pandas.Series

class pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)

One-dimensional ndarray with axis labels (including time series).

Labels need not be unique but must be any hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Statistical methods from ndarray have been overridden to automatically exclude missing data (currently represented as NaN)

Operations between Series (+, -, /, , *) align values based on their associated index values– they need not be the same length. The result index will be the sorted union of the two indexes.

Parameters:

data : array-like, dict, or scalar value

Contains data stored in Series

index : array-like or Index (1d)

Values must be unique and hashable, same length as data. Index object (or other iterable of same length as data) Will default to np.arange(len(data)) if not provided. If both a dict and index sequence are used, the index will override the keys found in the dict.

dtype : numpy.dtype or None

If None, dtype will be inferred

copy : boolean, default False

Copy input data

Attributes

T	return the transpose, which is by definition self
at	Fast label-based scalar accessor
axes
base	return the base object if the memory of the underlying data is shared
blocks	Internal property, property synonym for as_blocks()
data	return the data pointer of the underlying data
dtype	return the dtype object of the underlying data
dtypes	return the dtype object of the underlying data
empty	True if NDFrame is entirely empty [no items]
flags
ftype	return if the data is sparse|dense
ftypes	return if the data is sparse|dense
iat	Fast integer location scalar accessor.
iloc	Purely integer-location based indexing for selection by position.
imag
is_time_series
itemsize	return the size of the dtype of the item of the underlying data
ix	A primarily label-location based indexer, with integer position fallback.
loc	Purely label-location based indexer for selection by label.
nbytes	return the number of bytes in the underlying data
ndim	return the number of dimensions of the underlying data, by definition 1
real
shape	return a tuple of the shape of the underlying data
size	return the number of elements in the underlying data
strides	return the strides of the underlying data
values	Return Series as ndarray

is_copy

Methods

abs()	Return an object with absolute value taken.
add(other[, level, fill_value, axis])	Binary operator add with support to substitute a fill_value for missing data
add_prefix(prefix)	Concatenate prefix string with panel items names.
add_suffix(suffix)	Concatenate suffix string with panel items names
align(other[, join, axis, level, copy, ...])	Align two object on their axes with the
all([axis, bool_only, skipna, level])	Return whether all elements are True over requested axis
any([axis, bool_only, skipna, level])	Return whether any element is True over requested axis
append(to_append[, verify_integrity])	Concatenate two or more Series.
apply(func[, convert_dtype, args])	Invoke function on values of Series.
argmax([axis, out, skipna])	Index of first occurrence of maximum of values.
argmin([axis, out, skipna])	Index of first occurrence of minimum of values.
argsort([axis, kind, order])	Overrides ndarray.argsort.
as_blocks()	Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype.
as_matrix([columns])	Convert the frame to its Numpy-array representation.
asfreq(freq[, method, how, normalize])	Convert all TimeSeries inside to specified frequency using DateOffset objects.
asof(where)	Return last good (non-NaN) value in TimeSeries if value is NaN for requested date.
astype(dtype[, copy, raise_on_error])	Cast object to input numpy.dtype
at_time(time[, asof])	Select values at particular time of day (e.g.
autocorr([lag])	Lag-N autocorrelation
between(left, right[, inclusive])	Return boolean Series equivalent to left <= series <= right.
between_time(start_time, end_time[, ...])	Select values between particular times of the day (e.g., 9:00-9:30 AM)
bfill([axis, inplace, limit, downcast])	Synonym for NDFrame.fillna(method=’bfill’)
bool()	Return the bool of a single element PandasObject
cat	alias of CategoricalAccessor
clip([lower, upper, out])	Trim values at input threshold(s)
clip_lower(threshold)	Return copy of the input with values below given value truncated
clip_upper(threshold)	Return copy of input with values above given value truncated
combine(other, func[, fill_value])	Perform elementwise binary operation on two Series using given function
combine_first(other)	Combine Series values, choosing the calling Series’s values first.
compound([axis, skipna, level])	Return the compound percentage of the values for the requested axis
compress(condition[, axis, out])	Return selected slices of an array along given axis as a Series
consolidate([inplace])	Compute NDFrame with “consolidated” internals (data of each dtype grouped together in a single ndarray).
convert_objects([convert_dates, ...])	Attempt to infer better dtype for object columns
copy([deep])	Make a copy of this object
corr(other[, method, min_periods])	Compute correlation with other Series, excluding missing values
count([level])	Return number of non-NA/null observations in the Series
cov(other[, min_periods])	Compute covariance with Series, excluding missing values
cummax([axis, dtype, out, skipna])	Return cumulative max over requested axis.
cummin([axis, dtype, out, skipna])	Return cumulative min over requested axis.
cumprod([axis, dtype, out, skipna])	Return cumulative prod over requested axis.
cumsum([axis, dtype, out, skipna])	Return cumulative sum over requested axis.
describe([percentile_width, percentiles, ...])	Generate various summary statistics, excluding NaN values.
diff([periods])	1st discrete difference of object
div(other[, level, fill_value, axis])	Binary operator truediv with support to substitute a fill_value for missing data
divide(other[, level, fill_value, axis])	Binary operator truediv with support to substitute a fill_value for missing data
dot(other)	Matrix multiplication with DataFrame or inner-product with Series
drop(labels[, axis, level, inplace])	Return new object with labels in requested axis removed
drop_duplicates([take_last, inplace])	Return Series with duplicate values removed
dropna([axis, inplace])	Return Series without null values
dt	alias of CombinedDatetimelikeProperties
duplicated([take_last])	Return boolean Series denoting duplicate values
eq(other)
equals(other)	Determines if two NDFrame objects contain the same elements.
factorize([sort, na_sentinel])	Encode the object as an enumerated type or categorical variable
ffill([axis, inplace, limit, downcast])	Synonym for NDFrame.fillna(method=’ffill’)
fillna([value, method, axis, inplace, ...])	Fill NA/NaN values using the specified method
filter([items, like, regex, axis])	Restrict the info axis to set of items or wildcard
first(offset)	Convenience method for subsetting initial periods of time series data
first_valid_index()	Return label for first non-NA/null value
floordiv(other[, level, fill_value, axis])	Binary operator floordiv with support to substitute a fill_value for missing data
from_array(arr[, index, name, dtype, copy, ...])
from_csv(path[, sep, parse_dates, header, ...])	Read delimited file into Series
ge(other)
get(key[, default])	Get item from object for given key (DataFrame column, Panel slice, etc.).
get_dtype_counts()	Return the counts of dtypes in this object
get_ftype_counts()	Return the counts of ftypes in this object
get_value(label[, takeable])	Quickly retrieve single value at passed index label
get_values()	same as values (but handles sparseness conversions); is a view
groupby([by, axis, level, as_index, sort, ...])	Group series using mapper (dict or key function, apply given function
gt(other)
hasnans()	return if I have any nans; enables various perf speedups
head([n])	Returns first n rows
hist([by, ax, grid, xlabelsize, xrot, ...])	Draw histogram of the input series using matplotlib
idxmax([axis, out, skipna])	Index of first occurrence of maximum of values.
idxmin([axis, out, skipna])	Index of first occurrence of minimum of values.
iget(i[, axis])	Return the i-th value or values in the Series by location
iget_value(i[, axis])	Return the i-th value or values in the Series by location
interpolate([method, axis, limit, inplace, ...])	Interpolate values according to different methods.
irow(i[, axis])	Return the i-th value or values in the Series by location
isin(values)	Return a boolean Series showing whether each element in the Series is exactly contained in the passed sequence of values.
isnull()	Return a boolean same-sized object indicating if the values are null
item()	return the first element of the underlying data as a python scalar
iteritems()	Lazily iterate over (index, value) tuples
iterkv(*args, **kwargs)	iteritems alias used to get around 2to3. Deprecated
keys()	Alias for index
kurt([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis using Fishers definition of kurtosis (kurtosis of normal == 0.0).
kurtosis([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis using Fishers definition of kurtosis (kurtosis of normal == 0.0).
last(offset)	Convenience method for subsetting final periods of time series data
last_valid_index()	Return label for last non-NA/null value
le(other)
load(path)	Deprecated.
lt(other)
mad([axis, skipna, level])	Return the mean absolute deviation of the values for the requested axis
map(arg[, na_action])	Map values of Series using input correspondence (which can be
mask(cond)	Returns copy whose values are replaced with nan if the
max([axis, skipna, level, numeric_only])	This method returns the maximum of the values in the object.
mean([axis, skipna, level, numeric_only])	Return the mean of the values for the requested axis
median([axis, skipna, level, numeric_only])	Return the median of the values for the requested axis
min([axis, skipna, level, numeric_only])	This method returns the minimum of the values in the object.
mod(other[, level, fill_value, axis])	Binary operator mod with support to substitute a fill_value for missing data
mode()	Returns the mode(s) of the dataset.
mul(other[, level, fill_value, axis])	Binary operator mul with support to substitute a fill_value for missing data
multiply(other[, level, fill_value, axis])	Binary operator mul with support to substitute a fill_value for missing data
ne(other)
nlargest([n, take_last])	Return the largest n elements.
nonzero()	Return the indices of the elements that are non-zero
notnull()	Return a boolean same-sized object indicating if the values are
nsmallest([n, take_last])	Return the smallest n elements.
nunique([dropna])	Return number of unique elements in the object.
order([na_last, ascending, kind, ...])	Sorts Series object, by value, maintaining index-value link.
pct_change([periods, fill_method, limit, freq])	Percent change over given number of periods.
plot(data[, kind, ax, figsize, use_index, ...])	Make plots of Series using matplotlib / pylab.
pop(item)	Return item and drop from frame.
pow(other[, level, fill_value, axis])	Binary operator pow with support to substitute a fill_value for missing data
prod([axis, skipna, level, numeric_only])	Return the product of the values for the requested axis
product([axis, skipna, level, numeric_only])	Return the product of the values for the requested axis
ptp([axis, out])
put(*args, **kwargs)	return a ndarray with the values put
quantile([q])	Return value at the given quantile, a la numpy.percentile.
radd(other[, level, fill_value, axis])	Binary operator radd with support to substitute a fill_value for missing data
rank([method, na_option, ascending, pct])	Compute data ranks (1 through n).
ravel([order])	Return the flattened underlying data as an ndarray
rdiv(other[, level, fill_value, axis])	Binary operator rtruediv with support to substitute a fill_value for missing data
reindex([index])	Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
reindex_axis(labels[, axis])	for compatibility with higher dims
reindex_like(other[, method, copy, limit])	return an object with matching indicies to myself
rename([index])	Alter axes input function or functions.
rename_axis(mapper[, axis, copy, inplace])	Alter index and / or columns using input function or functions.
reorder_levels(order)	Rearrange index levels using input order.
repeat(reps)	return a new Series with the values repeated reps times
replace([to_replace, value, inplace, limit, ...])	Replace values given in ‘to_replace’ with ‘value’.
resample(rule[, how, axis, fill_method, ...])	Convenience method for frequency conversion and resampling of regular time-series data.
reset_index([level, drop, name, inplace])	Analogous to the pandas.DataFrame.reset_index() function, see docstring there.
reshape(*args, **kwargs)	return an ndarray with the values shape
rfloordiv(other[, level, fill_value, axis])	Binary operator rfloordiv with support to substitute a fill_value for missing data
rmod(other[, level, fill_value, axis])	Binary operator rmod with support to substitute a fill_value for missing data
rmul(other[, level, fill_value, axis])	Binary operator rmul with support to substitute a fill_value for missing data
round([decimals, out])	Return a with each element rounded to the given number of decimals.
rpow(other[, level, fill_value, axis])	Binary operator rpow with support to substitute a fill_value for missing data
rsub(other[, level, fill_value, axis])	Binary operator rsub with support to substitute a fill_value for missing data
rtruediv(other[, level, fill_value, axis])	Binary operator rtruediv with support to substitute a fill_value for missing data
save(path)	Deprecated.
searchsorted(v[, side, sorter])	Find indices where elements should be inserted to maintain order.
select(crit[, axis])	Return data corresponding to axis labels matching criteria
sem([axis, skipna, level, ddof, numeric_only])	Return unbiased standard error of the mean over requested axis.
set_axis(axis, labels)	public verson of axis assignment
set_value(label, value[, takeable])	Quickly set single value at passed label.
shift([periods, freq, axis])	Shift index by desired number of periods with an optional time freq
skew([axis, skipna, level, numeric_only])	Return unbiased skew over requested axis
slice_shift([periods, axis])	Equivalent to shift without copying data.
sort([axis, ascending, kind, na_position, ...])	Sort values and index labels by value.
sort_index([ascending])	Sort object by labels (along an axis)
sortlevel([level, ascending, sort_remaining])	Sort Series with MultiIndex by chosen level.
squeeze()	squeeze length 1 dimensions
std([axis, skipna, level, ddof, numeric_only])	Return unbiased standard deviation over requested axis.
str	alias of StringMethods
sub(other[, level, fill_value, axis])	Binary operator sub with support to substitute a fill_value for missing data
subtract(other[, level, fill_value, axis])	Binary operator sub with support to substitute a fill_value for missing data
sum([axis, skipna, level, numeric_only])	Return the sum of the values for the requested axis
swapaxes(axis1, axis2[, copy])	Interchange axes and swap values axes appropriately
swaplevel(i, j[, copy])	Swap levels i and j in a MultiIndex
tail([n])	Returns last n rows
take(indices[, axis, convert, is_copy])	return Series corresponding to requested indices
to_clipboard([excel, sep])	Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.
to_csv(path[, index, sep, na_rep, ...])	Write Series to a comma-separated values (csv) file
to_dense()	Return dense representation of NDFrame (as opposed to sparse)
to_dict()	Convert Series to {label -> value} dict
to_frame([name])	Convert Series to DataFrame
to_hdf(path_or_buf, key, **kwargs)	activate the HDFStore
to_json([path_or_buf, orient, date_format, ...])	Convert the object to a JSON string.
to_msgpack([path_or_buf])	msgpack (serialize) object to input file path
to_period([freq, copy])	Convert TimeSeries from DatetimeIndex to PeriodIndex with desired
to_pickle(path)	Pickle (serialize) object to input file path
to_sparse([kind, fill_value])	Convert Series to SparseSeries
to_sql(name, con[, flavor, schema, ...])	Write records stored in a DataFrame to a SQL database.
to_string([buf, na_rep, float_format, ...])	Render a string representation of the Series
to_timestamp([freq, how, copy])	Cast to datetimeindex of timestamps, at beginning of period
tolist()	Convert Series to a nested list
transpose()	return the transpose, which is by definition self
truediv(other[, level, fill_value, axis])	Binary operator truediv with support to substitute a fill_value for missing data
truncate([before, after, axis, copy])	Truncates a sorted NDFrame before and/or after some particular dates.
tshift([periods, freq, axis])	Shift the time index, using the index’s frequency if available
tz_convert(tz[, axis, level, copy])	Convert the axis to target time zone.
tz_localize(*args, **kwargs)	Localize tz-naive TimeSeries to target time zone
unique()	Return array of unique values in the object.
unstack([level])	Unstack, a.k.a.
update(other)	Modify Series in place using non-NA values from passed Series.
valid([inplace])
value_counts([normalize, sort, ascending, ...])	Returns object containing counts of unique values.
var([axis, skipna, level, ddof, numeric_only])	Return unbiased variance over requested axis.
view([dtype])
where(cond[, other, inplace, axis, level, ...])	Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
xs(key[, axis, level, copy, drop_level])	Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.