Fix quotes position in pandas.core, typos and misspelled parameters. (#25093)

Author: alanderex

pandas/core/accessor.py

@@ -16,11 +16,15 @@ class DirNamesMixin(object):
         ['asobject', 'base', 'data', 'flags', 'itemsize', 'strides'])
 
     def _dir_deletions(self):
-        """ delete unwanted __dir__ for this object """
+        """
+        Delete unwanted __dir__ for this object.
+        """
         return self._accessors | self._deprecations
 
     def _dir_additions(self):
-        """ add additional __dir__ for this object """
+        """
+        Add additional __dir__ for this object.
+        """
         rv = set()
         for accessor in self._accessors:
             try:
@@ -33,7 +37,7 @@ def _dir_additions(self):
     def __dir__(self):
         """
         Provide method name lookup and completion
-        Only provide 'public' methods
+        Only provide 'public' methods.
         """
         rv = set(dir(type(self)))
         rv = (rv - self._dir_deletions()) | self._dir_additions()
@@ -42,7 +46,7 @@ def __dir__(self):
 
 class PandasDelegate(object):
     """
-    an abstract base class for delegating methods/properties
+    An abstract base class for delegating methods/properties.
     """
 
     def _delegate_property_get(self, name, *args, **kwargs):
@@ -65,10 +69,10 @@ def _add_delegate_accessors(cls, delegate, accessors, typ,
         ----------
         cls : the class to add the methods/properties to
         delegate : the class to get methods/properties & doc-strings
-        acccessors : string list of accessors to add
+        accessors : string list of accessors to add
         typ : 'property' or 'method'
         overwrite : boolean, default False
-           overwrite the method/property in the target class if it exists
+           overwrite the method/property in the target class if it exists.
         """
 
         def _create_delegator_property(name):
@@ -117,7 +121,7 @@ def delegate_names(delegate, accessors, typ, overwrite=False):
     ----------
     delegate : object
         the class to get methods/properties & doc-strings
-    acccessors : Sequence[str]
+    accessors : Sequence[str]
         List of accessor to add
     typ : {'property', 'method'}
     overwrite : boolean, default False

pandas/core/common.py

@@ -32,7 +32,8 @@ class SettingWithCopyWarning(Warning):
 
 
 def flatten(l):
-    """Flatten an arbitrarily nested sequence.
+    """
+    Flatten an arbitrarily nested sequence.
 
     Parameters
     ----------
@@ -160,44 +161,56 @@ def cast_scalar_indexer(val):
 
 
 def _not_none(*args):
-    """Returns a generator consisting of the arguments that are not None"""
+    """
+    Returns a generator consisting of the arguments that are not None.
+    """
     return (arg for arg in args if arg is not None)
 
 
 def _any_none(*args):
-    """Returns a boolean indicating if any argument is None"""
+    """
+    Returns a boolean indicating if any argument is None.
+    """
     for arg in args:
         if arg is None:
             return True
     return False
 
 
 def _all_none(*args):
-    """Returns a boolean indicating if all arguments are None"""
+    """
+    Returns a boolean indicating if all arguments are None.
+    """
     for arg in args:
         if arg is not None:
             return False
     return True
 
 
 def _any_not_none(*args):
-    """Returns a boolean indicating if any argument is not None"""
+    """
+    Returns a boolean indicating if any argument is not None.
+    """
     for arg in args:
         if arg is not None:
             return True
     return False
 
 
 def _all_not_none(*args):
-    """Returns a boolean indicating if all arguments are not None"""
+    """
+    Returns a boolean indicating if all arguments are not None.
+    """
     for arg in args:
         if arg is None:
             return False
     return True
 
 
 def count_not_none(*args):
-    """Returns the count of arguments that are not None"""
+    """
+    Returns the count of arguments that are not None.
+    """
     return sum(x is not None for x in args)
 
 
@@ -277,7 +290,9 @@ def maybe_make_list(obj):
 
 
 def is_null_slice(obj):
-    """ we have a null slice """
+    """
+    We have a null slice.
+    """
     return (isinstance(obj, slice) and obj.start is None and
             obj.stop is None and obj.step is None)
 
@@ -291,7 +306,9 @@ def is_true_slices(l):
 
 # TODO: used only once in indexing; belongs elsewhere?
 def is_full_slice(obj, l):
-    """ we have a full length slice """
+    """
+    We have a full length slice.
+    """
     return (isinstance(obj, slice) and obj.start == 0 and obj.stop == l and
             obj.step is None)
 
@@ -316,7 +333,7 @@ def get_callable_name(obj):
 def apply_if_callable(maybe_callable, obj, **kwargs):
     """
     Evaluate possibly callable input using obj and kwargs if it is callable,
-    otherwise return as it is
+    otherwise return as it is.
 
     Parameters
     ----------
@@ -333,7 +350,8 @@ def apply_if_callable(maybe_callable, obj, **kwargs):
 
 def dict_compat(d):
     """
-    Helper function to convert datetimelike-keyed dicts to Timestamp-keyed dict
+    Helper function to convert datetimelike-keyed dicts
+    to Timestamp-keyed dict.
 
     Parameters
     ----------

pandas/core/internals/blocks.py

@@ -87,7 +87,8 @@ def __init__(self, values, placement, ndim=None):
                 '{mgr}'.format(val=len(self.values), mgr=len(self.mgr_locs)))
 
     def _check_ndim(self, values, ndim):
-        """ndim inference and validation.
+        """
+        ndim inference and validation.
 
         Infers ndim from 'values' if not provided to __init__.
         Validates that values.ndim and ndim are consistent if and only if

pandas/core/missing.py

@@ -1,5 +1,5 @@
 """
-Routines for filling missing data
+Routines for filling missing data.
 """
 from distutils.version import LooseVersion
 import operator
@@ -116,7 +116,7 @@ def interpolate_1d(xvalues, yvalues, method='linear', limit=None,
     xvalues and yvalues will each be 1-d arrays of the same length.
 
     Bounds_error is currently hardcoded to False since non-scipy ones don't
-    take it as an argumnet.
+    take it as an argument.
     """
     # Treat the original, non-scipy methods first.
 
@@ -244,9 +244,9 @@ def interpolate_1d(xvalues, yvalues, method='linear', limit=None,
 def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
                                bounds_error=False, order=None, **kwargs):
     """
-    passed off to scipy.interpolate.interp1d. method is scipy's kind.
+    Passed off to scipy.interpolate.interp1d. method is scipy's kind.
     Returns an array interpolated at new_x.  Add any new methods to
-    the list in _clean_interp_method
+    the list in _clean_interp_method.
     """
     try:
         from scipy import interpolate
@@ -314,7 +314,7 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
 
 def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
     """
-    Convenience function for interpolate.BPoly.from_derivatives
+    Convenience function for interpolate.BPoly.from_derivatives.
 
     Construct a piecewise polynomial in the Bernstein basis, compatible
     with the specified values and derivatives at breakpoints.
@@ -325,7 +325,7 @@ def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
         sorted 1D array of x-coordinates
     yi : array_like or list of array-likes
         yi[i][j] is the j-th derivative known at xi[i]
-    orders : None or int or array_like of ints. Default: None.
+    order: None or int or array_like of ints. Default: None.
         Specifies the degree of local polynomials. If not None, some
         derivatives are ignored.
     der : int or list
@@ -344,8 +344,7 @@ def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
     Returns
     -------
     y : scalar or array_like
-        The result, of length R or length M or M by R,
-
+        The result, of length R or length M or M by R.
     """
     import scipy
     from scipy import interpolate
@@ -418,8 +417,9 @@ def _akima_interpolate(xi, yi, x, der=0, axis=0):
 
 def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None,
                    dtype=None):
-    """ perform an actual interpolation of values, values will be make 2-d if
-    needed fills inplace, returns the result
+    """
+    Perform an actual interpolation of values, values will be make 2-d if
+    needed fills inplace, returns the result.
     """
 
     transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
@@ -533,13 +533,13 @@ def clean_reindex_fill_method(method):
 
 def fill_zeros(result, x, y, name, fill):
     """
-    if this is a reversed op, then flip x,y
+    If this is a reversed op, then flip x,y
 
-    if we have an integer value (or array in y)
+    If we have an integer value (or array in y)
     and we have 0's, fill them with the fill,
-    return the result
+    return the result.
 
-    mask the nan's from x
+    Mask the nan's from x.
     """
     if fill is None or is_float_dtype(result):
         return result

pandas/core/resample.py

@@ -36,7 +36,6 @@
 
 
 class Resampler(_GroupBy):
-
     """
     Class for resampling datetimelike data, a groupby-like operation.
     See aggregate, transform, and apply functions on this object.
@@ -107,7 +106,7 @@ def __iter__(self):
         Returns
         -------
         Generator yielding sequence of (name, subsetted object)
-        for each group
+        for each group.
 
         See Also
         --------
@@ -286,8 +285,8 @@ def transform(self, arg, *args, **kwargs):
 
         Parameters
         ----------
-        func : function
-            To apply to each group. Should return a Series with the same index
+        arg : function
+            To apply to each group. Should return a Series with the same index.
 
         Returns
         -------
@@ -423,7 +422,7 @@ def pad(self, limit=None):
 
         Returns
         -------
-        an upsampled Series
+        An upsampled Series.
 
         See Also
         --------

pandas/core/sparse/frame.py

@@ -194,15 +194,19 @@ def sp_maker(x):
         return to_manager(sdict, columns, index)
 
     def _init_matrix(self, data, index, columns, dtype=None):
-        """ Init self from ndarray or list of lists """
+        """
+        Init self from ndarray or list of lists.
+        """
         data = prep_ndarray(data, copy=False)
         index, columns = self._prep_index(data, index, columns)
         data = {idx: data[:, i] for i, idx in enumerate(columns)}
         return self._init_dict(data, index, columns, dtype)
 
     def _init_spmatrix(self, data, index, columns, dtype=None,
                        fill_value=None):
-        """ Init self from scipy.sparse matrix """
+        """
+        Init self from scipy.sparse matrix.
+        """
         index, columns = self._prep_index(data, index, columns)
         data = data.tocoo()
         N = len(index)
@@ -302,7 +306,9 @@ def __getstate__(self):
                     _default_kind=self._default_kind)
 
     def _unpickle_sparse_frame_compat(self, state):
-        """ original pickle format """
+        """
+        Original pickle format
+        """
         series, cols, idx, fv, kind = state
 
         if not isinstance(cols, Index):  # pragma: no cover
@@ -338,7 +344,9 @@ def to_dense(self):
         return DataFrame(data, index=self.index, columns=self.columns)
 
     def _apply_columns(self, func):
-        """ get new SparseDataFrame applying func to each columns """
+        """
+        Get new SparseDataFrame applying func to each columns
+        """
 
         new_data = {col: func(series)
                     for col, series in compat.iteritems(self)}

pandas/core/sparse/scipy_sparse.py

@@ -90,7 +90,8 @@ def _get_index_subset_to_coord_dict(index, subset, sort_labels=False):
 
 def _sparse_series_to_coo(ss, row_levels=(0, ), column_levels=(1, ),
                           sort_labels=False):
-    """ Convert a SparseSeries to a scipy.sparse.coo_matrix using index
+    """
+    Convert a SparseSeries to a scipy.sparse.coo_matrix using index
     levels row_levels, column_levels as the row and column
     labels respectively. Returns the sparse_matrix, row and column labels.
     """
@@ -116,7 +117,8 @@ def _sparse_series_to_coo(ss, row_levels=(0, ), column_levels=(1, ),
 
 
 def _coo_to_sparse_series(A, dense_index=False):
-    """ Convert a scipy.sparse.coo_matrix to a SparseSeries.
+    """
+    Convert a scipy.sparse.coo_matrix to a SparseSeries.
     Use the defaults given in the SparseSeries constructor.
     """
     s = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))