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.
Contains data stored in Series. If data is a dict, argument order is maintained.
Values must be hashable and have the same length as :None:None:`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.
Data type for the output Series. If not specified, this will be inferred from :None:None:`data`. See the user guide <basics.dtypes>
for more usages.
The name to give to the Series.
Copy input data. Only affects Series or 1d ndarray input. See examples.
One-dimensional ndarray with axis labels (including time series).
Constructing Series from a dictionary with an Index specified
This example is valid syntax, but we were not able to check execution>>> 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.
This example is valid syntax, but we were not able to check execution>>> 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 :None:None:`copy=False`.
>>> r = [1, 2]This example is valid syntax, but we were not able to check execution
... 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 :None:None:`copy=False`, so the data is unchanged.
Constructing Series from a 1d ndarray with :None:None:`copy=False`.
>>> r = np.array([1, 2])This example is valid syntax, but we were not able to check execution
... 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 :None:None:`view` on the original data, so the data is changed as well.
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.series.Series.reset_index
pandas.core.tools.numeric.to_numeric
pandas.plotting
pandas.core.frame.DataFrame.resample
pandas.core.series.Series.combine
pandas
pandas.core.dtypes.concat.union_categoricals
pandas.plotting._misc.radviz
pandas.core.series.Series.resample
pandas.core.construction.array
pandas.core.reshape.tile.cut
pandas.core.generic.NDFrame.resample
pandas.core.frame.DataFrame.insert
pandas.io.formats.format.get_series_repr_params
matplotlib.cbook.index_of
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