CLN: modified timmie PR 4631

Author: jreback

doc/source/basics.rst

@@ -465,9 +465,6 @@ take an optional ``axis`` argument:
    df.apply(lambda x: x.max() - x.min())
    df.apply(np.cumsum)
    df.apply(np.exp)
-   
-Please note that the default is the application along the DataFrame's index
-(axis=0) whereas for applying along the columns (axis=1) axis must be specified explicitly (see also :ref:`api.dataframe` and :ref:`api.series`).
 
 Depending on the return type of the function passed to ``apply``, the result
 will either be of lower dimension or the same dimension.

doc/source/dsintro.rst

@@ -431,9 +431,6 @@ available to insert at a particular location in the columns:
 
 Indexing / Selection
 ~~~~~~~~~~~~~~~~~~~~
-
-.. _dsintro.basics-of-indexing:
-
 The basics of indexing are as follows:
 
 .. csv-table::
@@ -453,20 +450,6 @@ DataFrame:
 
    df.loc['b']
    df.iloc[2]
-   
-There is also support for purely integer-based indexing provided by the following methods:
-
-.. _dsintro.integer-indexing:
-
-.. csv-table::
-    :header: "Method","Description"
-    :widths: 40,60
-
-	``Series.iget_value(i)``, Retrieve value stored at location ``i``
-	``Series.iget(i)``, Alias for ``iget_value``
-	``DataFrame.irow(i)``, Retrieve the ``i``-th row
-	``DataFrame.icol(j)``, Retrieve the ``j``-th column
-	"``DataFrame.iget_value(i, j)``", Retrieve the value at row ``i`` and column ``j``
 
 For a more exhaustive treatment of more sophisticated label-based indexing and
 slicing, see the :ref:`section on indexing <indexing>`. We will address the

doc/source/index.rst

@@ -133,5 +133,4 @@ See the package overview for more detail about what's in the library.
     related
     comparison_with_r
     api
-    shortcuts
     release

doc/source/release.rst

@@ -59,6 +59,9 @@ pandas 0.13
   - A Series of dtype ``timedelta64[ns]`` can now be divided by another
     ``timedelta64[ns]`` object to yield a ``float64`` dtyped Series. This
     is frequency conversion.
+  - read_excel (:issue:`4332`) supports a date_parser. This enables reading in hours
+    in a form of 01:00-24:00 in both `Excel datemodes <http://support.microsoft.com/kb/214330/en-us>`_
+    courtesy of @timmie
 
 **API Changes**
 

doc/source/shortcuts.rst

@@ -387,8 +387,6 @@ regularity will result in a ``DatetimeIndex`` (but frequency is lost):
 DateOffset objects
 ------------------
 
-.. _timeseries.dateoffset:
-
 In the preceding examples, we created DatetimeIndex objects at various
 frequencies by passing in frequency strings like 'M', 'W', and 'BM to the
 ``freq`` keyword. Under the hood, these frequency strings are being translated
@@ -549,8 +547,6 @@ calendars which account for local holidays and local weekend conventions.
 Offset Aliases
 ~~~~~~~~~~~~~~
 
-.. _timeseries.offset-aliases:
-
 A number of string aliases are given to useful common time series
 frequencies. We will refer to these aliases as *offset aliases*
 (referred to as *time rules* prior to v0.8.0).
@@ -1250,51 +1246,3 @@ The following are equivalent statements in the two versions of numpy.
        y / np.timedelta64(1,'D')
        y / np.timedelta64(1,'s')
 
-Working with timedate-based indices
--------------------------------------------------------
-
-The :func:`pd.datetime` allows for vectorised operations using datetime information stored in a :ref:`timeseries.datetimeindex`.
-
-Use cases are:
-
-* calculation of sunsunrise, sunset, daylength
-* boolean test of working hours
-
-An example contributed by a savvy user at `Stackoverflow <http://stackoverflow.com/a/15839530>`_:
-
-.. ipython:: python
-
-   import pandas as pd
-
-   ###1) create a date column from indiviadual year, month, day columns
-   df = pd.DataFrame({"year": [1992, 2003, 2014], "month": [2,3,4], "day": [10,20,30]})
-   df
-
-   df["Date"] = df.apply(lambda x: pd.datetime(x['year'], x['month'], x['day']), axis=1)
-   df
-   
-   ###2) alternatively, use the equivalent to datetime.datetime.combine
-   import numpy as np
-   
-   #create a hourly timeseries
-   data_randints = np.random.randint(1, 10, 4000)
-   data_randints = data_randints.reshape(1000, 4)
-   ts = pd.Series(randn(1000), index=pd.date_range('1/1/2000', periods=1000, freq='H'))
-   df = pd.DataFrame(data_randints, index=ts.index, columns=['A', 'B', 'C', 'D'])
-   df.head()
-      
-   #only for examplary purposes: get the date & time from the df.index
-   #                             in real world, these would be read in or generated from different columns
-   df['date'] = df.index.date
-   df.head()
-   
-   df['time'] = df.index.time
-   df.head()
-   
-   #combine both:
-   df['datetime'] = df.apply((lambda x: pd.datetime.combine(x['date'], x['time'])), axis=1)
-   df.head()
-   
-   #the index could be set to the created column
-   df = df.set_index(['datetime'])
-   df.head()

doc/source/timeseries.rst

@@ -270,10 +270,6 @@ Bug Fixes
 
   - Suppressed DeprecationWarning associated with internal calls issued by repr() (:issue:`4391`)
 
-  - read_excel (:issue:`4332`) supports a date_parser. This enabales to reading in hours like 01:00-24:00 in both `Excel datemodes <http://support.microsoft.com/kb/214330/en-us>`_
-
-  - (Excel) parser (:issue:`4340`) allows skipping an arbitrary number of lines between header and first row. 
-
 See the :ref:`full release notes
 <release>` or issue tracker
 on GitHub for a complete list.

doc/source/v0.13.0.txt

@@ -150,8 +150,18 @@ This change also has the same impact on DataFrame:
    In [5]: df.ix[3]
    KeyError: 3
 
-In order to support purely `integer-based indexing <dsintro.integer-indexing>`, `corresponding methods <dsintro.integer-indexing>` have been added.
-
+In order to support purely integer-based indexing, the following methods have
+been added:
+
+.. csv-table::
+    :header: "Method","Description"
+    :widths: 40,60
+
+	``Series.iget_value(i)``, Retrieve value stored at location ``i``
+	``Series.iget(i)``, Alias for ``iget_value``
+	``DataFrame.irow(i)``, Retrieve the ``i``-th row
+	``DataFrame.icol(j)``, Retrieve the ``j``-th column
+	"``DataFrame.iget_value(i, j)``", Retrieve the value at row ``i`` and column ``j``
 
 API tweaks regarding label-based slicing
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

doc/source/v0.7.0.txt

@@ -60,9 +60,9 @@ def _check_columns(cols):
     return N
 
 
-## Datetime Conversion for date_parsers
-## see also: create a community supported set of typical converters
-##           https://github.com/pydata/pandas/issues/1180
+# Datetime Conversion for date_parsers
+# see also: create a community supported set of typical converters
+# https://github.com/pydata/pandas/issues/1180
 
 def offset_datetime(dt_in, days=0, hours=0, minutes=0,
                     seconds=0, microseconds=0):
@@ -82,52 +82,20 @@ def offset_datetime(dt_in, days=0, hours=0, minutes=0,
 
     output
     ------
-    ti_corr : datetime.time or datetime.datetime object
+    ti_corr : datetime.time object (or pass thru if no conversion)
 
 
     '''
     # if a excel time like '23.07.2013 24:00' they actually mean
     # in Python '23.07.2013 23:59', must be converted
-#            offset = -10 # minutes
     delta = timedelta(days=days, hours=hours, minutes=minutes,
                       seconds=seconds, microseconds=microseconds)
 
-    #check if offset it to me applied on datetime or time
-    if type(dt_in) is time:
-        #create psydo datetime
-        dt_now = datetime.now()
-        dt_base = datetime.combine(dt_now, dt_in)
-    else:
-        dt_base = dt_in
-
-    dt_corr = (dt_base) + delta
-
-    #if input is time, we return it.
-    if type(dt_in) is time:
-        dt_corr = dt_corr.time()
-
-    return dt_corr
-
-
-def dt2ti(dt_in):
-    '''converts wrong datetime.datetime to datetime.time
-
-    input
-    -----
-    dt_in : dt_in : datetime.time or datetime.datetime object
-
-    output
-    -------
-    ti_corr : datetime.time object
-    '''
-    # so we correct those which are not of type :mod:datetime.time
-    # impdt2tiortant hint:
-    # http://stackoverflow.com/a/12906456
-    if type(dt_in) is not time:
-        dt_in = dt_in.time()
-    elif type(dt_in) is datetime:
-        dt_in = dt_in.time()
-    else:
-        pass
+    offsetter = lambda base: (base) + delta
 
+    # check if offset it to me applied on datetime or time
+    if isinstance(dt_in, time):
+        return offsetter(datetime.combine(datetime.now(), dt_in)).time()
+    elif isinstance(dt_in, datetime):
+        return offsetter(datetime.combine(datetime.now(), dt_in.time())).time()
     return dt_in

pandas/io/date_converters.py

@@ -127,18 +127,15 @@ def parse(self, sheetname, header=0, skiprows=None, skip_footer=0,
         skipfooter = kwds.pop('skipfooter', None)
         if skipfooter is not None:
             skip_footer = skipfooter
-        
-        # this now gives back a df
-        res = self._parse_excel(sheetname, header=header, skiprows=skiprows,
+
+        return self._parse_excel(sheetname, header=header, skiprows=skiprows,
                                  index_col=index_col,
                                  has_index_names=has_index_names,
                                  parse_cols=parse_cols,
                                  parse_dates=parse_dates,
                                  date_parser=date_parser, na_values=na_values,
                                  thousands=thousands, chunksize=chunksize,
                                  skip_footer=skip_footer, **kwds)
-                    
-        return res
 
     def _should_parse(self, i, parse_cols):
 
@@ -198,24 +195,21 @@ def _parse_excel(self, sheetname, header=0, skiprows=None, skip_footer=0,
                 if parse_cols is None or should_parse[j]:
                     if typ == XL_CELL_DATE:
                         dt = xldate_as_tuple(value, datemode)
-                        
+
                         # how to produce this first case?
                         # if the year is ZERO then values are time/hours
                         if dt[0] < datetime.MINYEAR:  # pragma: no cover
                             datemode = 1
                             dt = xldate_as_tuple(value, datemode)
-                            
-                            value = datetime.time(*dt[3:])  
-                                     
+                            value = datetime.time(*dt[3:])
 
                         #or insert a full date
                         else:
                             value = datetime.datetime(*dt)
-                        
-                        #apply eventual date_parser correction
+
                         if date_parser:
-                                value = date_parser(value)    
-                            
+                                value = date_parser(value)
+
                     elif typ == XL_CELL_ERROR:
                         value = np.nan
                     elif typ == XL_CELL_BOOLEAN:
@@ -237,13 +231,13 @@ def _parse_excel(self, sheetname, header=0, skiprows=None, skip_footer=0,
                             skip_footer=skip_footer,
                             chunksize=chunksize,
                             **kwds)
-        res = parser.read() 
-        
+
+        res = parser.read()
+
         if header is not None:
 
             if len(data[header]) == len(res.columns.tolist()):
                 res.columns = data[header]
-        
 
         return res
 

pandas/io/excel.py

@@ -1150,11 +1150,7 @@ def TextParser(*args, **kwds):
         returns Series if only one column
     """
     kwds['engine'] = 'python'
-    
-    res = TextFileReader(*args, **kwds)
-    
-    
-    return res
+    return TextFileReader(*args, **kwds)
 
 # delimiter=None, dialect=None, names=None, header=0,
 # index_col=None,
@@ -1389,7 +1385,6 @@ def _convert_data(self, data):
                                          clean_conv)
 
     def _infer_columns(self):
-        #TODO: this full part is too complex and somewhat strage!!!
         names = self.names
 
         if self.header is not None:
@@ -1401,20 +1396,13 @@ def _infer_columns(self):
                 header = list(header) + [header[-1]+1]
             else:
                 have_mi_columns = False
-                #TODO: explain why header (in this case 1 number) needs to be a list???
                 header = [ header ]
 
             columns = []
             for level, hr in enumerate(header):
-                #TODO: explain why self.buf is needed.
-                #      the header is correctly retrieved in excel.py by
-                #      data[header] = _trim_excel_header(data[header])
+
                 if len(self.buf) > 0:
                     line = self.buf[0]
-
-                elif (header[0] == hr) and (level == 0) and (header[0] > 0):
-                     line = self._get_header()
-                    
                 else:
                     line = self._next_line()
 
@@ -1468,24 +1456,8 @@ def _infer_columns(self):
                 columns = [ names ]
 
         return columns
-        
-    def _get_header(self):
-        ''' reads header if e.g. header 
-        FIXME: this tshoul be turned into something much less complicates
-        FIXME: all due to the header assuming that there is never a row between
-               data and header
-        '''
-        if isinstance(self.data, list):
-            line = self.data[self.header]
-            self.pos = self.header +1
-        else:
-            line = self._next_line()
-        
-        return line
 
     def _next_line(self):
-        #FIXME: why is self.data at times a list and sometimes a _scv.reader??
-        #       reduce complexity here!!!
         if isinstance(self.data, list):
             while self.pos in self.skiprows:
                 self.pos += 1