Python pandas.core.dtypes.common.is_list_like() Examples

def pivot(data, index=None, columns=None, values=None):
    if values is None:
        cols = [columns] if index is None else [index, columns]
        append = index is None
        indexed = data.set_index(cols, append=append)
    else:
        if index is None:
            index = data.index
        else:
            index = data[index]
        index = MultiIndex.from_arrays([index, data[columns]])

        if is_list_like(values) and not isinstance(values, tuple):
            # Exclude tuple because it is seen as a single column name
            indexed = data._constructor(data[values].values, index=index,
                                        columns=values)
        else:
            indexed = data._constructor_sliced(data[values].values,
                                               index=index)
    return indexed.unstack(columns) 

def pivot(data, index=None, columns=None, values=None):
    if values is None:
        cols = [columns] if index is None else [index, columns]
        append = index is None
        indexed = data.set_index(cols, append=append)
    else:
        if index is None:
            index = data.index
        else:
            index = data[index]
        index = MultiIndex.from_arrays([index, data[columns]])

        if is_list_like(values) and not isinstance(values, tuple):
            # Exclude tuple because it is seen as a single column name
            indexed = data._constructor(data[values].values, index=index,
                                        columns=values)
        else:
            indexed = data._constructor_sliced(data[values].values,
                                               index=index)
    return indexed.unstack(columns) 

def _validate_where(w):
    """
    Validate that the where statement is of the right type.

    The type may either be String, Expr, or list-like of Exprs.

    Parameters
    ----------
    w : String term expression, Expr, or list-like of Exprs.

    Returns
    -------
    where : The original where clause if the check was successful.

    Raises
    ------
    TypeError : An invalid data type was passed in for w (e.g. dict).
    """

    if not (isinstance(w, (Expr, string_types)) or is_list_like(w)):
        raise TypeError("where must be passed as a string, Expr, "
                        "or list-like of Exprs")

    return w 

def _validate_where(w):
    """
    Validate that the where statement is of the right type.

    The type may either be String, Expr, or list-like of Exprs.

    Parameters
    ----------
    w : String term expression, Expr, or list-like of Exprs.

    Returns
    -------
    where : The original where clause if the check was successful.

    Raises
    ------
    TypeError : An invalid data type was passed in for w (e.g. dict).
    """

    if not (isinstance(w, (Expr, string_types)) or is_list_like(w)):
        raise TypeError("where must be passed as a string, Expr, "
                        "or list-like of Exprs")

    return w 

def _validate_where(w):
    """
    Validate that the where statement is of the right type.

    The type may either be String, Expr, or list-like of Exprs.

    Parameters
    ----------
    w : String term expression, Expr, or list-like of Exprs.

    Returns
    -------
    where : The original where clause if the check was successful.

    Raises
    ------
    TypeError : An invalid data type was passed in for w (e.g. dict).
    """

    if not (isinstance(w, (Expr, string_types)) or is_list_like(w)):
        raise TypeError("where must be passed as a string, Expr, "
                        "or list-like of Exprs")

    return w 

def conform(self, rhs):
        """ inplace conform rhs """
        if not is_list_like(rhs):
            rhs = [rhs]
        if isinstance(rhs, np.ndarray):
            rhs = rhs.ravel()
        return rhs 

def _flatten(axes):
    if not is_list_like(axes):
        return np.array([axes])
    elif isinstance(axes, (np.ndarray, ABCIndexClass)):
        return axes.ravel()
    return np.array(axes) 

def _get_skiprows(skiprows):
    """Get an iterator given an integer, slice or container.

    Parameters
    ----------
    skiprows : int, slice, container
        The iterator to use to skip rows; can also be a slice.

    Raises
    ------
    TypeError
        * If `skiprows` is not a slice, integer, or Container

    Returns
    -------
    it : iterable
        A proper iterator to use to skip rows of a DataFrame.
    """
    if isinstance(skiprows, slice):
        return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1)
    elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows):
        return skiprows
    elif skiprows is None:
        return 0
    raise TypeError('%r is not a valid type for skipping rows' %
                    type(skiprows).__name__) 

def _flatten(axes):
    if not is_list_like(axes):
        return np.array([axes])
    elif isinstance(axes, (np.ndarray, Index)):
        return axes.ravel()
    return np.array(axes) 

def conform(self, rhs):
        """ inplace conform rhs """
        if not is_list_like(rhs):
            rhs = [rhs]
        if isinstance(rhs, np.ndarray):
            rhs = rhs.ravel()
        return rhs 

def _in(x, y):
    """Compute the vectorized membership of ``x in y`` if possible, otherwise
    use Python.
    """
    try:
        return x.isin(y)
    except AttributeError:
        if is_list_like(x):
            try:
                return y.isin(x)
            except AttributeError:
                pass
        return x in y 

def _get_skiprows(skiprows):
    """Get an iterator given an integer, slice or container.

    Parameters
    ----------
    skiprows : int, slice, container
        The iterator to use to skip rows; can also be a slice.

    Raises
    ------
    TypeError
        * If `skiprows` is not a slice, integer, or Container

    Returns
    -------
    it : iterable
        A proper iterator to use to skip rows of a DataFrame.
    """
    if isinstance(skiprows, slice):
        return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1)
    elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows):
        return skiprows
    elif skiprows is None:
        return 0
    raise TypeError('%r is not a valid type for skipping rows' %
                    type(skiprows).__name__) 

def _flatten(axes):
    if not is_list_like(axes):
        return np.array([axes])
    elif isinstance(axes, (np.ndarray, Index)):
        return axes.ravel()
    return np.array(axes) 

def check_simple_cmp_op(self, lhs, cmp1, rhs):
        ex = 'lhs {0} rhs'.format(cmp1)
        if cmp1 in ('in', 'not in') and not is_list_like(rhs):
            pytest.raises(TypeError, pd.eval, ex, engine=self.engine,
                          parser=self.parser, local_dict={'lhs': lhs,
                                                          'rhs': rhs})
        else:
            expected = _eval_single_bin(lhs, cmp1, rhs, self.engine)
            result = pd.eval(ex, engine=self.engine, parser=self.parser)
            self.check_equal(result, expected) 

def _gotitem(self, key, ndim, subset=None):
        """
        Sub-classes to define. Return a sliced object.

        Parameters
        ----------
        key : string / list of selections
        ndim : 1,2
            requested ndim of result
        subset : object, default None
            subset to act on
        """
        # create a new object to prevent aliasing
        if subset is None:
            subset = self.obj

        # we need to make a shallow copy of ourselves
        # with the same groupby
        kwargs = {attr: getattr(self, attr) for attr in self._attributes}

        # Try to select from a DataFrame, falling back to a Series
        try:
            groupby = self._groupby[key]
        except IndexError:
            groupby = self._groupby

        self = self.__class__(subset,
                              groupby=groupby,
                              parent=self,
                              **kwargs)
        self._reset_cache()
        if subset.ndim == 2:
            if is_scalar(key) and key in subset or is_list_like(key):
                self._selection = key
        return self


# special case to prevent duplicate plots when catching exceptions when
# forwarding methods from NDFrames 

def _not_in(x, y):
    """Compute the vectorized membership of ``x not in y`` if possible,
    otherwise use Python.
    """
    try:
        return ~x.isin(y)
    except AttributeError:
        if is_list_like(x):
            try:
                return ~y.isin(x)
            except AttributeError:
                pass
        return x not in y 

def _get_skiprows(skiprows):
    """Get an iterator given an integer, slice or container.

    Parameters
    ----------
    skiprows : int, slice, container
        The iterator to use to skip rows; can also be a slice.

    Raises
    ------
    TypeError
        * If `skiprows` is not a slice, integer, or Container

    Returns
    -------
    it : iterable
        A proper iterator to use to skip rows of a DataFrame.
    """
    if isinstance(skiprows, slice):
        return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1)
    elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows):
        return skiprows
    elif skiprows is None:
        return 0
    raise TypeError('%r is not a valid type for skipping rows' %
                    type(skiprows).__name__) 

def aggregate(self, func=None, *args, **kwargs):
        if self._axis != 0:
            # This is not implemented in pandas,
            # so we throw a different message
            raise NotImplementedError("axis other than 0 is not supported")

        if func is None or is_list_like(func):
            return self._default_to_pandas(
                lambda df: df.aggregate(func, *args, **kwargs)
            )
        return self._apply_agg_function(
            lambda df: df.aggregate(func, *args, **kwargs), drop=self._as_index
        ) 

def quantile(self, q=0.5, **kwargs):
        import numpy as np

        if self.ndim > 1:
            if self._df.dtypes.map(lambda x: x == np.dtype("O")).any():
                raise TypeError(
                    "'quantile' cannot be performed against 'object' dtypes!"
                )
        elif self._df.dtypes == np.dtype("O"):
            raise TypeError("'quantile' cannot be performed against 'object' dtypes!")
        if is_list_like(q):
            return self._default_to_pandas(lambda df: df.quantile(q=q, **kwargs))

        return self._apply_agg_function(lambda df: df.quantile(q, **kwargs)) 

def check_already_dropped(labels, dropped_cols, name, dropped_by='numeric_only',
                          warn=True):
    """Check if columns have already been dropped and return only those that
    haven't.

    Args:
        labels (single label or list-like): Column labels to check.
        dropped_cols (set or pandas.Index): Columns that were already dropped.
        name (str): Original arg that triggered the check (e.g. dropcols).
        dropped_by (str, optional): Original arg that caused dropped_cols``
            (e.g. numeric_only).
        warn (bool, optional): If ``True``, produces a
            :class:`ColumnAlreadyDroppedWarning` if there are columns in the
            intersection of dropped_cols and labels.

    Returns:
        list: Columns in labels which are not in dropped_cols.
    """
    if not is_list_like(labels):
        labels = [labels]
    str_labels = [c for c in labels if isinstance(c, str)]
    already_dropped = dropped_cols.intersection(str_labels)
    if warn and any(already_dropped):
        warnings.warn("Some column labels from `{}` were already dropped by "
                "`{}`:\n{}".format(name, dropped_by, already_dropped.tolist()),
                ColumnAlreadyDroppedWarning, stacklevel=2)
    return [c for c in labels if not isinstance(c, str) or c not in already_dropped] 

def _flatten(axes):
    if not is_list_like(axes):
        return np.array([axes])
    elif isinstance(axes, (np.ndarray, ABCIndexClass)):
        return axes.ravel()
    return np.array(axes) 

def _flatten(axes):
    if not is_list_like(axes):
        return np.array([axes])
    elif isinstance(axes, (np.ndarray, Index)):
        return axes.ravel()
    return np.array(axes) 

def check_simple_cmp_op(self, lhs, cmp1, rhs):
        ex = 'lhs {0} rhs'.format(cmp1)
        if cmp1 in ('in', 'not in') and not is_list_like(rhs):
            pytest.raises(TypeError, pd.eval, ex, engine=self.engine,
                          parser=self.parser, local_dict={'lhs': lhs,
                                                          'rhs': rhs})
        else:
            expected = _eval_single_bin(lhs, cmp1, rhs, self.engine)
            result = pd.eval(ex, engine=self.engine, parser=self.parser)
            self.check_equal(result, expected) 

def _in(x, y):
    """Compute the vectorized membership of ``x in y`` if possible, otherwise
    use Python.
    """
    try:
        return x.isin(y)
    except AttributeError:
        if is_list_like(x):
            try:
                return y.isin(x)
            except AttributeError:
                pass
        return x in y 

def check_simple_cmp_op(self, lhs, cmp1, rhs):
        ex = 'lhs {0} rhs'.format(cmp1)
        if cmp1 in ('in', 'not in') and not is_list_like(rhs):
            pytest.raises(TypeError, pd.eval, ex, engine=self.engine,
                          parser=self.parser, local_dict={'lhs': lhs,
                                                          'rhs': rhs})
        else:
            expected = _eval_single_bin(lhs, cmp1, rhs, self.engine)
            result = pd.eval(ex, engine=self.engine, parser=self.parser)
            self.check_equal(result, expected) 

def conform(self, rhs):
        """ inplace conform rhs """
        if not is_list_like(rhs):
            rhs = [rhs]
        if isinstance(rhs, np.ndarray):
            rhs = rhs.ravel()
        return rhs 

def _in(x, y):
    """Compute the vectorized membership of ``x in y`` if possible, otherwise
    use Python.
    """
    try:
        return x.isin(y)
    except AttributeError:
        if is_list_like(x):
            try:
                return y.isin(x)
            except AttributeError:
                pass
        return x in y 

def _not_in(x, y):
    """Compute the vectorized membership of ``x not in y`` if possible,
    otherwise use Python.
    """
    try:
        return ~x.isin(y)
    except AttributeError:
        if is_list_like(x):
            try:
                return ~y.isin(x)
            except AttributeError:
                pass
        return x not in y 

def _gotitem(self, key, ndim, subset=None):
        """
        Sub-classes to define. Return a sliced object.

        Parameters
        ----------
        key : string / list of selections
        ndim : 1,2
            requested ndim of result
        subset : object, default None
            subset to act on
        """
        # create a new object to prevent aliasing
        if subset is None:
            subset = self.obj

        # we need to make a shallow copy of ourselves
        # with the same groupby
        kwargs = {attr: getattr(self, attr) for attr in self._attributes}

        # Try to select from a DataFrame, falling back to a Series
        try:
            groupby = self._groupby[key]
        except IndexError:
            groupby = self._groupby

        self = self.__class__(subset,
                              groupby=groupby,
                              parent=self,
                              **kwargs)
        self._reset_cache()
        if subset.ndim == 2:
            if is_scalar(key) and key in subset or is_list_like(key):
                self._selection = key
        return self


# special case to prevent duplicate plots when catching exceptions when
# forwarding methods from NDFrames 

def _get_skiprows(skiprows):
    """Get an iterator given an integer, slice or container.

    Parameters
    ----------
    skiprows : int, slice, container
        The iterator to use to skip rows; can also be a slice.

    Raises
    ------
    TypeError
        * If `skiprows` is not a slice, integer, or Container

    Returns
    -------
    it : iterable
        A proper iterator to use to skip rows of a DataFrame.
    """
    if isinstance(skiprows, slice):
        return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1)
    elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows):
        return skiprows
    elif skiprows is None:
        return 0
    raise TypeError('%r is not a valid type for skipping rows' %
                    type(skiprows).__name__)