Misc typos (pandas-dev#19430)

Found via `codespell -q 3`

Author: luzpaz

asv_bench/benchmarks/replace.py

@@ -44,15 +44,15 @@ class Convert(object):
 
     goal_time = 0.5
     params = (['DataFrame', 'Series'], ['Timestamp', 'Timedelta'])
-    param_names = ['contructor', 'replace_data']
+    param_names = ['constructor', 'replace_data']
 
-    def setup(self, contructor, replace_data):
+    def setup(self, constructor, replace_data):
         N = 10**3
         data = {'Series': pd.Series(np.random.randint(N, size=N)),
                 'DataFrame': pd.DataFrame({'A': np.random.randint(N, size=N),
                                            'B': np.random.randint(N, size=N)})}
         self.to_replace = {i: getattr(pd, replace_data) for i in range(N)}
-        self.data = data[contructor]
+        self.data = data[constructor]
 
-    def time_replace(self, contructor, replace_data):
+    def time_replace(self, constructor, replace_data):
         self.data.replace(self.to_replace)

asv_bench/benchmarks/rolling.py

@@ -12,14 +12,14 @@ class Methods(object):
               ['int', 'float'],
               ['median', 'mean', 'max', 'min', 'std', 'count', 'skew', 'kurt',
                'sum', 'corr', 'cov'])
-    param_names = ['contructor', 'window', 'dtype', 'method']
+    param_names = ['constructor', 'window', 'dtype', 'method']
 
-    def setup(self, contructor, window, dtype, method):
+    def setup(self, constructor, window, dtype, method):
         N = 10**5
         arr = np.random.random(N).astype(dtype)
-        self.roll = getattr(pd, contructor)(arr).rolling(window)
+        self.roll = getattr(pd, constructor)(arr).rolling(window)
 
-    def time_rolling(self, contructor, window, dtype, method):
+    def time_rolling(self, constructor, window, dtype, method):
         getattr(self.roll, method)()
 
 
@@ -30,12 +30,12 @@ class Quantile(object):
               [10, 1000],
               ['int', 'float'],
               [0, 0.5, 1])
-    param_names = ['contructor', 'window', 'dtype', 'percentile']
+    param_names = ['constructor', 'window', 'dtype', 'percentile']
 
-    def setup(self, contructor, window, dtype, percentile):
+    def setup(self, constructor, window, dtype, percentile):
         N = 10**5
         arr = np.random.random(N).astype(dtype)
-        self.roll = getattr(pd, contructor)(arr).rolling(window)
+        self.roll = getattr(pd, constructor)(arr).rolling(window)
 
-    def time_quantile(self, contructor, window, dtype, percentile):
+    def time_quantile(self, constructor, window, dtype, percentile):
         self.roll.quantile(percentile)

doc/source/api.rst

@@ -2500,7 +2500,7 @@ Scalar introspection
 Extensions
 ----------
 
-These are primarily intented for library authors looking to extend pandas
+These are primarily intended for library authors looking to extend pandas
 objects.
 
 .. currentmodule:: pandas

doc/source/io.rst

@@ -2675,7 +2675,7 @@ file, and the ``sheet_name`` indicating which sheet to parse.
 +++++++++++++++++++
 
 To facilitate working with multiple sheets from the same file, the ``ExcelFile``
-class can be used to wrap the file and can be be passed into ``read_excel``
+class can be used to wrap the file and can be passed into ``read_excel``
 There will be a performance benefit for reading multiple sheets as the file is
 read into memory only once.
 

doc/sphinxext/numpydoc/tests/test_docscrape.py

@@ -42,7 +42,7 @@
   -------
   out : ndarray
       The drawn samples, arranged according to `shape`.  If the
-      shape given is (m,n,...), then the shape of `out` is is
+      shape given is (m,n,...), then the shape of `out` is
       (m,n,...,N).
 
       In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
@@ -222,7 +222,7 @@ def test_str():
 -------
 out : ndarray
     The drawn samples, arranged according to `shape`.  If the
-    shape given is (m,n,...), then the shape of `out` is is
+    shape given is (m,n,...), then the shape of `out` is
     (m,n,...,N).
 
     In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
@@ -340,7 +340,7 @@ def test_sphinx_str():
     **out** : ndarray
 
         The drawn samples, arranged according to `shape`.  If the
-        shape given is (m,n,...), then the shape of `out` is is
+        shape given is (m,n,...), then the shape of `out` is
         (m,n,...,N).
 
         In other words, each entry ``out[i,j,...,:]`` is an N-dimensional

pandas/_libs/tslibs/timedeltas.pyx

@@ -897,7 +897,7 @@ class Timedelta(_Timedelta):
     Represents a duration, the difference between two dates or times.
 
     Timedelta is the pandas equivalent of python's ``datetime.timedelta``
-    and is interchangable with it in most cases.
+    and is interchangeable with it in most cases.
 
     Parameters
     ----------

pandas/_libs/tslibs/timezones.pyx

@@ -295,7 +295,7 @@ cpdef bint tz_compare(object start, object end):
     timezones. For example
     `<DstTzInfo 'Europe/Paris' LMT+0:09:00 STD>` and
     `<DstTzInfo 'Europe/Paris' CET+1:00:00 STD>` are essentially same
-    timezones but aren't evaluted such, but the string representation
+    timezones but aren't evaluated such, but the string representation
     for both of these is `'Europe/Paris'`.
 
     This exists only to add a notion of equality to pytz-style zones

pandas/core/frame.py

@@ -4115,7 +4115,7 @@ def combine(self, other, func, fill_value=None, overwrite=True):
                 series[this_mask] = fill_value
                 otherSeries[other_mask] = fill_value
 
-            # if we have different dtypes, possibily promote
+            # if we have different dtypes, possibly promote
             new_dtype = this_dtype
             if not is_dtype_equal(this_dtype, other_dtype):
                 new_dtype = find_common_type([this_dtype, other_dtype])

pandas/core/indexes/datetimelike.py

@@ -332,7 +332,7 @@ def freqstr(self):
     @cache_readonly
     def inferred_freq(self):
         """
-        Trys to return a string representing a frequency guess,
+        Tryies to return a string representing a frequency guess,
         generated by infer_freq.  Returns None if it can't autodetect the
         frequency.
         """

pandas/core/sparse/frame.py

@@ -120,7 +120,7 @@ def __init__(self, data=None, index=None, columns=None, default_kind=None,
             if dtype is not None:
                 mgr = mgr.astype(dtype)
         else:
-            msg = ('SparseDataFrame called with unkown type "{data_type}" '
+            msg = ('SparseDataFrame called with unknown type "{data_type}" '
                    'for data argument')
             raise TypeError(msg.format(data_type=type(data).__name__))