Moving pandasjson back into mainline pandas

LICENSES/ULTRAJSON_LICENSE

@@ -0,0 +1,34 @@
+Copyright (c) 2011-2013, ESN Social Software AB and Jonas Tarnstrom
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the ESN Social Software AB nor the
+      names of its contributors may be used to endorse or promote products
+      derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL ESN SOCIAL SOFTWARE AB OR JONAS TARNSTROM BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc)
+http://code.google.com/p/stringencoders/
+Copyright (c) 2007  Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved.
+
+Numeric decoder derived from from TCL library
+http://www.opensource.apple.com/source/tcl/tcl-14/tcl/license.terms
+ * Copyright (c) 1988-1993 The Regents of the University of California.
+ * Copyright (c) 1994 Sun Microsystems, Inc.

pandas/core/frame.py

@@ -1659,8 +1659,8 @@ def convert_objects(self, convert_dates=True, convert_numeric=False, copy=True):
         -------
         converted : DataFrame
         """
-        return self._constructor(self._data.convert(convert_dates=convert_dates, 
-                                                    convert_numeric=convert_numeric, 
+        return self._constructor(self._data.convert(convert_dates=convert_dates,
+                                                    convert_numeric=convert_numeric,
                                                     copy=copy))
 
     #----------------------------------------------------------------------
@@ -3321,7 +3321,7 @@ def fillna(self, value=None, method=None, axis=0, inplace=False,
             a reference to the filled object, which is self if inplace=True
         limit : int, default None
             Maximum size gap to forward or backward fill
-        downcast : dict, default is None, a dict of item->dtype of what to 
+        downcast : dict, default is None, a dict of item->dtype of what to
             downcast if possible
 
         See also
@@ -3368,7 +3368,7 @@ def fillna(self, value=None, method=None, axis=0, inplace=False,
                     result[k].fillna(v, inplace=True)
                 return result
             else:
-                new_data = self._data.fillna(value, inplace=inplace, 
+                new_data = self._data.fillna(value, inplace=inplace,
                                              downcast=downcast)
 
         if inplace:
@@ -3756,8 +3756,8 @@ def combine(self, other, func, fill_value=None, overwrite=True):
             result[col] = arr
 
         # convert_objects just in case
-        return self._constructor(result, 
-                                 index=new_index, 
+        return self._constructor(result,
+                                 index=new_index,
                                  columns=new_columns).convert_objects(copy=False)
 
     def combine_first(self, other):
@@ -5278,6 +5278,106 @@ def mask(self, cond):
         """
         return self.where(~cond, NA)
 
+
+@classmethod
+def from_json(cls, json, orient="columns", dtype=None, numpy=True):
+    """
+    Convert JSON string to DataFrame
+
+    Parameters
+    ----------
+    json : The JSON string to parse.
+    orient : {'split', 'records', 'index', 'columns', 'values'},
+             default 'columns'
+        The format of the JSON string
+        split : dict like
+            {index -> [index], columns -> [columns], data -> [values]}
+        records : list like [{column -> value}, ... , {column -> value}]
+        index : dict like {index -> {column -> value}}
+        columns : dict like {column -> {index -> value}}
+        values : just the values array
+    dtype : dtype of the resulting DataFrame
+    nupmpy: direct decoding to numpy arrays. default True but falls back
+        to standard decoding if a problem occurs.
+
+    Returns
+    -------
+    result : DataFrame
+    """
+    from pandas.json import loads
+
+    df = None
+
+    if dtype is not None and orient == "split":
+        numpy = False
+
+    if numpy:
+        try:
+            if orient == "columns":
+                args = loads(json, dtype=dtype, numpy=True, labelled=True)
+                if args:
+                    args = (args[0].T, args[2], args[1])
+                df = DataFrame(*args)
+            elif orient == "split":
+                decoded = loads(json, dtype=dtype, numpy=True)
+                decoded = dict((str(k), v) for k, v in decoded.iteritems())
+                df = DataFrame(**decoded)
+            elif orient == "values":
+                df = DataFrame(loads(json, dtype=dtype, numpy=True))
+            else:
+                df = DataFrame(*loads(json, dtype=dtype, numpy=True,
+                                      labelled=True))
+        except ValueError:
+            numpy = False
+    if not numpy:
+        if orient == "columns":
+            df = DataFrame(loads(json), dtype=dtype)
+        elif orient == "split":
+            decoded = dict((str(k), v)
+                           for k, v in loads(json).iteritems())
+            df = DataFrame(dtype=dtype, **decoded)
+        elif orient == "index":
+            df = DataFrame(loads(json), dtype=dtype).T
+        else:
+            df = DataFrame(loads(json), dtype=dtype)
+
+    return df
+DataFrame.from_json = from_json
+
+
+def to_json(self, orient="columns", double_precision=10,
+            force_ascii=True):
+    """
+    Convert DataFrame to a JSON string.
+
+    Note NaN's and None will be converted to null and datetime objects
+    will be converted to UNIX timestamps.
+
+    Parameters
+    ----------
+    orient : {'split', 'records', 'index', 'columns', 'values'},
+             default 'columns'
+        The format of the JSON string
+        split : dict like
+            {index -> [index], columns -> [columns], data -> [values]}
+        records : list like [{column -> value}, ... , {column -> value}]
+        index : dict like {index -> {column -> value}}
+        columns : dict like {column -> {index -> value}}
+        values : just the values array
+    double_precision : The number of decimal places to use when encoding
+        floating point values, default 10.
+    force_ascii : force encoded string to be ASCII, default True.
+
+    Returns
+    -------
+    result : JSON compatible string
+    """
+    from pandas.json import dumps
+    return dumps(self, orient=orient, double_precision=double_precision,
+                 ensure_ascii=force_ascii)
+DataFrame.to_json = to_json
+
+
 _EMPTY_SERIES = Series([])
 
 

pandas/core/series.py

@@ -1962,7 +1962,7 @@ def dot(self, other):
 
         Parameters
         ----------
-        other : Series or DataFrame 
+        other : Series or DataFrame
 
         Returns
         -------
@@ -3241,6 +3241,88 @@ def str(self):
         from pandas.core.strings import StringMethods
         return StringMethods(self)
 
+
+@classmethod
+def from_json(cls, json, orient="index", dtype=None, numpy=True):
+    """
+    Convert JSON string to Series
+
+    Parameters
+    ----------
+    json : The JSON string to parse.
+    orient : {'split', 'records', 'index'}, default 'index'
+        The format of the JSON string
+        split : dict like
+            {index -> [index], name -> name, data -> [values]}
+        records : list like [value, ... , value]
+        index : dict like {index -> value}
+    dtype : dtype of the resulting Series
+    nupmpy: direct decoding to numpy arrays. default True but falls back
+        to standard decoding if a problem occurs.
+
+    Returns
+    -------
+    result : Series
+    """
+    from pandas.json import loads
+    s = None
+
+    if dtype is not None and orient == "split":
+        numpy = False
+
+    if numpy:
+        try:
+            if orient == "split":
+                decoded = loads(json, dtype=dtype, numpy=True)
+                decoded = dict((str(k), v) for k, v in decoded.iteritems())
+                s = Series(**decoded)
+            elif orient == "columns" or orient == "index":
+                s = Series(*loads(json, dtype=dtype, numpy=True,
+                                  labelled=True))
+            else:
+                s = Series(loads(json, dtype=dtype, numpy=True))
+        except ValueError:
+            numpy = False
+    if not numpy:
+        if orient == "split":
+            decoded = dict((str(k), v)
+                           for k, v in loads(json).iteritems())
+            s = Series(dtype=dtype, **decoded)
+        else:
+            s = Series(loads(json), dtype=dtype)
+
+    return s
+Series.from_json = from_json
+
+def to_json(self, orient="index", double_precision=10, force_ascii=True):
+    """
+    Convert Series to a JSON string
+
+    Note NaN's and None will be converted to null and datetime objects
+    will be converted to UNIX timestamps.
+
+    Parameters
+    ----------
+    orient : {'split', 'records', 'index'}, default 'index'
+        The format of the JSON string
+        split : dict like
+            {index -> [index], name -> name, data -> [values]}
+        records : list like [value, ... , value]
+        index : dict like {index -> value}
+    double_precision : The number of decimal places to use when encoding
+        floating point values, default 10.
+    force_ascii : force encoded string to be ASCII, default True.
+
+    Returns
+    -------
+    result : JSON compatible string
+    """
+    from pandas.json import dumps
+    return dumps(self, orient=orient, double_precision=double_precision,
+                 ensure_ascii=force_ascii)
+Series.to_json = to_json
+
+
 _INDEX_TYPES = ndarray, Index, list, tuple
 
 #------------------------------------------------------------------------------