pandas.Series — pandas 0.16.2 documentation (original) (raw)
abs()
Return an object with absolute value taken.
add(other[, level, fill_value, axis])
Addition of series and other, element-wise (binary operator add).
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.
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
alias of CategoricalAccessor
clip([lower, upper, out, axis])
Trim values at input threshold(s)
clip_lower(threshold[, axis])
Return copy of the input with values below given value(s) truncated
clip_upper(threshold[, axis])
Return copy of input with values above given value(s) 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])
Floating division of series and other, element-wise (binary operator truediv).
divide(other[, level, fill_value, axis])
Floating division of series and other, element-wise (binary operator truediv).
dot(other)
Matrix multiplication with DataFrame or inner-product with Series
drop(labels[, axis, level, inplace, errors])
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
alias of CombinedDatetimelikeProperties
duplicated([take_last])
Return boolean Series denoting duplicate values
eq(other[, axis])
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
Return label for first non-NA/null value
floordiv(other[, level, fill_value, axis])
Integer division of series and other, element-wise (binary operator floordiv).
from_array(arr[, index, name, dtype, copy, ...])
from_csv(path[, sep, parse_dates, header, ...])
Read delimited file into Series
ge(other[, axis])
get(key[, default])
Get item from object for given key (DataFrame column, Panel slice, etc.).
Return the counts of dtypes in this object
Return the counts of ftypes in this object
get_value(label[, takeable])
Quickly retrieve single value at passed index label
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[, axis])
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
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
Return label for last non-NA/null value
le(other[, axis])
load(path)
Deprecated.
lt(other[, axis])
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[, other, inplace, axis, level, ...])
Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.
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])
Modulo of series and other, element-wise (binary operator mod).
mode()
Returns the mode(s) of the dataset.
mul(other[, level, fill_value, axis])
Multiplication of series and other, element-wise (binary operator mul).
multiply(other[, level, fill_value, axis])
Multiplication of series and other, element-wise (binary operator mul).
ne(other[, axis])
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.
pipe(func, *args, **kwargs)
Apply func(self, *args, **kwargs)
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])
Exponential power of series and other, element-wise (binary operator pow).
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])
Addition of series and other, element-wise (binary operator radd).
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])
Floating division of series and other, element-wise (binary operator rtruediv).
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])
Integer division of series and other, element-wise (binary operator rfloordiv).
rmod(other[, level, fill_value, axis])
Modulo of series and other, element-wise (binary operator rmod).
rmul(other[, level, fill_value, axis])
Multiplication of series and other, element-wise (binary operator rmul).
round([decimals, out])
Return a with each element rounded to the given number of decimals.
rpow(other[, level, fill_value, axis])
Exponential power of series and other, element-wise (binary operator rpow).
rsub(other[, level, fill_value, axis])
Subtraction of series and other, element-wise (binary operator rsub).
rtruediv(other[, level, fill_value, axis])
Floating division of series and other, element-wise (binary operator rtruediv).
sample([n, frac, replace, weights, ...])
Returns a random sample of items from an axis of object.
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.
alias of StringMethods
sub(other[, level, fill_value, axis])
Subtraction of series and other, element-wise (binary operator sub).
subtract(other[, level, fill_value, axis])
Subtraction of series and other, element-wise (binary operator sub).
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
return the transpose, which is by definition self
truediv(other[, level, fill_value, axis])
Floating division of series and other, element-wise (binary operator truediv).
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 tz-aware 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.