merge.py

"""
SQL-style merge routines
"""

import copy
import warnings

import string

import numpy as np
from pandas.compat import range, lrange, lzip, zip, map, filter
import pandas.compat as compat

from pandas import (Categorical, DataFrame, Series,
                    Index, MultiIndex, Timedelta)
from pandas.core.categorical import (_factorize_from_iterable,
                                     _factorize_from_iterables)
from pandas.core.frame import _merge_doc
from pandas.types.generic import ABCSeries
from pandas.types.common import (is_datetime64tz_dtype,
                                 is_datetime64_dtype,
                                 needs_i8_conversion,
                                 is_int64_dtype,
                                 is_integer_dtype,
                                 is_float_dtype,
                                 is_integer,
                                 is_int_or_datetime_dtype,
                                 is_dtype_equal,
                                 is_bool,
                                 is_list_like,
                                 _ensure_int64,
                                 _ensure_float64,
                                 _ensure_object,
                                 _get_dtype)
from pandas.types.missing import na_value_for_dtype

from pandas.core.generic import NDFrame
from pandas.core.index import (_get_combined_index,
                               _ensure_index, _get_consensus_names,
                               _all_indexes_same)
from pandas.core.internals import (items_overlap_with_suffix,
                                   concatenate_block_managers)
from pandas.util.decorators import Appender, Substitution

import pandas.core.algorithms as algos
import pandas.core.common as com
import pandas.types.concat as _concat

import pandas._join as _join
import pandas.hashtable as _hash


@Substitution('\nleft : DataFrame')
@Appender(_merge_doc, indents=0)
def merge(left, right, how='inner', on=None, left_on=None, right_on=None,
          left_index=False, right_index=False, sort=False,
          suffixes=('_x', '_y'), copy=True, indicator=False):
    op = _MergeOperation(left, right, how=how, on=on, left_on=left_on,
                         right_on=right_on, left_index=left_index,
                         right_index=right_index, sort=sort, suffixes=suffixes,
                         copy=copy, indicator=indicator)
    return op.get_result()
if __debug__:
    merge.__doc__ = _merge_doc % '\nleft : DataFrame'


class MergeError(ValueError):
    pass


def _groupby_and_merge(by, on, left, right, _merge_pieces,
                       check_duplicates=True):
    """
    groupby & merge; we are always performing a left-by type operation

    Parameters
    ----------
    by: field to group
    on: duplicates field
    left: left frame
    right: right frame
    _merge_pieces: function for merging
    check_duplicates: boolean, default True
        should we check & clean duplicates
    """

    pieces = []
    if not isinstance(by, (list, tuple)):
        by = [by]

    lby = left.groupby(by, sort=False)

    # if we can groupby the rhs
    # then we can get vastly better perf
    try:

        # we will check & remove duplicates if indicated
        if check_duplicates:
            if on is None:
                on = []
            elif not isinstance(on, (list, tuple)):
                on = [on]

            if right.duplicated(by + on).any():
                right = right.drop_duplicates(by + on, keep='last')
        rby = right.groupby(by, sort=False)
    except KeyError:
        rby = None

    for key, lhs in lby:

        if rby is None:
            rhs = right
        else:
            try:
                rhs = right.take(rby.indices[key])
            except KeyError:
                # key doesn't exist in left
                lcols = lhs.columns.tolist()
                cols = lcols + [r for r in right.columns
                                if r not in set(lcols)]
                merged = lhs.reindex(columns=cols)
                merged.index = range(len(merged))
                pieces.append(merged)
                continue

        merged = _merge_pieces(lhs, rhs)

        # make sure join keys are in the merged
        # TODO, should _merge_pieces do this?
        for k in by:
            try:
                if k in merged:
                    merged[k] = key
            except:
                pass

        pieces.append(merged)

    # preserve the original order
    # if we have a missing piece this can be reset
    result = concat(pieces, ignore_index=True)
    result = result.reindex(columns=pieces[0].columns, copy=False)
    return result, lby


def ordered_merge(left, right, on=None,
                  left_on=None, right_on=None,
                  left_by=None, right_by=None,
                  fill_method=None, suffixes=('_x', '_y')):

    warnings.warn("ordered_merge is deprecated and replaced by merge_ordered",
                  FutureWarning, stacklevel=2)
    return merge_ordered(left, right, on=on,
                         left_on=left_on, right_on=right_on,
                         left_by=left_by, right_by=right_by,
                         fill_method=fill_method, suffixes=suffixes)


def merge_ordered(left, right, on=None,
                  left_on=None, right_on=None,
                  left_by=None, right_by=None,
                  fill_method=None, suffixes=('_x', '_y'),
                  how='outer'):
    """Perform merge with optional filling/interpolation designed for ordered
    data like time series data. Optionally perform group-wise merge (see
    examples)

    Parameters
    ----------
    left : DataFrame
    right : DataFrame
    on : label or list
        Field names to join on. Must be found in both DataFrames.
    left_on : label or list, or array-like
        Field names to join on in left DataFrame. Can be a vector or list of
        vectors of the length of the DataFrame to use a particular vector as
        the join key instead of columns
    right_on : label or list, or array-like
        Field names to join on in right DataFrame or vector/list of vectors per
        left_on docs
    left_by : column name or list of column names
        Group left DataFrame by group columns and merge piece by piece with
        right DataFrame
    right_by : column name or list of column names
        Group right DataFrame by group columns and merge piece by piece with
        left DataFrame
    fill_method : {'ffill', None}, default None
        Interpolation method for data
    suffixes : 2-length sequence (tuple, list, ...)
        Suffix to apply to overlapping column names in the left and right
        side, respectively
    how : {'left', 'right', 'outer', 'inner'}, default 'outer'
        * left: use only keys from left frame (SQL: left outer join)
        * right: use only keys from right frame (SQL: right outer join)
        * outer: use union of keys from both frames (SQL: full outer join)
        * inner: use intersection of keys from both frames (SQL: inner join)

        .. versionadded:: 0.19.0

    Examples
    --------
    >>> A                      >>> B
          key  lvalue group        key  rvalue
    0   a       1     a        0     b       1
    1   c       2     a        1     c       2
    2   e       3     a        2     d       3
    3   a       1     b
    4   c       2     b
    5   e       3     b

    >>> ordered_merge(A, B, fill_method='ffill', left_by='group')
       key  lvalue group  rvalue
    0    a       1     a     NaN
    1    b       1     a       1
    2    c       2     a       2
    3    d       2     a       3
    4    e       3     a       3
    5    f       3     a       4
    6    a       1     b     NaN
    7    b       1     b       1
    8    c       2     b       2
    9    d       2     b       3
    10   e       3     b       3
    11   f       3     b       4

    Returns
    -------
    merged : DataFrame
        The output type will the be same as 'left', if it is a subclass
        of DataFrame.

    See also
    --------
    merge
    merge_asof

    """
    def _merger(x, y):
        # perform the ordered merge operation
        op = _OrderedMerge(x, y, on=on, left_on=left_on, right_on=right_on,
                           suffixes=suffixes, fill_method=fill_method,
                           how=how)
        return op.get_result()

    if left_by is not None and right_by is not None:
        raise ValueError('Can only group either left or right frames')
    elif left_by is not None:
        result, _ = _groupby_and_merge(left_by, on, left, right,
                                       lambda x, y: _merger(x, y),
                                       check_duplicates=False)
    elif right_by is not None:
        result, _ = _groupby_and_merge(right_by, on, right, left,
                                       lambda x, y: _merger(y, x),
                                       check_duplicates=False)
    else:
        result = _merger(left, right)
    return result

ordered_merge.__doc__ = merge_ordered.__doc__


def merge_asof(left, right, on=None,
               left_on=None, right_on=None,
               left_index=False, right_index=False,
               by=None, left_by=None, right_by=None,
               suffixes=('_x', '_y'),
               tolerance=None,
               allow_exact_matches=True):
    """Perform an asof merge. This is similar to a left-join except that we
    match on nearest key rather than equal keys.

    For each row in the left DataFrame, we select the last row in the right
    DataFrame whose 'on' key is less than or equal to the left's key. Both
    DataFrames must be sorted by the key.

    Optionally match on equivalent keys with 'by' before searching for nearest
    match with 'on'.

    .. versionadded:: 0.19.0

    Parameters
    ----------
    left : DataFrame
    right : DataFrame
    on : label
        Field name to join on. Must be found in both DataFrames.
        The data MUST be ordered. Furthermore this must be a numeric column,
        such as datetimelike, integer, or float. On or left_on/right_on
        must be given.
    left_on : label
        Field name to join on in left DataFrame.
    right_on : label
        Field name to join on in right DataFrame.
    left_index : boolean
        Use the index of the left DataFrame as the join key.

        .. versionadded:: 0.19.2

    right_index : boolean
        Use the index of the right DataFrame as the join key.

        .. versionadded:: 0.19.2

    by : column name or list of column names
        Match on these columns before performing merge operation.
    left_by : column name
        Field names to match on in the left DataFrame.

        .. versionadded:: 0.19.2

    right_by : column name
        Field names to match on in the right DataFrame.

        .. versionadded:: 0.19.2

    suffixes : 2-length sequence (tuple, list, ...)
        Suffix to apply to overlapping column names in the left and right
        side, respectively
    tolerance : integer or Timedelta, optional, default None
        select asof tolerance within this range; must be compatible
        to the merge index.
    allow_exact_matches : boolean, default True

        - If True, allow matching the same 'on' value
          (i.e. less-than-or-equal-to)
        - If False, don't match the same 'on' value
          (i.e., stricly less-than)

    Returns
    -------
    merged : DataFrame

    Examples
    --------
    >>> left
        a left_val
    0   1        a
    1   5        b
    2  10        c

    >>> right
       a  right_val
    0  1          1
    1  2          2
    2  3          3
    3  6          6
    4  7          7

    >>> pd.merge_asof(left, right, on='a')
        a left_val  right_val
    0   1        a          1
    1   5        b          3
    2  10        c          7

    >>> pd.merge_asof(left, right, on='a', allow_exact_matches=False)
        a left_val  right_val
    0   1        a        NaN
    1   5        b        3.0
    2  10        c        7.0

    For this example, we can achieve a similar result thru
    ``pd.merge_ordered()``, though its not nearly as performant.

    >>> (pd.merge_ordered(left, right, on='a')
    ...    .ffill()
    ...    .drop_duplicates(['left_val'])
    ... )
        a left_val  right_val
    0   1        a        1.0
    3   5        b        3.0
    6  10        c        7.0

    We can use indexed DataFrames as well.

    >>> left
       left_val
    1         a
    5         b
    10        c

    >>> right
       right_val
    1          1
    2          2
    3          3
    6          6
    7          7

    >>> pd.merge_asof(left, right, left_index=True, right_index=True)
       left_val  right_val
    1         a          1
    5         b          3
    10        c          7

    Here is a real-world times-series example

    >>> quotes
                         time ticker     bid     ask
    0 2016-05-25 13:30:00.023   GOOG  720.50  720.93
    1 2016-05-25 13:30:00.023   MSFT   51.95   51.96
    2 2016-05-25 13:30:00.030   MSFT   51.97   51.98
    3 2016-05-25 13:30:00.041   MSFT   51.99   52.00
    4 2016-05-25 13:30:00.048   GOOG  720.50  720.93
    5 2016-05-25 13:30:00.049   AAPL   97.99   98.01
    6 2016-05-25 13:30:00.072   GOOG  720.50  720.88
    7 2016-05-25 13:30:00.075   MSFT   52.01   52.03

    >>> trades
                         time ticker   price  quantity
    0 2016-05-25 13:30:00.023   MSFT   51.95        75
    1 2016-05-25 13:30:00.038   MSFT   51.95       155
    2 2016-05-25 13:30:00.048   GOOG  720.77       100
    3 2016-05-25 13:30:00.048   GOOG  720.92       100
    4 2016-05-25 13:30:00.048   AAPL   98.00       100

    By default we are taking the asof of the quotes

    >>> pd.merge_asof(trades, quotes,
    ...                       on='time',
    ...                       by='ticker')
                         time ticker   price  quantity     bid     ask
    0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96
    1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
    2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
    3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN

    We only asof within 2ms betwen the quote time and the trade time

    >>> pd.merge_asof(trades, quotes,
    ...                       on='time',
    ...                       by='ticker',
    ...                       tolerance=pd.Timedelta('2ms'))
                         time ticker   price  quantity     bid     ask
    0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96
    1 2016-05-25 13:30:00.038   MSFT   51.95       155     NaN     NaN
    2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
    3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN

    We only asof within 10ms betwen the quote time and the trade time
    and we exclude exact matches on time. However *prior* data will
    propogate forward

    >>> pd.merge_asof(trades, quotes,
    ...                       on='time',
    ...                       by='ticker',
    ...                       tolerance=pd.Timedelta('10ms'),
    ...                       allow_exact_matches=False)
                         time ticker   price  quantity     bid     ask
    0 2016-05-25 13:30:00.023   MSFT   51.95        75     NaN     NaN
    1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
    2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
    3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN

    See also
    --------
    merge
    merge_ordered

    """
    op = _AsOfMerge(left, right,
                    on=on, left_on=left_on, right_on=right_on,
                    left_index=left_index, right_index=right_index,
                    by=by, left_by=left_by, right_by=right_by,
                    suffixes=suffixes,
                    how='asof', tolerance=tolerance,
                    allow_exact_matches=allow_exact_matches)
    return op.get_result()


# TODO: transformations??
# TODO: only copy DataFrames when modification necessary
class _MergeOperation(object):
    """
    Perform a database (SQL) merge operation between two DataFrame objects
    using either columns as keys or their row indexes
    """
    _merge_type = 'merge'

    def __init__(self, left, right, how='inner', on=None,
                 left_on=None, right_on=None, axis=1,
                 left_index=False, right_index=False, sort=True,
                 suffixes=('_x', '_y'), copy=True, indicator=False):
        self.left = self.orig_left = left
        self.right = self.orig_right = right
        self.how = how
        self.axis = axis

        self.on = com._maybe_make_list(on)
        self.left_on = com._maybe_make_list(left_on)
        self.right_on = com._maybe_make_list(right_on)

        self.copy = copy
        self.suffixes = suffixes
        self.sort = sort

        self.left_index = left_index
        self.right_index = right_index

        self.indicator = indicator

        if isinstance(self.indicator, compat.string_types):
            self.indicator_name = self.indicator
        elif isinstance(self.indicator, bool):
            self.indicator_name = '_merge' if self.indicator else None
        else:
            raise ValueError(
                'indicator option can only accept boolean or string arguments')

        if not isinstance(left, DataFrame):
            raise ValueError(
                'can not merge DataFrame with instance of '
                'type {0}'.format(type(left)))
        if not isinstance(right, DataFrame):
            raise ValueError(
                'can not merge DataFrame with instance of '
                'type {0}'.format(type(right)))

        if not is_bool(left_index):
            raise ValueError(
                'left_index parameter must be of type bool, not '
                '{0}'.format(type(left_index)))
        if not is_bool(right_index):
            raise ValueError(
                'right_index parameter must be of type bool, not '
                '{0}'.format(type(right_index)))

        # warn user when merging between different levels
        if left.columns.nlevels != right.columns.nlevels:
            msg = ('merging between different levels can give an unintended '
                   'result ({0} levels on the left, {1} on the right)')
            msg = msg.format(left.columns.nlevels, right.columns.nlevels)
            warnings.warn(msg, UserWarning)

        self._validate_specification()

        # note this function has side effects
        (self.left_join_keys,
         self.right_join_keys,
         self.join_names) = self._get_merge_keys()

    def get_result(self):
        if self.indicator:
            self.left, self.right = self._indicator_pre_merge(
                self.left, self.right)

        join_index, left_indexer, right_indexer = self._get_join_info()

        ldata, rdata = self.left._data, self.right._data
        lsuf, rsuf = self.suffixes

        llabels, rlabels = items_overlap_with_suffix(ldata.items, lsuf,
                                                     rdata.items, rsuf)

        lindexers = {1: left_indexer} if left_indexer is not None else {}
        rindexers = {1: right_indexer} if right_indexer is not None else {}

        result_data = concatenate_block_managers(
            [(ldata, lindexers), (rdata, rindexers)],
            axes=[llabels.append(rlabels), join_index],
            concat_axis=0, copy=self.copy)

        typ = self.left._constructor
        result = typ(result_data).__finalize__(self, method=self._merge_type)

        if self.indicator:
            result = self._indicator_post_merge(result)

        self._maybe_add_join_keys(result, left_indexer, right_indexer)

        return result

    def _indicator_pre_merge(self, left, right):

        columns = left.columns.union(right.columns)

        for i in ['_left_indicator', '_right_indicator']:
            if i in columns:
                raise ValueError("Cannot use `indicator=True` option when "
                                 "data contains a column named {}".format(i))
        if self.indicator_name in columns:
            raise ValueError(
                "Cannot use name of an existing column for indicator column")

        left = left.copy()
        right = right.copy()

        left['_left_indicator'] = 1
        left['_left_indicator'] = left['_left_indicator'].astype('int8')

        right['_right_indicator'] = 2
        right['_right_indicator'] = right['_right_indicator'].astype('int8')

        return left, right

    def _indicator_post_merge(self, result):

        result['_left_indicator'] = result['_left_indicator'].fillna(0)
        result['_right_indicator'] = result['_right_indicator'].fillna(0)

        result[self.indicator_name] = Categorical((result['_left_indicator'] +
                                                   result['_right_indicator']),
                                                  categories=[1, 2, 3])
        result[self.indicator_name] = (
            result[self.indicator_name]
            .cat.rename_categories(['left_only', 'right_only', 'both']))

        result = result.drop(labels=['_left_indicator', '_right_indicator'],
                             axis=1)
        return result

    def _maybe_add_join_keys(self, result, left_indexer, right_indexer):

        left_has_missing = None
        right_has_missing = None

        keys = zip(self.join_names, self.left_on, self.right_on)
        for i, (name, lname, rname) in enumerate(keys):
            if not _should_fill(lname, rname):
                continue

            take_left, take_right = None, None

            if name in result:

                if left_indexer is not None and right_indexer is not None:
                    if name in self.left:

                        if left_has_missing is None:
                            left_has_missing = (left_indexer == -1).any()

                        if left_has_missing:
                            take_right = self.right_join_keys[i]

                            if not is_dtype_equal(result[name].dtype,
                                                  self.left[name].dtype):
                                take_left = self.left[name]._values

                    elif name in self.right:

                        if right_has_missing is None:
                            right_has_missing = (right_indexer == -1).any()

                        if right_has_missing:
                            take_left = self.left_join_keys[i]

                            if not is_dtype_equal(result[name].dtype,
                                                  self.right[name].dtype):
                                take_right = self.right[name]._values

            elif left_indexer is not None \
                    and isinstance(self.left_join_keys[i], np.ndarray):

                take_left = self.left_join_keys[i]
                take_right = self.right_join_keys[i]

            if take_left is not None or take_right is not None:

                if take_left is None:
                    lvals = result[name]._values
                else:
                    lfill = na_value_for_dtype(take_left.dtype)
                    lvals = algos.take_1d(take_left, left_indexer,
                                          fill_value=lfill)

                if take_right is None:
                    rvals = result[name]._values
                else:
                    rfill = na_value_for_dtype(take_right.dtype)
                    rvals = algos.take_1d(take_right, right_indexer,
                                          fill_value=rfill)

                # if we have an all missing left_indexer
                # make sure to just use the right values
                mask = left_indexer == -1
                if mask.all():
                    key_col = rvals
                else:
                    key_col = Index(lvals).where(~mask, rvals)

                if name in result:
                    result[name] = key_col
                else:
                    result.insert(i, name or 'key_%d' % i, key_col)

    def _get_join_indexers(self):
        """ return the join indexers """
        return _get_join_indexers(self.left_join_keys,
                                  self.right_join_keys,
                                  sort=self.sort,
                                  how=self.how)

    def _get_join_info(self):
        left_ax = self.left._data.axes[self.axis]
        right_ax = self.right._data.axes[self.axis]

        if self.left_index and self.right_index and self.how != 'asof':
            join_index, left_indexer, right_indexer = \
                left_ax.join(right_ax, how=self.how, return_indexers=True)
        elif self.right_index and self.how == 'left':
            join_index, left_indexer, right_indexer = \
                _left_join_on_index(left_ax, right_ax, self.left_join_keys,
                                    sort=self.sort)

        elif self.left_index and self.how == 'right':
            join_index, right_indexer, left_indexer = \
                _left_join_on_index(right_ax, left_ax, self.right_join_keys,
                                    sort=self.sort)
        else:
            (left_indexer,
             right_indexer) = self._get_join_indexers()

            if self.right_index:
                if len(self.left) > 0:
                    join_index = self.left.index.take(left_indexer)
                else:
                    join_index = self.right.index.take(right_indexer)
                    left_indexer = np.array([-1] * len(join_index))
            elif self.left_index:
                if len(self.right) > 0:
                    join_index = self.right.index.take(right_indexer)
                else:
                    join_index = self.left.index.take(left_indexer)
                    right_indexer = np.array([-1] * len(join_index))
            else:
                join_index = Index(np.arange(len(left_indexer)))

        if len(join_index) == 0:
            join_index = join_index.astype(object)
        return join_index, left_indexer, right_indexer

    def _get_merge_data(self):
        """
        Handles overlapping column names etc.
        """
        ldata, rdata = self.left._data, self.right._data
        lsuf, rsuf = self.suffixes

        llabels, rlabels = items_overlap_with_suffix(
            ldata.items, lsuf, rdata.items, rsuf)

        if not llabels.equals(ldata.items):
            ldata = ldata.copy(deep=False)
            ldata.set_axis(0, llabels)

        if not rlabels.equals(rdata.items):
            rdata = rdata.copy(deep=False)
            rdata.set_axis(0, rlabels)

        return ldata, rdata

    def _get_merge_keys(self):
        """
        Note: has side effects (copy/delete key columns)

        Parameters
        ----------
        left
        right
        on

        Returns
        -------
        left_keys, right_keys
        """
        left_keys = []
        right_keys = []
        join_names = []
        right_drop = []
        left_drop = []
        left, right = self.left, self.right

        is_lkey = lambda x: isinstance(
            x, (np.ndarray, ABCSeries)) and len(x) == len(left)
        is_rkey = lambda x: isinstance(
            x, (np.ndarray, ABCSeries)) and len(x) == len(right)

        # Note that pd.merge_asof() has separate 'on' and 'by' parameters. A
        # user could, for example, request 'left_index' and 'left_by'. In a
        # regular pd.merge(), users cannot specify both 'left_index' and
        # 'left_on'. (Instead, users have a MultiIndex). That means the
        # self.left_on in this function is always empty in a pd.merge(), but
        # a pd.merge_asof(left_index=True, left_by=...) will result in a
        # self.left_on array with a None in the middle of it. This requires
        # a work-around as designated in the code below.
        # See _validate_specification() for where this happens.

        # ugh, spaghetti re #733
        if _any(self.left_on) and _any(self.right_on):
            for lk, rk in zip(self.left_on, self.right_on):
                if is_lkey(lk):
                    left_keys.append(lk)
                    if is_rkey(rk):
                        right_keys.append(rk)
                        join_names.append(None)  # what to do?
                    else:
                        if rk is not None:
                            right_keys.append(right[rk]._values)
                            join_names.append(rk)
                        else:
                            # work-around for merge_asof(right_index=True)
                            right_keys.append(right.index)
                            join_names.append(right.index.name)
                else:
                    if not is_rkey(rk):
                        if rk is not None:
                            right_keys.append(right[rk]._values)
                        else:
                            # work-around for merge_asof(right_index=True)
                            right_keys.append(right.index)
                        if lk is not None and lk == rk:
                            # avoid key upcast in corner case (length-0)
                            if len(left) > 0:
                                right_drop.append(rk)
                            else:
                                left_drop.append(lk)
                    else:
                        right_keys.append(rk)
                    if lk is not None:
                        left_keys.append(left[lk]._values)
                        join_names.append(lk)
                    else:
                        # work-around for merge_asof(left_index=True)
                        left_keys.append(left.index)
                        join_names.append(left.index.name)
        elif _any(self.left_on):
            for k in self.left_on:
                if is_lkey(k):
                    left_keys.append(k)
                    join_names.append(None)
                else:
                    left_keys.append(left[k]._values)
                    join_names.append(k)
            if isinstance(self.right.index, MultiIndex):
                right_keys = [lev._values.take(lab)
                              for lev, lab in zip(self.right.index.levels,
                                                  self.right.index.labels)]
            else:
                right_keys = [self.right.index.values]
        elif _any(self.right_on):
            for k in self.right_on:
                if is_rkey(k):
                    right_keys.append(k)
                    join_names.append(None)
                else:
                    right_keys.append(right[k]._values)
                    join_names.append(k)
            if isinstance(self.left.index, MultiIndex):
                left_keys = [lev._values.take(lab)
                             for lev, lab in zip(self.left.index.levels,
                                                 self.left.index.labels)]
            else:
                left_keys = [self.left.index.values]

        if left_drop:
            self.left = self.left.drop(left_drop, axis=1)

        if right_drop:
            self.right = self.right.drop(right_drop, axis=1)

        return left_keys, right_keys, join_names

    def _validate_specification(self):
        # Hm, any way to make this logic less complicated??
        if self.on is None and self.left_on is None and self.right_on is None:

            if self.left_index and self.right_index:
                self.left_on, self.right_on = (), ()
            elif self.left_index:
                if self.right_on is None:
                    raise MergeError('Must pass right_on or right_index=True')
            elif self.right_index:
                if self.left_on is None:
                    raise MergeError('Must pass left_on or left_index=True')
            else:
                # use the common columns
                common_cols = self.left.columns.intersection(
                    self.right.columns)
                if len(common_cols) == 0:
                    raise MergeError('No common columns to perform merge on')
                if not common_cols.is_unique:
                    raise MergeError("Data columns not unique: %s"
                                     % repr(common_cols))
                self.left_on = self.right_on = common_cols
        elif self.on is not None:
            if self.left_on is not None or self.right_on is not None:
                raise MergeError('Can only pass argument "on" OR "left_on" '
                                 'and "right_on", not a combination of both.')
            self.left_on = self.right_on = self.on
        elif self.left_on is not None:
            n = len(self.left_on)
            if self.right_index:
                if len(self.left_on) != self.right.index.nlevels:
                    raise ValueError('len(left_on) must equal the number '
                                     'of levels in the index of "right"')
                self.right_on = [None] * n
        elif self.right_on is not None:
            n = len(self.right_on)
            if self.left_index:
                if len(self.right_on) != self.left.index.nlevels:
                    raise ValueError('len(right_on) must equal the number '
                                     'of levels in the index of "left"')
                self.left_on = [None] * n
        if len(self.right_on) != len(self.left_on):
            raise ValueError("len(right_on) must equal len(left_on)")


def _get_join_indexers(left_keys, right_keys, sort=False, how='inner',
                       **kwargs):
    """

    Parameters
    ----------

    Returns
    -------

    """
    from functools import partial

    assert len(left_keys) == len(right_keys), \
        'left_key and right_keys must be the same length'

    # bind `sort` arg. of _factorize_keys
    fkeys = partial(_factorize_keys, sort=sort)

    # get left & right join labels and num. of levels at each location
    llab, rlab, shape = map(list, zip(* map(fkeys, left_keys, right_keys)))

    # get flat i8 keys from label lists
    lkey, rkey = _get_join_keys(llab, rlab, shape, sort)

    # factorize keys to a dense i8 space
    # `count` is the num. of unique keys
    # set(lkey) | set(rkey) == range(count)
    lkey, rkey, count = fkeys(lkey, rkey)

    # preserve left frame order if how == 'left' and sort == False
    kwargs = copy.copy(kwargs)
    if how == 'left':
        kwargs['sort'] = sort
    join_func = _join_functions[how]

    return join_func(lkey, rkey, count, **kwargs)


class _OrderedMerge(_MergeOperation):
    _merge_type = 'ordered_merge'

    def __init__(self, left, right, on=None, left_on=None, right_on=None,
                 left_index=False, right_index=False, axis=1,
                 suffixes=('_x', '_y'), copy=True,
                 fill_method=None, how='outer'):

        self.fill_method = fill_method
        _MergeOperation.__init__(self, left, right, on=on, left_on=left_on,
                                 left_index=left_index,
                                 right_index=right_index,
                                 right_on=right_on, axis=axis,
                                 how=how, suffixes=suffixes,
                                 sort=True  # factorize sorts
                                 )

    def get_result(self):
        join_index, left_indexer, right_indexer = self._get_join_info()

        # this is a bit kludgy
        ldata, rdata = self.left._data, self.right._data
        lsuf, rsuf = self.suffixes

        llabels, rlabels = items_overlap_with_suffix(ldata.items, lsuf,
                                                     rdata.items, rsuf)

        if self.fill_method == 'ffill':
            left_join_indexer = _join.ffill_indexer(left_indexer)
            right_join_indexer = _join.ffill_indexer(right_indexer)
        else:
            left_join_indexer = left_indexer
            right_join_indexer = right_indexer

        lindexers = {
            1: left_join_indexer} if left_join_indexer is not None else {}
        rindexers = {
            1: right_join_indexer} if right_join_indexer is not None else {}

        result_data = concatenate_block_managers(
            [(ldata, lindexers), (rdata, rindexers)],
            axes=[llabels.append(rlabels), join_index],
            concat_axis=0, copy=self.copy)

        typ = self.left._constructor
        result = typ(result_data).__finalize__(self, method=self._merge_type)

        self._maybe_add_join_keys(result, left_indexer, right_indexer)

        return result


def _asof_function(on_type):
    return getattr(_join, 'asof_join_%s' % on_type, None)


def _asof_by_function(on_type, by_type):
    return getattr(_join, 'asof_join_%s_by_%s' % (on_type, by_type), None)


_type_casters = {
    'int64_t': _ensure_int64,
    'double': _ensure_float64,
    'object': _ensure_object,
}

_cython_types = {
    'uint8': 'uint8_t',
    'uint32': 'uint32_t',
    'uint16': 'uint16_t',
    'uint64': 'uint64_t',
    'int8': 'int8_t',
    'int32': 'int32_t',
    'int16': 'int16_t',
    'int64': 'int64_t',
    'float16': 'error',
    'float32': 'float',
    'float64': 'double',
}


def _get_cython_type(dtype):
    """ Given a dtype, return a C name like 'int64_t' or 'double' """
    type_name = _get_dtype(dtype).name
    ctype = _cython_types.get(type_name, 'object')
    if ctype == 'error':
        raise MergeError('unsupported type: ' + type_name)
    return ctype


def _get_cython_type_upcast(dtype):
    """ Upcast a dtype to 'int64_t', 'double', or 'object' """
    if is_integer_dtype(dtype):
        return 'int64_t'
    elif is_float_dtype(dtype):
        return 'double'
    else:
        return 'object'


class _AsOfMerge(_OrderedMerge):
    _merge_type = 'asof_merge'

    def __init__(self, left, right, on=None, left_on=None, right_on=None,
                 left_index=False, right_index=False,
                 by=None, left_by=None, right_by=None,
                 axis=1, suffixes=('_x', '_y'), copy=True,
                 fill_method=None,
                 how='asof', tolerance=None,
                 allow_exact_matches=True):

        self.by = by
        self.left_by = left_by
        self.right_by = right_by
        self.tolerance = tolerance
        self.allow_exact_matches = allow_exact_matches

        _OrderedMerge.__init__(self, left, right, on=on, left_on=left_on,
                               right_on=right_on, left_index=left_index,
                               right_index=right_index, axis=axis,
                               how=how, suffixes=suffixes,
                               fill_method=fill_method)

    def _validate_specification(self):
        super(_AsOfMerge, self)._validate_specification()

        # we only allow on to be a single item for on
        if len(self.left_on) != 1 and not self.left_index:
            raise MergeError("can only asof on a key for left")

        if len(self.right_on) != 1 and not self.right_index:
            raise MergeError("can only asof on a key for right")

        if self.left_index and isinstance(self.left.index, MultiIndex):
            raise MergeError("left can only have one index")

        if self.right_index and isinstance(self.right.index, MultiIndex):
            raise MergeError("right can only have one index")

        # set 'by' columns
        if self.by is not None:
            if self.left_by is not None or self.right_by is not None:
                raise MergeError('Can only pass by OR left_by '
                                 'and right_by')
            self.left_by = self.right_by = self.by
        if self.left_by is None and self.right_by is not None:
            raise MergeError('missing left_by')
        if self.left_by is not None and self.right_by is None:
            raise MergeError('missing right_by')

        # add by to our key-list so we can have it in the
        # output as a key
        if self.left_by is not None:
            if not is_list_like(self.left_by):
                self.left_by = [self.left_by]
            if not is_list_like(self.right_by):
                self.right_by = [self.right_by]

            self.left_on = self.left_by + list(self.left_on)
            self.right_on = self.right_by + list(self.right_on)

    @property
    def _asof_key(self):
        """ This is our asof key, the 'on' """
        return self.left_on[-1]

    def _get_merge_keys(self):

        # note this function has side effects
        (left_join_keys,
         right_join_keys,
         join_names) = super(_AsOfMerge, self)._get_merge_keys()

        # validate index types are the same
        for lk, rk in zip(left_join_keys, right_join_keys):
            if not is_dtype_equal(lk.dtype, rk.dtype):
                raise MergeError("incompatible merge keys, "
                                 "must be the same type")

        # validate tolerance; must be a Timedelta if we have a DTI
        if self.tolerance is not None:

            lt = left_join_keys[-1]
            msg = "incompatible tolerance, must be compat " \
                  "with type {0}".format(type(lt))

            if is_datetime64_dtype(lt) or is_datetime64tz_dtype(lt):
                if not isinstance(self.tolerance, Timedelta):
                    raise MergeError(msg)
                if self.tolerance < Timedelta(0):
                    raise MergeError("tolerance must be positive")

            elif is_int64_dtype(lt):
                if not is_integer(self.tolerance):
                    raise MergeError(msg)
                if self.tolerance < 0:
                    raise MergeError("tolerance must be positive")

            else:
                raise MergeError("key must be integer or timestamp")

        # validate allow_exact_matches
        if not is_bool(self.allow_exact_matches):
            raise MergeError("allow_exact_matches must be boolean, "
                             "passed {0}".format(self.allow_exact_matches))

        return left_join_keys, right_join_keys, join_names

    def _get_join_indexers(self):
        """ return the join indexers """

        def flip(xs):
            """ unlike np.transpose, this returns an array of tuples """
            labels = list(string.ascii_lowercase[:len(xs)])
            dtypes = [x.dtype for x in xs]
            labeled_dtypes = list(zip(labels, dtypes))
            return np.array(lzip(*xs), labeled_dtypes)

        # values to compare
        left_values = (self.left.index.values if self.left_index else
                       self.left_join_keys[-1])
        right_values = (self.right.index.values if self.right_index else
                        self.right_join_keys[-1])
        tolerance = self.tolerance

        # we required sortedness in the join keys
        msg = " keys must be sorted"
        if not Index(left_values).is_monotonic:
            raise ValueError('left' + msg)
        if not Index(right_values).is_monotonic:
            raise ValueError('right' + msg)

        # initial type conversion as needed
        if needs_i8_conversion(left_values):
            left_values = left_values.view('i8')
            right_values = right_values.view('i8')
            if tolerance is not None:
                tolerance = tolerance.value

        # a "by" parameter requires special handling
        if self.left_by is not None:
            if len(self.left_join_keys) > 2:
                # get tuple representation of values if more than one
                left_by_values = flip(self.left_join_keys[0:-1])
                right_by_values = flip(self.right_join_keys[0:-1])
            else:
                left_by_values = self.left_join_keys[0]
                right_by_values = self.right_join_keys[0]

            # upcast 'by' parameter because HashTable is limited
            by_type = _get_cython_type_upcast(left_by_values.dtype)
            by_type_caster = _type_casters[by_type]
            left_by_values = by_type_caster(left_by_values)
            right_by_values = by_type_caster(right_by_values)

            # choose appropriate function by type
            on_type = _get_cython_type(left_values.dtype)
            func = _asof_by_function(on_type, by_type)
            return func(left_values,
                        right_values,
                        left_by_values,
                        right_by_values,
                        self.allow_exact_matches,
                        tolerance)
        else:
            # choose appropriate function by type
            on_type = _get_cython_type(left_values.dtype)
            func = _asof_function(on_type)
            return func(left_values,
                        right_values,
                        self.allow_exact_matches,
                        tolerance)


def _get_multiindex_indexer(join_keys, index, sort):
    from functools import partial

    # bind `sort` argument
    fkeys = partial(_factorize_keys, sort=sort)

    # left & right join labels and num. of levels at each location
    rlab, llab, shape = map(list, zip(* map(fkeys, index.levels, join_keys)))
    if sort:
        rlab = list(map(np.take, rlab, index.labels))
    else:
        i8copy = lambda a: a.astype('i8', subok=False, copy=True)
        rlab = list(map(i8copy, index.labels))

    # fix right labels if there were any nulls
    for i in range(len(join_keys)):
        mask = index.labels[i] == -1
        if mask.any():
            # check if there already was any nulls at this location
            # if there was, it is factorized to `shape[i] - 1`
            a = join_keys[i][llab[i] == shape[i] - 1]
            if a.size == 0 or not a[0] != a[0]:
                shape[i] += 1

            rlab[i][mask] = shape[i] - 1

    # get flat i8 join keys
    lkey, rkey = _get_join_keys(llab, rlab, shape, sort)

    # factorize keys to a dense i8 space
    lkey, rkey, count = fkeys(lkey, rkey)

    return _join.left_outer_join(lkey, rkey, count, sort=sort)


def _get_single_indexer(join_key, index, sort=False):
    left_key, right_key, count = _factorize_keys(join_key, index, sort=sort)

    left_indexer, right_indexer = _join.left_outer_join(
        _ensure_int64(left_key),
        _ensure_int64(right_key),
        count, sort=sort)

    return left_indexer, right_indexer


def _left_join_on_index(left_ax, right_ax, join_keys, sort=False):
    if len(join_keys) > 1:
        if not ((isinstance(right_ax, MultiIndex) and
                 len(join_keys) == right_ax.nlevels)):
            raise AssertionError("If more than one join key is given then "
                                 "'right_ax' must be a MultiIndex and the "
                                 "number of join keys must be the number of "
                                 "levels in right_ax")

        left_indexer, right_indexer = \
            _get_multiindex_indexer(join_keys, right_ax, sort=sort)
    else:
        jkey = join_keys[0]

        left_indexer, right_indexer = \
            _get_single_indexer(jkey, right_ax, sort=sort)

    if sort or len(left_ax) != len(left_indexer):
        # if asked to sort or there are 1-to-many matches
        join_index = left_ax.take(left_indexer)
        return join_index, left_indexer, right_indexer

    # left frame preserves order & length of its index
    return left_ax, None, right_indexer


def _right_outer_join(x, y, max_groups):
    right_indexer, left_indexer = _join.left_outer_join(y, x, max_groups)
    return left_indexer, right_indexer

_join_functions = {
    'inner': _join.inner_join,
    'left': _join.left_outer_join,
    'right': _right_outer_join,
    'outer': _join.full_outer_join,
}


def _factorize_keys(lk, rk, sort=True):
    if is_datetime64tz_dtype(lk) and is_datetime64tz_dtype(rk):
        lk = lk.values
        rk = rk.values
    if is_int_or_datetime_dtype(lk) and is_int_or_datetime_dtype(rk):
        klass = _hash.Int64Factorizer
        lk = _ensure_int64(com._values_from_object(lk))
        rk = _ensure_int64(com._values_from_object(rk))
    else:
        klass = _hash.Factorizer
        lk = _ensure_object(lk)
        rk = _ensure_object(rk)

    rizer = klass(max(len(lk), len(rk)))

    llab = rizer.factorize(lk)
    rlab = rizer.factorize(rk)

    count = rizer.get_count()

    if sort:
        uniques = rizer.uniques.to_array()
        llab, rlab = _sort_labels(uniques, llab, rlab)

    # NA group
    lmask = llab == -1
    lany = lmask.any()
    rmask = rlab == -1
    rany = rmask.any()

    if lany or rany:
        if lany:
            np.putmask(llab, lmask, count)
        if rany:
            np.putmask(rlab, rmask, count)
        count += 1

    return llab, rlab, count


def _sort_labels(uniques, left, right):
    if not isinstance(uniques, np.ndarray):
        # tuplesafe
        uniques = Index(uniques).values

    l = len(left)
    labels = np.concatenate([left, right])

    _, new_labels = algos.safe_sort(uniques, labels, na_sentinel=-1)
    new_labels = _ensure_int64(new_labels)
    new_left, new_right = new_labels[:l], new_labels[l:]

    return new_left, new_right


def _get_join_keys(llab, rlab, shape, sort):
    from pandas.core.groupby import _int64_overflow_possible

    # how many levels can be done without overflow
    pred = lambda i: not _int64_overflow_possible(shape[:i])
    nlev = next(filter(pred, range(len(shape), 0, -1)))

    # get keys for the first `nlev` levels
    stride = np.prod(shape[1:nlev], dtype='i8')
    lkey = stride * llab[0].astype('i8', subok=False, copy=False)
    rkey = stride * rlab[0].astype('i8', subok=False, copy=False)

    for i in range(1, nlev):
        stride //= shape[i]
        lkey += llab[i] * stride
        rkey += rlab[i] * stride

    if nlev == len(shape):  # all done!
        return lkey, rkey

    # densify current keys to avoid overflow
    lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort)

    llab = [lkey] + llab[nlev:]
    rlab = [rkey] + rlab[nlev:]
    shape = [count] + shape[nlev:]

    return _get_join_keys(llab, rlab, shape, sort)

# ---------------------------------------------------------------------
# Concatenate DataFrame objects


def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
           keys=None, levels=None, names=None, verify_integrity=False,
           copy=True):
    """
    Concatenate pandas objects along a particular axis with optional set logic
    along the other axes.

    Can also add a layer of hierarchical indexing on the concatenation axis,
    which may be useful if the labels are the same (or overlapping) on
    the passed axis number.

    Parameters
    ----------
    objs : a sequence or mapping of Series, DataFrame, or Panel objects
        If a dict is passed, the sorted keys will be used as the `keys`
        argument, unless it is passed, in which case the values will be
        selected (see below). Any None objects will be dropped silently unless
        they are all None in which case a ValueError will be raised
    axis : {0/'index', 1/'columns'}, default 0
        The axis to concatenate along
    join : {'inner', 'outer'}, default 'outer'
        How to handle indexes on other axis(es)
    join_axes : list of Index objects
        Specific indexes to use for the other n - 1 axes instead of performing
        inner/outer set logic
    ignore_index : boolean, default False
        If True, do not use the index values along the concatenation axis. The
        resulting axis will be labeled 0, ..., n - 1. This is useful if you are
        concatenating objects where the concatenation axis does not have
        meaningful indexing information. Note the index values on the other
        axes are still respected in the join.
    keys : sequence, default None
        If multiple levels passed, should contain tuples. Construct
        hierarchical index using the passed keys as the outermost level
    levels : list of sequences, default None
        Specific levels (unique values) to use for constructing a
        MultiIndex. Otherwise they will be inferred from the keys
    names : list, default None
        Names for the levels in the resulting hierarchical index
    verify_integrity : boolean, default False
        Check whether the new concatenated axis contains duplicates. This can
        be very expensive relative to the actual data concatenation
    copy : boolean, default True
        If False, do not copy data unnecessarily

    Returns
    -------
    concatenated : type of objects

    Notes
    -----
    The keys, levels, and names arguments are all optional.

    A walkthrough of how this method fits in with other tools for combining
    panda objects can be found `here
    <http://pandas.pydata.org/pandas-docs/stable/merging.html>`__.

    See Also
    --------
    Series.append
    DataFrame.append
    DataFrame.join
    DataFrame.merge

    Examples
    --------
    Combine two ``Series``.

    >>> s1 = pd.Series(['a', 'b'])
    >>> s2 = pd.Series(['c', 'd'])
    >>> pd.concat([s1, s2])
    0    a
    1    b
    0    c
    1    d
    dtype: object

    Clear the existing index and reset it in the result
    by setting the ``ignore_index`` option to ``True``.

    >>> pd.concat([s1, s2], ignore_index=True)
    0    a
    1    b
    2    c
    3    d
    dtype: object

    Add a hierarchical index at the outermost level of
    the data with the ``keys`` option.

    >>> pd.concat([s1, s2], keys=['s1', 's2',])
    s1  0    a
        1    b
    s2  0    c
        1    d
    dtype: object

    Label the index keys you create with the ``names`` option.

    >>> pd.concat([s1, s2], keys=['s1', 's2'],
    ...           names=['Series name', 'Row ID'])
    Series name  Row ID
    s1           0         a
                 1         b
    s2           0         c
                 1         d
    dtype: object

    Combine two ``DataFrame`` objects with identical columns.

    >>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
    ...                    columns=['letter', 'number'])
    >>> df1
      letter  number
    0      a       1
    1      b       2
    >>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
    ...                    columns=['letter', 'number'])
    >>> df2
      letter  number
    0      c       3
    1      d       4
    >>> pd.concat([df1, df2])
      letter  number
    0      a       1
    1      b       2
    0      c       3
    1      d       4

    Combine ``DataFrame`` objects with overlapping columns
    and return everything. Columns outside the intersection will
    be filled with ``NaN`` values.

    >>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
    ...                    columns=['letter', 'number', 'animal'])
    >>> df3
      letter  number animal
    0      c       3    cat
    1      d       4    dog
    >>> pd.concat([df1, df3])
      animal letter  number
    0    NaN      a       1
    1    NaN      b       2
    0    cat      c       3
    1    dog      d       4

    Combine ``DataFrame`` objects with overlapping columns
    and return only those that are shared by passing ``inner`` to
    the ``join`` keyword argument.

    >>> pd.concat([df1, df3], join="inner")
      letter  number
    0      a       1
    1      b       2
    0      c       3
    1      d       4

    Combine ``DataFrame`` objects horizontally along the x axis by
    passing in ``axis=1``.

    >>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],
    ...                    columns=['animal', 'name'])
    >>> pd.concat([df1, df4], axis=1)
      letter  number  animal    name
    0      a       1    bird   polly
    1      b       2  monkey  george

    Prevent the result from including duplicate index values with the
    ``verify_integrity`` option.

    >>> df5 = pd.DataFrame([1], index=['a'])
    >>> df5
       0
    a  1
    >>> df6 = pd.DataFrame([2], index=['a'])
    >>> df6
       0
    a  2
    >>> pd.concat([df5, df6], verify_integrity=True)
    ValueError: Indexes have overlapping values: ['a']
    """
    op = _Concatenator(objs, axis=axis, join_axes=join_axes,
                       ignore_index=ignore_index, join=join,
                       keys=keys, levels=levels, names=names,
                       verify_integrity=verify_integrity,
                       copy=copy)
    return op.get_result()


class _Concatenator(object):
    """
    Orchestrates a concatenation operation for BlockManagers
    """

    def __init__(self, objs, axis=0, join='outer', join_axes=None,
                 keys=None, levels=None, names=None,
                 ignore_index=False, verify_integrity=False, copy=True):
        if isinstance(objs, (NDFrame, compat.string_types)):
            raise TypeError('first argument must be an iterable of pandas '
                            'objects, you passed an object of type '
                            '"{0}"'.format(type(objs).__name__))

        if join == 'outer':
            self.intersect = False
        elif join == 'inner':
            self.intersect = True
        else:  # pragma: no cover
            raise ValueError('Only can inner (intersect) or outer (union) '
                             'join the other axis')

        if isinstance(objs, dict):
            if keys is None:
                keys = sorted(objs)
            objs = [objs[k] for k in keys]
        else:
            objs = list(objs)

        if len(objs) == 0:
            raise ValueError('No objects to concatenate')

        if keys is None:
            objs = [obj for obj in objs if obj is not None]
        else:
            # #1649
            clean_keys = []
            clean_objs = []
            for k, v in zip(keys, objs):
                if v is None:
                    continue
                clean_keys.append(k)
                clean_objs.append(v)
            objs = clean_objs
            name = getattr(keys, 'name', None)
            keys = Index(clean_keys, name=name)

        if len(objs) == 0:
            raise ValueError('All objects passed were None')

        # consolidate data & figure out what our result ndim is going to be
        ndims = set()
        for obj in objs:
            if not isinstance(obj, NDFrame):
                raise TypeError("cannot concatenate a non-NDFrame object")

            # consolidate
            obj.consolidate(inplace=True)
            ndims.add(obj.ndim)

        # get the sample
        # want the higest ndim that we have, and must be non-empty
        # unless all objs are empty
        sample = None
        if len(ndims) > 1:
            max_ndim = max(ndims)
            for obj in objs:
                if obj.ndim == max_ndim and np.sum(obj.shape):
                    sample = obj
                    break

        else:
            # filter out the empties if we have not multi-index possibiltes
            # note to keep empty Series as it affect to result columns / name
            non_empties = [obj for obj in objs
                           if sum(obj.shape) > 0 or isinstance(obj, Series)]

            if (len(non_empties) and (keys is None and names is None and
                                      levels is None and join_axes is None)):
                objs = non_empties
                sample = objs[0]

        if sample is None:
            sample = objs[0]
        self.objs = objs

        # Standardize axis parameter to int
        if isinstance(sample, Series):
            axis = DataFrame()._get_axis_number(axis)
        else:
            axis = sample._get_axis_number(axis)

        # Need to flip BlockManager axis in the DataFrame special case
        self._is_frame = isinstance(sample, DataFrame)
        if self._is_frame:
            axis = 1 if axis == 0 else 0

        self._is_series = isinstance(sample, ABCSeries)
        if not 0 <= axis <= sample.ndim:
            raise AssertionError("axis must be between 0 and {0}, "
                                 "input was {1}".format(sample.ndim, axis))

        # if we have mixed ndims, then convert to highest ndim
        # creating column numbers as needed
        if len(ndims) > 1:
            current_column = 0
            max_ndim = sample.ndim
            self.objs, objs = [], self.objs
            for obj in objs:

                ndim = obj.ndim
                if ndim == max_ndim:
                    pass

                elif ndim != max_ndim - 1:
                    raise ValueError("cannot concatenate unaligned mixed "
                                     "dimensional NDFrame objects")

                else:
                    name = getattr(obj, 'name', None)
                    if ignore_index or name is None:
                        name = current_column
                        current_column += 1

                    # doing a row-wise concatenation so need everything
                    # to line up
                    if self._is_frame and axis == 1:
                        name = 0
                    obj = sample._constructor({name: obj})

                self.objs.append(obj)

        # note: this is the BlockManager axis (since DataFrame is transposed)
        self.axis = axis
        self.join_axes = join_axes
        self.keys = keys
        self.names = names or getattr(keys, 'names', None)
        self.levels = levels

        self.ignore_index = ignore_index
        self.verify_integrity = verify_integrity
        self.copy = copy

        self.new_axes = self._get_new_axes()

    def get_result(self):

        # series only
        if self._is_series:

            # stack blocks
            if self.axis == 0:
                # concat Series with length to keep dtype as much
                non_empties = [x for x in self.objs if len(x) > 0]
                if len(non_empties) > 0:
                    values = [x._values for x in non_empties]
                else:
                    values = [x._values for x in self.objs]
                new_data = _concat._concat_compat(values)

                name = com._consensus_name_attr(self.objs)
                cons = _concat._get_series_result_type(new_data)

                return (cons(new_data, index=self.new_axes[0],
                             name=name, dtype=new_data.dtype)
                        .__finalize__(self, method='concat'))

            # combine as columns in a frame
            else:
                data = dict(zip(range(len(self.objs)), self.objs))
                cons = _concat._get_series_result_type(data)

                index, columns = self.new_axes
                df = cons(data, index=index)
                df.columns = columns
                return df.__finalize__(self, method='concat')

        # combine block managers
        else:
            mgrs_indexers = []
            for obj in self.objs:
                mgr = obj._data
                indexers = {}
                for ax, new_labels in enumerate(self.new_axes):
                    if ax == self.axis:
                        # Suppress reindexing on concat axis
                        continue

                    obj_labels = mgr.axes[ax]
                    if not new_labels.equals(obj_labels):
                        indexers[ax] = obj_labels.reindex(new_labels)[1]

                mgrs_indexers.append((obj._data, indexers))

            new_data = concatenate_block_managers(
                mgrs_indexers, self.new_axes, concat_axis=self.axis,
                copy=self.copy)
            if not self.copy:
                new_data._consolidate_inplace()

            cons = _concat._get_frame_result_type(new_data, self.objs)
            return (cons._from_axes(new_data, self.new_axes)
                    .__finalize__(self, method='concat'))

    def _get_result_dim(self):
        if self._is_series and self.axis == 1:
            return 2
        else:
            return self.objs[0].ndim

    def _get_new_axes(self):
        ndim = self._get_result_dim()
        new_axes = [None] * ndim

        if self.join_axes is None:
            for i in range(ndim):
                if i == self.axis:
                    continue
                new_axes[i] = self._get_comb_axis(i)
        else:
            if len(self.join_axes) != ndim - 1:
                raise AssertionError("length of join_axes must not be "
                                     "equal to {0}".format(ndim - 1))

            # ufff...
            indices = lrange(ndim)
            indices.remove(self.axis)

            for i, ax in zip(indices, self.join_axes):
                new_axes[i] = ax

        new_axes[self.axis] = self._get_concat_axis()
        return new_axes

    def _get_comb_axis(self, i):
        if self._is_series:
            all_indexes = [x.index for x in self.objs]
        else:
            try:
                all_indexes = [x._data.axes[i] for x in self.objs]
            except IndexError:
                types = [type(x).__name__ for x in self.objs]
                raise TypeError("Cannot concatenate list of %s" % types)

        return _get_combined_index(all_indexes, intersect=self.intersect)

    def _get_concat_axis(self):
        """
        Return index to be used along concatenation axis.
        """
        if self._is_series:
            if self.axis == 0:
                indexes = [x.index for x in self.objs]
            elif self.ignore_index:
                idx = com._default_index(len(self.objs))
                return idx
            elif self.keys is None:
                names = [None] * len(self.objs)
                num = 0
                has_names = False
                for i, x in enumerate(self.objs):
                    if not isinstance(x, Series):
                        raise TypeError("Cannot concatenate type 'Series' "
                                        "with object of type "
                                        "%r" % type(x).__name__)
                    if x.name is not None:
                        names[i] = x.name
                        has_names = True
                    else:
                        names[i] = num
                        num += 1
                if has_names:
                    return Index(names)
                else:
                    return com._default_index(len(self.objs))
            else:
                return _ensure_index(self.keys)
        else:
            indexes = [x._data.axes[self.axis] for x in self.objs]

        if self.ignore_index:
            idx = com._default_index(sum(len(i) for i in indexes))
            return idx

        if self.keys is None:
            concat_axis = _concat_indexes(indexes)
        else:
            concat_axis = _make_concat_multiindex(indexes, self.keys,
                                                  self.levels, self.names)

        self._maybe_check_integrity(concat_axis)

        return concat_axis

    def _maybe_check_integrity(self, concat_index):
        if self.verify_integrity:
            if not concat_index.is_unique:
                overlap = concat_index.get_duplicates()
                raise ValueError('Indexes have overlapping values: %s'
                                 % str(overlap))


def _concat_indexes(indexes):
    return indexes[0].append(indexes[1:])


def _make_concat_multiindex(indexes, keys, levels=None, names=None):

    if ((levels is None and isinstance(keys[0], tuple)) or
            (levels is not None and len(levels) > 1)):
        zipped = lzip(*keys)
        if names is None:
            names = [None] * len(zipped)

        if levels is None:
            _, levels = _factorize_from_iterables(zipped)
        else:
            levels = [_ensure_index(x) for x in levels]
    else:
        zipped = [keys]
        if names is None:
            names = [None]

        if levels is None:
            levels = [_ensure_index(keys)]
        else:
            levels = [_ensure_index(x) for x in levels]

    if not _all_indexes_same(indexes):
        label_list = []

        # things are potentially different sizes, so compute the exact labels
        # for each level and pass those to MultiIndex.from_arrays

        for hlevel, level in zip(zipped, levels):
            to_concat = []
            for key, index in zip(hlevel, indexes):
                try:
                    i = level.get_loc(key)
                except KeyError:
                    raise ValueError('Key %s not in level %s'
                                     % (str(key), str(level)))

                to_concat.append(np.repeat(i, len(index)))
            label_list.append(np.concatenate(to_concat))

        concat_index = _concat_indexes(indexes)

        # these go at the end
        if isinstance(concat_index, MultiIndex):
            levels.extend(concat_index.levels)
            label_list.extend(concat_index.labels)
        else:
            codes, categories = _factorize_from_iterable(concat_index)
            levels.append(categories)
            label_list.append(codes)

        if len(names) == len(levels):
            names = list(names)
        else:
            # make sure that all of the passed indices have the same nlevels
            if not len(set([idx.nlevels for idx in indexes])) == 1:
                raise AssertionError("Cannot concat indices that do"
                                     " not have the same number of levels")

            # also copies
            names = names + _get_consensus_names(indexes)

        return MultiIndex(levels=levels, labels=label_list, names=names,
                          verify_integrity=False)

    new_index = indexes[0]
    n = len(new_index)
    kpieces = len(indexes)

    # also copies
    new_names = list(names)
    new_levels = list(levels)

    # construct labels
    new_labels = []

    # do something a bit more speedy

    for hlevel, level in zip(zipped, levels):
        hlevel = _ensure_index(hlevel)
        mapped = level.get_indexer(hlevel)

        mask = mapped == -1
        if mask.any():
            raise ValueError('Values not found in passed level: %s'
                             % str(hlevel[mask]))

        new_labels.append(np.repeat(mapped, n))

    if isinstance(new_index, MultiIndex):
        new_levels.extend(new_index.levels)
        new_labels.extend([np.tile(lab, kpieces) for lab in new_index.labels])
    else:
        new_levels.append(new_index)
        new_labels.append(np.tile(np.arange(n), kpieces))

    if len(new_names) < len(new_levels):
        new_names.extend(new_index.names)

    return MultiIndex(levels=new_levels, labels=new_labels, names=new_names,
                      verify_integrity=False)


def _should_fill(lname, rname):
    if (not isinstance(lname, compat.string_types) or
            not isinstance(rname, compat.string_types)):
        return True
    return lname == rname


def _any(x):
    return x is not None and len(x) > 0 and any([y is not None for y in x])