Series([data, index, dtype, name, copy, …])
Series
One-dimensional ndarray with axis labels (including time series).
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(self[, index, deep])
Series.memory_usage
Return the memory usage of the Series.
Series.hasnans
Return if I have any nans; enables various perf speedups.
Series.empty
Series.dtypes
Series.name
Series.astype(self, dtype, copy, errors)
Series.astype
Cast a pandas object to a specified dtype dtype.
dtype
Series.infer_objects(self)
Series.infer_objects
Attempt to infer better dtypes for object columns.
Series.copy(self, deep)
Series.copy
Make a copy of this object’s indices and data.
Series.bool(self)
Series.bool
Return the bool of a single element PandasObject.
Series.to_numpy(self[, dtype, copy, na_value])
Series.to_numpy
A NumPy ndarray representing the values in this Series or Index.
Series.to_period(self[, freq, copy])
Series.to_period
Convert Series from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).
Series.to_timestamp(self[, freq, how, copy])
Series.to_timestamp
Cast to DatetimeIndex of Timestamps, at beginning of period.
Series.to_list(self)
Series.to_list
Return a list of the values.
Series.__array__(self[, dtype])
Series.__array__
Return the values as a NumPy array.
Series.get(self, key[, default])
Series.get
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
Purely integer-location based indexing for selection by position.
Series.__iter__(self)
Series.__iter__
Return an iterator of the values.
Series.items(self)
Series.items
Lazily iterate over (index, value) tuples.
Series.iteritems(self)
Series.iteritems
Series.keys(self)
Series.keys
Return alias for index.
Series.pop(self, item)
Series.pop
Return item and drop from frame.
Series.item(self)
Series.item
Return the first element of the underlying data as a python scalar.
Series.xs(self, key[, axis, level])
Series.xs
Return cross-section from the Series/DataFrame.
For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.
.at
.iat
.loc
.iloc
Series.add(self, other[, level, fill_value, …])
Series.add
Return Addition of series and other, element-wise (binary operator add).
Series.sub(self, other[, level, fill_value, …])
Series.sub
Return Subtraction of series and other, element-wise (binary operator sub).
Series.mul(self, other[, level, fill_value, …])
Series.mul
Return Multiplication of series and other, element-wise (binary operator mul).
Series.div(self, other[, level, fill_value, …])
Series.div
Return Floating division of series and other, element-wise (binary operator truediv).
Series.truediv(self, other[, level, …])
Series.truediv
Series.floordiv(self, other[, level, …])
Series.floordiv
Return Integer division of series and other, element-wise (binary operator floordiv).
Series.mod(self, other[, level, fill_value, …])
Series.mod
Return Modulo of series and other, element-wise (binary operator mod).
Series.pow(self, other[, level, fill_value, …])
Series.pow
Return Exponential power of series and other, element-wise (binary operator pow).
Series.radd(self, other[, level, …])
Series.radd
Return Addition of series and other, element-wise (binary operator radd).
Series.rsub(self, other[, level, …])
Series.rsub
Return Subtraction of series and other, element-wise (binary operator rsub).
Series.rmul(self, other[, level, …])
Series.rmul
Return Multiplication of series and other, element-wise (binary operator rmul).
Series.rdiv(self, other[, level, …])
Series.rdiv
Return Floating division of series and other, element-wise (binary operator rtruediv).
Series.rtruediv(self, other[, level, …])
Series.rtruediv
Series.rfloordiv(self, other[, level, …])
Series.rfloordiv
Return Integer division of series and other, element-wise (binary operator rfloordiv).
Series.rmod(self, other[, level, …])
Series.rmod
Return Modulo of series and other, element-wise (binary operator rmod).
Series.rpow(self, other[, level, …])
Series.rpow
Return Exponential power of series and other, element-wise (binary operator rpow).
Series.combine(self, other, func[, fill_value])
Series.combine
Combine the Series with a Series or scalar according to func.
Series.combine_first(self, other)
Series.combine_first
Combine Series values, choosing the calling Series’s values first.
Series.round(self[, decimals])
Series.round
Round each value in a Series to the given number of decimals.
Series.lt(self, other[, level, fill_value, axis])
Series.lt
Return Less than of series and other, element-wise (binary operator lt).
Series.gt(self, other[, level, fill_value, axis])
Series.gt
Return Greater than of series and other, element-wise (binary operator gt).
Series.le(self, other[, level, fill_value, axis])
Series.le
Return Less than or equal to of series and other, element-wise (binary operator le).
Series.ge(self, other[, level, fill_value, axis])
Series.ge
Return Greater than or equal to of series and other, element-wise (binary operator ge).
Series.ne(self, other[, level, fill_value, axis])
Series.ne
Return Not equal to of series and other, element-wise (binary operator ne).
Series.eq(self, other[, level, fill_value, axis])
Series.eq
Return Equal to of series and other, element-wise (binary operator eq).
Series.product(self[, axis, skipna, level, …])
Series.product
Return the product of the values for the requested axis.
Series.dot(self, other)
Series.dot
Compute the dot product between the Series and the columns of other.
Series.apply(self, func[, convert_dtype, args])
Series.apply
Invoke function on values of Series.
Series.agg(self, func[, axis])
Series.agg
Aggregate using one or more operations over the specified axis.
Series.aggregate(self, func[, axis])
Series.aggregate
Series.transform(self, func[, axis])
Series.transform
Call func on self producing a Series with transformed values.
func
Series.map(self, arg[, na_action])
Series.map
Map values of Series according to input correspondence.
Series.groupby(self[, by, axis, level])
Series.groupby
Group Series using a mapper or by a Series of columns.
Series.rolling(self, window[, min_periods, …])
Series.rolling
Provide rolling window calculations.
Series.expanding(self[, min_periods, …])
Series.expanding
Provide expanding transformations.
Series.ewm(self[, com, span, halflife, …])
Series.ewm
Provide exponential weighted functions.
Series.pipe(self, func, \*args, \*\*kwargs)
Series.pipe
Apply func(self, *args, **kwargs).
Series.abs(self)
Series.abs
Return a Series/DataFrame with absolute numeric value of each element.
Series.all(self[, axis, bool_only, skipna, …])
Series.all
Return whether all elements are True, potentially over an axis.
Series.any(self[, axis, bool_only, skipna, …])
Series.any
Return whether any element is True, potentially over an axis.
Series.autocorr(self[, lag])
Series.autocorr
Compute the lag-N autocorrelation.
Series.between(self, left, right[, inclusive])
Series.between
Return boolean Series equivalent to left <= series <= right.
Series.clip(self[, lower, upper, axis])
Series.clip
Trim values at input threshold(s).
Series.corr(self, other[, method, min_periods])
Series.corr
Compute correlation with other Series, excluding missing values.
Series.count(self[, level])
Series.count
Return number of non-NA/null observations in the Series.
Series.cov(self, other[, min_periods])
Series.cov
Compute covariance with Series, excluding missing values.
Series.cummax(self[, axis, skipna])
Series.cummax
Return cumulative maximum over a DataFrame or Series axis.
Series.cummin(self[, axis, skipna])
Series.cummin
Return cumulative minimum over a DataFrame or Series axis.
Series.cumprod(self[, axis, skipna])
Series.cumprod
Return cumulative product over a DataFrame or Series axis.
Series.cumsum(self[, axis, skipna])
Series.cumsum
Return cumulative sum over a DataFrame or Series axis.
Series.describe(self[, percentiles, …])
Series.describe
Generate descriptive statistics.
Series.diff(self[, periods])
Series.diff
First discrete difference of element.
Series.factorize(self[, sort, na_sentinel])
Series.factorize
Encode the object as an enumerated type or categorical variable.
Series.kurt(self[, axis, skipna, level, …])
Series.kurt
Return unbiased kurtosis over requested axis.
Series.mad(self[, axis, skipna, level])
Series.mad
Return the mean absolute deviation of the values for the requested axis.
Series.max(self[, axis, skipna, level, …])
Series.max
Return the maximum of the values for the requested axis.
Series.mean(self[, axis, skipna, level, …])
Series.mean
Return the mean of the values for the requested axis.
Series.median(self[, axis, skipna, level, …])
Series.median
Return the median of the values for the requested axis.
Series.min(self[, axis, skipna, level, …])
Series.min
Return the minimum of the values for the requested axis.
Series.mode(self[, dropna])
Series.mode
Return the mode(s) of the dataset.
Series.nlargest(self[, n, keep])
Series.nlargest
Return the largest n elements.
Series.nsmallest(self[, n, keep])
Series.nsmallest
Return the smallest n elements.
Series.pct_change(self[, periods, …])
Series.pct_change
Percentage change between the current and a prior element.
Series.prod(self[, axis, skipna, level, …])
Series.prod
Series.quantile(self[, q, interpolation])
Series.quantile
Return value at the given quantile.
Series.rank(self[, axis])
Series.rank
Compute numerical data ranks (1 through n) along axis.
Series.sem(self[, axis, skipna, level, …])
Series.sem
Return unbiased standard error of the mean over requested axis.
Series.skew(self[, axis, skipna, level, …])
Series.skew
Return unbiased skew over requested axis.
Series.std(self[, axis, skipna, level, …])
Series.std
Return sample standard deviation over requested axis.
Series.sum(self[, axis, skipna, level, …])
Series.sum
Return the sum of the values for the requested axis.
Series.var(self[, axis, skipna, level, …])
Series.var
Return unbiased variance over requested axis.
Series.kurtosis(self[, axis, skipna, level, …])
Series.kurtosis
Series.unique(self)
Series.unique
Return unique values of Series object.
Series.nunique(self[, dropna])
Series.nunique
Return number of unique elements in the object.
Series.is_unique
Return boolean if values in the object are unique.
Series.is_monotonic
Return boolean if values in the object are monotonic_increasing.
Series.is_monotonic_increasing
Series.is_monotonic_decreasing
Return boolean if values in the object are monotonic_decreasing.
Series.value_counts(self[, normalize, sort, …])
Series.value_counts
Return a Series containing counts of unique values.
Series.align(self, other[, join, axis, …])
Series.align
Align two objects on their axes with the specified join method.
Series.drop(self[, labels, axis, index, …])
Series.drop
Return Series with specified index labels removed.
Series.droplevel(self, level[, axis])
Series.droplevel
Return DataFrame with requested index / column level(s) removed.
Series.drop_duplicates(self[, keep, inplace])
Series.drop_duplicates
Return Series with duplicate values removed.
Series.duplicated(self[, keep])
Series.duplicated
Indicate duplicate Series values.
Series.equals(self, other)
Series.equals
Test whether two objects contain the same elements.
Series.first(self, offset)
Series.first
Method to subset initial periods of time series data based on a date offset.
Series.head(self, n)
Series.head
Return the first n rows.
Series.idxmax(self[, axis, skipna])
Series.idxmax
Return the row label of the maximum value.
Series.idxmin(self[, axis, skipna])
Series.idxmin
Return the row label of the minimum value.
Series.isin(self, values)
Series.isin
Check whether values are contained in Series.
Series.last(self, offset)
Series.last
Method to subset final periods of time series data based on a date offset.
Series.reindex(self[, index])
Series.reindex
Conform Series to new index with optional filling logic.
Series.reindex_like(self, other, method, …)
Series.reindex_like
Return an object with matching indices as other object.
Series.rename(self[, index, axis, copy, …])
Series.rename
Alter Series index labels or name.
Series.rename_axis(self[, mapper, index, …])
Series.rename_axis
Set the name of the axis for the index or columns.
Series.reset_index(self[, level, drop, …])
Series.reset_index
Generate a new DataFrame or Series with the index reset.
Series.sample(self[, n, frac, replace, …])
Series.sample
Return a random sample of items from an axis of object.
Series.set_axis(self, labels[, axis, inplace])
Series.set_axis
Assign desired index to given axis.
Series.take(self, indices[, axis, is_copy])
Series.take
Return the elements in the given positional indices along an axis.
Series.tail(self, n)
Series.tail
Return the last n rows.
Series.truncate(self[, before, after, axis])
Series.truncate
Truncate a Series or DataFrame before and after some index value.
Series.where(self, cond[, other, inplace, …])
Series.where
Replace values where the condition is False.
Series.mask(self, cond[, other, inplace, …])
Series.mask
Replace values where the condition is True.
Series.add_prefix(self, prefix)
Series.add_prefix
Prefix labels with string prefix.
Series.add_suffix(self, suffix)
Series.add_suffix
Suffix labels with string suffix.
Series.filter(self[, items, axis])
Series.filter
Subset the dataframe rows or columns according to the specified index labels.
Series.isna(self)
Series.isna
Detect missing values.
Series.notna(self)
Series.notna
Detect existing (non-missing) values.
Series.dropna(self[, axis, inplace, how])
Series.dropna
Return a new Series with missing values removed.
Series.fillna(self[, value, method, axis, …])
Series.fillna
Fill NA/NaN values using the specified method.
Series.interpolate(self[, method, axis, …])
Series.interpolate
Interpolate values according to different methods.
Series.argsort(self[, axis, kind, order])
Series.argsort
Override ndarray.argsort.
Series.argmin(self[, axis, skipna])
Series.argmin
Return a ndarray of the minimum argument indexer.
Series.argmax(self[, axis, skipna])
Series.argmax
Return an ndarray of the maximum argument indexer.
Series.reorder_levels(self, order)
Series.reorder_levels
Rearrange index levels using input order.
Series.sort_values(self[, axis, ascending])
Series.sort_values
Sort by the values.
Series.sort_index(self[, axis, level, …])
Series.sort_index
Sort Series by index labels.
Series.swaplevel(self[, i, j, copy])
Series.swaplevel
Swap levels i and j in a MultiIndex.
MultiIndex
Series.unstack(self[, level, fill_value])
Series.unstack
Unstack, a.k.a.
Series.explode(self)
Series.explode
Transform each element of a list-like to a row, replicating the index values.
Series.searchsorted(self, value[, side, sorter])
Series.searchsorted
Find indices where elements should be inserted to maintain order.
Series.ravel(self[, order])
Series.ravel
Return the flattened underlying data as an ndarray.
Series.repeat(self, repeats[, axis])
Series.repeat
Repeat elements of a Series.
Series.squeeze(self[, axis])
Series.squeeze
Squeeze 1 dimensional axis objects into scalars.
Series.view(self[, dtype])
Series.view
Create a new view of the Series.
Series.append(self, to_append[, …])
Series.append
Concatenate two or more Series.
Series.replace(self[, to_replace, value, …])
Series.replace
Replace values given in to_replace with value.
Series.update(self, other)
Series.update
Modify Series in place using non-NA values from passed Series.
Series.asfreq(self, freq[, method, fill_value])
Series.asfreq
Convert TimeSeries to specified frequency.
Series.asof(self, where[, subset])
Series.asof
Return the last row(s) without any NaNs before where.
Series.shift(self[, periods, freq, axis, …])
Series.shift
Shift index by desired number of periods with an optional time freq.
Series.first_valid_index(self)
Series.first_valid_index
Return index for first non-NA/null value.
Series.last_valid_index(self)
Series.last_valid_index
Return index for last non-NA/null value.
Series.resample(self, rule[, axis, loffset, …])
Series.resample
Resample time-series data.
Series.tz_convert(self, tz[, axis, level])
Series.tz_convert
Convert tz-aware axis to target time zone.
Series.tz_localize(self, tz[, axis, level, …])
Series.tz_localize
Localize tz-naive index of a Series or DataFrame to target time zone.
Series.at_time(self, time, asof[, axis])
Series.at_time
Select values at particular time of day (e.g.
Series.between_time(self, start_time, …[, …])
Series.between_time
Select values between particular times of the day (e.g., 9:00-9:30 AM).
Series.tshift(self, periods[, freq, axis])
Series.tshift
Shift the time index, using the index’s frequency if available.
Series.slice_shift(self, periods[, axis])
Series.slice_shift
Equivalent to shift without copying data.
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
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
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(self, \*args, \*\*kwargs)
Series.dt.to_period
Cast to PeriodArray/Index at a particular frequency.
Series.dt.to_pydatetime(self)
Series.dt.to_pydatetime
Return the data as an array of native Python datetime objects.
Series.dt.tz_localize(self, \*args, \*\*kwargs)
Series.dt.tz_localize
Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index.
Series.dt.tz_convert(self, \*args, \*\*kwargs)
Series.dt.tz_convert
Convert tz-aware Datetime Array/Index from one time zone to another.
Series.dt.normalize(self, \*args, \*\*kwargs)
Series.dt.normalize
Convert times to midnight.
Series.dt.strftime(self, \*args, \*\*kwargs)
Series.dt.strftime
Convert to Index using specified date_format.
Series.dt.round(self, \*args, \*\*kwargs)
Series.dt.round
Perform round operation on the data to the specified freq.
Series.dt.floor(self, \*args, \*\*kwargs)
Series.dt.floor
Perform floor operation on the data to the specified freq.
Series.dt.ceil(self, \*args, \*\*kwargs)
Series.dt.ceil
Perform ceil operation on the data to the specified freq.
Series.dt.month_name(self, \*args, \*\*kwargs)
Series.dt.month_name
Return the month names of the DateTimeIndex with specified locale.
Series.dt.day_name(self, \*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(self)
Series.dt.to_pytimedelta
Return an array of native datetime.timedelta objects.
Series.dt.total_seconds(self, \*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(self)
Series.str.capitalize
Convert strings in the Series/Index to be capitalized.
Series.str.casefold(self)
Series.str.casefold
Convert strings in the Series/Index to be casefolded.
Series.str.cat(self[, others, sep, na_rep, join])
Series.str.cat
Concatenate strings in the Series/Index with given separator.
Series.str.center(self, width[, fillchar])
Series.str.center
Filling left and right side of strings in the Series/Index with an additional character.
Series.str.contains(self, pat[, case, …])
Series.str.contains
Test if pattern or regex is contained within a string of a Series or Index.
Series.str.count(self, pat[, flags])
Series.str.count
Count occurrences of pattern in each string of the Series/Index.
Series.str.decode(self, encoding[, errors])
Series.str.decode
Decode character string in the Series/Index using indicated encoding.
Series.str.encode(self, encoding[, errors])
Series.str.encode
Encode character string in the Series/Index using indicated encoding.
Series.str.endswith(self, pat[, na])
Series.str.endswith
Test if the end of each string element matches a pattern.
Series.str.extract(self, pat[, flags, expand])
Series.str.extract
Extract capture groups in the regex pat as columns in a DataFrame.
Series.str.extractall(self, pat[, flags])
Series.str.extractall
For each subject string in the Series, extract groups from all matches of regular expression pat.
Series.str.find(self, sub[, start, end])
Series.str.find
Return lowest indexes in each strings in the Series/Index where the substring is fully contained between [start:end].
Series.str.findall(self, pat[, flags])
Series.str.findall
Find all occurrences of pattern or regular expression in the Series/Index.
Series.str.get(self, i)
Series.str.get
Extract element from each component at specified position.
Series.str.index(self, sub[, start, end])
Series.str.index
Return lowest indexes in each strings where the substring is fully contained between [start:end].
Series.str.join(self, sep)
Series.str.join
Join lists contained as elements in the Series/Index with passed delimiter.
Series.str.len(self)
Series.str.len
Compute the length of each element in the Series/Index.
Series.str.ljust(self, width[, fillchar])
Series.str.ljust
Filling right side of strings in the Series/Index with an additional character.
Series.str.lower(self)
Series.str.lower
Convert strings in the Series/Index to lowercase.
Series.str.lstrip(self[, to_strip])
Series.str.lstrip
Remove leading and trailing characters.
Series.str.match(self, pat[, case, flags, na])
Series.str.match
Determine if each string matches a regular expression.
Series.str.normalize(self, form)
Series.str.normalize
Return the Unicode normal form for the strings in the Series/Index.
Series.str.pad(self, width[, side, fillchar])
Series.str.pad
Pad strings in the Series/Index up to width.
Series.str.partition(self[, sep, expand])
Series.str.partition
Split the string at the first occurrence of sep.
Series.str.repeat(self, repeats)
Series.str.repeat
Duplicate each string in the Series or Index.
Series.str.replace(self, pat, repl[, n, …])
Series.str.replace
Replace occurrences of pattern/regex in the Series/Index with some other string.
Series.str.rfind(self, sub[, start, end])
Series.str.rfind
Return highest indexes in each strings in the Series/Index where the substring is fully contained between [start:end].
Series.str.rindex(self, sub[, start, end])
Series.str.rindex
Return highest indexes in each strings where the substring is fully contained between [start:end].
Series.str.rjust(self, width[, fillchar])
Series.str.rjust
Filling left side of strings in the Series/Index with an additional character.
Series.str.rpartition(self[, sep, expand])
Series.str.rpartition
Split the string at the last occurrence of sep.
Series.str.rstrip(self[, to_strip])
Series.str.rstrip
Series.str.slice(self[, start, stop, step])
Series.str.slice
Slice substrings from each element in the Series or Index.
Series.str.slice_replace(self[, start, …])
Series.str.slice_replace
Replace a positional slice of a string with another value.
Series.str.split(self[, pat, n, expand])
Series.str.split
Split strings around given separator/delimiter.
Series.str.rsplit(self[, pat, n, expand])
Series.str.rsplit
Series.str.startswith(self, pat[, na])
Series.str.startswith
Test if the start of each string element matches a pattern.
Series.str.strip(self[, to_strip])
Series.str.strip
Series.str.swapcase(self)
Series.str.swapcase
Convert strings in the Series/Index to be swapcased.
Series.str.title(self)
Series.str.title
Convert strings in the Series/Index to titlecase.
Series.str.translate(self, table)
Series.str.translate
Map all characters in the string through the given mapping table.
Series.str.upper(self)
Series.str.upper
Convert strings in the Series/Index to uppercase.
Series.str.wrap(self, width, \*\*kwargs)
Series.str.wrap
Wrap long strings in the Series/Index to be formatted in paragraphs with length less than a given width.
Series.str.zfill(self, width)
Series.str.zfill
Pad strings in the Series/Index by prepending ‘0’ characters.
Series.str.isalnum(self)
Series.str.isalnum
Check whether all characters in each string are alphanumeric.
Series.str.isalpha(self)
Series.str.isalpha
Check whether all characters in each string are alphabetic.
Series.str.isdigit(self)
Series.str.isdigit
Check whether all characters in each string are digits.
Series.str.isspace(self)
Series.str.isspace
Check whether all characters in each string are whitespace.
Series.str.islower(self)
Series.str.islower
Check whether all characters in each string are lowercase.
Series.str.isupper(self)
Series.str.isupper
Check whether all characters in each string are uppercase.
Series.str.istitle(self)
Series.str.istitle
Check whether all characters in each string are titlecase.
Series.str.isnumeric(self)
Series.str.isnumeric
Check whether all characters in each string are numeric.
Series.str.isdecimal(self)
Series.str.isdecimal
Check whether all characters in each string are decimal.
Series.str.get_dummies(self[, sep])
Series.str.get_dummies
Split each string in the Series by sep and return a DataFrame of dummy/indicator variables.
Categorical-dtype specific methods and attributes are available under the Series.cat accessor.
Series.cat
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(self, \*args, …)
Series.cat.rename_categories
Rename categories.
Series.cat.reorder_categories(self, \*args, …)
Series.cat.reorder_categories
Reorder categories as specified in new_categories.
Series.cat.add_categories(self, \*args, …)
Series.cat.add_categories
Add new categories.
Series.cat.remove_categories(self, \*args, …)
Series.cat.remove_categories
Remove the specified categories.
Series.cat.remove_unused_categories(self, …)
Series.cat.remove_unused_categories
Remove categories which are not used.
Series.cat.set_categories(self, \*args, …)
Series.cat.set_categories
Set the categories to the specified new_categories.
Series.cat.as_ordered(self, \*args, \*\*kwargs)
Series.cat.as_ordered
Set the Categorical to be ordered.
Series.cat.as_unordered(self, \*args, \*\*kwargs)
Series.cat.as_unordered
Set the Categorical to be unordered.
Sparse-dtype specific methods and attributes are provided under the Series.sparse accessor.
Series.sparse
Series.sparse.npoints
The number of non- fill_value points.
fill_value
Series.sparse.density
The percent of non- fill_value points, as decimal.
Series.sparse.fill_value
Elements in data that are fill_value are not stored.
Series.sparse.sp_values
An ndarray containing the non- fill_value values.
Series.sparse.from_coo(A[, dense_index])
Series.sparse.from_coo
Create a Series with sparse values from a scipy.sparse.coo_matrix.
Series.sparse.to_coo(self[, row_levels, …])
Series.sparse.to_coo
Create a scipy.sparse.coo_matrix from a Series with MultiIndex.
Series.attrs is a dictionary for storing global metadata for this Series.
Series.attrs
Warning
Series.attrs is considered experimental and may change without warning.
Dictionary of global attributes on this object.
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>
Series.plot([kind, ax, figsize, ….])
Series plotting accessor and method
Series.plot.area(self[, x, y])
Series.plot.area
Draw a stacked area plot.
Series.plot.bar(self[, x, y])
Series.plot.bar
Vertical bar plot.
Series.plot.barh(self[, x, y])
Series.plot.barh
Make a horizontal bar plot.
Series.plot.box(self[, by])
Series.plot.box
Make a box plot of the DataFrame columns.
Series.plot.density(self[, bw_method, ind])
Series.plot.density
Generate Kernel Density Estimate plot using Gaussian kernels.
Series.plot.hist(self[, by, bins])
Series.plot.hist
Draw one histogram of the DataFrame’s columns.
Series.plot.kde(self[, bw_method, ind])
Series.plot.kde
Series.plot.line(self[, x, y])
Series.plot.line
Plot Series or DataFrame as lines.
Series.plot.pie(self, \*\*kwargs)
Series.plot.pie
Generate a pie plot.
Series.hist(self[, by, ax, grid, …])
Series.hist
Draw histogram of the input series using matplotlib.
Series.to_pickle(self, path, compression, …)
Series.to_pickle
Pickle (serialize) object to file.
Series.to_csv(self, path_or_buf, …)
Series.to_csv
Write object to a comma-separated values (csv) file.
Series.to_dict(self[, into])
Series.to_dict
Convert Series to {label -> value} dict or dict-like object.
Series.to_excel(self, excel_writer[, …])
Series.to_excel
Write object to an Excel sheet.
Series.to_frame(self[, name])
Series.to_frame
Convert Series to DataFrame.
Series.to_xarray(self)
Series.to_xarray
Return an xarray object from the pandas object.
Series.to_hdf(self, path_or_buf, key, mode, …)
Series.to_hdf
Write the contained data to an HDF5 file using HDFStore.
Series.to_sql(self, name, con[, schema, …])
Series.to_sql
Write records stored in a DataFrame to a SQL database.
Series.to_json(self, path_or_buf, …)
Series.to_json
Convert the object to a JSON string.
Series.to_string(self[, buf, na_rep, …])
Series.to_string
Render a string representation of the Series.
Series.to_clipboard(self, excel, sep, …)
Series.to_clipboard
Copy object to the system clipboard.
Series.to_latex(self[, buf, columns, …])
Series.to_latex
Render object to a LaTeX tabular, longtable, or nested table/tabular.
Series.to_markdown(self, buf, …)
Series.to_markdown
Print Series in Markdown-friendly format.