= Python Pandas Types =

The '''types''' defined by the `pandas` module.

<<TableOfContents>>

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== DataFrame ==

For details on `pandas.core.frame.DataFrame`, see [[Python/Pandas/DataFrame|here]].

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== Index ==

The `pandas.core.indexes.base.Index` type is returned by the `columns` attribute on other data types, such as '''`DataFrame`'''.

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== RangeIndex ==

The `pandas.core.indexes.range.RangeIndex` type is returned by the `index` attribute on other data types, such as '''`Series`'''.

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== Series ==

For details on `pandas.core.series.Series`, see [[Python/Pandas/Series|here]].

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== _ILocIndexer ==

`pandas.core.indexing._iLocIndexer` is an accessor object returned by the `iloc` method. Even if there is a set index for the original object, this always looks up sequential integer indices.

{{{
s.iloc[0]         # first value by integer index
s.iloc[-1]        # last value
s.iloc[:5]        # slice of first 5 values 
s.iloc[:]         # slice of all values
s.iloc[[0, -1]]   # first and last value
}}}

If there is more than one selected value, a new instance of the original data type is returned. If there is only one selected value, the underlying stored type is returned.

{{{
s = pd.Series(["foo", "bar", "baz"])

type(s.iloc[:])  # pandas.core.series.Series
type(s.iloc[0])  # str
}}}

Indexing can raise an [[Python/Builtins/Exceptions#IndexError|IndexError]].

Note that this is ''not'' a read-only view.

{{{
s.iloc[0] = "Foo"
s.iloc[[0, 1, 2]] = ["foo", "bar", "baz"]
s.iloc[0:3] = ["foo", "bar", "baz"]
}}}



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== _LocIndexer ==

`pandas.core.indexing._LocIndexer` is an accessor object returned by the `loc` method. This works exactly like the '''`_iLocIndexer`''' except that it looks up the set index, rather than the underlying sequential integer index.



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