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diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
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+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import sys
+
+if sys.version >= '3':
+    basestring = str
+    long = int
+
+from pyspark.context import SparkContext
+from pyspark.rdd import ignore_unicode_prefix
+from pyspark.sql.types import *
+
+__all__ = ["DataFrame", "Column", "SchemaRDD", "DataFrameNaFunctions",
+           "DataFrameStatFunctions"]
+
+
+def _create_column_from_literal(literal):
+    sc = SparkContext._active_spark_context
+    return sc._jvm.functions.lit(literal)
+
+
+def _create_column_from_name(name):
+    sc = SparkContext._active_spark_context
+    return sc._jvm.functions.col(name)
+
+
+def _to_java_column(col):
+    if isinstance(col, Column):
+        jcol = col._jc
+    else:
+        jcol = _create_column_from_name(col)
+    return jcol
+
+
+def _to_seq(sc, cols, converter=None):
+    """
+    Convert a list of Column (or names) into a JVM Seq of Column.
+
+    An optional `converter` could be used to convert items in `cols`
+    into JVM Column objects.
+    """
+    if converter:
+        cols = [converter(c) for c in cols]
+    return sc._jvm.PythonUtils.toSeq(cols)
+
+
+def _unary_op(name, doc="unary operator"):
+    """ Create a method for given unary operator """
+    def _(self):
+        jc = getattr(self._jc, name)()
+        return Column(jc)
+    _.__doc__ = doc
+    return _
+
+
+def _func_op(name, doc=''):
+    def _(self):
+        sc = SparkContext._active_spark_context
+        jc = getattr(sc._jvm.functions, name)(self._jc)
+        return Column(jc)
+    _.__doc__ = doc
+    return _
+
+
+def _bin_op(name, doc="binary operator"):
+    """ Create a method for given binary operator
+    """
+    def _(self, other):
+        jc = other._jc if isinstance(other, Column) else other
+        njc = getattr(self._jc, name)(jc)
+        return Column(njc)
+    _.__doc__ = doc
+    return _
+
+
+def _reverse_op(name, doc="binary operator"):
+    """ Create a method for binary operator (this object is on right side)
+    """
+    def _(self, other):
+        jother = _create_column_from_literal(other)
+        jc = getattr(jother, name)(self._jc)
+        return Column(jc)
+    _.__doc__ = doc
+    return _
+
+
+class Column(object):
+
+    """
+    A column in a DataFrame.
+
+    :class:`Column` instances can be created by::
+
+        # 1. Select a column out of a DataFrame
+
+        df.colName
+        df["colName"]
+
+        # 2. Create from an expression
+        df.colName + 1
+        1 / df.colName
+    """
+
+    def __init__(self, jc):
+        self._jc = jc
+
+    # arithmetic operators
+    __neg__ = _func_op("negate")
+    __add__ = _bin_op("plus")
+    __sub__ = _bin_op("minus")
+    __mul__ = _bin_op("multiply")
+    __div__ = _bin_op("divide")
+    __truediv__ = _bin_op("divide")
+    __mod__ = _bin_op("mod")
+    __radd__ = _bin_op("plus")
+    __rsub__ = _reverse_op("minus")
+    __rmul__ = _bin_op("multiply")
+    __rdiv__ = _reverse_op("divide")
+    __rtruediv__ = _reverse_op("divide")
+    __rmod__ = _reverse_op("mod")
+
+    # logistic operators
+    __eq__ = _bin_op("equalTo")
+    __ne__ = _bin_op("notEqual")
+    __lt__ = _bin_op("lt")
+    __le__ = _bin_op("leq")
+    __ge__ = _bin_op("geq")
+    __gt__ = _bin_op("gt")
+
+    # `and`, `or`, `not` cannot be overloaded in Python,
+    # so use bitwise operators as boolean operators
+    __and__ = _bin_op('and')
+    __or__ = _bin_op('or')
+    __invert__ = _func_op('not')
+    __rand__ = _bin_op("and")
+    __ror__ = _bin_op("or")
+
+    # container operators
+    __contains__ = _bin_op("contains")
+    __getitem__ = _bin_op("apply")
+
+    # bitwise operators
+    bitwiseOR = _bin_op("bitwiseOR")
+    bitwiseAND = _bin_op("bitwiseAND")
+    bitwiseXOR = _bin_op("bitwiseXOR")
+
+    def getItem(self, key):
+        """An expression that gets an item at position `ordinal` out of a list,
+         or gets an item by key out of a dict.
+
+        >>> df = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["l", "d"])
+        >>> df.select(df.l.getItem(0), df.d.getItem("key")).show()
+        +----+------+
+        |l[0]|d[key]|
+        +----+------+
+        |   1| value|
+        +----+------+
+        >>> df.select(df.l[0], df.d["key"]).show()
+        +----+------+
+        |l[0]|d[key]|
+        +----+------+
+        |   1| value|
+        +----+------+
+        """
+        return self[key]
+
+    def getField(self, name):
+        """An expression that gets a field by name in a StructField.
+
+        >>> from pyspark.sql import Row
+        >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF()
+        >>> df.select(df.r.getField("b")).show()
+        +----+
+        |r[b]|
+        +----+
+        |   b|
+        +----+
+        >>> df.select(df.r.a).show()
+        +----+
+        |r[a]|
+        +----+
+        |   1|
+        +----+
+        """
+        return self[name]
+
+    def __getattr__(self, item):
+        if item.startswith("__"):
+            raise AttributeError(item)
+        return self.getField(item)
+
+    # string methods
+    rlike = _bin_op("rlike")
+    like = _bin_op("like")
+    startswith = _bin_op("startsWith")
+    endswith = _bin_op("endsWith")
+
+    @ignore_unicode_prefix
+    def substr(self, startPos, length):
+        """
+        Return a :class:`Column` which is a substring of the column
+
+        :param startPos: start position (int or Column)
+        :param length:  length of the substring (int or Column)
+
+        >>> df.select(df.name.substr(1, 3).alias("col")).collect()
+        [Row(col=u'Ali'), Row(col=u'Bob')]
+        """
+        if type(startPos) != type(length):
+            raise TypeError("Can not mix the type")
+        if isinstance(startPos, (int, long)):
+            jc = self._jc.substr(startPos, length)
+        elif isinstance(startPos, Column):
+            jc = self._jc.substr(startPos._jc, length._jc)
+        else:
+            raise TypeError("Unexpected type: %s" % type(startPos))
+        return Column(jc)
+
+    __getslice__ = substr
+
+    @ignore_unicode_prefix
+    def inSet(self, *cols):
+        """ A boolean expression that is evaluated to true if the value of this
+        expression is contained by the evaluated values of the arguments.
+
+        >>> df[df.name.inSet("Bob", "Mike")].collect()
+        [Row(age=5, name=u'Bob')]
+        >>> df[df.age.inSet([1, 2, 3])].collect()
+        [Row(age=2, name=u'Alice')]
+        """
+        if len(cols) == 1 and isinstance(cols[0], (list, set)):
+            cols = cols[0]
+        cols = [c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols]
+        sc = SparkContext._active_spark_context
+        jc = getattr(self._jc, "in")(_to_seq(sc, cols))
+        return Column(jc)
+
+    # order
+    asc = _unary_op("asc", "Returns a sort expression based on the"
+                           " ascending order of the given column name.")
+    desc = _unary_op("desc", "Returns a sort expression based on the"
+                             " descending order of the given column name.")
+
+    isNull = _unary_op("isNull", "True if the current expression is null.")
+    isNotNull = _unary_op("isNotNull", "True if the current expression is not null.")
+
+    def alias(self, *alias):
+        """Returns this column aliased with a new name or names (in the case of expressions that
+        return more than one column, such as explode).
+
+        >>> df.select(df.age.alias("age2")).collect()
+        [Row(age2=2), Row(age2=5)]
+        """
+
+        if len(alias) == 1:
+            return Column(getattr(self._jc, "as")(alias[0]))
+        else:
+            sc = SparkContext._active_spark_context
+            return Column(getattr(self._jc, "as")(_to_seq(sc, list(alias))))
+
+    @ignore_unicode_prefix
+    def cast(self, dataType):
+        """ Convert the column into type `dataType`
+
+        >>> df.select(df.age.cast("string").alias('ages')).collect()
+        [Row(ages=u'2'), Row(ages=u'5')]
+        >>> df.select(df.age.cast(StringType()).alias('ages')).collect()
+        [Row(ages=u'2'), Row(ages=u'5')]
+        """
+        if isinstance(dataType, basestring):
+            jc = self._jc.cast(dataType)
+        elif isinstance(dataType, DataType):
+            sc = SparkContext._active_spark_context
+            ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc())
+            jdt = ssql_ctx.parseDataType(dataType.json())
+            jc = self._jc.cast(jdt)
+        else:
+            raise TypeError("unexpected type: %s" % type(dataType))
+        return Column(jc)
+
+    @ignore_unicode_prefix
+    def between(self, lowerBound, upperBound):
+        """ A boolean expression that is evaluated to true if the value of this
+        expression is between the given columns.
+        """
+        return (self >= lowerBound) & (self <= upperBound)
+
+    @ignore_unicode_prefix
+    def when(self, condition, value):
+        """Evaluates a list of conditions and returns one of multiple possible result expressions.
+        If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.
+
+        See :func:`pyspark.sql.functions.when` for example usage.
+
+        :param condition: a boolean :class:`Column` expression.
+        :param value: a literal value, or a :class:`Column` expression.
+
+        """
+        sc = SparkContext._active_spark_context
+        if not isinstance(condition, Column):
+            raise TypeError("condition should be a Column")
+        v = value._jc if isinstance(value, Column) else value
+        jc = sc._jvm.functions.when(condition._jc, v)
+        return Column(jc)
+
+    @ignore_unicode_prefix
+    def otherwise(self, value):
+        """Evaluates a list of conditions and returns one of multiple possible result expressions.
+        If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.
+
+        See :func:`pyspark.sql.functions.when` for example usage.
+
+        :param value: a literal value, or a :class:`Column` expression.
+        """
+        v = value._jc if isinstance(value, Column) else value
+        jc = self._jc.otherwise(value)
+        return Column(jc)
+
+    def __repr__(self):
+        return 'Column<%s>' % self._jc.toString().encode('utf8')
+
+
+def _test():
+    import doctest
+    from pyspark.context import SparkContext
+    from pyspark.sql import SQLContext
+    import pyspark.sql.column
+    globs = pyspark.sql.column.__dict__.copy()
+    sc = SparkContext('local[4]', 'PythonTest')
+    globs['sc'] = sc
+    globs['sqlContext'] = SQLContext(sc)
+    globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')]) \
+        .toDF(StructType([StructField('age', IntegerType()),
+                          StructField('name', StringType())]))
+
+    (failure_count, test_count) = doctest.testmod(
+        pyspark.sql.column, globs=globs,
+        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
+    globs['sc'].stop()
+    if failure_count:
+        exit(-1)
+
+
+if __name__ == "__main__":
+    _test()