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.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.T`	Return the transpose, which is by definition self.
`Series.memory_usage`([index, deep])	Return the memory usage of the Series.
`Series.hasnans`	Return True if there are any NaNs.
`Series.empty`	Indicator whether Series/DataFrame is empty.
`Series.dtypes`	Return the dtype object of the underlying data.
`Series.name`	Return the name of the Series.
`Series.flags`	Get the properties associated with this pandas object.
`Series.set_flags`(*[, copy, ...])	Return a new object with updated flags.

Conversion#

`Series.astype`(dtype[, copy, errors])	Cast a pandas object to a specified dtype`dtype`.
`Series.convert_dtypes`([infer_objects, ...])	Convert columns to the best possible dtypes using dtypes supporting`pd.NA`.
`Series.infer_objects`([copy])	Attempt to infer better dtypes for object columns.
`Series.copy`([deep])	Make a copy of this object's indices and data.
`Series.bool`()	(DEPRECATED) Return the bool of a single element Series or DataFrame.
`Series.to_numpy`([dtype, copy, na_value])	A NumPy ndarray representing the values in this Series or Index.
`Series.to_period`([freq, copy])	Convert Series from DatetimeIndex to PeriodIndex.
`Series.to_timestamp`([freq, how, copy])	Cast to DatetimeIndex of Timestamps, atbeginning of period.
`Series.to_list`()	Return a list of the values.
`Series.__array__`([dtype, copy])	Return the values as a NumPy array.

Indexing, iteration#

`Series.get`(key[, default])	Get item from object for given key (ex: DataFrame column).
`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`	(DEPRECATED) Purely integer-location based indexing for selection by position.
`Series.__iter__`()	Return an iterator of the values.
`Series.items`()	Lazily iterate over (index, value) tuples.
`Series.keys`()	Return alias for index.
`Series.pop`(item)	Return item and drops from series.
`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 theindexing documentation.

Binary operator functions#

`Series.add`(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operatoradd).
`Series.sub`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operatorsub).
`Series.mul`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operatormul).
`Series.div`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operatortruediv).
`Series.truediv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operatortruediv).
`Series.floordiv`(other[, level, fill_value, axis])	Return Integer division of series and other, element-wise (binary operatorfloordiv).
`Series.mod`(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operatormod).
`Series.pow`(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operatorpow).
`Series.radd`(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operatorradd).
`Series.rsub`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operatorrsub).
`Series.rmul`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operatorrmul).
`Series.rdiv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operatorrtruediv).
`Series.rtruediv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operatorrtruediv).
`Series.rfloordiv`(other[, level, fill_value, ...])	Return Integer division of series and other, element-wise (binary operatorrfloordiv).
`Series.rmod`(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operatorrmod).
`Series.rpow`(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operatorrpow).
`Series.combine`(other, func[, fill_value])	Combine the Series with a Series or scalar according tofunc.
`Series.combine_first`(other)	Update null elements with value in the same location in 'other'.
`Series.round`([decimals])	Round each value in a Series to the given number of decimals.
`Series.lt`(other[, level, fill_value, axis])	Return Less than of series and other, element-wise (binary operatorlt).
`Series.gt`(other[, level, fill_value, axis])	Return Greater than of series and other, element-wise (binary operatorgt).
`Series.le`(other[, level, fill_value, axis])	Return Less than or equal to of series and other, element-wise (binary operatorle).
`Series.ge`(other[, level, fill_value, axis])	Return Greater than or equal to of series and other, element-wise (binary operatorge).
`Series.ne`(other[, level, fill_value, axis])	Return Not equal to of series and other, element-wise (binary operatorne).
`Series.eq`(other[, level, fill_value, axis])	Return Equal to of series and other, element-wise (binary operatoreq).
`Series.product`([axis, skipna, numeric_only, ...])	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, args, by_row])	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 the same axis shape as self.
`Series.map`(arg[, na_action])	Map values of Series according to an input mapping or function.
`Series.groupby`([by, axis, level, as_index, ...])	Group Series using a mapper or by a Series of columns.
`Series.rolling`(window[, min_periods, ...])	Provide rolling window calculations.
`Series.expanding`([min_periods, axis, method])	Provide expanding window calculations.
`Series.ewm`([com, span, halflife, alpha, ...])	Provide exponentially weighted (EW) calculations.
`Series.pipe`(func, args, *kwargs)	Apply chainable functions that expect Series or DataFrames.

Computations / descriptive stats#

`Series.abs`()	Return a Series/DataFrame with absolute numeric value of each element.
`Series.all`([axis, bool_only, skipna])	Return whether all elements are True, potentially over an axis.
`Series.any`(*[, axis, bool_only, skipna])	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.corr`(other[, method, min_periods])	Compute correlation withother Series, excluding missing values.
`Series.count`()	Return number of non-NA/null observations in the Series.
`Series.cov`(other[, min_periods, ddof])	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.
`Series.diff`([periods])	First discrete difference of element.
`Series.factorize`([sort, use_na_sentinel])	Encode the object as an enumerated type or categorical variable.
`Series.kurt`([axis, skipna, numeric_only])	Return unbiased kurtosis over requested axis.
`Series.max`([axis, skipna, numeric_only])	Return the maximum of the values over the requested axis.
`Series.mean`([axis, skipna, numeric_only])	Return the mean of the values over the requested axis.
`Series.median`([axis, skipna, numeric_only])	Return the median of the values over the requested axis.
`Series.min`([axis, skipna, numeric_only])	Return the minimum of the values over the requested axis.
`Series.mode`([dropna])	Return the mode(s) of the Series.
`Series.nlargest`([n, keep])	Return the largestn elements.
`Series.nsmallest`([n, keep])	Return the smallestn elements.
`Series.pct_change`([periods, fill_method, ...])	Fractional change between the current and a prior element.
`Series.prod`([axis, skipna, numeric_only, ...])	Return the product of the values over 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, ddof, numeric_only])	Return unbiased standard error of the mean over requested axis.
`Series.skew`([axis, skipna, numeric_only])	Return unbiased skew over requested axis.
`Series.std`([axis, skipna, ddof, numeric_only])	Return sample standard deviation over requested axis.
`Series.sum`([axis, skipna, numeric_only, ...])	Return the sum of the values over the requested axis.
`Series.var`([axis, skipna, ddof, numeric_only])	Return unbiased variance over requested axis.
`Series.kurtosis`([axis, skipna, numeric_only])	Return unbiased kurtosis over requested axis.
`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_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.case_when`(caselist)	Replace values where the conditions are True.
`Series.drop`([labels, axis, index, columns, ...])	Return Series with specified index labels removed.
`Series.droplevel`(level[, axis])	Return Series/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)	(DEPRECATED) Select initial periods of time series data based on a date offset.
`Series.head`([n])	Return the firstn 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)	Whether elements in Series are contained invalues.
`Series.last`(offset)	(DEPRECATED) Select final periods of time series data based on a date offset.
`Series.reindex`([index, axis, method, copy, ...])	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, axis, ...])	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, copy])	Assign desired index to given axis.
`Series.take`(indices[, axis])	Return the elements in the givenpositional indices along an axis.
`Series.tail`([n])	Return the lastn rows.
`Series.truncate`([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.
`Series.where`(cond[, other, inplace, axis, level])	Replace values where the condition is False.
`Series.mask`(cond[, other, inplace, axis, level])	Replace values where the condition is True.
`Series.add_prefix`(prefix[, axis])	Prefix labels with stringprefix.
`Series.add_suffix`(suffix[, axis])	Suffix labels with stringsuffix.
`Series.filter`([items, like, regex, axis])	Subset the dataframe rows or columns according to the specified index labels.

Missing data handling#

`Series.backfill`(*[, axis, inplace, limit, ...])	(DEPRECATED) Fill NA/NaN values by using the next valid observation to fill the gap.
`Series.bfill`(*[, axis, inplace, limit, ...])	Fill NA/NaN values by using the next valid observation to fill the gap.
`Series.dropna`(*[, axis, inplace, how, ...])	Return a new Series with missing values removed.
`Series.ffill`(*[, axis, inplace, limit, ...])	Fill NA/NaN values by propagating the last valid observation to next valid.
`Series.fillna`([value, method, axis, ...])	Fill NA/NaN values using the specified method.
`Series.interpolate`([method, axis, limit, ...])	Fill NaN values using an interpolation 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])	(DEPRECATED) Fill NA/NaN values by propagating the last valid observation to next valid.
`Series.replace`([to_replace, value, inplace, ...])	Replace values given into_replace withvalue.

Reshaping, sorting#

`Series.argsort`([axis, kind, order, stable])	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.reorder_levels`(order)	Rearrange index levels using input order.
`Series.sort_values`(*[, axis, ascending, ...])	Sort by the values.
`Series.sort_index`(*[, axis, level, ...])	Sort Series by index labels.
`Series.swaplevel`([i, j, copy])	Swap levels i and j in a`MultiIndex`.
`Series.unstack`([level, fill_value, sort])	Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
`Series.explode`([ignore_index])	Transform each element of a list-like to a row.
`Series.searchsorted`(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
`Series.ravel`([order])	(DEPRECATED) Return the flattened underlying data as an ndarray or ExtensionArray.
`Series.repeat`(repeats[, axis])	Repeat elements of a Series.
`Series.squeeze`([axis])	Squeeze 1 dimensional axis objects into scalars.
`Series.view`([dtype])	(DEPRECATED) Create a new view of the Series.

Combining / comparing / joining / merging#

`Series.compare`(other[, align_axis, ...])	Compare to another Series and show the differences.
`Series.update`(other)	Modify Series in place using values from passed Series.

Time Series-related#

`Series.asfreq`(freq[, method, how, ...])	Convert time series to specified frequency.
`Series.asof`(where[, subset])	Return the last row(s) without any NaNs beforewhere.
`Series.shift`([periods, freq, axis, ...])	Shift index by desired number of periods with an optional timefreq.
`Series.first_valid_index`()	Return index for first non-NA value or None, if no non-NA value is found.
`Series.last_valid_index`()	Return index for last non-NA value or None, if no non-NA value is found.
`Series.resample`(rule[, axis, closed, label, ...])	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., 9:30AM).
`Series.between_time`(start_time, end_time[, ...])	Select values between particular times of the day (e.g., 9:00-9:30 AM).

Accessors#

pandas provides dtype-specific methods under various accessors.These are separate namespaces withinSeries that only applyto specific data types.

`Series.str`	alias of`StringMethods`
`Series.cat`	alias of`CategoricalAccessor`
`Series.dt`	alias of`CombinedDatetimelikeProperties`
`Series.sparse`	alias of`SparseAccessor`
`DataFrame.sparse`	alias of`SparseFrameAccessor`
`Index.str`	alias of`StringMethods`

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 asdatetimelike and return several properties.These can be accessed likeSeries.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.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.days_in_month`	The number of days in the month.
`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`	Return the frequency object for this PeriodArray.
`Series.dt.unit`

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/PeriodIndex at a particular frequency.
`Series.dt.to_pydatetime`()	(DEPRECATED) 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 specifiedfreq.
`Series.dt.floor`(args, *kwargs)	Perform floor operation on the data to the specifiedfreq.
`Series.dt.ceil`(args, *kwargs)	Perform ceil operation on the data to the specifiedfreq.
`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.
`Series.dt.as_unit`(args, *kwargs)

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.
`Series.dt.unit`

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.
`Series.dt.as_unit`(args, *kwargs)

String handling#

Series.str can be used to access the values of the series asstrings and apply several methods to it. These can be accessed likeSeries.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 regexpat as columns in a DataFrame.
`Series.str.extractall`(pat[, flags])	Extract capture groups in the regexpat 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 ofsep.
`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 ofsep.
`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, regex])	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.
`Series.str.get_dummies`([sep])	Return DataFrame of dummy/indicator variables for Series.

Categorical accessor#

Categorical-dtype specific methods and attributes are available undertheSeries.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)	Rename categories.
`Series.cat.reorder_categories`(args, *kwargs)	Reorder categories as specified in new_categories.
`Series.cat.add_categories`(args, *kwargs)	Add new categories.
`Series.cat.remove_categories`(args, *kwargs)	Remove the specified categories.
`Series.cat.remove_unused_categories`(*args, ...)	Remove categories which are not used.
`Series.cat.set_categories`(args, *kwargs)	Set 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 theSeries.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 indata that arefill_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 Series with sparse values from a scipy.sparse.coo_matrix.
`Series.sparse.to_coo`([row_levels, ...])	Create a scipy.sparse.coo_matrix from a Series with MultiIndex.

List accessor#

Arrow list-dtype specific methods and attributes are provided under theSeries.list accessor.

`Series.list.flatten`()	Flatten list values.
`Series.list.len`()	Return the length of each list in the Series.
`Series.list.__getitem__`(key)	Index or slice lists in the Series.

Struct accessor#

Arrow struct-dtype specific methods and attributes are provided under theSeries.struct accessor.

Series.struct.dtypes

Return the dtype object of each child field of the struct.

`Series.struct.field`(name_or_index)	Extract a child field of a struct as a Series.
`Series.struct.explode`()	Extract all child fields of a struct as a DataFrame.

Flags#

Flags refer to attributes of the pandas object. Properties of the dataset (likethe date is was recorded, the URL it was accessed from, etc.) should be storedinSeries.attrs.

Flags(obj, *, allows_duplicate_labels)

Flags that apply to pandas objects.

Metadata#

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

Warning

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

Series.attrs

Dictionary of global attributes of this dataset.

Plotting#

Series.plot is both a callable method and a namespace attribute forspecific plotting methods of the formSeries.plot.<kind>.

Series.plot([kind, ax, figsize, ....])

Series plotting accessor and method

`Series.plot.area`([x, y, stacked])	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`([by])	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`([by, 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 Series or DataFrame as lines.
`Series.plot.pie`(**kwargs)	Generate a pie plot.

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

Draw histogram of the input series using matplotlib.

Serialization / IO / conversion#

`Series.to_pickle`(path, *[, compression, ...])	Pickle (serialize) object to file.
`Series.to_csv`([path_or_buf, sep, na_rep, ...])	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, *[, ...])	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[, mode, ...])	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_json`([path_or_buf, orient, ...])	Convert the object to a JSON string.
`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, header, ...])	Render object to a LaTeX tabular, longtable, or nested table.
`Series.to_markdown`([buf, mode, index, ...])	Print Series in Markdown-friendly format.

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Series#