Python functions.concat方法代碼示例

# 需要導入模塊: from pyspark.sql import functions [as 別名]
# 或者: from pyspark.sql.functions import concat [as 別名]
def compile_string_concat(t, expr, scope, **kwargs):
    op = expr.op()

    src_columns = t.translate(op.arg, scope)
    return F.concat(*src_columns) 

# 需要導入模塊: from pyspark.sql import functions [as 別名]
# 或者: from pyspark.sql.functions import concat [as 別名]
def compile_array_concat(t, expr, scope, **kwargs):
    op = expr.op()

    left = t.translate(op.left, scope)
    right = t.translate(op.right, scope)
    return F.concat(left, right) 

# 需要導入模塊: from pyspark.sql import functions [as 別名]
# 或者: from pyspark.sql.functions import concat [as 別名]
def getData(self):
        spk         = SparkSession.builder.getOrCreate()
        dataDF      = spk.read.parquet(self.input)
        providerDF  = dataDF.select(concat(concat('provider_domain', 'content_path'), \
                            when(col('content_query_string') != '', concat(lit('?'), col('content_query_string')))\
                            .otherwise(lit(''))).alias('url'), \
                            concat('warc_segment', lit('/warc/'), 'warc_filename').alias('warc_filename'), \
                                     'content_offset', 'deflate_length')\
                            .where(col('provider_domain').like('%{}'.format(self.domain)))\
                            .dropDuplicates(['url'])

        providerData = providerDF.rdd.map(lambda row: '\t'.join([str(col) for col in row])).collect() #convert dataframe into a list of tab delimited elements

        return providerData 

# 需要導入模塊: from pyspark.sql import functions [as 別名]
# 或者: from pyspark.sql.functions import concat [as 別名]
def __add__(self, other):
        if isinstance(self.spark.data_type, StringType):
            # Concatenate string columns
            if isinstance(other, IndexOpsMixin) and isinstance(other.spark.data_type, StringType):
                return column_op(F.concat)(self, other)
            # Handle df['col'] + 'literal'
            elif isinstance(other, str):
                return column_op(F.concat)(self, F.lit(other))
            else:
                raise TypeError("string addition can only be applied to string series or literals.")
        else:
            return column_op(Column.__add__)(self, other) 

# 需要導入模塊: from pyspark.sql import functions [as 別名]
# 或者: from pyspark.sql.functions import concat [as 別名]
def __radd__(self, other):
        # Handle 'literal' + df['col']
        if isinstance(self.spark.data_type, StringType) and isinstance(other, str):
            return self._with_new_scol(F.concat(F.lit(other), self.spark.column))
        else:
            return column_op(Column.__radd__)(self, other) 

# 需要導入模塊: from pyspark.sql import functions [as 別名]
# 或者: from pyspark.sql.functions import concat [as 別名]
def add_prefix(self, prefix):
        """
        Prefix labels with string `prefix`.

        For Series, the row labels are prefixed.
        For DataFrame, the column labels are prefixed.

        Parameters
        ----------
        prefix : str
           The string to add before each label.

        Returns
        -------
        Series
           New Series with updated labels.

        See Also
        --------
        Series.add_suffix: Suffix column labels with string `suffix`.
        DataFrame.add_suffix: Suffix column labels with string `suffix`.
        DataFrame.add_prefix: Prefix column labels with string `prefix`.

        Examples
        --------
        >>> s = ks.Series([1, 2, 3, 4])
        >>> s
        0    1
        1    2
        2    3
        3    4
        Name: 0, dtype: int64

        >>> s.add_prefix('item_')
        item_0    1
        item_1    2
        item_2    3
        item_3    4
        Name: 0, dtype: int64
        """
        assert isinstance(prefix, str)
        internal = self.to_frame()._internal
        sdf = internal.spark_frame.select(
            [
                F.concat(F.lit(prefix), index_spark_column).alias(index_spark_column_name)
                for index_spark_column, index_spark_column_name in zip(
                    internal.index_spark_columns, internal.index_spark_column_names
                )
            ]
            + internal.data_spark_columns
        )
        return first_series(DataFrame(internal.with_new_sdf(sdf))) 

# 需要導入模塊: from pyspark.sql import functions [as 別名]
# 或者: from pyspark.sql.functions import concat [as 別名]
def add_suffix(self, suffix):
        """
        Suffix labels with string suffix.

        For Series, the row labels are suffixed.
        For DataFrame, the column labels are suffixed.

        Parameters
        ----------
        suffix : str
           The string to add after each label.

        Returns
        -------
        Series
           New Series with updated labels.

        See Also
        --------
        Series.add_prefix: Prefix row labels with string `prefix`.
        DataFrame.add_prefix: Prefix column labels with string `prefix`.
        DataFrame.add_suffix: Suffix column labels with string `suffix`.

        Examples
        --------
        >>> s = ks.Series([1, 2, 3, 4])
        >>> s
        0    1
        1    2
        2    3
        3    4
        Name: 0, dtype: int64

        >>> s.add_suffix('_item')
        0_item    1
        1_item    2
        2_item    3
        3_item    4
        Name: 0, dtype: int64
        """
        assert isinstance(suffix, str)
        internal = self.to_frame()._internal
        sdf = internal.spark_frame.select(
            [
                F.concat(index_spark_column, F.lit(suffix)).alias(index_spark_column_name)
                for index_spark_column, index_spark_column_name in zip(
                    internal.index_spark_columns, internal.index_spark_column_names
                )
            ]
            + internal.data_spark_columns
        )
        return first_series(DataFrame(internal.with_new_sdf(sdf)))