuples.
Series.items()	Lazily iterate over (index, value) tuples.
Series.keys()	Alias for index.
Series.pop(item)	Return item and drop from frame.
Series.item()	Return the first element of the underlying data as a python scalar.
Series.xs(key[, axis, level, drop_level])	Return cross-section from the Series/DataFrame.

For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.

Binary operator functions

Series.add(other[, level, fill_value, axis])	Addition of series and other, element-wise (binary operator add).
Series.sub(other[, level, fill_value, axis])	Subtraction of series and other, element-wise (binary operator sub).
Series.mul(other[, level, fill_value, axis])	Multiplication of series and other, element-wise (binary operator mul).
Series.div(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator truediv).
Series.truediv(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator truediv).
Series.floordiv(other[, level, fill_value, axis])	Integer division of series and other, element-wise (binary operator floordiv).
Series.mod(other[, level, fill_value, axis])	Modulo of series and other, element-wise (binary operator mod).
Series.pow(other[, level, fill_value, axis])	Exponential power of series and other, element-wise (binary operator pow).
Series.radd(other[, level, fill_value, axis])	Addition of series and other, element-wise (binary operator radd).
Series.rsub(other[, level, fill_value, axis])	Subtraction of series and other, element-wise (binary operator rsub).
Series.rmul(other[, level, fill_value, axis])	Multiplication of series and other, element-wise (binary operator rmul).
Series.rdiv(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator rtruediv).
Series.rtruediv(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator rtruediv).
Series.rfloordiv(other[, level, fill_value, …])	Integer division of series and other, element-wise (binary operator rfloordiv).
Series.rmod(other[, level, fill_value, axis])	Modulo of series and other, element-wise (binary operator rmod).
Series.rpow(other[, level, fill_value, axis])	Exponential power of series and other, element-wise (binary operator rpow).
Series.combine(other, func[, fill_value])	Combine the Series with a Series or scalar according to func.
Series.combine_first(other)	Combine Series values, choosing the calling Series’s values first.
Series.round([decimals])	Round each value in a Series to the given number of decimals.
Series.lt(other[, level, fill_value, axis])	Less than of series and other, element-wise (binary operator lt).
Series.gt(other[, level, fill_value, axis])	Greater than of series and other, element-wise (binary operator gt).
Series.le(other[, level, fill_value, axis])	Less than or equal to of series and other, element-wise (binary operator le).
Series.ge(other[, level, fill_value, axis])	Greater than or equal to of series and other, element-wise (binary operator ge).
Series.ne(other[, level, fill_value, axis])	Not equal to of series and other, element-wise (binary operator ne).
Series.eq(other[, level, fill_value, axis])	Equal to of series and other, element-wise (binary operator eq).
Series.product([axis, skipna, level, …])	Return the product of the values for the requested axis.
Series.dot(other)	Compute the dot product between the Series and the columns of other.

Function application, GroupBy & Window

Series.apply(func[, convert_dtype, args])	Invoke function on values of Series.
Series.agg(func[, axis])	Aggregate using one or more operations over the specified axis.
Series.aggregate(func[, axis])	Aggregate using one or more operations over the specified axis.
Series.transform(func[, axis])	Call func on self producing a Series with transformed values and that has the same axis length as self.
Series.map(arg[, na_action])	Map values of Series according to input correspondence.
Series.groupby([by, axis, level, as_index, …])	Group DataFrame or Series using a mapper or by a Series of columns.
Series.rolling(window[, min_periods, …])	Provides rolling window calculations.
Series.expanding([min_periods, center, axis])	Provides expanding transformations.
Series.ewm([com, span, halflife, alpha, …])	Provides exponential weighted functions.
Series.pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs).

Computations / Descriptive Stats

Series.abs()	Return a Series/DataFrame with absolute numeric value of each element.
Series.all([axis, bool_only, skipna, level])	Return whether all elements are True, potentially over an axis.
Series.any([axis, bool_only, skipna, level])	Return whether any element is True, potentially over an axis.
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, axis, inplace])	Trim values at input threshold(s).
Series.clip_lower(threshold[, axis, inplace])	(DEPRECATED) Trim values below a given threshold.
Series.clip_upper(threshold[, axis, inplace])	(DEPRECATED) Trim values above a given threshold.
Series.corr(other[, method, min_periods])	Compute correlation with other Series, excluding missing values.
Series.count([level])	Return number of non-NA/null observations in the Series.
Series.cov(other[, min_periods])	Compute covariance with Series, excluding missing values.
Series.cummax([axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
Series.cummin([axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
Series.cumprod([axis, skipna])	Return cumulative product over a DataFrame or Series axis.
Series.cumsum([axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
Series.describe([percentiles, include, exclude])	Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
Series.diff([periods])	First discrete difference of element.
Series.factorize([sort, na_sentinel])	Encode the object as an enumerated type or categorical variable.
Series.kurt([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
Series.mad([axis, skipna, level])	Return the mean absolute deviation of the values for the requested axis.
Series.max([axis, skipna, level, numeric_only])	Return the maximum of the values for the requested axis.
Series.mean([axis, skipna, level, numeric_only])	Return the mean of the values for the requested axis.
Series.median([axis, skipna, level, …])	Return the median of the values for the requested axis.
Series.min([axis, skipna, level, numeric_only])	Return the minimum of the values for the requested axis.
Series.mode([dropna])	Return the mode(s) of the dataset.
Series.nlargest([n, keep])	Return the largest n elements.
Series.nsmallest([n, keep])	Return the smallest n elements.
Series.pct_change([periods, fill_method, …])	Percentage change between the current and a prior element.
Series.prod([axis, skipna, level, …])	Return the product of the values for the requested axis.
Series.quantile([q, interpolation])	Return value at the given quantile.
Series.rank([axis, method, numeric_only, …])	Compute numerical data ranks (1 through n) along axis.
Series.sem([axis, skipna, level, ddof, …])	Return unbiased standard error of the mean over requested axis.
Series.skew([axis, skipna, level, numeric_only])	Return unbiased skew over requested axis Normalized by N-1.
Series.std([axis, skipna, level, ddof, …])	Return sample standard deviation over requested axis.
Series.sum([axis, skipna, level, …])	Return the sum of the values for the requested axis.
Series.var([axis, skipna, level, ddof, …])	Return unbiased variance over requested axis.
Series.kurtosis([axis, skipna, level, …])	Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
Series.unique()	Return unique values of Series object.
Series.nunique([dropna])	Return number of unique elements in the object.
Series.is_unique	Return boolean if values in the object are unique.
Series.is_monotonic	Return boolean if values in the object are monotonic_increasing.
Series.is_monotonic_increasing	Return boolean if values in the object are monotonic_increasing.
Series.is_monotonic_decreasing	Return boolean if values in the object are monotonic_decreasing.
Series.value_counts([normalize, sort, …])	Return a Series containing counts of unique values.
Series.compound([axis, skipna, level])	Return the compound percentage of the values for the requested axis.

Reindexing / Selection / Label manipulation

Series.align(other[, join, axis, level, …])	Align two objects on their axes with the specified join method for each axis Index.
Series.drop([labels, axis, index, columns, …])	Return Series with specified index labels removed.
Series.droplevel(level[, axis])	Return DataFrame with requested index / column level(s) removed.
Series.drop_duplicates([keep, inplace])	Return Series with duplicate values removed.
Series.duplicated([keep])	Indicate duplicate Series values.
Series.equals(other)	Test whether two objects contain the same elements.
Series.first(offset)	Convenience method for subsetting initial periods of time series data based on a date offset.
Series.head([n])	Return the first n rows.
Series.idxmax([axis, skipna])	Return the row label of the maximum value.
Series.idxmin([axis, skipna])	Return the row label of the minimum value.
Series.isin(values)	Check whether values are contained in Series.
Series.last(offset)	Convenience method for subsetting final periods of time series data based on a date offset.
Series.reindex([index])	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[, method, copy, …])	Return an object with matching indices as other object.
Series.rename([index])	Alter Series index labels or name.
Series.rename_axis([mapper, index, columns, …])	Set the name of the axis for the index or columns.
Series.reset_index([level, drop, name, inplace])	Generate a new DataFrame or Series with the index reset.
Series.sample([n, frac, replace, weights, …])	Return a random sample of items from an axis of object.
Series.select(crit[, axis])	(DEPRECATED) Return data corresponding to axis labels matching criteria.
Series.set_axis(labels[, axis, inplace])	Assign desired index to given axis.
Series.take(indices[, axis, convert, is_copy])	Return the elements in the given positional indices along an axis.
Series.tail([n])	Return the last n rows.
Series.truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.
Series.where(cond[, other, inplace, axis, …])	Replace values where the condition is False.
Series.mask(cond[, other, inplace, axis, …])	Replace values where the condition is True.
Series.add_prefix(prefix)	Prefix labels with string prefix.
Series.add_suffix(suffix)	Suffix labels with string suffix.
Series.filter([items, like, regex, axis])	Subset rows or columns of dataframe according to labels in the specified index.

Missing data handling

Series.isna()	Detect missing values.
Series.notna()	Detect existing (non-missing) values.
Series.dropna([axis, inplace])	Return a new Series with missing values removed.
Series.fillna([value, method, axis, …])	Fill NA/NaN values using the specified method.
Series.interpolate([method, axis, limit, …])	Interpolate values according to different methods.

Reshaping, sorting

Series.argsort([axis, kind, order])	Overrides ndarray.argsort.
Series.argmin([axis, skipna])	(DEPRECATED) Return the row label of the minimum value.
Series.argmax([axis, skipna])	(DEPRECATED) Return the row label of the maximum value.
Series.reorder_levels(order)	Rearrange index levels using input order.
Series.sort_values([axis, ascending, …])	Sort by the values.
Series.sort_index([axis, level, ascending, …])	Sort Series by index labels.
Series.swaplevel([i, j, copy])	Swap levels i and j in a MultiIndex.
Series.unstack([level, fill_value])	Unstack, a.k.a.
Series.searchsorted(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
Series.ravel([order])	Return the flattened underlying data as an ndarray.
Series.repeat(repeats[, axis])	Repeat elements of a Series.
Series.squeeze([axis])	Squeeze 1 dimensional axis objects into scalars.
Series.view([dtype])	Create a new view of the Series.

Combining / joining / merging

Series.append(to_append[, ignore_index, …])	Concatenate two or more Series.
Series.replace([to_replace, value, inplace, …])	Replace values given in to_replace with value.
Series.update(other)	Modify Series in place using non-NA values from passed Series.

Time series-related

Series.asfreq(freq[, method, how, …])	Convert TimeSeries to specified frequency.
Series.asof(where[, subset])	Return the last row(s) without any NaNs before where.
Series.shift([periods, freq, axis, fill_value])	Shift index by desired number of periods with an optional time freq.
Series.first_valid_index()	Return index for first non-NA/null value.
Series.last_valid_index()	Return index for last non-NA/null value.
Series.resample(rule[, how, axis, …])	Resample time-series data.
Series.tz_convert(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
Series.tz_localize(tz[, axis, level, copy, …])	Localize tz-naive index of a Series or DataFrame to target time zone.
Series.at_time(time[, asof, axis])	Select values at particular time of day (e.g.
Series.between_time(start_time, end_time[, …])	Select values between particular times of the day (e.g., 9:00-9:30 AM).
Series.tshift([periods, freq, axis])	Shift the time index, using the index’s frequency if available.
Series.slice_shift([periods, axis])	Equivalent to shift without copying data.

Accessors

Pandas provides dtype-specific methods under various accessors. These are separate namespaces within Series that only apply to specific data types.

Data Type	Accessor
Datetime, Timedelta, Period	dt
String	str
Categorical	cat
Sparse	sparse

Datetimelike Properties

Series.dt can be used to access the values of the series as datetimelike and return several properties. These can be accessed like Series.dt.<property>.

Datetime Properties

Series.dt.date	Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information).
Series.dt.time	Returns numpy array of datetime.time.
Series.dt.timetz	Returns numpy array of datetime.time also containing timezone information.
Series.dt.year	The year of the datetime.
Series.dt.month	The month as January=1, December=12.
Series.dt.day	The days of the datetime.
Series.dt.hour	The hours of the datetime.
Series.dt.minute	The minutes of the datetime.
Series.dt.second	The seconds of the datetime.
Series.dt.microsecond	The microseconds of the datetime.
Series.dt.nanosecond	The nanoseconds of the datetime.
Series.dt.week	The week ordinal of the year.
Series.dt.weekofyear	The week ordinal of the year.
Series.dt.dayofweek	The day of the week with Monday=0, Sunday=6.
Series.dt.weekday	The day of the week with Monday=0, Sunday=6.
Series.dt.dayofyear	The ordinal day of the year.
Series.dt.quarter	The quarter of the date.
Series.dt.is_month_start	Indicates whether the date is the first day of the month.
Series.dt.is_month_end	Indicates whether the date is the last day of the month.
Series.dt.is_quarter_start	Indicator for whether the date is the first day of a quarter.
Series.dt.is_quarter_end	Indicator for whether the date is the last day of a quarter.
Series.dt.is_year_start	Indicate whether the date is the first day of a year.
Series.dt.is_year_end	Indicate whether the date is the last day of the year.
Series.dt.is_leap_year	Boolean indicator if the date belongs to a leap year.
Series.dt.daysinmonth	The number of days in the month.
Series.dt.days_in_month	The number of days in the month.
Series.dt.tz	Return timezone, if any.
Series.dt.freq

Datetime Methods

Series.dt.to_period(*args, **kwargs)	Cast to PeriodArray/Index at a particular frequency.
Series.dt.to_pydatetime()	Return the data as an array of native Python datetime objects.
Series.dt.tz_localize(*args, **kwargs)	Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index.
Series.dt.tz_convert(*args, **kwargs)	Convert tz-aware Datetime Array/Index from one time zone to another.
Series.dt.normalize(*args, **kwargs)	Convert times to midnight.
Series.dt.strftime(*args, **kwargs)	Convert to Index using specified date_format.
Series.dt.round(*args, **kwargs)	Perform round operation on the data to the specified freq.
Series.dt.floor(*args, **kwargs)	Perform floor operation on the data to the specified freq.
Series.dt.ceil(*args, **kwargs)	Perform ceil operation on the data to the specified freq.
Series.dt.month_name(*args, **kwargs)	Return the month names of the DateTimeIndex with specified locale.
Series.dt.day_name(*args, **kwargs)	Return the day names of the DateTimeIndex with specified locale.

Period Properties

Series.dt.qyear
Series.dt.start_time
Series.dt.end_time

Timedelta Properties

Series.dt.days	Number of days for each element.
Series.dt.seconds	Number of seconds (>= 0 and less than 1 day) for each element.
Series.dt.microseconds	Number of microseconds (>= 0 and less than 1 second) for each element.
Series.dt.nanoseconds	Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.
Series.dt.components	Return a Dataframe of the components of the Timedeltas.

Timedelta Methods

Series.dt.to_pytimedelta()	Return an array of native datetime.timedelta objects.
Series.dt.total_seconds(*args, **kwargs)	Return total duration of each element expressed in seconds.

String handling

Series.str can be used to access the values of the series as strings and apply several methods to it. These can be accessed like Series.str.<function/property>.

Series.str.capitalize()	Convert strings in the Series/Index to be capitalized.
Series.str.cat([others, sep, na_rep, join])	Concatenate strings in the Series/Index with given separator.
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 or Index.
Series.str.count(pat[, flags])	Count occurrences of pattern in each string of the Series/Index.
Series.str.decode(encoding[, errors])	Decode character string in the Series/Index using indicated encoding.
Series.str.encode(encoding[, errors])	Encode character string in the Series/Index using indicated encoding.
Series.str.endswith(pat[, na])	Test if the end of each string element matches a pattern.
Series.str.extract(pat[, flags, expand])	Extract capture groups in the regex pat as columns in a DataFrame.
Series.str.extractall(pat[, flags])	For each subject string in the Series, extract groups from all matches of regular expression pat.
Series.str.find(sub[, start, end])	Return lowest indexes in each strings in the Series/Index 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/Index.
Series.str.get(i)	Extract element from each component at specified position.
Series.str.index(sub[, start, end])	Return lowest indexes in each strings where the substring is fully contained between [start:end].
Series.str.join(sep)	Join lists contained as elements in the Series/Index with passed delimiter.
Series.str.len()	Computes the length of each element in the Series/Index.
Series.str.ljust(width[, fillchar])	Filling right side of strings in the Series/Index with an additional character.
Series.str.lower()	Convert strings in the Series/Index to lowercase.
Series.str.lstrip([to_strip])	Remove leading and trailing 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/Index.
Series.str.pad(width[, side, fillchar])	Pad strings in the Series/Index up to width.
Series.str.partition([sep, expand])	Split the string at the first occurrence of sep.
Series.str.repeat(repeats)	Duplicate each string in the Series or Index.
Series.str.replace(pat, repl[, n, case, …])	Replace occurrences of pattern/regex in the Series/Index with some other string.
Series.str.rfind(sub[, start, end])	Return highest indexes in each strings in the Series/Index where the substring is fully contained between [start:end].
Series.str.rindex(sub[, start, end])	Return highest indexes in each strings where the substring is fully contained between [start:end].
Series.str.rjust(width[, fillchar])	Filling left side of strings in the Series/Index with an additional character.
Series.str.rpartition([sep, expand])	Split the string at the last occurrence of sep.
Series.str.rstrip([to_strip])	Remove leading and trailing characters.
Series.str.slice([start, stop, step])	Slice substrings from each element in the Series or Index.
Series.str.slice_replace([start, stop, repl])	Replace a positional slice of a string with another value.
Series.str.split([pat, n, expand])	Split strings around given separator/delimiter.
Series.str.rsplit([pat, n, expand])	Split strings around given separator/delimiter.
Series.str.startswith(pat[, 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 swapcased.
Series.str.title()	Convert strings in the Series/Index to titlecase.
Series.str.translate(table[, deletechars])	Map all characters in the string through the given mapping table.
Series.str.upper()	Convert strings in the Series/Index to uppercase.
Series.str.wrap(width, **kwargs)	Wrap long strings in the Series/Index to be formatted in paragraphs with length less than a given width.
Series.str.zfill(width)	Pad strings in the Series/Index by prepending ‘0’ characters.
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 whitespace.
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 titlecase.
Series.str.isnumeric()	Check whether all characters in each string are numeric.
Series.str.isdecimal()	Check whether all characters in each string are decimal.
Series.str.get_dummies([sep])	Split each string in the Series by sep and return a frame of dummy/indicator variables.

Categorical Accessor

Categorical-dtype specific methods and attributes are available under the Series.cat accessor.

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(*args, **kwargs)	Renames categories.
Series.cat.reorder_categories(*args, **kwargs)	Reorders categories as specified in new_categories.
Series.cat.add_categories(*args, **kwargs)	Add new categories.
Series.cat.remove_categories(*args, **kwargs)	Removes the specified categories.
Series.cat.remove_unused_categories(*args, …)	Removes categories which are not used.
Series.cat.set_categories(*args, **kwargs)	Sets the categories to the specified new_categories.
Series.cat.as_ordered(*args, **kwargs)	Set the Categorical to be ordered.
Series.cat.as_unordered(*args, **kwargs)	Set the Categorical to be unordered.

Sparse Accessor

Sparse-dtype specific methods and attributes are provided under the Series.sparse accessor.

Series.sparse.npoints	The number of non- fill_value points.
Series.sparse.density	The percent of non- fill_value points, as decimal.
Series.sparse.fill_value	Elements in data that are fill_value are not stored.
Series.sparse.sp_values	An ndarray containing the non- fill_value values.

Series.sparse.from_coo(A[, dense_index])	Create a SparseSeries from a scipy.sparse.coo_matrix.
Series.sparse.to_coo([row_levels, …])	Create a scipy.sparse.coo_matrix from a SparseSeries with MultiIndex.

Plotting

Series.plot is both a callable method and a namespace attribute for specific plotting methods of the form Series.plot.<kind>.

Series.plot([kind, ax, figsize, ….])

Series plotting accessor and method

Series.plot.area(**kwds)	Area plot.
Series.plot.bar(**kwds)	Vertical bar plot.
Series.plot.barh(**kwds)	Horizontal bar plot.
Series.plot.box(**kwds)	Boxplot.
Series.plot.density([bw_method, ind])	Generate Kernel Density Estimate plot using Gaussian kernels.
Series.plot.hist([bins])	Histogram.
Series.plot.kde([bw_method, ind])	Generate Kernel Density Estimate plot using Gaussian kernels.
Series.plot.line(**kwds)	Line plot.
Series.plot.pie(**kwds)	Pie chart.

Series.hist([by, ax, grid, xlabelsize, …])

Draw histogram of the input series using matplotlib.

Serialization / IO / Conversion

Series.to_pickle(path[, compression, protocol])	Pickle (serialize) object to file.
Series.to_csv(*args, **kwargs)	Write object to a comma-separated values (csv) file.
Series.to_dict([into])	Convert Series to {label -> value} dict or dict-like object.
Series.to_excel(excel_writer[, sheet_name, …])	Write object to an Excel sheet.
Series.to_frame([name])	Convert Series to DataFrame.
Series.to_xarray()	Return an xarray object from the pandas object.
Series.to_hdf(path_or_buf, key, **kwargs)	Write the contained data to an HDF5 file using HDFStore.
Series.to_sql(name, con[, schema, …])	Write records stored in a DataFrame to a SQL database.
Series.to_msgpack([path_or_buf, encoding])	Serialize object to input file path using msgpack format.
Series.to_json([path_or_buf, orient, …])	Convert the object to a JSON string.
Series.to_sparse([kind, fill_value])	Convert Series to SparseSeries.
Series.to_dense()	Return dense representation of NDFrame (as opposed to sparse).
Series.to_string([buf, na_rep, …])	Render a string representation of the Series.
Series.to_clipboard([excel, sep])	Copy object to the system clipboard.
Series.to_latex([buf, columns, col_space, …])	Render an object to a LaTeX tabular environment table.

Sparse

SparseSeries.to_coo([row_levels, …])	Create a scipy.sparse.coo_matrix from a SparseSeries with MultiIndex.
SparseSeries.from_coo(A[, dense_index])	Create a SparseSeries from a scipy.sparse.coo_matrix.