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DOC: add missing links to introduction to pandas (pandas-dev#32198)
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doc/source/getting_started/10min.rst

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@@ -39,7 +39,7 @@ and labeled columns:
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df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
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df
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Creating a ``DataFrame`` by passing a dict of objects that can be converted to series-like.
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Creating a :class:`DataFrame` by passing a dict of objects that can be converted to series-like.
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.. ipython:: python
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@@ -51,7 +51,7 @@ Creating a ``DataFrame`` by passing a dict of objects that can be converted to s
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'F': 'foo'})
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df2
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The columns of the resulting ``DataFrame`` have different
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The columns of the resulting :class:`DataFrame` have different
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:ref:`dtypes <basics.dtypes>`.
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.. ipython:: python
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Getting
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~~~~~~~
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Selecting a single column, which yields a ``Series``,
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Selecting a single column, which yields a :class:`Series`,
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equivalent to ``df.A``:
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.. ipython:: python
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pd.concat(pieces)
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.. note::
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Adding a column to a ``DataFrame`` is relatively fast. However, adding
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Adding a column to a :class:`DataFrame` is relatively fast. However, adding
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a row requires a copy, and may be expensive. We recommend passing a
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pre-built list of records to the ``DataFrame`` constructor instead
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of building a ``DataFrame`` by iteratively appending records to it.
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pre-built list of records to the :class:`DataFrame` constructor instead
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of building a :class:`DataFrame` by iteratively appending records to it.
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See :ref:`Appending to dataframe <merging.concatenation>` for more.
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Join
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'D': np.random.randn(8)})
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df
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Grouping and then applying the :meth:`~DataFrame.sum` function to the resulting
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Grouping and then applying the :meth:`~pandas.core.groupby.GroupBy.sum` function to the resulting
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groups.
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.. ipython:: python
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df.groupby('A').sum()
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Grouping by multiple columns forms a hierarchical index, and again we can
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apply the ``sum`` function.
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apply the :meth:`~pandas.core.groupby.GroupBy.sum` function.
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.. ipython:: python
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Categoricals
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------------
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pandas can include categorical data in a ``DataFrame``. For full docs, see the
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pandas can include categorical data in a :class:`DataFrame`. For full docs, see the
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:ref:`categorical introduction <categorical>` and the :ref:`API documentation <api.arrays.categorical>`.
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.. ipython:: python
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df["grade"]
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Rename the categories to more meaningful names (assigning to
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``Series.cat.categories`` is inplace!).
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:meth:`Series.cat.categories` is inplace!).
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.. ipython:: python
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df["grade"].cat.categories = ["very good", "good", "very bad"]
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Reorder the categories and simultaneously add the missing categories (methods under ``Series
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.cat`` return a new ``Series`` by default).
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Reorder the categories and simultaneously add the missing categories (methods under :meth:`Series.cat` return a new :class:`Series` by default).
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.. ipython:: python
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