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 resulting index will be the sorted union of the two indexes. Parametersdataarray-like, Iterable, dict, or scalar value

Contains data stored in Series. If data is a dict, argument order is maintained.indexarray-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 values in the index are used to reindex the Series after it is created using the keys in the data.dtypestr, 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.namestr, optional

The name to give to the Series.copybool, default False

Copy input data.

Attributes

1. pandas.Series.T

property Series.T

Return the transpose, which is by definition self.

2. pandas.Series.array

property Series.array

The ExtensionArray of the data backing this Series or Index.ReturnsExtensionArray

An ExtensionArray of the values stored within. For extension types, this is the actual array. For NumPy native types, this is a thin (no copy) wrapper around numpy.ndarray.

.array differs .values which may require converting the data to a different form.

3. pandas.Series.at

property Series.at

Access a single value for a row/column label pair.

Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series.RaisesKeyError

If ‘label’ does not exist in DataFrame.

4. pandas.Series.attrs

property Series.attrs

Dictionary of global attributes of this dataset.

5. pandas.Series.axes

property Series.axes

Return a list of the row axis labels.

6.pandas.Series.dtype

property Series.dtype

Return the dtype object of the underlying data.

7.pandas.Series.dtypes

property Series.dtypes

Return the dtype object of the underlying data.

8. pandas.Series.flags

property Series.flags

Get the properties associated with this pandas object.

The available flags are

• Flags.allows_duplicate_labels

9. pandas.Series.hasnans

property Series.hasnans

Return True if there are any NaNs.

Enables various performance speedups.

10. pandas.Series.iat

property Series.iat

Access a single value for a row/column pair by integer position.

Similar to iloc, in that both provide integer-based lookups. Use iat if you only need to get or set a single value in a DataFrame or Series.RaisesIndexError

When integer position is out of bounds.

11. pandas.Series.iloc

property Series.iloc

Purely integer-location based indexing for selection by position.

.iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.

Allowed inputs are:

• An integer, e.g. 5.
• A list or array of integers, e.g. [4, 3, 0].
• A slice object with ints, e.g. 1:7.
• A boolean array.
• A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value.

.iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).

12. pandas.Series.index

Series.index

The index (axis labels) of the Series.

13. pandas.Series.is_monotonic

property Series.is_monotonic

Return boolean if values in the object are monotonic_increasing.Returns bool

14. pandas.Series.is_monotonic_decreasing

property Series.is_monotonic_decreasing

Return boolean if values in the object are monotonic_decreasing.Returns bool

15. pandas.Series.is_monotonic_increasing

property Series.is_monotonic_increasing

Alias for is_monotonic.

16. pandas.Series.is_unique

property Series.is_unique

Return boolean if values in the object are unique.
Returns bool

17. pandas.Series.loc

property Series.loc

Access a group of rows and columns by label(s) or a boolean array.

.loc[] is primarily label based, but may also be used with a boolean array.

Allowed inputs are:

• A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
• A list or array of labels, e.g. ['a', 'b', 'c'].
• A slice object with labels, e.g. 'a':'f'.WarningNote that contrary to usual python slices, both the start and the stop are included
• A boolean array of the same length as the axis being sliced, e.g. [True, False, True].
• An alignable boolean Series. The index of the key will be aligned before masking.
• An alignable Index. The Index of the returned selection will be the input.
• A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above)

See more at Selection by Label.RaisesKeyError

If any items are not found.IndexingError

If an indexed key is passed and its index is unalignable to the frame index.

18. pandas.Series.name

property Series.name

Return the name of the Series.

The name of a Series becomes its index or column name if it is used to form a DataFrame. It is also used whenever displaying the Series using the interpreter.Returnslabel (hashable object)

The name of the Series, also the column name if part of a DataFrame.

See alsoSeries.rename

Sets the Series name when given a scalar input.Index.name

Corresponding Index property.

Examples

The Series name can be set initially when calling the constructor.

>>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers')
>>> s
0    1
1    2
2    3
Name: Numbers, dtype: int64
>>> s.name = "Integers"
>>> s
0    1
1    2
2    3
Name: Integers, dtype: int64

The name of a Series within a DataFrame is its column name.

>>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]],
...                   columns=["Odd Numbers", "Even Numbers"])
>>> df
   Odd Numbers  Even Numbers
0            1             2
1            3             4
2            5             6
>>> df["Even Numbers"].name
'Even Numbers'

19. pandas.Series.nbytes

property Series.nbytes

Return the number of bytes in the underlying data.

20. pandas.Series.ndim

property Series.ndim

Number of dimensions of the underlying data, by definition 1.

21. pandas.Series.shape

property Series.shape

Return a tuple of the shape of the underlying data.

22. pandas.Series.size

property Series.size

Return the number of elements in the underlying data.

23. pandas.Series.values

property Series.values

Return Series as ndarray or ndarray-like depending on the dtype.

Warning

We recommend using Series.array or Series.to_numpy(), depending on whether you need a reference to the underlying data or a NumPy array.Returnsnumpy.ndarray or ndarray-like

See alsoSeries.array

Reference to the underlying data.Series.to_numpy

A NumPy array representing the underlying data.

Examples

>>> pd.Series([1, 2, 3]).values
array([1, 2, 3])

>>> pd.Series(list('aabc')).values
array(['a', 'a', 'b', 'c'], dtype=object)

>>> pd.Series(list('aabc')).astype('category').values
['a', 'a', 'b', 'c']
Categories (3, object): ['a', 'b', 'c']

Timezone aware datetime data is converted to UTC:

>>> pd.Series(pd.date_range('20130101', periods=3,
...                         tz='US/Eastern')).values
array(['2013-01-01T05:00:00.000000000',
       '2013-01-02T05:00:00.000000000',
       '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')

Methods

1.pandas.Series.add

Series.add(other, level=None, fill_value=None, axis=0)[source]

Return Addition of series and other, element-wise (binary operator add).

Equivalent to series + other, but with support to substitute a fill_value for missing data in either one of the inputs.ParametersotherSeries or scalar valuefill_valueNone or float value, default None (NaN)

Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result of filling (at that location) will be missing.levelint or name

Broadcast across a level, matching Index values on the passed MultiIndex level.ReturnsSeries

The result of the operation.

See alsoSeries.radd

Reverse of the Addition operator, see Python documentation for more details.

Examples

>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a    1.0
b    1.0
c    1.0
d    NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a    1.0
b    NaN
d    1.0
e    NaN
dtype: float64
>>> a.add(b, fill_value=0)
a    2.0
b    1.0
c    1.0
d    1.0
e    NaN
dtype: float64

2.pandas.Series.add

Series.add(other, level=None, fill_value=None, axis=0)[source]

Return Addition of series and other, element-wise (binary operator add).

Equivalent to series + other, but with support to substitute a fill_value for missing data in either one of the inputs.ParametersotherSeries or scalar valuefill_valueNone or float value, default None (NaN)

Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result of filling (at that location) will be missing.levelint or name

Broadcast across a level, matching Index values on the passed MultiIndex level.ReturnsSeries

The result of the operation.

See alsoSeries.radd

Reverse of the Addition operator, see Python documentation for more details.

Examples

>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a    1.0
b    1.0
c    1.0
d    NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a    1.0
b    NaN
d    1.0
e    NaN
dtype: float64
>>> a.add(b, fill_value=0)
a    2.0
b    1.0
c    1.0
d    1.0
e    NaN
dtype: float64

3.pandas.Series.add_prefix

Series.add_prefix(prefix)[source]

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.Parametersprefixstr

The string to add before each label.ReturnsSeries or DataFrame

New Series or DataFrame with updated labels.

See alsoSeries.add_suffix

Suffix row labels with string suffix.DataFrame.add_suffix

Suffix column labels with string suffix.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_prefix('item_')
item_0    1
item_1    2
item_2    3
item_3    4
dtype: int64

>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_prefix('col_')
     col_A  col_B
0       1       3
1       2       4
2       3       5
3       4       6

4.pandas.Series.add_suffix

Series.add_suffix(suffix)[source]

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.Parameterssuffixstr

The string to add after each label.ReturnsSeries or DataFrame

New Series or DataFrame with updated labels.

See alsoSeries.add_prefix

Prefix row labels with string prefix.DataFrame.add_prefix

Prefix column labels with string prefix.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_suffix('_item')
0_item    1
1_item    2
2_item    3
3_item    4
dtype: int64

>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_suffix('_col')
     A_col  B_col
0       1       3
1       2       4
2       3       5
3       4       6