3, dtype: int64 On their own, a PandasArray isn’t a very useful object. But if you need write low-level code that works generically for any ExtensionArray, PandasArray satisfies that need. Notice that pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization stored. See also: pandas.DataFrame.to_hdf Write a HDF file from a DataFrame. pandas.HDFStore Low-level access to HDF files. Examples >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization level for that MultiIndex. • Bug where pd.read_gbq() could throw ImportError: No module named discovery as a re- sult of a naming conflict with another python package called apiclient (GH13454) • Bug categorical data and the underlying Categorical is implemented as a python object and not as a low-level numpy array dtype. This leads to some problems. numpy itself doesn’t know about the new dtype:

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization level for that MultiIndex. • Bug where pd.read_gbq() could throw ImportError: No module named discovery as a re- sult of a naming conflict with another python package called apiclient (GH13454) • Bug categorical data and the underlying Categorical is implemented as a python object and not as a low-level numpy array dtype. This leads to some problems. numpy itself doesn’t know about the new dtype:

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization categorical data and the underlying Categorical is implemented as a python object and not as a low-level numpy array dtype. This leads to some problems. numpy itself doesn’t know about the new dtype: backfill / bfill: use NEXT valid observation to fill gap Returns indexer : ndarray Notes This is a low-level method and probably should be used at your own risk Examples >>> indexer = index.get_indexer(new_index)

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization categorical data and the underlying Categorical is implemented as a python object and not as a low-level numpy array dtype. This leads to some problems. numpy itself doesn’t know about the new dtype: 7. Index 1293 pandas: powerful Python data analysis toolkit, Release 0.15.1 Notes This is a low-level method and probably should be used at your own risk Examples >>> indexer = index.get_indexer(new_index)

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization powerful Python data analysis toolkit, Release 0.13.1 Returns indexer : ndarray Notes This is a low-level method and probably should be used at your own risk Examples >>> indexer = index.get_indexer(new_index) backfill / bfill: use NEXT valid observation to fill gap Returns indexer : ndarray Notes This is a low-level method and probably should be used at your own risk Examples >>> indexer = index.get_indexer(new_index)

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization backfill / bfill: use NEXT valid observation to fill gap Returns indexer : ndarray Notes This is a low-level method and probably should be used at your own risk Examples >>> indexer = index.get_indexer(new_index) backfill / bfill: use NEXT valid observation to fill gap Returns indexer : ndarray Notes This is a low-level method and probably should be used at your own risk Examples >>> indexer = index.get_indexer(new_index)

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization categorical data and the underlying Categorical is implemented as a Python object and not as a low-level NumPy array dtype. This leads to some problems. NumPy itself doesn’t know about the new dtype: on the object stored. See also: DataFrame.to_hdf Write a HDF file from a DataFrame. HDFStore Low-level access to HDF files. Examples >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization categorical data and the underlying Categorical is implemented as a Python object and not as a low-level NumPy array dtype. This leads to some problems. NumPy itself doesn’t know about the new dtype: on the object stored. See also: DataFrame.to_hdf Write a HDF file from a DataFrame. HDFStore Low-level access to HDF files. Examples >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])

pandas is the ideal tool for all of these tasks. Some other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization on the object stored. See also: DataFrame.to_hdf Write a HDF file from a DataFrame. HDFStore Low-level access to HDF files. Examples >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z']) Return StataReader object. Returns DataFrame or StataReader See also: io.stata.StataReader Low-level reader for Stata data files. DataFrame.to_stata Export Stata data files. 958 Chapter 3. API reference