DOC: fixed some minor typos

pandas/core/algorithms.py

@@ -223,7 +223,7 @@ def quantile(x, q, interpolation_method='fraction'):
     score : float
         Score at percentile.
 
-    Examplesb
+    Examples
     --------
     >>> from scipy import stats
     >>> a = np.arange(100)

pandas/core/frame.py

@@ -1161,7 +1161,7 @@ def to_excel(self, excel_writer, sheet_name='sheet1', na_rep='',
         sheet_name : string, default 'sheet1'
             Name of sheet which will contain DataFrame
         na_rep : string, default ''
-            Missing data rep'n
+            Missing data representation
         cols : sequence, optional
             Columns to write
         header : boolean or list of string, default True
@@ -4303,7 +4303,7 @@ def _reduce(self, op, axis=0, skipna=True, numeric_only=None,
 
     def idxmin(self, axis=0, skipna=True):
         """
-        Return index of first occurence of minimum over requested axis.
+        Return index of first occurrence of minimum over requested axis.
         NA/null values are excluded.
 
         Parameters
@@ -4325,7 +4325,7 @@ def idxmin(self, axis=0, skipna=True):
 
     def idxmax(self, axis=0, skipna=True):
         """
-        Return index of first occurence of maximum over requested axis.
+        Return index of first occurrence of maximum over requested axis.
         NA/null values are excluded.
 
         Parameters

pandas/core/groupby.py

@@ -74,7 +74,7 @@ class GroupBy(object):
     -----
     After grouping, see aggregate, apply, and transform functions. Here are
     some other brief notes about usage. When grouping by multiple groups, the
-    result index will be a MultiIndex (hierarhical) by default.
+    result index will be a MultiIndex (hierarchical) by default.
 
     Iteration produces (key, group) tuples, i.e. chunking the data by group. So
     you can write code like:

pandas/core/index.py

@@ -2291,7 +2291,7 @@ def insert(self, loc, item):
         new_index : Index
         """
         if not isinstance(item, tuple) or len(item) != self.nlevels:
-            raise Exception("%s cannot be inserted in this MultIndex"
+            raise Exception("%s cannot be inserted in this MultiIndex"
                             % str(item))
 
         new_levels = []

pandas/core/panel.py

@@ -487,7 +487,7 @@ def to_excel(self, path, na_rep=''):
         excel_writer : string or ExcelWriter object
             File path or existing ExcelWriter
         na_rep : string, default ''
-            Missing data rep'n
+            Missing data representation
         """
         from pandas.io.parsers import ExcelWriter
         writer = ExcelWriter(path)

pandas/core/series.py

@@ -752,7 +752,7 @@ def set_value(self, label, value):
 
     def reset_index(self, drop=False, name=None):
         """
-        Analagous to the DataFrame.reset_index function, see docstring there.
+        Analogous to the DataFrame.reset_index function, see docstring there.
 
         Parameters
         ----------
@@ -1139,7 +1139,7 @@ def _agg_by_level(self, name, level=0, skipna=True, **kwds):
 
     def idxmin(self, axis=None, out=None, skipna=True):
         """
-        Index of first occurence of minimum of values.
+        Index of first occurrence of minimum of values.
 
         Parameters
         ----------
@@ -1148,7 +1148,7 @@ def idxmin(self, axis=None, out=None, skipna=True):
 
         Returns
         -------
-        idxmin : Index of mimimum of values
+        idxmin : Index of minimum of values
         """
         i = nanops.nanargmin(self.values, skipna=skipna)
         if i == -1:
@@ -1157,7 +1157,7 @@ def idxmin(self, axis=None, out=None, skipna=True):
 
     def idxmax(self, axis=None, out=None, skipna=True):
         """
-        Index of first occurence of maximum of values.
+        Index of first occurrence of maximum of values.
 
         Parameters
         ----------
@@ -1166,7 +1166,7 @@ def idxmax(self, axis=None, out=None, skipna=True):
 
         Returns
         -------
-        idxmax : Index of mimimum of values
+        idxmax : Index of minimum of values
         """
         i = nanops.nanargmax(self.values, skipna=skipna)
         if i == -1:
@@ -1757,7 +1757,7 @@ def order(self, na_last=True, ascending=True, kind='mergesort'):
             Sort ascending. Passing False sorts descending
         kind : {'mergesort', 'quicksort', 'heapsort'}, default 'mergesort'
             Choice of sorting algorithm. See np.sort for more
-            information. 'mergesort' is the only stable algorith
+            information. 'mergesort' is the only stable algorithm
 
         Returns
         -------
@@ -2341,7 +2341,7 @@ def to_csv(self, path, index=True, sep=",", na_rep='', header=False,
         ----------
         path : string file path or file handle / StringIO
         na_rep : string, default ''
-            Missing data rep'n
+            Missing data representation
         header : boolean, default False
             Write out series name
         index : boolean, default True
@@ -2465,7 +2465,7 @@ def asof(self, where):
 
         Parameters
         ----------
-        wehre : date or array of dates
+        where : date or array of dates
 
         Notes
         -----

pandas/core/strings.py

@@ -417,7 +417,7 @@ def str_findall(arr, pat, flags=0):
     Parameters
     ----------
     pat : string
-        Pattern or regular expressino
+        Pattern or regular expression
     flags : int, default 0 (no flags)
         re module flags, e.g. re.IGNORECASE
 

pandas/sparse/frame.py

@@ -253,7 +253,7 @@ def density(self):
         return tot_nonsparse / float(tot)
 
     #----------------------------------------------------------------------
-    # Support different internal rep'n of SparseDataFrame
+    # Support different internal representation of SparseDataFrame
 
     def _set_item(self, key, value):
         sp_maker = lambda x: SparseSeries(x, index=self.index,

pandas/stats/misc.py

@@ -141,7 +141,7 @@ def bucketcat(series, cats):
     ----------
     series : Series
     cat : Series or same-length array
-        bucket by category; mutually exxlusive with 'by'
+        bucket by category; mutually exclusive with 'by'
 
     Returns
     -------
@@ -175,7 +175,7 @@ def bucketpanel(series, bins=None, by=None, cat=None):
     by : tuple of Series
         bucket by value
     cat : tuple of Series
-        bucket by category; mutually exxlusive with 'by'
+        bucket by category; mutually exclusive with 'by'
 
     Returns
     -------

pandas/tools/plotting.py

@@ -38,7 +38,7 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
     grid : setting this to True will show the grid
     diagonal : pick between 'kde' and 'hist' for
         either Kernel Density Estimation or Histogram
-        plon in the diagonal
+        plot in the diagonal
     kwds : other plotting keyword arguments
         To be passed to scatter function
 
@@ -1304,7 +1304,7 @@ def plot_series(series, label=None, kind='line', use_index=True, rot=None,
 def boxplot(data, column=None, by=None, ax=None, fontsize=None,
             rot=0, grid=True, figsize=None, **kwds):
     """
-    Make a box plot from DataFrame column optionally grouped b ysome columns or
+    Make a box plot from DataFrame column optionally grouped by some columns or
     other inputs
 
     Parameters
@@ -1733,7 +1733,7 @@ def _subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True,
         - for NxM subplots with N>1 and M>1 are returned as a 2d array.
 
       If False, no squeezing at all is done: the returned axis object is always
-      a 2-d array contaning Axis instances, even if it ends up being 1x1.
+      a 2-d array containing Axis instances, even if it ends up being 1x1.
 
     subplot_kw : dict
       Dict with keywords passed to the add_subplot() call used to create each
@@ -1753,7 +1753,7 @@ def _subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True,
     fig, ax : tuple
       - fig is the Matplotlib Figure object
       - ax can be either a single axis object or an array of axis objects if
-      more than one supblot was created.  The dimensions of the resulting array
+      more than one subplot was created.  The dimensions of the resulting array
       can be controlled with the squeeze keyword, see above.
 
     **Examples:**

pandas/tseries/frequencies.py

@@ -703,7 +703,7 @@ def infer_freq(index, warn=True):
     Returns
     -------
     freq : string or None
-        None if no discernable frequency
+        None if no discernible frequency
     """
     from pandas.tseries.index import DatetimeIndex
 

pandas/tseries/index.py

@@ -602,7 +602,7 @@ def order(self, return_indexer=False, ascending=True):
 
     def snap(self, freq='S'):
         """
-        Snap time stamps to nearest occuring frequency
+        Snap time stamps to nearest occurring frequency
 
         """
         # Superdumb, punting on any optimizing

pandas/tseries/offsets.py

@@ -529,7 +529,7 @@ class WeekOfMonth(DateOffset, CacheableOffset):
         0 is 1st week of month, 1 2nd week, etc.
     weekday : {0, 1, ..., 6}
         0: Mondays
-        1: Tuedays
+        1: Tuesdays
         2: Wednesdays
         3: Thursdays
         4: Fridays