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.shape

Series.ndim

Return an int representing the number of axes / array dimensions.

Series.name

Conversion

Series.astype(dtype[, copy, errors])

Cast a pandas object to a specified dtype dtype.

Series.copy([deep])

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

Series.to_frame([name])

Convert Series to DataFrame.

Series.to_tensor([dtype])

Indexing, iteration

Series.at

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

Series.iat

Series.loc

Series.iloc

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, 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.dot(other)

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

Function application, groupby & window

Series.apply(func[, convert_dtype, args])

Series.agg(func[, axis])

Series.aggregate(func[, axis])

Series.transform(func[, convert_dtype, …])

Series.map(arg[, na_action, dtype])

Series.groupby([by, level, as_index, sort, …])

Series.rolling(window[, min_periods, …])

Provide rolling window calculations.

Series.expanding([min_periods, center, axis])

Provide expanding transformations.

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

Provide exponential weighted functions.

Computations / descriptive stats

Series.abs()

Series.count([level, combine_size])

Series.cummax([axis, skipna])

Series.cummin([axis, skipna])

Series.cumprod([axis, skipna])

Series.cumsum([axis, skipna])

Series.describe([percentiles, include, exclude])

Series.max([axis, skipna, level, combine_size])

Series.mean([axis, skipna, level, combine_size])

Series.min([axis, skipna, level, combine_size])

Series.prod([axis, skipna, level, …])

Series.product([axis, skipna, level, …])

Series.quantile([q, interpolation])

Return value at the given quantile.

Series.round([decimals])

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

Series.std([axis, skipna, level, ddof, …])

Series.sum([axis, skipna, level, min_count, …])

Series.var([axis, skipna, level, ddof, …])

Series.nunique([dropna, combine_size])

Return number of unique elements in the object.

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

Return a Series containing counts of unique values.

Reindexing / selection / label manipulation

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.head([n])

Return the first n rows.

Series.isin(values)

Series.rename([index, axis, copy, inplace, …])

Alter Series index labels or name.

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

Series.tail([n])

Return the last n rows.

Missing data handling

Series.isna()

Detect missing values.

Series.notna()

Detect existing (non-missing) values.

Series.dropna([axis, inplace, how])

Return a new Series with missing values removed.

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

Fill NA/NaN values using the specified method.

Reshaping, sorting

Series.sort_values([axis, ascending, …])

Sort by the values.

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

Sort object by labels (along an axis).

Combining / joining / merging

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

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

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 month as January=1, December=12.

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.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])

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()

Compute 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)

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.

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 mars.dataframe.plotting.core.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_sql(name, con[, schema, …])

Write records stored in a DataFrame to a SQL database.