Series

Constructor

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

Attributes

Axes

Series.index

The index (axis labels) of the Series.

Series.dtype	Return the dtype object of the underlying data.
Series.memory_usage([index, deep])	Return the memory usage of the Series.
Series.ndim	Return an int representing the number of axes / array dimensions.
Series.name
Series.shape
Series.T	Return the transpose, which is by definition self.

Conversion

Series.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
Series.convert_dtypes([infer_objects, ...])	Convert columns to best possible dtypes using dtypes supporting pd.NA.
Series.copy([deep])	Make a copy of this object's indices and data.
Series.infer_objects([copy])	Attempt to infer better dtypes for object columns.
Series.to_frame([name])	Convert Series to DataFrame.

Index, iteration

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.iloc	Purely integer-location based indexing for selection by position.
Series.loc	Access a group of rows and columns by label(s) or a boolean array.
Series.mask(cond[, other, inplace, axis, ...])	Replace values where the condition is True.
Series.pop(item)	Return item and drops from series.
Series.xs(key[, axis, level, drop_level])	Return cross-section from the Series/DataFrame.
Series.where(cond[, other, inplace, axis, ...])	Replace values where the condition is False.

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 subtract).
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 rsubtract).
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.lt(other[, level, fill_value, axis])	Return Less than of series and other, element-wise (binary operator lt).
Series.gt(other[, level, fill_value, axis])	Return Greater than of series and other, element-wise (binary operator gt).
Series.le(other[, level, fill_value, axis])	Return Less than or equal to of series and other, element-wise (binary operator le).
Series.ge(other[, level, fill_value, axis])	Return Greater than or equal to of series and other, element-wise (binary operator ge).
Series.ne(other[, level, fill_value, axis])	Return Not equal to of series and other, element-wise (binary operator ne).
Series.eq(other[, level, fill_value, axis])	Return Equal to of series and other, element-wise (binary operator eq).
Series.combine(other, func[, fill_value])	Combine the Series with a Series or scalar according to func.
Series.combine_first(other)	Update null elements with value in the same location in '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.ewm([com, span, halflife, alpha, ...])	Provide exponential weighted functions.
Series.expanding([min_periods, shift, ...])	Provide expanding transformations.
Series.groupby([by, level, as_index, sort, ...])	Group DataFrame using a mapper or by a Series of columns.
Series.map(arg[, na_action, dtype, ...])	Map values of Series according to input correspondence.
Series.rolling(window[, min_periods, ...])	Provide rolling window calculations.
Series.transform(func[, convert_dtype, ...])	Call func on self producing a Series with transformed values.

Computations / descriptive stats

Series.abs()
Series.all([axis, bool_only, skipna, level, ...])
Series.any([axis, bool_only, skipna, level, ...])
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 with other Series, excluding missing values.
Series.count([level])
Series.cov(other[, min_periods, ddof])	Compute covariance with Series, excluding missing values.
Series.describe([percentiles, include, exclude])	Generate descriptive statistics.
Series.factorize([sort, use_na_sentinel])	Encode the object as an enumerated type or categorical variable.
Series.is_monotonic_increasing	Return boolean scalar if values in the object are monotonic_increasing.
Series.is_monotonic_decreasing	Return boolean scalar if values in the object are monotonic_decreasing.
Series.is_unique	Return boolean if values in the object are unique.
Series.max([axis, skipna, level, method])
Series.mean([axis, skipna, level, method])
Series.median([axis, skipna, level, method])
Series.min([axis, skipna, level, method])
Series.mode([dropna, combine_size])	Return the mode(s) of the Series.
Series.nlargest(n[, keep])	Return the largest n elements.
Series.nsmallest(n[, keep])	Return the smallest n elements.
Series.nunique([dropna])	Return number of unique elements in the object.
Series.prod([axis, skipna, level, ...])
Series.product([axis, skipna, level, ...])
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.round([decimals])	Round each value in a Series to the given number of decimals.
Series.sem([axis, skipna, level, ddof, method])
Series.std([axis, skipna, level, ddof, method])
Series.sum([axis, skipna, level, min_count, ...])
Series.unique([method])	Uniques are returned in order of appearance.
Series.value_counts([normalize, sort, ...])	Return a Series containing counts of unique values.
Series.var([axis, skipna, level, ddof, method])

Reindexing / selection / label manipulation

Series.add_prefix(prefix)	Prefix labels with string prefix.
Series.add_suffix(suffix)	Suffix labels with string suffix.
Series.align(other[, join, axis, level, ...])	Align two objects on their axes with the specified join method.
Series.at_time(time[, 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).
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.drop_duplicates([keep, inplace, ...])	Return Series with duplicate values removed.
Series.droplevel(level[, axis])	Return Series/DataFrame with requested index / column level(s) removed.
Series.filter([items, like, regex, axis])	Subset the dataframe rows or columns according to the specified index labels.
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)	Whether elements in Series are contained in values.
Series.reindex([labels, index, columns, ...])	Conform Series/DataFrame 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.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.set_axis(labels[, axis, inplace])	Assign desired index to given axis.
Series.take(indices[, axis])	Return the elements in the given positional indices along an axis.
Series.truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.

Missing data handling

Series.dropna([axis, inplace, how, ignore_index])	Return a new Series with missing values removed.
Series.fillna([value, method, axis, ...])	Fill NA/NaN values using the specified method.
Series.isna()	Detect missing values.
Series.notna()	Detect existing (non-missing) values.
Series.dropna([axis, inplace, how, ignore_index])	Return a new Series with missing values removed.
Series.fillna([value, method, axis, ...])	Fill NA/NaN values using the specified method.

Reshaping, sorting

Series.argmax([axis, skipna])	Return int position of the smallest value in the Series.
Series.argmin([axis, skipna])	Return int position of the smallest value in the Series.
Series.argsort([axis, kind, order, stable])	Return the integer indices that would sort the Series values.
Series.explode([ignore_index, ...])	Transform each element of a list-like to a row.
Series.reorder_levels(order)	Rearrange index levels using input order.
Series.repeat(repeats[, axis])	Repeat elements of a Series.
Series.sort_values([axis, ascending, ...])	Sort by the values.
Series.sort_index([axis, level, ascending, ...])	Sort object by labels (along an axis).
Series.swaplevel([i, j])	Swap levels i and j in a MultiIndex.
Series.unstack([level, fill_value])	Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.

Combining / comparing / joining / merging

Series.append(other[, ignore_index, ...])	Append rows of other to the end of caller, returning a new object.
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.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.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
Dict	dict

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.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.

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 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.

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.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.endswith(pat[, na])	Test if the end of each string element matches a pattern.
Series.str.find(sub[, start, end])	Return lowest indexes in each strings in the Series/Index.
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.pad(width[, side, fillchar])	Pad strings in the Series/Index up to width.
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.rjust(width[, fillchar])	Pad left side of strings in the Series/Index.
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.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.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.

Dict properties

Series.dict can be used to access the methods of the series with dict values. These can be accessed like Series.dict.<method>.

Dict methods

Series.dict.__getitem__(query_key)	Get the value by the key of each dict in the Series.
Series.dict.__setitem__(query_key, value)	Set the value with the key to each dict of the Series.
Series.dict.contains(query_key)	Check whether the key is in each dict of the Series.
Series.dict.get(query_key[, default_value])	Get the value by the key of each dict in the Series.
Series.dict.len()	Get the length of each dict of the Series.
Series.dict.remove(query_key[, ignore_key_error])	Remove the item by the key from each dict of the Series.

List properties

Series.list can be used to access the methods of the series with list values. These can be accessed like Series.list.<method>.

List methods

Series.list.__getitem__(query_index)	Get the value by the index of each list in the Series.
Series.list.len()	Get the length of each list of the Series.

Struct properties

Series.struct can be used to access the methods of the series with struct values. These can be accessed like Series.struct.<method>.

Struct methods

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.

Plotting

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

Series.plot

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_json([path, orient, date_format, ...])	Convert the object to a JSON string.
Series.to_list([batch_size, session])	Return a list of the values.

MaxFrame Extensions

Series.mf.apply_chunk(func[, batch_rows, ...])	Apply a function that takes pandas Series and outputs pandas DataFrame/Series.
Series.mf.flatmap(func[, dtypes, dtype, ...])	Apply the given function to each row and then flatten results.
Series.mf.flatjson(query_paths[, dtypes, ...])	Flat JSON object in the series to a dataframe according to JSON query.
Series.rechunk(chunk_size[, reassign_worker])	Rechunk DataFrame, Series or Index data.

Series.mf The Series.mf provides methods unique to MaxFrame. These methods are collated from application scenarios in MaxCompute and these can be accessed like Series.mf.<function/property>.