xorbits.pandas.Series#

class xorbits.pandas.Series(*args, **kwargs)[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, Iterable, dict, or scalar value (Not supported yet)) – Contains data stored in Series. If data is a dict, argument order is maintained.
index (array-like or Index (1d) (Not supported yet)) – Values must be hashable and have the same length as data. Non-unique index values are allowed. Will default to RangeIndex (0, 1, 2, …, n) if not provided. If data is dict-like and index is None, then the keys in the data are used as the index. If the index is not None, the resulting Series is reindexed with the index values.
dtype (str, numpy.dtype, or ExtensionDtype, optional) – Data type for the output Series. If not specified, this will be inferred from data. See the user guide for more usages.
name (Hashable, default None (Not supported yet)) – The name to give to the Series.
copy (bool, default False (Not supported yet)) – Copy input data. Only affects Series or 1d ndarray input. See examples.

Notes

Please reference the User Guide for more information.

Examples

Constructing Series from a dictionary with an Index specified

>>> d = {'a': 1, 'b': 2, 'c': 3}  
>>> ser = pd.Series(data=d, index=['a', 'b', 'c'])  
>>> ser  
a   1
b   2
c   3
dtype: int64

The keys of the dictionary match with the Index values, hence the Index values have no effect.

>>> d = {'a': 1, 'b': 2, 'c': 3}  
>>> ser = pd.Series(data=d, index=['x', 'y', 'z'])  
>>> ser  
x   NaN
y   NaN
z   NaN
dtype: float64

Note that the Index is first build with the keys from the dictionary. After this the Series is reindexed with the given Index values, hence we get all NaN as a result.

Constructing Series from a list with copy=False.

>>> r = [1, 2]  
>>> ser = pd.Series(r, copy=False)  
>>> ser.iloc[0] = 999  
>>> r  
[1, 2]
>>> ser  
0    999
1      2
dtype: int64

Due to input data type the Series has a copy of the original data even though copy=False, so the data is unchanged.

Constructing Series from a 1d ndarray with copy=False.

>>> r = np.array([1, 2])  
>>> ser = pd.Series(r, copy=False)  
>>> ser.iloc[0] = 999  
>>> r  
array([999,   2])
>>> ser  
0    999
1      2
dtype: int64

Due to input data type the Series has a view on the original data, so the data is changed as well.

This docstring was copied from pandas.

__init__(*args, **kwargs)[source]#

Methods

__init__(*args, **kwargs)

Attributes

`at`	Access a single value for a row/column label pair.
`iat`	Access a single value for a row/column pair by integer position.
`iloc`	Purely integer-location based indexing for selection by position.
`loc`	Access a group of rows and columns by label(s) or a boolean array.
`shape`
`data`