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pandas.Series() ⚬｜Documentation｜1st｜20251021002348-00-⌔

pandas.Series — pandas 2.3.3 documentation#pandas.Series

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

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

Contains data stored in Series. If data is a dict, argument order is maintained. Unordered sets are not supported.

index: array-like or Index (1d)

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

The name to give to the Series.

copy: bool, default None

Whether to copy input data, only relevant for array, Series, and Index inputs (for other input, e.g. a list, a new array is created anyway). Defaults to True for array input and False for Index/Series. Even when False for Index/Series, a shallow copy of the data is made. Set to False to avoid copying array input at your own risk (if you know the input data won’t be modified elsewhere). Set to True to force copying Series/Index input up front.

See also:

DataFrame

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Index

Immutable sequence used for indexing and alignment.

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 built 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.

Attributes:

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Methods:

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Printed 2026-06-28.

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