class pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)
[source]
One-dimensional ndarray with axis labels (including time series).
Labels need not be unique but must be a 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 hashable and have the same length as dtype : numpy.dtype or None If None, dtype will be inferred copy : boolean, default False Copy input data |
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T | return the transpose, which is by definition self |
asobject | return object Series which contains boxed values |
at | Fast label-based scalar accessor |
axes | Return a list of the row axis labels |
base | return the base object if the memory of the underlying data is |
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 | |
flags | |
ftype | return if the data is sparse|dense |
ftypes | return if the data is sparse|dense |
hasnans | |
iat | Fast integer location scalar accessor. |
iloc | Purely integer-location based indexing for selection by position. |
imag | |
is_copy | |
is_monotonic | Return boolean if values in the object are |
is_monotonic_decreasing | Return boolean if values in the object are |
is_monotonic_increasing | Return boolean if values in the object are |
is_unique | Return boolean if values in the object are unique |
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. |
name | |
nbytes | return the number of bytes in the underlying data |
ndim | return the number of dimensions of the underlying data, |
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 or ndarray-like |
abs () | Return an object with absolute value taken–only applicable to objects that are all numeric. |
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. |
agg (func[, axis]) | Aggregate using callable, string, dict, or list of string/callables |
aggregate (func[, axis]) | Aggregate using callable, string, dict, or list of string/callables |
align (other[, join, axis, level, copy, ...]) | Align two objects 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[, ignore_index, ...]) | Concatenate two or more Series. |
apply (func[, convert_dtype, args]) | Invoke function on values of Series. |
argmax (*args, **kwargs) | |
argmin (*args, **kwargs) | |
argsort ([axis, kind, order]) | Overrides ndarray.argsort. |
as_blocks ([copy]) | 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 TimeSeries to specified frequency. |
asof (where[, subset]) | The last row without any NaN is taken (or the last row without |
astype (dtype[, copy, errors]) | Cast a pandas object to a specified dtype 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 DataFrame.fillna(method='bfill')
|
bool () | Return the bool of a single element PandasObject. |
cat | alias of CategoricalAccessor
|
clip ([lower, upper, axis, inplace]) | Trim values at input threshold(s). |
clip_lower (threshold[, axis, inplace]) | Return copy of the input with values below given value(s) truncated. |
clip_upper (threshold[, axis, inplace]) | 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, *args, **kwargs) | Return selected slices of an array along given axis as a Series |
consolidate ([inplace]) | DEPRECATED: consolidate will be an internal implementation only. |
convert_objects ([convert_dates, ...]) | Deprecated. |
copy ([deep]) | Make a copy of this objects data. |
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, skipna]) | Return cumulative max over requested axis. |
cummin ([axis, skipna]) | Return cumulative minimum over requested axis. |
cumprod ([axis, skipna]) | Return cumulative product over requested axis. |
cumsum ([axis, skipna]) | Return cumulative sum over requested axis. |
describe ([percentiles, include, exclude]) | Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, 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, index, columns, level, ...]) | Return new object with labels in requested axis removed. |
drop_duplicates ([keep, inplace]) | Return Series with duplicate values removed |
dropna ([axis, inplace]) | Return Series without null values |
dt | alias of CombinedDatetimelikeProperties
|
duplicated ([keep]) | Return boolean Series denoting duplicate values |
eq (other[, level, fill_value, axis]) | Equal to of series and other, element-wise (binary operator eq ). |
equals (other) | Determines if two NDFrame objects contain the same elements. |
ewm ([com, span, halflife, alpha, ...]) | Provides exponential weighted functions |
expanding ([min_periods, freq, center, axis]) | Provides expanding transformations. |
factorize ([sort, na_sentinel]) | Encode the object as an enumerated type or categorical variable |
ffill ([axis, inplace, limit, downcast]) | Synonym for DataFrame.fillna(method='ffill')
|
fillna ([value, method, axis, inplace, ...]) | Fill NA/NaN values using the specified method |
filter ([items, like, regex, axis]) | Subset rows or columns of dataframe according to labels in the specified index. |
first (offset) | Convenience method for subsetting initial periods of time series data based on a date offset. |
first_valid_index () | Return index 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 CSV file (DEPRECATED, please use pandas.read_csv() instead). |
ge (other[, level, fill_value, axis]) | Greater than or equal to of series and other, element-wise (binary operator ge ). |
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 to group, return result as series) or by a series of columns. |
gt (other[, level, fill_value, axis]) | Greater than of series and other, element-wise (binary operator gt ). |
head ([n]) | Return the first n rows. |
hist ([by, ax, grid, xlabelsize, xrot, ...]) | Draw histogram of the input series using matplotlib |
idxmax ([axis, skipna]) | Index label of the first occurrence of maximum of values. |
idxmin ([axis, skipna]) | Index label of the first occurrence of minimum of values. |
infer_objects () | Attempt to infer better dtypes for object columns. |
interpolate ([method, axis, limit, inplace, ...]) | Interpolate values according to different methods. |
isin (values) | Return a boolean Series showing whether each element in the Series is exactly contained in the passed sequence of values . |
isna () | Return a boolean same-sized object indicating if the values are NA. |
isnull () | Return a boolean same-sized object indicating if the values are NA. |
item () | return the first element of the underlying data as a python |
items () | Lazily iterate over (index, value) tuples |
iteritems () | Lazily iterate over (index, value) tuples |
keys () | Alias for index |
kurt ([axis, skipna, level, numeric_only]) | Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). |
kurtosis ([axis, skipna, level, numeric_only]) | Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). |
last (offset) | Convenience method for subsetting final periods of time series data based on a date offset. |
last_valid_index () | Return index for last non-NA/null value. |
le (other[, level, fill_value, axis]) | Less than or equal to of series and other, element-wise (binary operator le ). |
lt (other[, level, fill_value, axis]) | Less than of series and other, element-wise (binary operator lt ). |
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 |
memory_usage ([index, deep]) | Memory usage of the Series |
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 () | Return 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[, level, fill_value, axis]) | Not equal to of series and other, element-wise (binary operator ne ). |
nlargest ([n, keep]) | Return the largest n elements. |
nonzero () | Return the indices of the elements that are non-zero |
notna () | Return a boolean same-sized object indicating if the values are not NA. |
notnull () | Return a boolean same-sized object indicating if the values are not NA. |
nsmallest ([n, keep]) | Return the smallest n elements. |
nunique ([dropna]) | Return number of unique elements in the object. |
pct_change ([periods, fill_method, limit, freq]) | Percent change over given number of periods. |
pipe (func, *args, **kwargs) | Apply func(self, *args, **kwargs) |
plot | alias of SeriesPlotMethods
|
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, skipna, level, numeric_only]) | Returns the difference between the maximum value and the minimum value in the object. |
put (*args, **kwargs) | Applies the put method to its values attribute if it has one. |
quantile ([q, interpolation]) | 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 ([axis, method, numeric_only, ...]) | Compute numerical data ranks (1 through n) along axis. |
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 indices to myself. |
rename ([index]) | Alter Series index labels or name |
rename_axis (mapper[, axis, copy, inplace]) | Alter the name of the index or columns. |
reorder_levels (order) | Rearrange index levels using input order. |
repeat (repeats, *args, **kwargs) | Repeat elements of an Series. |
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 time series. |
reset_index ([level, drop, name, inplace]) | Analogous to the pandas.DataFrame.reset_index() function, see docstring there. |
reshape (*args, **kwargs) |
Deprecated since version 0.19.0. |
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 ). |
rolling (window[, min_periods, freq, center, ...]) | Provides rolling window calculations. |
round ([decimals]) | Round each value in a Series 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. |
searchsorted (value[, 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 (labels[, axis, inplace]) | Assign desired index to given axis |
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_index ([axis, level, ascending, ...]) | Sort object by labels (along an axis) |
sort_values ([axis, ascending, inplace, ...]) | Sort by the values along either axis |
sortlevel ([level, ascending, sort_remaining]) | DEPRECATED: use Series.sort_index()
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squeeze ([axis]) | Squeeze length 1 dimensions. |
std ([axis, skipna, level, ddof, numeric_only]) | Return sample standard deviation over requested axis. |
str | alias of StringMethods
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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]) | Return the last n rows. |
take (indices[, axis, convert, is_copy]) | Return the elements in the given positional indices along an axis. |
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 ([into]) | Convert Series to {label -> value} dict or dict-like object. |
to_excel (excel_writer[, sheet_name, na_rep, ...]) | Write Series to an excel sheet |
to_frame ([name]) | Convert Series to DataFrame |
to_hdf (path_or_buf, key, **kwargs) | Write the contained data to an HDF5 file using HDFStore. |
to_json ([path_or_buf, orient, date_format, ...]) | Convert the object to a JSON string. |
to_latex ([buf, columns, col_space, header, ...]) | Render an object to a tabular environment table. |
to_msgpack ([path_or_buf, encoding]) | msgpack (serialize) object to input file path |
to_period ([freq, copy]) | Convert Series from DatetimeIndex to PeriodIndex with desired |
to_pickle (path[, compression, protocol]) | 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 |
to_xarray () | Return an xarray object from the pandas object. |
tolist () | Return a list of the values. |
transform (func, *args, **kwargs) | Call function producing a like-indexed NDFrame |
transpose (*args, **kwargs) | 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 DataFrame/Series before and/or after some particular index value. |
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 (tz[, axis, level, copy, ambiguous]) | Localize tz-naive TimeSeries to target time zone. |
unique () | Return unique values in the object. |
unstack ([level, fill_value]) | 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, drop_level]) | Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. |
© 2008–2012, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.22.0/generated/pandas.Series.html