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Minor updates to docs on missing values.
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doc/source/missing_data.rst

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@@ -64,13 +64,15 @@ not ``NaN``, I think you will find this is a worthwhile trade-off (Zen of
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Python: "practicality beats purity").
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To make detecting missing values easier (and across different array dtypes),
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pandas provides the ``isnull`` and ``notnull`` functions:
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pandas provides the :func:`~pandas.core.common.isnull` and
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:func:`~pandas.core.common.notnull` functions, which are also methods on
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``Series`` objects:
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.. ipython:: python
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df2['one']
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isnull(df2['one'])
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notnull(df2['four'])
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df2['four'].notnull()
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**Summary:** ``NaN``, ``inf``, ``-inf``, and ``None`` (in object arrays) are
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all considered missing by the ``isnull`` and ``notnull`` functions.
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pandas objects are equipped with various data manipulation methods for dealing
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with missing data.
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dropna:
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.. _missing_data.fillna:
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Filling missing values: fillna
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With time series data, using pad/ffill is extremely common so that the "last
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known value" is available at every time point.
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.. _missing_data.dropna:
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Dropping axis labels with missing data: dropna
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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