Series

Constructor

Series(*args, **kwargs)

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

Attributes

Axes

Series.index	The index (axis labels) of the Series.
Series.values	Return Series as ndarray or ndarray-like depending on the dtype.
Series.dtype	Return the dtype object of the underlying data.
Series.shape	Return a tuple of the shape of 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.T	Return the transpose, which is by definition self.
Series.memory_usage([index, deep])	Return the memory usage of the Series.
Series.empty	Indicator whether Series/DataFrame is empty.
Series.name	Return the name of the Series.

Conversion

Series.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
Series.copy()	Make a copy of this object's indices and data.
Series.__array__(*args, **kwargs)

Indexing, iteration

Series.items([batch_size, session])	Lazily iterate over (index, value) tuples.
Series.iteritems([batch_size, session])	Lazily iterate over (index, value) tuples.
Series.keys()	Return alias for index.

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

Binary operator functions

Series.add(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operator add).
Series.sub(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator sub).
Series.mul(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator mul).
Series.div(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
Series.truediv(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
Series.floordiv(other[, level, fill_value, axis])	Return Integer division of series and other, element-wise (binary operator floordiv).
Series.mod(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operator mod).
Series.pow(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operator pow).
Series.radd(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operator radd).
Series.rsub(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator rsub).
Series.rmul(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator rmul).
Series.rdiv(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator rtruediv).
Series.rtruediv(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator rtruediv).
Series.rfloordiv(other[, level, fill_value, ...])	Return Integer division of series and other, element-wise (binary operator rfloordiv).
Series.rmod(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operator rmod).
Series.rpow(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operator rpow).
Series.round([decimals])	Round each value in a Series to the given number of decimals.
Series.lt(other[, level, axis])	Return Less than of series and other, element-wise (binary operator lt).
Series.gt(other[, level, axis])	Return Greater than of series and other, element-wise (binary operator gt).
Series.le(other[, level, axis])	Return Less than or equal to of series and other, element-wise (binary operator le).
Series.ge(other[, level, axis])	Return Greater than or equal to of series and other, element-wise (binary operator ge).
Series.ne(other[, level, axis])	Return Not equal to of series and other, element-wise (binary operator ne).
Series.eq(other[, level, axis])	Return Equal to of series and other, element-wise (binary operator eq).
Series.product([axis, skipna, level, ...])	Return the product of the values over 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, ...])	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[, convert_dtype, ...])	Call func on self producing a Series with the same axis shape as self.
Series.map(arg[, na_action, dtype, ...])	Map values of Series according to an input mapping or function.
Series.groupby([by, level, as_index, sort, ...])	Group Series using a mapper or by a Series of columns.
Series.rolling(window[, min_periods, ...])	Provide rolling window calculations.
Series.expanding([min_periods, center, axis])	Provide expanding window calculations.
Series.ewm([com, span, halflife, alpha, ...])	Provide exponentially weighted (EW) calculations.

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.corr(other[, method, min_periods])	Compute correlation with other Series, excluding missing values.
Series.count([level, combine_size])	Return number of non-NA/null observations in the Series.
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.
Series.diff([periods])	First discrete difference of element.
Series.kurt([axis, skipna, level, ...])	Return unbiased kurtosis over requested axis.
Series.max([axis, skipna, level, ...])	Return the maximum of the values over the requested axis.
Series.mean([axis, skipna, level, ...])	Return the mean of the values over the requested axis.
Series.median([axis, skipna, out, ...])	Return the median of the values over the requested axis.
Series.min([axis, skipna, level, ...])	Return the minimum of the values over the requested axis.
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 over the requested axis.
Series.quantile([q, interpolation])	Return value at the given quantile.
Series.sem([axis, skipna, level, ddof, ...])	Return unbiased standard error of the mean over requested axis.
Series.skew([axis, skipna, level, ...])	Return unbiased skew over requested axis.
Series.std([axis, skipna, level, ddof, ...])	Return sample standard deviation over requested axis.
Series.sum([axis, skipna, level, min_count, ...])	Return the sum of the values over 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.
Series.unique([method])	Return unique values of Series object.
Series.nunique([dropna, combine_size])	Return number of unique elements in the object.
Series.is_monotonic	Return boolean if values in the object are monotonically increasing.
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.value_counts([normalize, sort, ...])	Return a Series containing counts of unique values.

Reindexing / selection / label manipulation

Series.align(other[, join, axis, level, ...])	Align two objects on their axes with the specified join method.
Series.drop([labels, axis, index, columns, ...])	Return Series with specified index labels removed.
Series.drop_duplicates([keep, inplace, method])	Return Series with duplicate values removed.
Series.duplicated([keep, method])	Indicate duplicate Series values.
Series.head([n])	Return the first n rows.
Series.isin(values)	Whether elements in Series are contained in values.
Series.reindex(*args, **kwargs)	Conform Series to new index with optional filling logic.
Series.reindex_like(other[, method, copy, ...])	Return an object with matching indices as other object.
Series.rename([index, axis, copy, inplace, ...])	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, ...])	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.set_axis(labels[, axis, inplace])	Assign desired index to given axis.
Series.tail([n])	Return the last n rows.
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.

Missing data handling

Series.backfill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='bfill'.
Series.bfill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='bfill'.
Series.dropna([axis, inplace, how])	Return a new Series with missing values removed.
Series.ffill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='ffill'.
Series.fillna([value, method, axis, ...])	Fill NA/NaN values using the specified method.
Series.isna()	Detect missing values.
Series.isnull()	Series.isnull is an alias for Series.isna.
Series.notna()	Detect existing (non-missing) values.
Series.notnull()	Series.notnull is an alias for Series.notna.
Series.pad([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='ffill'.
Series.replace([to_replace, value, inplace, ...])	Replace values given in to_replace with value.

Reshaping, sorting

Series.sort_values([axis, ascending, ...])	Sort by the values.
Series.sort_index([axis, level, ascending, ...])	Sort Series by index labels.
Series.explode([ignore_index])	Transform each element of a list-like to a row.

Combining / comparing / joining / merging

Series.append(other[, ignore_index, ...])

Concatenate two or more Series.

Time Series-related

Series.shift([periods, freq, axis, fill_value])	Shift index by desired number of periods with an optional time freq.
Series.tshift([periods, freq, axis])	Shift the time index, using the index's frequency if available.

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.
Series.dt.time	Returns numpy array of datetime.time objects.
Series.dt.timetz	Returns numpy array of datetime.time objects with timezones.
Series.dt.year	The year of the datetime.
Series.dt.month	The month as January=1, December=12.
Series.dt.day	The day 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 according to the ISO 8601 standard.
Series.dt.weekofyear	The week ordinal of the year according to the ISO 8601 standard.
Series.dt.dayofweek	The day of the week with Monday=0, Sunday=6.
Series.dt.day_of_week	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.day_of_year	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 the timezone.
Series.dt.freq

Datetime methods

Series.dt.isocalendar()	Calculate year, week, and day according to the ISO 8601 standard.
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 datetime.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 with specified locale.
Series.dt.day_name(*args, **kwargs)	Return the day names with specified locale.

Period properties

Series.dt.qyear
Series.dt.start_time	Get the Timestamp for the start of the period.
Series.dt.end_time	Get the Timestamp for the end of the period.

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.casefold()	Convert strings in the Series/Index to be casefolded.
Series.str.cat([others, sep, na_rep, join])	Concatenate strings in the Series/Index with given separator.
Series.str.center(width[, fillchar])	Pad left and right side of strings in the Series/Index.
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])	Extract capture groups in the regex pat as columns in DataFrame.
Series.str.find(sub[, start, end])	Return lowest indexes in each strings in the Series/Index.
Series.str.findall(pat[, flags])	Find all occurrences of pattern or regular expression in the Series/Index.
Series.str.fullmatch(pat[, case, flags, na])	Determine if each string entirely matches a regular expression.
Series.str.get(i)	Extract element from each component at specified position or with specified key.
Series.str.index(sub[, start, end])	Return lowest indexes in each string in Series/Index.
Series.str.join(sep)	Join lists contained as elements in the Series/Index with passed delimiter.
Series.str.len()	Compute the length of each element in the Series/Index.
Series.str.ljust(width[, fillchar])	Pad right side of strings in the Series/Index.
Series.str.lower()	Convert strings in the Series/Index to lowercase.
Series.str.lstrip([to_strip])	Remove leading characters.
Series.str.match(pat[, case, flags, na])	Determine if each string starts with a match of 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.removeprefix(prefix)	Remove a prefix from an object series.
Series.str.removesuffix(suffix)	Remove a suffix from an object series.
Series.str.repeat(repeats)	Duplicate each string in the Series or Index.
Series.str.replace(pat, repl[, n, case, ...])	Replace each occurrence of pattern/regex in the Series/Index.
Series.str.rfind(sub[, start, end])	Return highest indexes in each strings in the Series/Index.
Series.str.rindex(sub[, start, end])	Return highest indexes in each string in Series/Index.
Series.str.rjust(width[, fillchar])	Pad left side of strings in the Series/Index.
Series.str.rpartition([sep, expand])	Split the string at the last occurrence of sep.
Series.str.rstrip([to_strip])	Remove 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)	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 strings in Series/Index at specified line 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.

Categorical accessor

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

Sparse accessor

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

Flags

Flags refer to attributes of the xorbits.pandas object. Properties of the dataset (like the date is was recorded, the URL it was accessed from, etc.) should be stored in Series.attrs.

Metadata

Series.attrs is a dictionary for storing global metadata for this Series.

Warning

Series.attrs is considered experimental and may change without warning.

Plotting

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

Series.plot

alias of xorbits.pandas.plotting.PlotAccessor

Series.plot.area(*args, **kwargs)	Draw a stacked area plot.
Series.plot.bar(*args, **kwargs)	Vertical bar plot.
Series.plot.barh(*args, **kwargs)	Make a horizontal bar plot.
Series.plot.box(*args, **kwargs)	Make a box plot of the DataFrame columns.
Series.plot.density(*args, **kwargs)	Generate Kernel Density Estimate plot using Gaussian kernels.
Series.plot.hist(*args, **kwargs)	Draw one histogram of the DataFrame's columns.
Series.plot.kde(*args, **kwargs)	Generate Kernel Density Estimate plot using Gaussian kernels.
Series.plot.line(*args, **kwargs)	Plot Series or DataFrame as lines.
Series.plot.pie(*args, **kwargs)	Generate a pie plot.

Serialization / IO / conversion

Series.to_csv(path[, sep, na_rep, ...])	Write object to a comma-separated values (csv) file.
Series.to_dict([into, batch_size, session])	Convert Series to {label -> value} dict or dict-like object.
Series.to_frame([name])	Convert Series to DataFrame.
Series.to_sql(name, con[, schema, ...])	Write records stored in a DataFrame to a SQL database.