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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