Series([data, index, dtype, name, copy, …])
Series
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
Series.memory_usage([index, deep])
Series.memory_usage
Return the memory usage of the Series.
Series.astype(dtype[, copy, errors])
Series.astype
Cast a pandas object to a specified dtype dtype.
dtype
Series.copy([deep])
Series.copy
Make a copy of this object’s indices and data.
Series.to_frame([name])
Series.to_frame
Convert Series to DataFrame.
Series.to_tensor([dtype])
Series.to_tensor
Series.at
Access a single value for a row/column label pair.
Series.iat
Series.loc
Series.iloc
Series.where(cond[, other, inplace, axis, …])
Series.where
Replace values where the condition is False.
Series.mask(cond[, other, inplace, axis, …])
Series.mask
Replace values where the condition is True.
Series.add(other[, level, fill_value, axis])
Series.add
Return Addition of series and other, element-wise (binary operator add).
Series.sub(other[, level, fill_value, axis])
Series.sub
Return Subtraction of series and other, element-wise (binary operator subtract).
Series.mul(other[, level, fill_value, axis])
Series.mul
Return Multiplication of series and other, element-wise (binary operator mul).
Series.div(other[, level, fill_value, axis])
Series.div
Return Floating division of series and other, element-wise (binary operator truediv).
Series.truediv(other[, level, fill_value, axis])
Series.truediv
Series.floordiv(other[, level, fill_value, axis])
Series.floordiv
Return Integer division of series and other, element-wise (binary operator floordiv).
Series.mod(other[, level, fill_value, axis])
Series.mod
Return Modulo of series and other, element-wise (binary operator mod).
Series.pow(other[, level, fill_value, axis])
Series.pow
Return Exponential power of series and other, element-wise (binary operator pow).
Series.radd(other[, level, fill_value, axis])
Series.radd
Return Addition of series and other, element-wise (binary operator radd).
Series.rsub(other[, level, fill_value, axis])
Series.rsub
Return Subtraction of series and other, element-wise (binary operator rsubtract).
Series.rmul(other[, level, fill_value, axis])
Series.rmul
Return Multiplication of series and other, element-wise (binary operator rmul).
Series.rdiv(other[, level, fill_value, axis])
Series.rdiv
Return Floating division of series and other, element-wise (binary operator rtruediv).
Series.rtruediv(other[, level, fill_value, axis])
Series.rtruediv
Series.rfloordiv(other[, level, fill_value, …])
Series.rfloordiv
Return Integer division of series and other, element-wise (binary operator rfloordiv).
Series.rmod(other[, level, fill_value, axis])
Series.rmod
Return Modulo of series and other, element-wise (binary operator rmod).
Series.rpow(other[, level, fill_value, axis])
Series.rpow
Return Exponential power of series and other, element-wise (binary operator rpow).
Series.lt(other[, level, axis])
Series.lt
Return Less than of series and other, element-wise (binary operator lt).
Series.gt(other[, level, axis])
Series.gt
Return Greater than of series and other, element-wise (binary operator gt).
Series.le(other[, level, axis])
Series.le
Return Less than or equal to of series and other, element-wise (binary operator le).
Series.ge(other[, level, axis])
Series.ge
Return Greater than or equal to of series and other, element-wise (binary operator ge).
Series.ne(other[, level, axis])
Series.ne
Return Not equal to of series and other, element-wise (binary operator ne).
Series.eq(other[, level, axis])
Series.eq
Return Equal to of series and other, element-wise (binary operator eq).
Series.dot(other)
Series.dot
Compute the dot product between the Series and the columns of other.
Series.apply(func[, convert_dtype, …])
Series.apply
Series.agg([func, axis])
Series.agg
Series.aggregate([func, axis])
Series.aggregate
Series.transform(func[, convert_dtype, …])
Series.transform
Series.map(arg[, na_action, dtype])
Series.map
Series.groupby([by, level, as_index, sort, …])
Series.groupby
Series.rolling(window[, min_periods, …])
Series.rolling
Provide rolling window calculations.
Series.expanding([min_periods, center, axis])
Series.expanding
Provide expanding transformations.
Series.ewm([com, span, halflife, alpha, …])
Series.ewm
Provide exponential weighted functions.
Series.abs()
Series.abs
Series.all([axis, bool_only, skipna, level, …])
Series.all
Series.any([axis, bool_only, skipna, level, …])
Series.any
Series.autocorr([lag])
Series.autocorr
Compute the lag-N autocorrelation.
Series.corr(other[, method, min_periods])
Series.corr
Compute correlation with other Series, excluding missing values.
Series.count([level, combine_size])
Series.count
Series.cummax([axis, skipna])
Series.cummax
Series.cummin([axis, skipna])
Series.cummin
Series.cumprod([axis, skipna])
Series.cumprod
Series.cumsum([axis, skipna])
Series.cumsum
Series.describe([percentiles, include, exclude])
Series.describe
Series.kurt([axis, skipna, level, …])
Series.kurt
Series.kurtosis([axis, skipna, level, …])
Series.kurtosis
Series.max([axis, skipna, level, combine_size])
Series.max
Series.mean([axis, skipna, level, combine_size])
Series.mean
Series.min([axis, skipna, level, combine_size])
Series.min
Series.prod([axis, skipna, level, …])
Series.prod
Series.product([axis, skipna, level, …])
Series.product
Series.quantile([q, interpolation])
Series.quantile
Return value at the given quantile.
Series.round([decimals])
Series.round
Round each value in a Series to the given number of decimals.
Series.sem([axis, skipna, level, ddof, …])
Series.sem
Series.skew([axis, skipna, level, …])
Series.skew
Series.std([axis, skipna, level, ddof, …])
Series.std
Series.sum([axis, skipna, level, min_count, …])
Series.sum
Series.var([axis, skipna, level, ddof, …])
Series.var
Series.nunique([dropna, combine_size])
Series.nunique
Return number of unique elements in the object.
Series.value_counts([normalize, sort, …])
Series.value_counts
Return a Series containing counts of unique values.
Series.drop([labels, axis, index, columns, …])
Series.drop
Return Series with specified index labels removed.
Series.drop_duplicates([keep, inplace, method])
Series.drop_duplicates
Return Series with duplicate values removed.
Series.head([n])
Series.head
Return the first n rows.
Series.isin(values)
Series.isin
Whether elements in Series are contained in values.
Series.reindex(*args, **kwargs)
Series.reindex
Conform Series/DataFrame to new index with optional filling logic.
Series.rename([index, axis, copy, inplace, …])
Series.rename
Alter Series index labels or name.
Series.reset_index([level, drop, name, inplace])
Series.reset_index
Generate a new DataFrame or Series with the index reset.
Series.tail([n])
Series.tail
Return the last n rows.
Series.isna()
Series.isna
Detect missing values.
Series.notna()
Series.notna
Detect existing (non-missing) values.
Series.dropna([axis, inplace, how])
Series.dropna
Return a new Series with missing values removed.
Series.fillna([value, method, axis, …])
Series.fillna
Fill NA/NaN values using the specified method.
Series.explode([ignore_index])
Series.explode
Transform each element of a list-like to a row.
Series.sort_values([axis, ascending, …])
Series.sort_values
Sort by the values.
Series.sort_index([axis, level, ascending, …])
Series.sort_index
Sort object by labels (along an axis).
Series.append(other[, ignore_index, …])
Series.append
Series.diff([periods])
Series.diff
First discrete difference of element.
Series.shift([periods, freq, axis, fill_value])
Series.shift
Shift index by desired number of periods with an optional time freq.
Series.tshift([periods, freq, axis])
Series.tshift
Shift the time index, using the index’s frequency if available.
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
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>.
Series.dt
Series.dt.<property>
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 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.
Series.dt.weekofyear
Series.dt.dayofweek
The day of the week with Monday=0, Sunday=6.
Series.dt.weekday
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
Series.dt.tz
Return timezone, if any.
Series.dt.freq
Series.dt.to_period(*args, **kwargs)
Series.dt.to_period
Cast to PeriodArray/Index at a particular frequency.
Series.dt.to_pydatetime()
Series.dt.to_pydatetime
Return the data as an array of native Python datetime objects.
Series.dt.tz_localize(*args, **kwargs)
Series.dt.tz_localize
Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index.
Series.dt.tz_convert(*args, **kwargs)
Series.dt.tz_convert
Convert tz-aware Datetime Array/Index from one time zone to another.
Series.dt.normalize(*args, **kwargs)
Series.dt.normalize
Convert times to midnight.
Series.dt.strftime(*args, **kwargs)
Series.dt.strftime
Convert to Index using specified date_format.
Series.dt.round(*args, **kwargs)
Series.dt.round
Perform round operation on the data to the specified freq.
Series.dt.floor(*args, **kwargs)
Series.dt.floor
Perform floor operation on the data to the specified freq.
Series.dt.ceil(*args, **kwargs)
Series.dt.ceil
Perform ceil operation on the data to the specified freq.
Series.dt.month_name(*args, **kwargs)
Series.dt.month_name
Return the month names of the DateTimeIndex with specified locale.
Series.dt.day_name(*args, **kwargs)
Series.dt.day_name
Return the day names of the DateTimeIndex with specified locale.
Series.dt.qyear
Series.dt.start_time
Series.dt.end_time
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.to_pytimedelta()
Series.dt.to_pytimedelta
Return an array of native datetime.timedelta objects.
Series.dt.total_seconds(*args, **kwargs)
Series.dt.total_seconds
Return total duration of each element expressed in seconds.
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
Series.str.<function/property>
Series.str.capitalize()
Series.str.capitalize
Convert strings in the Series/Index to be capitalized.
Series.str.casefold()
Series.str.casefold
Convert strings in the Series/Index to be casefolded.
Series.str.cat([others, sep, na_rep, join])
Series.str.cat
Concatenate strings in the Series/Index with given separator.
Series.str.center(width[, fillchar])
Series.str.center
Pad left and right side of strings in the Series/Index.
Series.str.contains(pat[, case, flags, na, …])
Series.str.contains
Test if pattern or regex is contained within a string of a Series or Index.
Series.str.count(pat[, flags])
Series.str.count
Count occurrences of pattern in each string of the Series/Index.
Series.str.decode(encoding[, errors])
Series.str.decode
Decode character string in the Series/Index using indicated encoding.
Series.str.encode(encoding[, errors])
Series.str.encode
Encode character string in the Series/Index using indicated encoding.
Series.str.endswith(pat[, na])
Series.str.endswith
Test if the end of each string element matches a pattern.
Series.str.extract(pat[, flags, expand])
Series.str.extract
Extract capture groups in the regex pat as columns in a DataFrame.
Series.str.extractall(pat[, flags])
Series.str.extractall
Extract capture groups in the regex pat as columns in DataFrame.
Series.str.find(sub[, start, end])
Series.str.find
Return lowest indexes in each strings in the Series/Index.
Series.str.findall(pat[, flags])
Series.str.findall
Find all occurrences of pattern or regular expression in the Series/Index.
Series.str.get(i)
Series.str.get
Extract element from each component at specified position.
Series.str.index(sub[, start, end])
Series.str.index
Return lowest indexes in each string in Series/Index.
Series.str.join(sep)
Series.str.join
Join lists contained as elements in the Series/Index with passed delimiter.
Series.str.len()
Series.str.len
Compute the length of each element in the Series/Index.
Series.str.ljust(width[, fillchar])
Series.str.ljust
Pad right side of strings in the Series/Index.
Series.str.lower()
Series.str.lower
Convert strings in the Series/Index to lowercase.
Series.str.lstrip([to_strip])
Series.str.lstrip
Remove leading characters.
Series.str.match(pat[, case, flags, na])
Series.str.match
Determine if each string starts with a match of a regular expression.
Series.str.normalize(form)
Series.str.normalize
Return the Unicode normal form for the strings in the Series/Index.
Series.str.pad(width[, side, fillchar])
Series.str.pad
Pad strings in the Series/Index up to width.
Series.str.partition([sep, expand])
Series.str.partition
Split the string at the first occurrence of sep.
Series.str.repeat(repeats)
Series.str.repeat
Duplicate each string in the Series or Index.
Series.str.replace(pat, repl[, n, case, …])
Series.str.replace
Replace each occurrence of pattern/regex in the Series/Index.
Series.str.rfind(sub[, start, end])
Series.str.rfind
Return highest indexes in each strings in the Series/Index.
Series.str.rindex(sub[, start, end])
Series.str.rindex
Return highest indexes in each string in Series/Index.
Series.str.rjust(width[, fillchar])
Series.str.rjust
Pad left side of strings in the Series/Index.
Series.str.rpartition([sep, expand])
Series.str.rpartition
Split the string at the last occurrence of sep.
Series.str.rstrip([to_strip])
Series.str.rstrip
Remove trailing characters.
Series.str.slice([start, stop, step])
Series.str.slice
Slice substrings from each element in the Series or Index.
Series.str.slice_replace([start, stop, repl])
Series.str.slice_replace
Replace a positional slice of a string with another value.
Series.str.split([pat, n, expand])
Series.str.split
Split strings around given separator/delimiter.
Series.str.rsplit([pat, n, expand])
Series.str.rsplit
Series.str.startswith(pat[, na])
Series.str.startswith
Test if the start of each string element matches a pattern.
Series.str.strip([to_strip])
Series.str.strip
Remove leading and trailing characters.
Series.str.swapcase()
Series.str.swapcase
Convert strings in the Series/Index to be swapcased.
Series.str.title()
Series.str.title
Convert strings in the Series/Index to titlecase.
Series.str.translate(table)
Series.str.translate
Map all characters in the string through the given mapping table.
Series.str.upper()
Series.str.upper
Convert strings in the Series/Index to uppercase.
Series.str.wrap(width, **kwargs)
Series.str.wrap
Wrap strings in Series/Index at specified line width.
Series.str.zfill(width)
Series.str.zfill
Pad strings in the Series/Index by prepending ‘0’ characters.
Series.str.isalnum()
Series.str.isalnum
Check whether all characters in each string are alphanumeric.
Series.str.isalpha()
Series.str.isalpha
Check whether all characters in each string are alphabetic.
Series.str.isdigit()
Series.str.isdigit
Check whether all characters in each string are digits.
Series.str.isspace()
Series.str.isspace
Check whether all characters in each string are whitespace.
Series.str.islower()
Series.str.islower
Check whether all characters in each string are lowercase.
Series.str.isupper()
Series.str.isupper
Check whether all characters in each string are uppercase.
Series.str.istitle()
Series.str.istitle
Check whether all characters in each string are titlecase.
Series.str.isnumeric()
Series.str.isnumeric
Check whether all characters in each string are numeric.
Series.str.isdecimal()
Series.str.isdecimal
Check whether all characters in each string are decimal.
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.<kind>
alias of mars.dataframe.plotting.core.PlotAccessor
mars.dataframe.plotting.core.PlotAccessor
Series.plot.area(*args, **kwargs)
Series.plot.area
Draw a stacked area plot.
Series.plot.bar(*args, **kwargs)
Series.plot.bar
Vertical bar plot.
Series.plot.barh(*args, **kwargs)
Series.plot.barh
Make a horizontal bar plot.
Series.plot.box(*args, **kwargs)
Series.plot.box
Make a box plot of the DataFrame columns.
Series.plot.density(*args, **kwargs)
Series.plot.density
Generate Kernel Density Estimate plot using Gaussian kernels.
Series.plot.hist(*args, **kwargs)
Series.plot.hist
Draw one histogram of the DataFrame’s columns.
Series.plot.kde(*args, **kwargs)
Series.plot.kde
Series.plot.line(*args, **kwargs)
Series.plot.line
Plot Series or DataFrame as lines.
Series.plot.pie(*args, **kwargs)
Series.plot.pie
Generate a pie plot.
Series.to_csv(path[, sep, na_rep, …])
Series.to_csv
Write object to a comma-separated values (csv) file.
Series.to_sql(name, con[, schema, …])
Series.to_sql
Write records stored in a DataFrame to a SQL database.
Series.map_chunk(func[, args])
Series.map_chunk
Apply function to each chunk.