BUG: Patch handling of keep_default_na=False

doc/source/io.rst

@@ -214,8 +214,20 @@ na_values : scalar, str, list-like, or dict, default ``None``
   for a list of the values interpreted as NaN by default.
 
 keep_default_na : boolean, default ``True``
-  If na_values are specified and keep_default_na is ``False`` the default NaN
-  values are overridden, otherwise they're appended to.
+  Whether or not to include the default NaN values when parsing the data.
+  Depending on whether `na_values` is passed in, the behavior is as follows:
+
+  * If `keep_default_na` is True, and `na_values` are specified, `na_values`
+    is appended to the default NaN values used for parsing.
+  * If `keep_default_na` is True, and `na_values` are not specified, only
+    the default NaN values are used for parsing.
+  * If `keep_default_na` is False, and `na_values` are specified, only
+    the NaN values specified `na_values` are used for parsing.
+  * If `keep_default_na` is False, and `na_values` are not specified, no
+    strings will be parsed as NaN.
+
+  Note that if `na_filter` is passed in as False, the `keep_default_na` and
+  `na_values` parameters will be ignored.
 na_filter : boolean, default ``True``
   Detect missing value markers (empty strings and the value of na_values). In
   data without any NAs, passing ``na_filter=False`` can improve the performance

doc/source/whatsnew/v0.23.0.txt

@@ -463,6 +463,7 @@ I/O
 - :func:`read_html` now rewinds seekable IO objects after parse failure, before attempting to parse with a new parser. If a parser errors and the object is non-seekable, an informative error is raised suggesting the use of a different parser (:issue:`17975`)
 - Bug in :func:`read_msgpack` with a non existent file is passed in Python 2 (:issue:`15296`)
 - Bug in :func:`read_csv` where a ``MultiIndex`` with duplicate columns was not being mangled appropriately (:issue:`18062`)
+- Bug in :func:`read_csv` where missing values were not being handled properly when ``keep_default_na=False`` with dictionary ``na_values`` (:issue:`19227`)
 - Bug in :func:`read_sas` where a file with 0 variables gave an ``AttributeError`` incorrectly. Now it gives an ``EmptyDataError`` (:issue:`18184`)
 - Bug in :func:`DataFrame.to_latex()` where pairs of braces meant to serve as invisible placeholders were escaped (:issue:`18667`)
 - Bug in :func:`read_json` where large numeric values were causing an ``OverflowError`` (:issue:`18842`)

pandas/_libs/parsers.pyx

@@ -288,7 +288,7 @@ cdef class TextReader:
         object file_handle, na_fvalues
         object true_values, false_values
         object handle
-        bint na_filter, verbose, has_usecols, has_mi_columns
+        bint na_filter, keep_default_na, verbose, has_usecols, has_mi_columns
         int64_t parser_start
         list clocks
         char *c_encoding
@@ -352,6 +352,8 @@ cdef class TextReader:
                   na_filter=True,
                   na_values=None,
                   na_fvalues=None,
+                  keep_default_na=True,
+
                   true_values=None,
                   false_values=None,
                   allow_leading_cols=True,
@@ -378,8 +380,8 @@ cdef class TextReader:
         self.parser = parser_new()
         self.parser.chunksize = tokenize_chunksize
 
-        self.mangle_dupe_cols=mangle_dupe_cols
-        self.tupleize_cols=tupleize_cols
+        self.mangle_dupe_cols = mangle_dupe_cols
+        self.tupleize_cols = tupleize_cols
 
         # For timekeeping
         self.clocks = []
@@ -477,6 +479,7 @@ cdef class TextReader:
         self.true_set = kset_from_list(self.true_values)
         self.false_set = kset_from_list(self.false_values)
 
+        self.keep_default_na = keep_default_na
         self.converters = converters
         self.na_filter = na_filter
 
@@ -1299,7 +1302,10 @@ cdef class TextReader:
             elif i in self.na_values:
                 key = i
             else:  # No na_values provided for this column.
-                return _NA_VALUES, set()
+                if self.keep_default_na:
+                    return _NA_VALUES, set()
+
+                return list(), set()
 
             values = self.na_values[key]
             if values is not None and not isinstance(values, list):

pandas/io/parsers.py

@@ -149,8 +149,20 @@
     NaN: '""" + fill("', '".join(sorted(_NA_VALUES)),
                      70, subsequent_indent="    ") + """'.
 keep_default_na : bool, default True
-    If na_values are specified and keep_default_na is False the default NaN
-    values are overridden, otherwise they're appended to.
+    Whether or not to include the default NaN values when parsing the data.
+    Depending on whether `na_values` is passed in, the behavior is as follows:
+
+    * If `keep_default_na` is True, and `na_values` are specified, `na_values`
+      is appended to the default NaN values used for parsing.
+    * If `keep_default_na` is True, and `na_values` are not specified, only
+      the default NaN values are used for parsing.
+    * If `keep_default_na` is False, and `na_values` are specified, only
+      the NaN values specified `na_values` are used for parsing.
+    * If `keep_default_na` is False, and `na_values` are not specified, no
+      strings will be parsed as NaN.
+
+    Note that if `na_filter` is passed in as False, the `keep_default_na` and
+    `na_values` parameters will be ignored.
 na_filter : boolean, default True
     Detect missing value markers (empty strings and the value of na_values). In
     data without any NAs, passing na_filter=False can improve the performance
@@ -910,9 +922,6 @@ def _clean_options(self, options, engine):
         na_values = options['na_values']
         skiprows = options['skiprows']
 
-        # really delete this one
-        keep_default_na = result.pop('keep_default_na')
-
         _validate_header_arg(options['header'])
 
         depr_warning = ''
@@ -957,6 +966,7 @@ def _clean_options(self, options, engine):
             converters = {}
 
         # Converting values to NA
+        keep_default_na = options['keep_default_na']
         na_values, na_fvalues = _clean_na_values(na_values, keep_default_na)
 
         # handle skiprows; this is internally handled by the
@@ -1225,6 +1235,7 @@ def __init__(self, kwds):
         self.na_values = kwds.get('na_values')
         self.na_fvalues = kwds.get('na_fvalues')
         self.na_filter = kwds.get('na_filter', False)
+        self.keep_default_na = kwds.get('keep_default_na', True)
 
         self.true_values = kwds.get('true_values')
         self.false_values = kwds.get('false_values')
@@ -1487,7 +1498,8 @@ def _agg_index(self, index, try_parse_dates=True):
                 col_name = self.index_names[i]
                 if col_name is not None:
                     col_na_values, col_na_fvalues = _get_na_values(
-                        col_name, self.na_values, self.na_fvalues)
+                        col_name, self.na_values, self.na_fvalues,
+                        self.keep_default_na)
 
             arr, _ = self._infer_types(arr, col_na_values | col_na_fvalues)
             arrays.append(arr)
@@ -1510,7 +1522,7 @@ def _convert_to_ndarrays(self, dct, na_values, na_fvalues, verbose=False,
 
             if self.na_filter:
                 col_na_values, col_na_fvalues = _get_na_values(
-                    c, na_values, na_fvalues)
+                    c, na_values, na_fvalues, self.keep_default_na)
             else:
                 col_na_values, col_na_fvalues = set(), set()
 
@@ -3097,16 +3109,23 @@ def _clean_na_values(na_values, keep_default_na=True):
             na_values = set()
         na_fvalues = set()
     elif isinstance(na_values, dict):
-        na_values = na_values.copy()  # Prevent aliasing.
-        if keep_default_na:
-            for k, v in compat.iteritems(na_values):
-                if not is_list_like(v):
-                    v = [v]
+        old_na_values = na_values.copy()
+        na_values = {}  # Prevent aliasing.
+
+        # Convert the values in the na_values dictionary
+        # into array-likes for further use. This is also
+        # where we append the default NaN values, provided
+        # that `keep_default_na=True`.
+        for k, v in compat.iteritems(old_na_values):
+            if not is_list_like(v):
+                v = [v]
+
+            if keep_default_na:
                 v = set(v) | _NA_VALUES
-                na_values[k] = v
-        na_fvalues = dict(
-            (k, _floatify_na_values(v)) for k, v in na_values.items()  # noqa
-        )
+
+            na_values[k] = v
+        na_fvalues = dict((k, _floatify_na_values(v))
+                          for k, v in na_values.items())
     else:
         if not is_list_like(na_values):
             na_values = [na_values]
@@ -3225,12 +3244,38 @@ def _stringify_na_values(na_values):
     return set(result)
 
 
-def _get_na_values(col, na_values, na_fvalues):
+def _get_na_values(col, na_values, na_fvalues, keep_default_na):
+    """
+    Get the NaN values for a given column.
+
+    Parameters
+    ----------
+    col : str
+        The name of the column.
+    na_values : array-like, dict
+        The object listing the NaN values as strings.
+    na_fvalues : array-like, dict
+        The object listing the NaN values as floats.
+    keep_default_na : bool
+        If `na_values` is a dict, and the column is not mapped in the
+        dictionary, whether to return the default NaN values or the empty set.
+
+    Returns
+    -------
+    nan_tuple : A length-two tuple composed of
+
+        1) na_values : the string NaN values for that column.
+        2) na_fvalues : the float NaN values for that column.
+    """
+
     if isinstance(na_values, dict):
         if col in na_values:
             return na_values[col], na_fvalues[col]
         else:
-            return _NA_VALUES, set()
+            if keep_default_na:
+                return _NA_VALUES, set()
+
+            return set(), set()
     else:
         return na_values, na_fvalues
 

pandas/tests/io/parser/na_values.py

@@ -224,6 +224,45 @@ def test_na_values_keep_default(self):
                                   'seven']})
         tm.assert_frame_equal(xp.reindex(columns=df.columns), df)
 
+    def test_no_keep_default_na_dict_na_values(self):
+        # see gh-19227
+        data = "a,b\n,2"
+
+        df = self.read_csv(StringIO(data), na_values={"b": ["2"]},
+                           keep_default_na=False)
+        expected = DataFrame({"a": [""], "b": [np.nan]})
+        tm.assert_frame_equal(df, expected)
+
+        # Scalar values shouldn't cause the parsing to crash or fail.
+        data = "a,b\n1,2"
+
+        df = self.read_csv(StringIO(data), na_values={"b": 2},
+                           keep_default_na=False)
+        expected = DataFrame({"a": [1], "b": [np.nan]})
+        tm.assert_frame_equal(df, expected)
+
+        data = """\
+113125,"blah","/blaha",kjsdkj,412.166,225.874,214.008
+729639,"qwer","",asdfkj,466.681,,252.373
+"""
+        expected = DataFrame({0: [np.nan, 729639.0],
+                              1: [np.nan, "qwer"],
+                              2: ["/blaha", np.nan],
+                              3: ["kjsdkj", "asdfkj"],
+                              4: [412.166, 466.681],
+                              5: ["225.874", ""],
+                              6: [np.nan, 252.373]})
+
+        df = self.read_csv(StringIO(data), header=None, keep_default_na=False,
+                           na_values={2: "", 6: "214.008",
+                                      1: "blah", 0: 113125})
+        tm.assert_frame_equal(df, expected)
+
+        df = self.read_csv(StringIO(data), header=None, keep_default_na=False,
+                           na_values={2: "", 6: "214.008",
+                                      1: "blah", 0: "113125"})
+        tm.assert_frame_equal(df, expected)
+
     def test_na_values_na_filter_override(self):
         data = """\
 A,B