reshape.py

# pylint: disable=E1101,E1103
# pylint: disable=W0703,W0622,W0613,W0201
from pandas.compat import range, text_type, zip
from pandas import compat
from functools import partial
import itertools

import numpy as np

from pandas.core.dtypes.common import (
    _ensure_platform_int,
    is_list_like, is_bool_dtype,
    needs_i8_conversion, is_sparse, is_object_dtype)
from pandas.core.dtypes.cast import maybe_promote
from pandas.core.dtypes.missing import notna

from pandas.core.series import Series
from pandas.core.frame import DataFrame

from pandas.core.sparse.api import SparseDataFrame, SparseSeries
from pandas.core.sparse.array import SparseArray
from pandas._libs.sparse import IntIndex

from pandas.core.categorical import Categorical, _factorize_from_iterable
from pandas.core.sorting import (get_group_index, get_compressed_ids,
                                 compress_group_index, decons_obs_group_ids)

import pandas.core.algorithms as algos
from pandas._libs import algos as _algos, reshape as _reshape

from pandas.core.index import Index, MultiIndex, _get_na_value


class _Unstacker(object):
    """
    Helper class to unstack data / pivot with multi-level index

    Parameters
    ----------
    level : int or str, default last level
        Level to "unstack". Accepts a name for the level.

    Examples
    --------
    >>> import pandas as pd
    >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
    ...                                    ('two', 'a'), ('two', 'b')])
    >>> s = pd.Series(np.arange(1, 5, dtype=np.int64), index=index)
    >>> s
    one  a    1
         b    2
    two  a    3
         b    4
    dtype: int64

    >>> s.unstack(level=-1)
         a  b
    one  1  2
    two  3  4

    >>> s.unstack(level=0)
       one  two
    a    1    3
    b    2    4

    Returns
    -------
    unstacked : DataFrame
    """

    def __init__(self, values, index, level=-1, value_columns=None,
                 fill_value=None):

        self.is_categorical = None
        self.is_sparse = is_sparse(values)
        if values.ndim == 1:
            if isinstance(values, Categorical):
                self.is_categorical = values
                values = np.array(values)
            elif self.is_sparse:
                # XXX: Makes SparseArray *dense*, but it's supposedly
                # a single column at a time, so it's "doable"
                values = values.values
            values = values[:, np.newaxis]
        self.values = values
        self.value_columns = value_columns
        self.fill_value = fill_value

        if value_columns is None and values.shape[1] != 1:  # pragma: no cover
            raise ValueError('must pass column labels for multi-column data')

        self.index = index

        self.level = self.index._get_level_number(level)

        # when index includes `nan`, need to lift levels/strides by 1
        self.lift = 1 if -1 in self.index.labels[self.level] else 0

        self.new_index_levels = list(index.levels)
        self.new_index_names = list(index.names)

        self.removed_name = self.new_index_names.pop(self.level)
        self.removed_level = self.new_index_levels.pop(self.level)

        self._make_sorted_values_labels()
        self._make_selectors()

    def _make_sorted_values_labels(self):
        v = self.level

        labs = list(self.index.labels)
        levs = list(self.index.levels)
        to_sort = labs[:v] + labs[v + 1:] + [labs[v]]
        sizes = [len(x) for x in levs[:v] + levs[v + 1:] + [levs[v]]]

        comp_index, obs_ids = get_compressed_ids(to_sort, sizes)
        ngroups = len(obs_ids)

        indexer = _algos.groupsort_indexer(comp_index, ngroups)[0]
        indexer = _ensure_platform_int(indexer)

        self.sorted_values = algos.take_nd(self.values, indexer, axis=0)
        self.sorted_labels = [l.take(indexer) for l in to_sort]

    def _make_selectors(self):
        new_levels = self.new_index_levels

        # make the mask
        remaining_labels = self.sorted_labels[:-1]
        level_sizes = [len(x) for x in new_levels]

        comp_index, obs_ids = get_compressed_ids(remaining_labels, level_sizes)
        ngroups = len(obs_ids)

        comp_index = _ensure_platform_int(comp_index)
        stride = self.index.levshape[self.level] + self.lift
        self.full_shape = ngroups, stride

        selector = self.sorted_labels[-1] + stride * comp_index + self.lift
        mask = np.zeros(np.prod(self.full_shape), dtype=bool)
        mask.put(selector, True)

        if mask.sum() < len(self.index):
            raise ValueError('Index contains duplicate entries, '
                             'cannot reshape')

        self.group_index = comp_index
        self.mask = mask
        self.unique_groups = obs_ids
        self.compressor = comp_index.searchsorted(np.arange(ngroups))

    def get_result(self):
        # TODO: find a better way than this masking business

        values, value_mask = self.get_new_values()
        columns = self.get_new_columns()
        index = self.get_new_index()

        # filter out missing levels
        if values.shape[1] > 0:
            col_inds, obs_ids = compress_group_index(self.sorted_labels[-1])
            # rare case, level values not observed
            if len(obs_ids) < self.full_shape[1]:
                inds = (value_mask.sum(0) > 0).nonzero()[0]
                values = algos.take_nd(values, inds, axis=1)
                columns = columns[inds]

        # may need to coerce categoricals here
        if self.is_categorical is not None:
            categories = self.is_categorical.categories
            ordered = self.is_categorical.ordered
            values = [Categorical(values[:, i], categories=categories,
                                  ordered=ordered)
                      for i in range(values.shape[-1])]

        klass = SparseDataFrame if self.is_sparse else DataFrame
        return klass(values, index=index, columns=columns)

    def get_new_values(self):
        values = self.values

        # place the values
        length, width = self.full_shape
        stride = values.shape[1]
        result_width = width * stride
        result_shape = (length, result_width)
        mask = self.mask
        mask_all = mask.all()

        # we can simply reshape if we don't have a mask
        if mask_all and len(values):
            new_values = (self.sorted_values
                              .reshape(length, width, stride)
                              .swapaxes(1, 2)
                              .reshape(result_shape)
                          )
            new_mask = np.ones(result_shape, dtype=bool)
            return new_values, new_mask

        # if our mask is all True, then we can use our existing dtype
        if mask_all:
            dtype = values.dtype
            new_values = np.empty(result_shape, dtype=dtype)
        else:
            dtype, fill_value = maybe_promote(values.dtype, self.fill_value)
            new_values = np.empty(result_shape, dtype=dtype)
            new_values.fill(fill_value)

        new_mask = np.zeros(result_shape, dtype=bool)

        name = np.dtype(dtype).name
        sorted_values = self.sorted_values

        # we need to convert to a basic dtype
        # and possibly coerce an input to our output dtype
        # e.g. ints -> floats
        if needs_i8_conversion(values):
            sorted_values = sorted_values.view('i8')
            new_values = new_values.view('i8')
            name = 'int64'
        elif is_bool_dtype(values):
            sorted_values = sorted_values.astype('object')
            new_values = new_values.astype('object')
            name = 'object'
        else:
            sorted_values = sorted_values.astype(name, copy=False)

        # fill in our values & mask
        f = getattr(_reshape, "unstack_{name}".format(name=name))
        f(sorted_values,
          mask.view('u1'),
          stride,
          length,
          width,
          new_values,
          new_mask.view('u1'))

        # reconstruct dtype if needed
        if needs_i8_conversion(values):
            new_values = new_values.view(values.dtype)

        return new_values, new_mask

    def get_new_columns(self):
        if self.value_columns is None:
            if self.lift == 0:
                return self.removed_level

            lev = self.removed_level
            return lev.insert(0, _get_na_value(lev.dtype.type))

        stride = len(self.removed_level) + self.lift
        width = len(self.value_columns)
        propagator = np.repeat(np.arange(width), stride)
        if isinstance(self.value_columns, MultiIndex):
            new_levels = self.value_columns.levels + (self.removed_level,)
            new_names = self.value_columns.names + (self.removed_name,)

            new_labels = [lab.take(propagator)
                          for lab in self.value_columns.labels]
        else:
            new_levels = [self.value_columns, self.removed_level]
            new_names = [self.value_columns.name, self.removed_name]
            new_labels = [propagator]

        new_labels.append(np.tile(np.arange(stride) - self.lift, width))
        return MultiIndex(levels=new_levels, labels=new_labels,
                          names=new_names, verify_integrity=False)

    def get_new_index(self):
        result_labels = [lab.take(self.compressor)
                         for lab in self.sorted_labels[:-1]]

        # construct the new index
        if len(self.new_index_levels) == 1:
            lev, lab = self.new_index_levels[0], result_labels[0]
            if (lab == -1).any():
                lev = lev.insert(len(lev), _get_na_value(lev.dtype.type))
            return lev.take(lab)

        return MultiIndex(levels=self.new_index_levels, labels=result_labels,
                          names=self.new_index_names, verify_integrity=False)


def _unstack_multiple(data, clocs, fill_value=None):
    if len(clocs) == 0:
        return data

    # NOTE: This doesn't deal with hierarchical columns yet

    index = data.index

    clocs = [index._get_level_number(i) for i in clocs]

    rlocs = [i for i in range(index.nlevels) if i not in clocs]

    clevels = [index.levels[i] for i in clocs]
    clabels = [index.labels[i] for i in clocs]
    cnames = [index.names[i] for i in clocs]
    rlevels = [index.levels[i] for i in rlocs]
    rlabels = [index.labels[i] for i in rlocs]
    rnames = [index.names[i] for i in rlocs]

    shape = [len(x) for x in clevels]
    group_index = get_group_index(clabels, shape, sort=False, xnull=False)

    comp_ids, obs_ids = compress_group_index(group_index, sort=False)
    recons_labels = decons_obs_group_ids(comp_ids, obs_ids, shape, clabels,
                                         xnull=False)

    if rlocs == []:
        # Everything is in clocs, so the dummy df has a regular index
        dummy_index = Index(obs_ids, name='__placeholder__')
    else:
        dummy_index = MultiIndex(levels=rlevels + [obs_ids],
                                 labels=rlabels + [comp_ids],
                                 names=rnames + ['__placeholder__'],
                                 verify_integrity=False)

    if isinstance(data, Series):
        dummy = data.copy()
        dummy.index = dummy_index
        unstacked = dummy.unstack('__placeholder__', fill_value=fill_value)
        new_levels = clevels
        new_names = cnames
        new_labels = recons_labels
    else:
        if isinstance(data.columns, MultiIndex):
            result = data
            for i in range(len(clocs)):
                val = clocs[i]
                result = result.unstack(val)
                clocs = [v if i > v else v - 1 for v in clocs]

            return result

        dummy = data.copy()
        dummy.index = dummy_index

        unstacked = dummy.unstack('__placeholder__', fill_value=fill_value)
        if isinstance(unstacked, Series):
            unstcols = unstacked.index
        else:
            unstcols = unstacked.columns
        new_levels = [unstcols.levels[0]] + clevels
        new_names = [data.columns.name] + cnames

        new_labels = [unstcols.labels[0]]
        for rec in recons_labels:
            new_labels.append(rec.take(unstcols.labels[-1]))

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

    if isinstance(unstacked, Series):
        unstacked.index = new_columns
    else:
        unstacked.columns = new_columns

    return unstacked


def pivot(self, index=None, columns=None, values=None):
    """
    See DataFrame.pivot
    """
    if values is None:
        cols = [columns] if index is None else [index, columns]
        append = index is None
        indexed = self.set_index(cols, append=append)
        return indexed.unstack(columns)
    else:
        if index is None:
            index = self.index
        else:
            index = self[index]
        indexed = Series(self[values].values,
                         index=MultiIndex.from_arrays([index, self[columns]]))
        return indexed.unstack(columns)


def pivot_simple(index, columns, values):
    """
    Produce 'pivot' table based on 3 columns of this DataFrame.
    Uses unique values from index / columns and fills with values.

    Parameters
    ----------
    index : ndarray
        Labels to use to make new frame's index
    columns : ndarray
        Labels to use to make new frame's columns
    values : ndarray
        Values to use for populating new frame's values

    Notes
    -----
    Obviously, all 3 of the input arguments must have the same length

    Returns
    -------
    DataFrame

    See also
    --------
    DataFrame.pivot_table : generalization of pivot that can handle
        duplicate values for one index/column pair
    """
    if (len(index) != len(columns)) or (len(columns) != len(values)):
        raise AssertionError('Length of index, columns, and values must be the'
                             ' same')

    if len(index) == 0:
        return DataFrame(index=[])

    hindex = MultiIndex.from_arrays([index, columns])
    series = Series(values.ravel(), index=hindex)
    series = series.sort_index(level=0)
    return series.unstack()


def _slow_pivot(index, columns, values):
    """
    Produce 'pivot' table based on 3 columns of this DataFrame.
    Uses unique values from index / columns and fills with values.

    Parameters
    ----------
    index : string or object
        Column name to use to make new frame's index
    columns : string or object
        Column name to use to make new frame's columns
    values : string or object
        Column name to use for populating new frame's values

    Could benefit from some Cython here.
    """
    tree = {}
    for i, (idx, col) in enumerate(zip(index, columns)):
        if col not in tree:
            tree[col] = {}
        branch = tree[col]
        branch[idx] = values[i]

    return DataFrame(tree)


def unstack(obj, level, fill_value=None):
    if isinstance(level, (tuple, list)):
        if len(level) != 1:
            # _unstack_multiple only handles MultiIndexes,
            # and isn't needed for a single level
            return _unstack_multiple(obj, level, fill_value=fill_value)
        else:
            level = level[0]

    if isinstance(obj, DataFrame):
        if isinstance(obj.index, MultiIndex):
            return _unstack_frame(obj, level, fill_value=fill_value)
        else:
            return obj.T.stack(dropna=False)
    else:
        unstacker = _Unstacker(obj.values, obj.index, level=level,
                               fill_value=fill_value)
        return unstacker.get_result()


def _unstack_frame(obj, level, fill_value=None):
    if obj._is_mixed_type:
        unstacker = partial(_Unstacker, index=obj.index,
                            level=level, fill_value=fill_value)
        blocks = obj._data.unstack(unstacker)
        klass = type(obj)
        return klass(blocks)
    else:
        unstacker = _Unstacker(obj.values, obj.index, level=level,
                               value_columns=obj.columns,
                               fill_value=fill_value)
        return unstacker.get_result()


def stack(frame, level=-1, dropna=True):
    """
    Convert DataFrame to Series with multi-level Index. Columns become the
    second level of the resulting hierarchical index

    Returns
    -------
    stacked : Series
    """

    def factorize(index):
        if index.is_unique:
            return index, np.arange(len(index))
        codes, categories = _factorize_from_iterable(index)
        return categories, codes

    N, K = frame.shape

    # Will also convert negative level numbers and check if out of bounds.
    level_num = frame.columns._get_level_number(level)

    if isinstance(frame.columns, MultiIndex):
        return _stack_multi_columns(frame, level_num=level_num, dropna=dropna)
    elif isinstance(frame.index, MultiIndex):
        new_levels = list(frame.index.levels)
        new_labels = [lab.repeat(K) for lab in frame.index.labels]

        clev, clab = factorize(frame.columns)
        new_levels.append(clev)
        new_labels.append(np.tile(clab, N).ravel())

        new_names = list(frame.index.names)
        new_names.append(frame.columns.name)
        new_index = MultiIndex(levels=new_levels, labels=new_labels,
                               names=new_names, verify_integrity=False)
    else:
        levels, (ilab, clab) = zip(*map(factorize, (frame.index,
                                                    frame.columns)))
        labels = ilab.repeat(K), np.tile(clab, N).ravel()
        new_index = MultiIndex(levels=levels, labels=labels,
                               names=[frame.index.name, frame.columns.name],
                               verify_integrity=False)

    new_values = frame.values.ravel()
    if dropna:
        mask = notna(new_values)
        new_values = new_values[mask]
        new_index = new_index[mask]

    klass = type(frame)._constructor_sliced
    return klass(new_values, index=new_index)


def stack_multiple(frame, level, dropna=True):
    # If all passed levels match up to column names, no
    # ambiguity about what to do
    if all(lev in frame.columns.names for lev in level):
        result = frame
        for lev in level:
            result = stack(result, lev, dropna=dropna)

    # Otherwise, level numbers may change as each successive level is stacked
    elif all(isinstance(lev, int) for lev in level):
        # As each stack is done, the level numbers decrease, so we need
        #  to account for that when level is a sequence of ints
        result = frame
        # _get_level_number() checks level numbers are in range and converts
        # negative numbers to positive
        level = [frame.columns._get_level_number(lev) for lev in level]

        # Can't iterate directly through level as we might need to change
        # values as we go
        for index in range(len(level)):
            lev = level[index]
            result = stack(result, lev, dropna=dropna)
            # Decrement all level numbers greater than current, as these
            # have now shifted down by one
            updated_level = []
            for other in level:
                if other > lev:
                    updated_level.append(other - 1)
                else:
                    updated_level.append(other)
            level = updated_level

    else:
        raise ValueError("level should contain all level names or all level "
                         "numbers, not a mixture of the two.")

    return result


def _stack_multi_columns(frame, level_num=-1, dropna=True):
    def _convert_level_number(level_num, columns):
        """
        Logic for converting the level number to something we can safely pass
        to swaplevel:

        We generally want to convert the level number into a level name, except
        when columns do not have names, in which case we must leave as a level
        number
        """
        if level_num in columns.names:
            return columns.names[level_num]
        else:
            if columns.names[level_num] is None:
                return level_num
            else:
                return columns.names[level_num]

    this = frame.copy()

    # this makes life much simpler
    if level_num != frame.columns.nlevels - 1:
        # roll levels to put selected level at end
        roll_columns = this.columns
        for i in range(level_num, frame.columns.nlevels - 1):
            # Need to check if the ints conflict with level names
            lev1 = _convert_level_number(i, roll_columns)
            lev2 = _convert_level_number(i + 1, roll_columns)
            roll_columns = roll_columns.swaplevel(lev1, lev2)
        this.columns = roll_columns

    if not this.columns.is_lexsorted():
        # Workaround the edge case where 0 is one of the column names,
        # which interferes with trying to sort based on the first
        # level
        level_to_sort = _convert_level_number(0, this.columns)
        this = this.sort_index(level=level_to_sort, axis=1)

    # tuple list excluding level for grouping columns
    if len(frame.columns.levels) > 2:
        tuples = list(zip(*[lev.take(lab)
                            for lev, lab in zip(this.columns.levels[:-1],
                                                this.columns.labels[:-1])]))
        unique_groups = [key for key, _ in itertools.groupby(tuples)]
        new_names = this.columns.names[:-1]
        new_columns = MultiIndex.from_tuples(unique_groups, names=new_names)
    else:
        new_columns = unique_groups = this.columns.levels[0]

    # time to ravel the values
    new_data = {}
    level_vals = this.columns.levels[-1]
    level_labels = sorted(set(this.columns.labels[-1]))
    level_vals_used = level_vals[level_labels]
    levsize = len(level_labels)
    drop_cols = []
    for key in unique_groups:
        try:
            loc = this.columns.get_loc(key)
        except KeyError:
            drop_cols.append(key)
            continue

        # can make more efficient?
        # we almost always return a slice
        # but if unsorted can get a boolean
        # indexer
        if not isinstance(loc, slice):
            slice_len = len(loc)
        else:
            slice_len = loc.stop - loc.start

        if slice_len != levsize:
            chunk = this.loc[:, this.columns[loc]]
            chunk.columns = level_vals.take(chunk.columns.labels[-1])
            value_slice = chunk.reindex(columns=level_vals_used).values
        else:
            if frame._is_mixed_type:
                value_slice = this.loc[:, this.columns[loc]].values
            else:
                value_slice = this.values[:, loc]

        new_data[key] = value_slice.ravel()

    if len(drop_cols) > 0:
        new_columns = new_columns.difference(drop_cols)

    N = len(this)

    if isinstance(this.index, MultiIndex):
        new_levels = list(this.index.levels)
        new_names = list(this.index.names)
        new_labels = [lab.repeat(levsize) for lab in this.index.labels]
    else:
        new_levels = [this.index]
        new_labels = [np.arange(N).repeat(levsize)]
        new_names = [this.index.name]  # something better?

    new_levels.append(level_vals)
    new_labels.append(np.tile(level_labels, N))
    new_names.append(frame.columns.names[level_num])

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

    result = DataFrame(new_data, index=new_index, columns=new_columns)

    # more efficient way to go about this? can do the whole masking biz but
    # will only save a small amount of time...
    if dropna:
        result = result.dropna(axis=0, how='all')

    return result


def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
                columns=None, sparse=False, drop_first=False, dtype=None):
    """
    Convert categorical variable into dummy/indicator variables

    Parameters
    ----------
    data : array-like, Series, or DataFrame
    prefix : string, list of strings, or dict of strings, default None
        String to append DataFrame column names
        Pass a list with length equal to the number of columns
        when calling get_dummies on a DataFrame. Alternatively, `prefix`
        can be a dictionary mapping column names to prefixes.
    prefix_sep : string, default '_'
        If appending prefix, separator/delimiter to use. Or pass a
        list or dictionary as with `prefix.`
    dummy_na : bool, default False
        Add a column to indicate NaNs, if False NaNs are ignored.
    columns : list-like, default None
        Column names in the DataFrame to be encoded.
        If `columns` is None then all the columns with
        `object` or `category` dtype will be converted.
    sparse : bool, default False
        Whether the dummy columns should be sparse or not.  Returns
        SparseDataFrame if `data` is a Series or if all columns are included.
        Otherwise returns a DataFrame with some SparseBlocks.
    drop_first : bool, default False
        Whether to get k-1 dummies out of k categorical levels by removing the
        first level.

        .. versionadded:: 0.18.0

    dtype : dtype, default np.uint8
        Data type for new columns. Only a single dtype is allowed.

        .. versionadded:: 0.23.0

    Returns
    -------
    dummies : DataFrame or SparseDataFrame

    Examples
    --------
    >>> import pandas as pd
    >>> s = pd.Series(list('abca'))

    >>> pd.get_dummies(s)
       a  b  c
    0  1  0  0
    1  0  1  0
    2  0  0  1
    3  1  0  0

    >>> s1 = ['a', 'b', np.nan]

    >>> pd.get_dummies(s1)
       a  b
    0  1  0
    1  0  1
    2  0  0

    >>> pd.get_dummies(s1, dummy_na=True)
       a  b  NaN
    0  1  0    0
    1  0  1    0
    2  0  0    1

    >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
    ...                    'C': [1, 2, 3]})

    >>> pd.get_dummies(df, prefix=['col1', 'col2'])
       C  col1_a  col1_b  col2_a  col2_b  col2_c
    0  1       1       0       0       1       0
    1  2       0       1       1       0       0
    2  3       1       0       0       0       1

    >>> pd.get_dummies(pd.Series(list('abcaa')))
       a  b  c
    0  1  0  0
    1  0  1  0
    2  0  0  1
    3  1  0  0
    4  1  0  0

    >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)
       b  c
    0  0  0
    1  1  0
    2  0  1
    3  0  0
    4  0  0

    >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)
         a    b    c
    0  1.0  0.0  0.0
    1  0.0  1.0  0.0
    2  0.0  0.0  1.0

    See Also
    --------
    Series.str.get_dummies
    """
    from pandas.core.reshape.concat import concat
    from itertools import cycle

    if isinstance(data, DataFrame):
        # determine columns being encoded

        if columns is None:
            columns_to_encode = data.select_dtypes(
                include=['object', 'category']).columns
        else:
            columns_to_encode = columns

        # validate prefixes and separator to avoid silently dropping cols
        def check_len(item, name):
            len_msg = ("Length of '{name}' ({len_item}) did not match the "
                       "length of the columns being encoded ({len_enc}).")

            if is_list_like(item):
                if not len(item) == len(columns_to_encode):
                    len_msg = len_msg.format(name=name, len_item=len(item),
                                             len_enc=len(columns_to_encode))
                    raise ValueError(len_msg)

        check_len(prefix, 'prefix')
        check_len(prefix_sep, 'prefix_sep')
        if isinstance(prefix, compat.string_types):
            prefix = cycle([prefix])
        if isinstance(prefix, dict):
            prefix = [prefix[col] for col in columns_to_encode]

        if prefix is None:
            prefix = columns_to_encode

        # validate separators
        if isinstance(prefix_sep, compat.string_types):
            prefix_sep = cycle([prefix_sep])
        elif isinstance(prefix_sep, dict):
            prefix_sep = [prefix_sep[col] for col in columns_to_encode]

        if set(columns_to_encode) == set(data.columns):
            with_dummies = []
        else:
            with_dummies = [data.drop(columns_to_encode, axis=1)]

        for (col, pre, sep) in zip(columns_to_encode, prefix, prefix_sep):

            dummy = _get_dummies_1d(data[col], prefix=pre, prefix_sep=sep,
                                    dummy_na=dummy_na, sparse=sparse,
                                    drop_first=drop_first, dtype=dtype)
            with_dummies.append(dummy)
        result = concat(with_dummies, axis=1)
    else:
        result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na,
                                 sparse=sparse,
                                 drop_first=drop_first,
                                 dtype=dtype)
    return result


def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False,
                    sparse=False, drop_first=False, dtype=None):
    # Series avoids inconsistent NaN handling
    codes, levels = _factorize_from_iterable(Series(data))

    if dtype is None:
        dtype = np.uint8
    dtype = np.dtype(dtype)

    if is_object_dtype(dtype):
        raise ValueError("dtype=object is not a valid dtype for get_dummies")

    def get_empty_Frame(data, sparse):
        if isinstance(data, Series):
            index = data.index
        else:
            index = np.arange(len(data))
        if not sparse:
            return DataFrame(index=index)
        else:
            return SparseDataFrame(index=index, default_fill_value=0)

    # if all NaN
    if not dummy_na and len(levels) == 0:
        return get_empty_Frame(data, sparse)

    codes = codes.copy()
    if dummy_na:
        codes[codes == -1] = len(levels)
        levels = np.append(levels, np.nan)

    # if dummy_na, we just fake a nan level. drop_first will drop it again
    if drop_first and len(levels) == 1:
        return get_empty_Frame(data, sparse)

    number_of_cols = len(levels)

    if prefix is not None:
        dummy_strs = [u'{prefix}{sep}{level}' if isinstance(v, text_type)
                      else '{prefix}{sep}{level}' for v in levels]
        dummy_cols = [dummy_str.format(prefix=prefix, sep=prefix_sep, level=v)
                      for dummy_str, v in zip(dummy_strs, levels)]
    else:
        dummy_cols = levels

    if isinstance(data, Series):
        index = data.index
    else:
        index = None

    if sparse:
        sparse_series = {}
        N = len(data)
        sp_indices = [[] for _ in range(len(dummy_cols))]
        for ndx, code in enumerate(codes):
            if code == -1:
                # Blank entries if not dummy_na and code == -1, #GH4446
                continue
            sp_indices[code].append(ndx)

        if drop_first:
            # remove first categorical level to avoid perfect collinearity
            # GH12042
            sp_indices = sp_indices[1:]
            dummy_cols = dummy_cols[1:]
        for col, ixs in zip(dummy_cols, sp_indices):
            sarr = SparseArray(np.ones(len(ixs), dtype=dtype),
                               sparse_index=IntIndex(N, ixs), fill_value=0,
                               dtype=dtype)
            sparse_series[col] = SparseSeries(data=sarr, index=index)

        out = SparseDataFrame(sparse_series, index=index, columns=dummy_cols,
                              default_fill_value=0,
                              dtype=dtype)
        return out

    else:
        dummy_mat = np.eye(number_of_cols, dtype=dtype).take(codes, axis=0)

        if not dummy_na:
            # reset NaN GH4446
            dummy_mat[codes == -1] = 0

        if drop_first:
            # remove first GH12042
            dummy_mat = dummy_mat[:, 1:]
            dummy_cols = dummy_cols[1:]
        return DataFrame(dummy_mat, index=index, columns=dummy_cols)


def make_axis_dummies(frame, axis='minor', transform=None):
    """
    Construct 1-0 dummy variables corresponding to designated axis
    labels

    Parameters
    ----------
    frame : DataFrame
    axis : {'major', 'minor'}, default 'minor'
    transform : function, default None
        Function to apply to axis labels first. For example, to
        get "day of week" dummies in a time series regression
        you might call::

            make_axis_dummies(panel, axis='major',
                              transform=lambda d: d.weekday())
    Returns
    -------
    dummies : DataFrame
        Column names taken from chosen axis
    """
    numbers = {'major': 0, 'minor': 1}
    num = numbers.get(axis, axis)

    items = frame.index.levels[num]
    labels = frame.index.labels[num]
    if transform is not None:
        mapped_items = items.map(transform)
        labels, items = _factorize_from_iterable(mapped_items.take(labels))

    values = np.eye(len(items), dtype=float)
    values = values.take(labels, axis=0)

    return DataFrame(values, columns=items, index=frame.index)