ENH: pull pandasjson back into pandas

Author: wesm

pandas/core/frame.py

@@ -5593,6 +5593,106 @@ 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

@@ -3298,6 +3298,88 @@ 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
 
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