sqlglot.dataframe.sql API documentation

Edit on GitHub

# sqlglot.dataframe.sql

View Source

     1from sqlglot.dataframe.sql.column import Column
     2from sqlglot.dataframe.sql.dataframe import DataFrame, DataFrameNaFunctions
     3from sqlglot.dataframe.sql.group import GroupedData
     4from sqlglot.dataframe.sql.readwriter import DataFrameReader, DataFrameWriter
     5from sqlglot.dataframe.sql.session import SparkSession
     6from sqlglot.dataframe.sql.window import Window, WindowSpec
     7
     8__all__ = [
     9    "SparkSession",
    10    "DataFrame",
    11    "GroupedData",
    12    "Column",
    13    "DataFrameNaFunctions",
    14    "Window",
    15    "WindowSpec",
    16    "DataFrameReader",
    17    "DataFrameWriter",
    18]

class SparkSession: View Source

     21class SparkSession:
     22    DEFAULT_DIALECT = "spark"
     23    _instance = None
     24
     25    def __init__(self):
     26        if not hasattr(self, "known_ids"):
     27            self.known_ids = set()
     28            self.known_branch_ids = set()
     29            self.known_sequence_ids = set()
     30            self.name_to_sequence_id_mapping = defaultdict(list)
     31            self.incrementing_id = 1
     32            self.dialect = Dialect.get_or_raise(self.DEFAULT_DIALECT)()
     33
     34    def __new__(cls, *args, **kwargs) -> SparkSession:
     35        if cls._instance is None:
     36            cls._instance = super().__new__(cls)
     37        return cls._instance
     38
     39    @property
     40    def read(self) -> DataFrameReader:
     41        return DataFrameReader(self)
     42
     43    def table(self, tableName: str) -> DataFrame:
     44        return self.read.table(tableName)
     45
     46    def createDataFrame(
     47        self,
     48        data: t.Sequence[t.Union[t.Dict[str, ColumnLiterals], t.List[ColumnLiterals], t.Tuple]],
     49        schema: t.Optional[SchemaInput] = None,
     50        samplingRatio: t.Optional[float] = None,
     51        verifySchema: bool = False,
     52    ) -> DataFrame:
     53        from sqlglot.dataframe.sql.dataframe import DataFrame
     54
     55        if samplingRatio is not None or verifySchema:
     56            raise NotImplementedError("Sampling Ratio and Verify Schema are not supported")
     57        if schema is not None and (
     58            not isinstance(schema, (StructType, str, list))
     59            or (isinstance(schema, list) and not isinstance(schema[0], str))
     60        ):
     61            raise NotImplementedError("Only schema of either list or string of list supported")
     62        if not data:
     63            raise ValueError("Must provide data to create into a DataFrame")
     64
     65        column_mapping: t.Dict[str, t.Optional[str]]
     66        if schema is not None:
     67            column_mapping = get_column_mapping_from_schema_input(schema)
     68        elif isinstance(data[0], dict):
     69            column_mapping = {col_name.strip(): None for col_name in data[0]}
     70        else:
     71            column_mapping = {f"_{i}": None for i in range(1, len(data[0]) + 1)}
     72
     73        data_expressions = [
     74            exp.Tuple(
     75                expressions=list(
     76                    map(
     77                        lambda x: F.lit(x).expression,
     78                        row if not isinstance(row, dict) else row.values(),
     79                    )
     80                )
     81            )
     82            for row in data
     83        ]
     84
     85        sel_columns = [
     86            F.col(name).cast(data_type).alias(name).expression
     87            if data_type is not None
     88            else F.col(name).expression
     89            for name, data_type in column_mapping.items()
     90        ]
     91
     92        select_kwargs = {
     93            "expressions": sel_columns,
     94            "from": exp.From(
     95                this=exp.Values(
     96                    expressions=data_expressions,
     97                    alias=exp.TableAlias(
     98                        this=exp.to_identifier(self._auto_incrementing_name),
     99                        columns=[exp.to_identifier(col_name) for col_name in column_mapping],
    100                    ),
    101                ),
    102            ),
    103        }
    104
    105        sel_expression = exp.Select(**select_kwargs)
    106        return DataFrame(self, sel_expression)
    107
    108    def sql(self, sqlQuery: str) -> DataFrame:
    109        expression = sqlglot.parse_one(sqlQuery, read=self.dialect)
    110        if isinstance(expression, exp.Select):
    111            df = DataFrame(self, expression)
    112            df = df._convert_leaf_to_cte()
    113        elif isinstance(expression, (exp.Create, exp.Insert)):
    114            select_expression = expression.expression.copy()
    115            if isinstance(expression, exp.Insert):
    116                select_expression.set("with", expression.args.get("with"))
    117                expression.set("with", None)
    118            del expression.args["expression"]
    119            df = DataFrame(self, select_expression, output_expression_container=expression)  # type: ignore
    120            df = df._convert_leaf_to_cte()
    121        else:
    122            raise ValueError(
    123                "Unknown expression type provided in the SQL. Please create an issue with the SQL."
    124            )
    125        return df
    126
    127    @property
    128    def _auto_incrementing_name(self) -> str:
    129        name = f"a{self.incrementing_id}"
    130        self.incrementing_id += 1
    131        return name
    132
    133    @property
    134    def _random_branch_id(self) -> str:
    135        id = self._random_id
    136        self.known_branch_ids.add(id)
    137        return id
    138
    139    @property
    140    def _random_sequence_id(self):
    141        id = self._random_id
    142        self.known_sequence_ids.add(id)
    143        return id
    144
    145    @property
    146    def _random_id(self) -> str:
    147        id = "r" + uuid.uuid4().hex
    148        self.known_ids.add(id)
    149        return id
    150
    151    @property
    152    def _join_hint_names(self) -> t.Set[str]:
    153        return {"BROADCAST", "MERGE", "SHUFFLE_HASH", "SHUFFLE_REPLICATE_NL"}
    154
    155    def _add_alias_to_mapping(self, name: str, sequence_id: str):
    156        self.name_to_sequence_id_mapping[name].append(sequence_id)
    157
    158    class Builder:
    159        SQLFRAME_DIALECT_KEY = "sqlframe.dialect"
    160
    161        def __init__(self):
    162            self.dialect = "spark"
    163
    164        def __getattr__(self, item) -> SparkSession.Builder:
    165            return self
    166
    167        def __call__(self, *args, **kwargs):
    168            return self
    169
    170        def config(
    171            self,
    172            key: t.Optional[str] = None,
    173            value: t.Optional[t.Any] = None,
    174            *,
    175            map: t.Optional[t.Dict[str, t.Any]] = None,
    176            **kwargs: t.Any,
    177        ) -> SparkSession.Builder:
    178            if key == self.SQLFRAME_DIALECT_KEY:
    179                self.dialect = value
    180            elif map and self.SQLFRAME_DIALECT_KEY in map:
    181                self.dialect = map[self.SQLFRAME_DIALECT_KEY]
    182            return self
    183
    184        def getOrCreate(self) -> SparkSession:
    185            spark = SparkSession()
    186            spark.dialect = Dialect.get_or_raise(self.dialect)()
    187            return spark
    188
    189    @classproperty
    190    def builder(cls) -> Builder:
    191        return cls.Builder()

DEFAULT_DIALECT = 'spark'
read: DataFrameReader
def table(self, tableName: str) -> DataFrame: View Source

    43    def table(self, tableName: str) -> DataFrame:
    44        return self.read.table(tableName)

def createDataFrame( self, data: Sequence[Union[Dict[str, <MagicMock id='140452569658896'>], List[<MagicMock id='140452569658896'>], Tuple]], schema: Optional[<MagicMock id='140452569891840'>] = None, samplingRatio: Optional[float] = None, verifySchema: bool = False) -> DataFrame: View Source

     46    def createDataFrame(
     47        self,
     48        data: t.Sequence[t.Union[t.Dict[str, ColumnLiterals], t.List[ColumnLiterals], t.Tuple]],
     49        schema: t.Optional[SchemaInput] = None,
     50        samplingRatio: t.Optional[float] = None,
     51        verifySchema: bool = False,
     52    ) -> DataFrame:
     53        from sqlglot.dataframe.sql.dataframe import DataFrame
     54
     55        if samplingRatio is not None or verifySchema:
     56            raise NotImplementedError("Sampling Ratio and Verify Schema are not supported")
     57        if schema is not None and (
     58            not isinstance(schema, (StructType, str, list))
     59            or (isinstance(schema, list) and not isinstance(schema[0], str))
     60        ):
     61            raise NotImplementedError("Only schema of either list or string of list supported")
     62        if not data:
     63            raise ValueError("Must provide data to create into a DataFrame")
     64
     65        column_mapping: t.Dict[str, t.Optional[str]]
     66        if schema is not None:
     67            column_mapping = get_column_mapping_from_schema_input(schema)
     68        elif isinstance(data[0], dict):
     69            column_mapping = {col_name.strip(): None for col_name in data[0]}
     70        else:
     71            column_mapping = {f"_{i}": None for i in range(1, len(data[0]) + 1)}
     72
     73        data_expressions = [
     74            exp.Tuple(
     75                expressions=list(
     76                    map(
     77                        lambda x: F.lit(x).expression,
     78                        row if not isinstance(row, dict) else row.values(),
     79                    )
     80                )
     81            )
     82            for row in data
     83        ]
     84
     85        sel_columns = [
     86            F.col(name).cast(data_type).alias(name).expression
     87            if data_type is not None
     88            else F.col(name).expression
     89            for name, data_type in column_mapping.items()
     90        ]
     91
     92        select_kwargs = {
     93            "expressions": sel_columns,
     94            "from": exp.From(
     95                this=exp.Values(
     96                    expressions=data_expressions,
     97                    alias=exp.TableAlias(
     98                        this=exp.to_identifier(self._auto_incrementing_name),
     99                        columns=[exp.to_identifier(col_name) for col_name in column_mapping],
    100                    ),
    101                ),
    102            ),
    103        }
    104
    105        sel_expression = exp.Select(**select_kwargs)
    106        return DataFrame(self, sel_expression)

def sql(self, sqlQuery: str) -> DataFrame: View Source

    108    def sql(self, sqlQuery: str) -> DataFrame:
    109        expression = sqlglot.parse_one(sqlQuery, read=self.dialect)
    110        if isinstance(expression, exp.Select):
    111            df = DataFrame(self, expression)
    112            df = df._convert_leaf_to_cte()
    113        elif isinstance(expression, (exp.Create, exp.Insert)):
    114            select_expression = expression.expression.copy()
    115            if isinstance(expression, exp.Insert):
    116                select_expression.set("with", expression.args.get("with"))
    117                expression.set("with", None)
    118            del expression.args["expression"]
    119            df = DataFrame(self, select_expression, output_expression_container=expression)  # type: ignore
    120            df = df._convert_leaf_to_cte()
    121        else:
    122            raise ValueError(
    123                "Unknown expression type provided in the SQL. Please create an issue with the SQL."
    124            )
    125        return df

builder: SparkSession.Builder
class SparkSession.Builder: View Source

    158    class Builder:
    159        SQLFRAME_DIALECT_KEY = "sqlframe.dialect"
    160
    161        def __init__(self):
    162            self.dialect = "spark"
    163
    164        def __getattr__(self, item) -> SparkSession.Builder:
    165            return self
    166
    167        def __call__(self, *args, **kwargs):
    168            return self
    169
    170        def config(
    171            self,
    172            key: t.Optional[str] = None,
    173            value: t.Optional[t.Any] = None,
    174            *,
    175            map: t.Optional[t.Dict[str, t.Any]] = None,
    176            **kwargs: t.Any,
    177        ) -> SparkSession.Builder:
    178            if key == self.SQLFRAME_DIALECT_KEY:
    179                self.dialect = value
    180            elif map and self.SQLFRAME_DIALECT_KEY in map:
    181                self.dialect = map[self.SQLFRAME_DIALECT_KEY]
    182            return self
    183
    184        def getOrCreate(self) -> SparkSession:
    185            spark = SparkSession()
    186            spark.dialect = Dialect.get_or_raise(self.dialect)()
    187            return spark

SQLFRAME_DIALECT_KEY = 'sqlframe.dialect'
dialect
def config( self, key: Optional[str] = None, value: Optional[Any] = None, *, map: Optional[Dict[str, Any]] = None, **kwargs: Any) -> SparkSession.Builder: View Source

    170        def config(
    171            self,
    172            key: t.Optional[str] = None,
    173            value: t.Optional[t.Any] = None,
    174            *,
    175            map: t.Optional[t.Dict[str, t.Any]] = None,
    176            **kwargs: t.Any,
    177        ) -> SparkSession.Builder:
    178            if key == self.SQLFRAME_DIALECT_KEY:
    179                self.dialect = value
    180            elif map and self.SQLFRAME_DIALECT_KEY in map:
    181                self.dialect = map[self.SQLFRAME_DIALECT_KEY]
    182            return self

def getOrCreate(self) -> SparkSession: View Source

    184        def getOrCreate(self) -> SparkSession:
    185            spark = SparkSession()
    186            spark.dialect = Dialect.get_or_raise(self.dialect)()
    187            return spark

class DataFrame: View Source

     49class DataFrame:
     50    def __init__(
     51        self,
     52        spark: SparkSession,
     53        expression: exp.Select,
     54        branch_id: t.Optional[str] = None,
     55        sequence_id: t.Optional[str] = None,
     56        last_op: Operation = Operation.INIT,
     57        pending_hints: t.Optional[t.List[exp.Expression]] = None,
     58        output_expression_container: t.Optional[OutputExpressionContainer] = None,
     59        **kwargs,
     60    ):
     61        self.spark = spark
     62        self.expression = expression
     63        self.branch_id = branch_id or self.spark._random_branch_id
     64        self.sequence_id = sequence_id or self.spark._random_sequence_id
     65        self.last_op = last_op
     66        self.pending_hints = pending_hints or []
     67        self.output_expression_container = output_expression_container or exp.Select()
     68
     69    def __getattr__(self, column_name: str) -> Column:
     70        return self[column_name]
     71
     72    def __getitem__(self, column_name: str) -> Column:
     73        column_name = f"{self.branch_id}.{column_name}"
     74        return Column(column_name)
     75
     76    def __copy__(self):
     77        return self.copy()
     78
     79    @property
     80    def sparkSession(self):
     81        return self.spark
     82
     83    @property
     84    def write(self):
     85        return DataFrameWriter(self)
     86
     87    @property
     88    def latest_cte_name(self) -> str:
     89        if not self.expression.ctes:
     90            from_exp = self.expression.args["from"]
     91            if from_exp.alias_or_name:
     92                return from_exp.alias_or_name
     93            table_alias = from_exp.find(exp.TableAlias)
     94            if not table_alias:
     95                raise RuntimeError(
     96                    f"Could not find an alias name for this expression: {self.expression}"
     97                )
     98            return table_alias.alias_or_name
     99        return self.expression.ctes[-1].alias
    100
    101    @property
    102    def pending_join_hints(self):
    103        return [hint for hint in self.pending_hints if isinstance(hint, exp.JoinHint)]
    104
    105    @property
    106    def pending_partition_hints(self):
    107        return [hint for hint in self.pending_hints if isinstance(hint, exp.Anonymous)]
    108
    109    @property
    110    def columns(self) -> t.List[str]:
    111        return self.expression.named_selects
    112
    113    @property
    114    def na(self) -> DataFrameNaFunctions:
    115        return DataFrameNaFunctions(self)
    116
    117    def _replace_cte_names_with_hashes(self, expression: exp.Select):
    118        replacement_mapping = {}
    119        for cte in expression.ctes:
    120            old_name_id = cte.args["alias"].this
    121            new_hashed_id = exp.to_identifier(
    122                self._create_hash_from_expression(cte.this), quoted=old_name_id.args["quoted"]
    123            )
    124            replacement_mapping[old_name_id] = new_hashed_id
    125            expression = expression.transform(replace_id_value, replacement_mapping)
    126        return expression
    127
    128    def _create_cte_from_expression(
    129        self,
    130        expression: exp.Expression,
    131        branch_id: t.Optional[str] = None,
    132        sequence_id: t.Optional[str] = None,
    133        **kwargs,
    134    ) -> t.Tuple[exp.CTE, str]:
    135        name = self._create_hash_from_expression(expression)
    136        expression_to_cte = expression.copy()
    137        expression_to_cte.set("with", None)
    138        cte = exp.Select().with_(name, as_=expression_to_cte, **kwargs).ctes[0]
    139        cte.set("branch_id", branch_id or self.branch_id)
    140        cte.set("sequence_id", sequence_id or self.sequence_id)
    141        return cte, name
    142
    143    @t.overload
    144    def _ensure_list_of_columns(self, cols: t.Collection[ColumnOrLiteral]) -> t.List[Column]:
    145        ...
    146
    147    @t.overload
    148    def _ensure_list_of_columns(self, cols: ColumnOrLiteral) -> t.List[Column]:
    149        ...
    150
    151    def _ensure_list_of_columns(self, cols):
    152        return Column.ensure_cols(ensure_list(cols))
    153
    154    def _ensure_and_normalize_cols(self, cols, expression: t.Optional[exp.Select] = None):
    155        cols = self._ensure_list_of_columns(cols)
    156        normalize(self.spark, expression or self.expression, cols)
    157        return cols
    158
    159    def _ensure_and_normalize_col(self, col):
    160        col = Column.ensure_col(col)
    161        normalize(self.spark, self.expression, col)
    162        return col
    163
    164    def _convert_leaf_to_cte(self, sequence_id: t.Optional[str] = None) -> DataFrame:
    165        df = self._resolve_pending_hints()
    166        sequence_id = sequence_id or df.sequence_id
    167        expression = df.expression.copy()
    168        cte_expression, cte_name = df._create_cte_from_expression(
    169            expression=expression, sequence_id=sequence_id
    170        )
    171        new_expression = df._add_ctes_to_expression(
    172            exp.Select(), expression.ctes + [cte_expression]
    173        )
    174        sel_columns = df._get_outer_select_columns(cte_expression)
    175        new_expression = new_expression.from_(cte_name).select(
    176            *[x.alias_or_name for x in sel_columns]
    177        )
    178        return df.copy(expression=new_expression, sequence_id=sequence_id)
    179
    180    def _resolve_pending_hints(self) -> DataFrame:
    181        df = self.copy()
    182        if not self.pending_hints:
    183            return df
    184        expression = df.expression
    185        hint_expression = expression.args.get("hint") or exp.Hint(expressions=[])
    186        for hint in df.pending_partition_hints:
    187            hint_expression.append("expressions", hint)
    188            df.pending_hints.remove(hint)
    189
    190        join_aliases = {
    191            join_table.alias_or_name
    192            for join_table in get_tables_from_expression_with_join(expression)
    193        }
    194        if join_aliases:
    195            for hint in df.pending_join_hints:
    196                for sequence_id_expression in hint.expressions:
    197                    sequence_id_or_name = sequence_id_expression.alias_or_name
    198                    sequence_ids_to_match = [sequence_id_or_name]
    199                    if sequence_id_or_name in df.spark.name_to_sequence_id_mapping:
    200                        sequence_ids_to_match = df.spark.name_to_sequence_id_mapping[
    201                            sequence_id_or_name
    202                        ]
    203                    matching_ctes = [
    204                        cte
    205                        for cte in reversed(expression.ctes)
    206                        if cte.args["sequence_id"] in sequence_ids_to_match
    207                    ]
    208                    for matching_cte in matching_ctes:
    209                        if matching_cte.alias_or_name in join_aliases:
    210                            sequence_id_expression.set("this", matching_cte.args["alias"].this)
    211                            df.pending_hints.remove(hint)
    212                            break
    213                hint_expression.append("expressions", hint)
    214        if hint_expression.expressions:
    215            expression.set("hint", hint_expression)
    216        return df
    217
    218    def _hint(self, hint_name: str, args: t.List[Column]) -> DataFrame:
    219        hint_name = hint_name.upper()
    220        hint_expression = (
    221            exp.JoinHint(
    222                this=hint_name,
    223                expressions=[exp.to_table(parameter.alias_or_name) for parameter in args],
    224            )
    225            if hint_name in JOIN_HINTS
    226            else exp.Anonymous(
    227                this=hint_name, expressions=[parameter.expression for parameter in args]
    228            )
    229        )
    230        new_df = self.copy()
    231        new_df.pending_hints.append(hint_expression)
    232        return new_df
    233
    234    def _set_operation(self, klass: t.Callable, other: DataFrame, distinct: bool):
    235        other_df = other._convert_leaf_to_cte()
    236        base_expression = self.expression.copy()
    237        base_expression = self._add_ctes_to_expression(base_expression, other_df.expression.ctes)
    238        all_ctes = base_expression.ctes
    239        other_df.expression.set("with", None)
    240        base_expression.set("with", None)
    241        operation = klass(this=base_expression, distinct=distinct, expression=other_df.expression)
    242        operation.set("with", exp.With(expressions=all_ctes))
    243        return self.copy(expression=operation)._convert_leaf_to_cte()
    244
    245    def _cache(self, storage_level: str):
    246        df = self._convert_leaf_to_cte()
    247        df.expression.ctes[-1].set("cache_storage_level", storage_level)
    248        return df
    249
    250    @classmethod
    251    def _add_ctes_to_expression(cls, expression: exp.Select, ctes: t.List[exp.CTE]) -> exp.Select:
    252        expression = expression.copy()
    253        with_expression = expression.args.get("with")
    254        if with_expression:
    255            existing_ctes = with_expression.expressions
    256            existsing_cte_names = {x.alias_or_name for x in existing_ctes}
    257            for cte in ctes:
    258                if cte.alias_or_name not in existsing_cte_names:
    259                    existing_ctes.append(cte)
    260        else:
    261            existing_ctes = ctes
    262        expression.set("with", exp.With(expressions=existing_ctes))
    263        return expression
    264
    265    @classmethod
    266    def _get_outer_select_columns(cls, item: t.Union[exp.Expression, DataFrame]) -> t.List[Column]:
    267        expression = item.expression if isinstance(item, DataFrame) else item
    268        return [Column(x) for x in (expression.find(exp.Select) or exp.Select()).expressions]
    269
    270    @classmethod
    271    def _create_hash_from_expression(cls, expression: exp.Expression) -> str:
    272        from sqlglot.dataframe.sql.session import SparkSession
    273
    274        value = expression.sql(dialect=SparkSession().dialect).encode("utf-8")
    275        return f"t{zlib.crc32(value)}"[:6]
    276
    277    def _get_select_expressions(
    278        self,
    279    ) -> t.List[t.Tuple[t.Union[t.Type[exp.Cache], OutputExpressionContainer], exp.Select]]:
    280        select_expressions: t.List[
    281            t.Tuple[t.Union[t.Type[exp.Cache], OutputExpressionContainer], exp.Select]
    282        ] = []
    283        main_select_ctes: t.List[exp.CTE] = []
    284        for cte in self.expression.ctes:
    285            cache_storage_level = cte.args.get("cache_storage_level")
    286            if cache_storage_level:
    287                select_expression = cte.this.copy()
    288                select_expression.set("with", exp.With(expressions=copy(main_select_ctes)))
    289                select_expression.set("cte_alias_name", cte.alias_or_name)
    290                select_expression.set("cache_storage_level", cache_storage_level)
    291                select_expressions.append((exp.Cache, select_expression))
    292            else:
    293                main_select_ctes.append(cte)
    294        main_select = self.expression.copy()
    295        if main_select_ctes:
    296            main_select.set("with", exp.With(expressions=main_select_ctes))
    297        expression_select_pair = (type(self.output_expression_container), main_select)
    298        select_expressions.append(expression_select_pair)  # type: ignore
    299        return select_expressions
    300
    301    def sql(
    302        self, dialect: t.Optional[DialectType] = None, optimize: bool = True, **kwargs
    303    ) -> t.List[str]:
    304        from sqlglot.dataframe.sql.session import SparkSession
    305
    306        if dialect and Dialect.get_or_raise(dialect)() != SparkSession().dialect:
    307            logger.warning(
    308                f"The recommended way of defining a dialect is by doing `SparkSession.builder.config('sqlframe.dialect', '{dialect}').getOrCreate()`. It is no longer needed then when calling `sql`. If you run into issues try updating your query to use this pattern."
    309            )
    310        df = self._resolve_pending_hints()
    311        select_expressions = df._get_select_expressions()
    312        output_expressions: t.List[t.Union[exp.Select, exp.Cache, exp.Drop]] = []
    313        replacement_mapping: t.Dict[exp.Identifier, exp.Identifier] = {}
    314        for expression_type, select_expression in select_expressions:
    315            select_expression = select_expression.transform(replace_id_value, replacement_mapping)
    316            if optimize:
    317                quote_identifiers(select_expression)
    318                select_expression = t.cast(
    319                    exp.Select, optimize_func(select_expression, dialect=SparkSession().dialect)
    320                )
    321            select_expression = df._replace_cte_names_with_hashes(select_expression)
    322            expression: t.Union[exp.Select, exp.Cache, exp.Drop]
    323            if expression_type == exp.Cache:
    324                cache_table_name = df._create_hash_from_expression(select_expression)
    325                cache_table = exp.to_table(cache_table_name)
    326                original_alias_name = select_expression.args["cte_alias_name"]
    327
    328                replacement_mapping[exp.to_identifier(original_alias_name)] = exp.to_identifier(  # type: ignore
    329                    cache_table_name
    330                )
    331                sqlglot.schema.add_table(
    332                    cache_table_name,
    333                    {
    334                        expression.alias_or_name: expression.type.sql(
    335                            dialect=SparkSession().dialect
    336                        )
    337                        for expression in select_expression.expressions
    338                    },
    339                    dialect=SparkSession().dialect,
    340                )
    341                cache_storage_level = select_expression.args["cache_storage_level"]
    342                options = [
    343                    exp.Literal.string("storageLevel"),
    344                    exp.Literal.string(cache_storage_level),
    345                ]
    346                expression = exp.Cache(
    347                    this=cache_table, expression=select_expression, lazy=True, options=options
    348                )
    349                # We will drop the "view" if it exists before running the cache table
    350                output_expressions.append(exp.Drop(this=cache_table, exists=True, kind="VIEW"))
    351            elif expression_type == exp.Create:
    352                expression = df.output_expression_container.copy()
    353                expression.set("expression", select_expression)
    354            elif expression_type == exp.Insert:
    355                expression = df.output_expression_container.copy()
    356                select_without_ctes = select_expression.copy()
    357                select_without_ctes.set("with", None)
    358                expression.set("expression", select_without_ctes)
    359                if select_expression.ctes:
    360                    expression.set("with", exp.With(expressions=select_expression.ctes))
    361            elif expression_type == exp.Select:
    362                expression = select_expression
    363            else:
    364                raise ValueError(f"Invalid expression type: {expression_type}")
    365            output_expressions.append(expression)
    366
    367        return [
    368            expression.sql(**{"dialect": SparkSession().dialect, **kwargs})
    369            for expression in output_expressions
    370        ]
    371
    372    def copy(self, **kwargs) -> DataFrame:
    373        return DataFrame(**object_to_dict(self, **kwargs))
    374
    375    @operation(Operation.SELECT)
    376    def select(self, *cols, **kwargs) -> DataFrame:
    377        cols = self._ensure_and_normalize_cols(cols)
    378        kwargs["append"] = kwargs.get("append", False)
    379        if self.expression.args.get("joins"):
    380            ambiguous_cols = [
    381                col
    382                for col in cols
    383                if isinstance(col.column_expression, exp.Column) and not col.column_expression.table
    384            ]
    385            if ambiguous_cols:
    386                join_table_identifiers = [
    387                    x.this for x in get_tables_from_expression_with_join(self.expression)
    388                ]
    389                cte_names_in_join = [x.this for x in join_table_identifiers]
    390                # If we have columns that resolve to multiple CTE expressions then we want to use each CTE left-to-right
    391                # and therefore we allow multiple columns with the same name in the result. This matches the behavior
    392                # of Spark.
    393                resolved_column_position: t.Dict[Column, int] = {col: -1 for col in ambiguous_cols}
    394                for ambiguous_col in ambiguous_cols:
    395                    ctes_with_column = [
    396                        cte
    397                        for cte in self.expression.ctes
    398                        if cte.alias_or_name in cte_names_in_join
    399                        and ambiguous_col.alias_or_name in cte.this.named_selects
    400                    ]
    401                    # Check if there is a CTE with this column that we haven't used before. If so, use it. Otherwise,
    402                    # use the same CTE we used before
    403                    cte = seq_get(ctes_with_column, resolved_column_position[ambiguous_col] + 1)
    404                    if cte:
    405                        resolved_column_position[ambiguous_col] += 1
    406                    else:
    407                        cte = ctes_with_column[resolved_column_position[ambiguous_col]]
    408                    ambiguous_col.expression.set("table", cte.alias_or_name)
    409        return self.copy(
    410            expression=self.expression.select(*[x.expression for x in cols], **kwargs), **kwargs
    411        )
    412
    413    @operation(Operation.NO_OP)
    414    def alias(self, name: str, **kwargs) -> DataFrame:
    415        new_sequence_id = self.spark._random_sequence_id
    416        df = self.copy()
    417        for join_hint in df.pending_join_hints:
    418            for expression in join_hint.expressions:
    419                if expression.alias_or_name == self.sequence_id:
    420                    expression.set("this", Column.ensure_col(new_sequence_id).expression)
    421        df.spark._add_alias_to_mapping(name, new_sequence_id)
    422        return df._convert_leaf_to_cte(sequence_id=new_sequence_id)
    423
    424    @operation(Operation.WHERE)
    425    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    426        col = self._ensure_and_normalize_col(column)
    427        return self.copy(expression=self.expression.where(col.expression))
    428
    429    filter = where
    430
    431    @operation(Operation.GROUP_BY)
    432    def groupBy(self, *cols, **kwargs) -> GroupedData:
    433        columns = self._ensure_and_normalize_cols(cols)
    434        return GroupedData(self, columns, self.last_op)
    435
    436    @operation(Operation.SELECT)
    437    def agg(self, *exprs, **kwargs) -> DataFrame:
    438        cols = self._ensure_and_normalize_cols(exprs)
    439        return self.groupBy().agg(*cols)
    440
    441    @operation(Operation.FROM)
    442    def join(
    443        self,
    444        other_df: DataFrame,
    445        on: t.Union[str, t.List[str], Column, t.List[Column]],
    446        how: str = "inner",
    447        **kwargs,
    448    ) -> DataFrame:
    449        other_df = other_df._convert_leaf_to_cte()
    450        join_columns = self._ensure_list_of_columns(on)
    451        # We will determine actual "join on" expression later so we don't provide it at first
    452        join_expression = self.expression.join(
    453            other_df.latest_cte_name, join_type=how.replace("_", " ")
    454        )
    455        join_expression = self._add_ctes_to_expression(join_expression, other_df.expression.ctes)
    456        self_columns = self._get_outer_select_columns(join_expression)
    457        other_columns = self._get_outer_select_columns(other_df)
    458        # Determines the join clause and select columns to be used passed on what type of columns were provided for
    459        # the join. The columns returned changes based on how the on expression is provided.
    460        if isinstance(join_columns[0].expression, exp.Column):
    461            """
    462            Unique characteristics of join on column names only:
    463            * The column names are put at the front of the select list
    464            * The column names are deduplicated across the entire select list and only the column names (other dups are allowed)
    465            """
    466            table_names = [
    467                table.alias_or_name
    468                for table in get_tables_from_expression_with_join(join_expression)
    469            ]
    470            potential_ctes = [
    471                cte
    472                for cte in join_expression.ctes
    473                if cte.alias_or_name in table_names
    474                and cte.alias_or_name != other_df.latest_cte_name
    475            ]
    476            # Determine the table to reference for the left side of the join by checking each of the left side
    477            # tables and see if they have the column being referenced.
    478            join_column_pairs = []
    479            for join_column in join_columns:
    480                num_matching_ctes = 0
    481                for cte in potential_ctes:
    482                    if join_column.alias_or_name in cte.this.named_selects:
    483                        left_column = join_column.copy().set_table_name(cte.alias_or_name)
    484                        right_column = join_column.copy().set_table_name(other_df.latest_cte_name)
    485                        join_column_pairs.append((left_column, right_column))
    486                        num_matching_ctes += 1
    487                if num_matching_ctes > 1:
    488                    raise ValueError(
    489                        f"Column {join_column.alias_or_name} is ambiguous. Please specify the table name."
    490                    )
    491                elif num_matching_ctes == 0:
    492                    raise ValueError(
    493                        f"Column {join_column.alias_or_name} does not exist in any of the tables."
    494                    )
    495            join_clause = functools.reduce(
    496                lambda x, y: x & y,
    497                [left_column == right_column for left_column, right_column in join_column_pairs],
    498            )
    499            join_column_names = [left_col.alias_or_name for left_col, _ in join_column_pairs]
    500            # To match spark behavior only the join clause gets deduplicated and it gets put in the front of the column list
    501            select_column_names = [
    502                column.alias_or_name
    503                if not isinstance(column.expression.this, exp.Star)
    504                else column.sql()
    505                for column in self_columns + other_columns
    506            ]
    507            select_column_names = [
    508                column_name
    509                for column_name in select_column_names
    510                if column_name not in join_column_names
    511            ]
    512            select_column_names = join_column_names + select_column_names
    513        else:
    514            """
    515            Unique characteristics of join on expressions:
    516            * There is no deduplication of the results.
    517            * The left join dataframe columns go first and right come after. No sort preference is given to join columns
    518            """
    519            join_columns = self._ensure_and_normalize_cols(join_columns, join_expression)
    520            if len(join_columns) > 1:
    521                join_columns = [functools.reduce(lambda x, y: x & y, join_columns)]
    522            join_clause = join_columns[0]
    523            select_column_names = [column.alias_or_name for column in self_columns + other_columns]
    524
    525        # Update the on expression with the actual join clause to replace the dummy one from before
    526        join_expression.args["joins"][-1].set("on", join_clause.expression)
    527        new_df = self.copy(expression=join_expression)
    528        new_df.pending_join_hints.extend(self.pending_join_hints)
    529        new_df.pending_hints.extend(other_df.pending_hints)
    530        new_df = new_df.select.__wrapped__(new_df, *select_column_names)
    531        return new_df
    532
    533    @operation(Operation.ORDER_BY)
    534    def orderBy(
    535        self,
    536        *cols: t.Union[str, Column],
    537        ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
    538    ) -> DataFrame:
    539        """
    540        This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
    541        has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
    542        is unlikely to come up.
    543        """
    544        columns = self._ensure_and_normalize_cols(cols)
    545        pre_ordered_col_indexes = [
    546            x
    547            for x in [
    548                i if isinstance(col.expression, exp.Ordered) else None
    549                for i, col in enumerate(columns)
    550            ]
    551            if x is not None
    552        ]
    553        if ascending is None:
    554            ascending = [True] * len(columns)
    555        elif not isinstance(ascending, list):
    556            ascending = [ascending] * len(columns)
    557        ascending = [bool(x) for i, x in enumerate(ascending)]
    558        assert len(columns) == len(
    559            ascending
    560        ), "The length of items in ascending must equal the number of columns provided"
    561        col_and_ascending = list(zip(columns, ascending))
    562        order_by_columns = [
    563            exp.Ordered(this=col.expression, desc=not asc)
    564            if i not in pre_ordered_col_indexes
    565            else columns[i].column_expression
    566            for i, (col, asc) in enumerate(col_and_ascending)
    567        ]
    568        return self.copy(expression=self.expression.order_by(*order_by_columns))
    569
    570    sort = orderBy
    571
    572    @operation(Operation.FROM)
    573    def union(self, other: DataFrame) -> DataFrame:
    574        return self._set_operation(exp.Union, other, False)
    575
    576    unionAll = union
    577
    578    @operation(Operation.FROM)
    579    def unionByName(self, other: DataFrame, allowMissingColumns: bool = False):
    580        l_columns = self.columns
    581        r_columns = other.columns
    582        if not allowMissingColumns:
    583            l_expressions = l_columns
    584            r_expressions = l_columns
    585        else:
    586            l_expressions = []
    587            r_expressions = []
    588            r_columns_unused = copy(r_columns)
    589            for l_column in l_columns:
    590                l_expressions.append(l_column)
    591                if l_column in r_columns:
    592                    r_expressions.append(l_column)
    593                    r_columns_unused.remove(l_column)
    594                else:
    595                    r_expressions.append(exp.alias_(exp.Null(), l_column, copy=False))
    596            for r_column in r_columns_unused:
    597                l_expressions.append(exp.alias_(exp.Null(), r_column, copy=False))
    598                r_expressions.append(r_column)
    599        r_df = (
    600            other.copy()._convert_leaf_to_cte().select(*self._ensure_list_of_columns(r_expressions))
    601        )
    602        l_df = self.copy()
    603        if allowMissingColumns:
    604            l_df = l_df._convert_leaf_to_cte().select(*self._ensure_list_of_columns(l_expressions))
    605        return l_df._set_operation(exp.Union, r_df, False)
    606
    607    @operation(Operation.FROM)
    608    def intersect(self, other: DataFrame) -> DataFrame:
    609        return self._set_operation(exp.Intersect, other, True)
    610
    611    @operation(Operation.FROM)
    612    def intersectAll(self, other: DataFrame) -> DataFrame:
    613        return self._set_operation(exp.Intersect, other, False)
    614
    615    @operation(Operation.FROM)
    616    def exceptAll(self, other: DataFrame) -> DataFrame:
    617        return self._set_operation(exp.Except, other, False)
    618
    619    @operation(Operation.SELECT)
    620    def distinct(self) -> DataFrame:
    621        return self.copy(expression=self.expression.distinct())
    622
    623    @operation(Operation.SELECT)
    624    def dropDuplicates(self, subset: t.Optional[t.List[str]] = None):
    625        if not subset:
    626            return self.distinct()
    627        column_names = ensure_list(subset)
    628        window = Window.partitionBy(*column_names).orderBy(*column_names)
    629        return (
    630            self.copy()
    631            .withColumn("row_num", F.row_number().over(window))
    632            .where(F.col("row_num") == F.lit(1))
    633            .drop("row_num")
    634        )
    635
    636    @operation(Operation.FROM)
    637    def dropna(
    638        self,
    639        how: str = "any",
    640        thresh: t.Optional[int] = None,
    641        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    642    ) -> DataFrame:
    643        minimum_non_null = thresh or 0  # will be determined later if thresh is null
    644        new_df = self.copy()
    645        all_columns = self._get_outer_select_columns(new_df.expression)
    646        if subset:
    647            null_check_columns = self._ensure_and_normalize_cols(subset)
    648        else:
    649            null_check_columns = all_columns
    650        if thresh is None:
    651            minimum_num_nulls = 1 if how == "any" else len(null_check_columns)
    652        else:
    653            minimum_num_nulls = len(null_check_columns) - minimum_non_null + 1
    654        if minimum_num_nulls > len(null_check_columns):
    655            raise RuntimeError(
    656                f"The minimum num nulls for dropna must be less than or equal to the number of columns. "
    657                f"Minimum num nulls: {minimum_num_nulls}, Num Columns: {len(null_check_columns)}"
    658            )
    659        if_null_checks = [
    660            F.when(column.isNull(), F.lit(1)).otherwise(F.lit(0)) for column in null_check_columns
    661        ]
    662        nulls_added_together = functools.reduce(lambda x, y: x + y, if_null_checks)
    663        num_nulls = nulls_added_together.alias("num_nulls")
    664        new_df = new_df.select(num_nulls, append=True)
    665        filtered_df = new_df.where(F.col("num_nulls") < F.lit(minimum_num_nulls))
    666        final_df = filtered_df.select(*all_columns)
    667        return final_df
    668
    669    @operation(Operation.FROM)
    670    def fillna(
    671        self,
    672        value: t.Union[ColumnLiterals],
    673        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    674    ) -> DataFrame:
    675        """
    676        Functionality Difference: If you provide a value to replace a null and that type conflicts
    677        with the type of the column then PySpark will just ignore your replacement.
    678        This will try to cast them to be the same in some cases. So they won't always match.
    679        Best to not mix types so make sure replacement is the same type as the column
    680
    681        Possibility for improvement: Use `typeof` function to get the type of the column
    682        and check if it matches the type of the value provided. If not then make it null.
    683        """
    684        from sqlglot.dataframe.sql.functions import lit
    685
    686        values = None
    687        columns = None
    688        new_df = self.copy()
    689        all_columns = self._get_outer_select_columns(new_df.expression)
    690        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    691        if isinstance(value, dict):
    692            values = list(value.values())
    693            columns = self._ensure_and_normalize_cols(list(value))
    694        if not columns:
    695            columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    696        if not values:
    697            values = [value] * len(columns)
    698        value_columns = [lit(value) for value in values]
    699
    700        null_replacement_mapping = {
    701            column.alias_or_name: (
    702                F.when(column.isNull(), value).otherwise(column).alias(column.alias_or_name)
    703            )
    704            for column, value in zip(columns, value_columns)
    705        }
    706        null_replacement_mapping = {**all_column_mapping, **null_replacement_mapping}
    707        null_replacement_columns = [
    708            null_replacement_mapping[column.alias_or_name] for column in all_columns
    709        ]
    710        new_df = new_df.select(*null_replacement_columns)
    711        return new_df
    712
    713    @operation(Operation.FROM)
    714    def replace(
    715        self,
    716        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    717        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    718        subset: t.Optional[t.Collection[ColumnOrName] | ColumnOrName] = None,
    719    ) -> DataFrame:
    720        from sqlglot.dataframe.sql.functions import lit
    721
    722        old_values = None
    723        new_df = self.copy()
    724        all_columns = self._get_outer_select_columns(new_df.expression)
    725        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    726
    727        columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    728        if isinstance(to_replace, dict):
    729            old_values = list(to_replace)
    730            new_values = list(to_replace.values())
    731        elif not old_values and isinstance(to_replace, list):
    732            assert isinstance(value, list), "value must be a list since the replacements are a list"
    733            assert len(to_replace) == len(
    734                value
    735            ), "the replacements and values must be the same length"
    736            old_values = to_replace
    737            new_values = value
    738        else:
    739            old_values = [to_replace] * len(columns)
    740            new_values = [value] * len(columns)
    741        old_values = [lit(value) for value in old_values]
    742        new_values = [lit(value) for value in new_values]
    743
    744        replacement_mapping = {}
    745        for column in columns:
    746            expression = Column(None)
    747            for i, (old_value, new_value) in enumerate(zip(old_values, new_values)):
    748                if i == 0:
    749                    expression = F.when(column == old_value, new_value)
    750                else:
    751                    expression = expression.when(column == old_value, new_value)  # type: ignore
    752            replacement_mapping[column.alias_or_name] = expression.otherwise(column).alias(
    753                column.expression.alias_or_name
    754            )
    755
    756        replacement_mapping = {**all_column_mapping, **replacement_mapping}
    757        replacement_columns = [replacement_mapping[column.alias_or_name] for column in all_columns]
    758        new_df = new_df.select(*replacement_columns)
    759        return new_df
    760
    761    @operation(Operation.SELECT)
    762    def withColumn(self, colName: str, col: Column) -> DataFrame:
    763        col = self._ensure_and_normalize_col(col)
    764        existing_col_names = self.expression.named_selects
    765        existing_col_index = (
    766            existing_col_names.index(colName) if colName in existing_col_names else None
    767        )
    768        if existing_col_index:
    769            expression = self.expression.copy()
    770            expression.expressions[existing_col_index] = col.expression
    771            return self.copy(expression=expression)
    772        return self.copy().select(col.alias(colName), append=True)
    773
    774    @operation(Operation.SELECT)
    775    def withColumnRenamed(self, existing: str, new: str):
    776        expression = self.expression.copy()
    777        existing_columns = [
    778            expression
    779            for expression in expression.expressions
    780            if expression.alias_or_name == existing
    781        ]
    782        if not existing_columns:
    783            raise ValueError("Tried to rename a column that doesn't exist")
    784        for existing_column in existing_columns:
    785            if isinstance(existing_column, exp.Column):
    786                existing_column.replace(exp.alias_(existing_column, new))
    787            else:
    788                existing_column.set("alias", exp.to_identifier(new))
    789        return self.copy(expression=expression)
    790
    791    @operation(Operation.SELECT)
    792    def drop(self, *cols: t.Union[str, Column]) -> DataFrame:
    793        all_columns = self._get_outer_select_columns(self.expression)
    794        drop_cols = self._ensure_and_normalize_cols(cols)
    795        new_columns = [
    796            col
    797            for col in all_columns
    798            if col.alias_or_name not in [drop_column.alias_or_name for drop_column in drop_cols]
    799        ]
    800        return self.copy().select(*new_columns, append=False)
    801
    802    @operation(Operation.LIMIT)
    803    def limit(self, num: int) -> DataFrame:
    804        return self.copy(expression=self.expression.limit(num))
    805
    806    @operation(Operation.NO_OP)
    807    def hint(self, name: str, *parameters: t.Optional[t.Union[str, int]]) -> DataFrame:
    808        parameter_list = ensure_list(parameters)
    809        parameter_columns = (
    810            self._ensure_list_of_columns(parameter_list)
    811            if parameters
    812            else Column.ensure_cols([self.sequence_id])
    813        )
    814        return self._hint(name, parameter_columns)
    815
    816    @operation(Operation.NO_OP)
    817    def repartition(
    818        self, numPartitions: t.Union[int, ColumnOrName], *cols: ColumnOrName
    819    ) -> DataFrame:
    820        num_partition_cols = self._ensure_list_of_columns(numPartitions)
    821        columns = self._ensure_and_normalize_cols(cols)
    822        args = num_partition_cols + columns
    823        return self._hint("repartition", args)
    824
    825    @operation(Operation.NO_OP)
    826    def coalesce(self, numPartitions: int) -> DataFrame:
    827        num_partitions = Column.ensure_cols([numPartitions])
    828        return self._hint("coalesce", num_partitions)
    829
    830    @operation(Operation.NO_OP)
    831    def cache(self) -> DataFrame:
    832        return self._cache(storage_level="MEMORY_AND_DISK")
    833
    834    @operation(Operation.NO_OP)
    835    def persist(self, storageLevel: str = "MEMORY_AND_DISK_SER") -> DataFrame:
    836        """
    837        Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html
    838        """
    839        return self._cache(storageLevel)

DataFrame( spark: <MagicMock id='140452574562864'>, expression: sqlglot.expressions.Select, branch_id: Optional[str] = None, sequence_id: Optional[str] = None, last_op: sqlglot.dataframe.sql.operations.Operation = <Operation.INIT: -1>, pending_hints: Optional[List[sqlglot.expressions.Expression]] = None, output_expression_container: Optional[<MagicMock id='140452574768448'>] = None, **kwargs) View Source

    50    def __init__(
    51        self,
    52        spark: SparkSession,
    53        expression: exp.Select,
    54        branch_id: t.Optional[str] = None,
    55        sequence_id: t.Optional[str] = None,
    56        last_op: Operation = Operation.INIT,
    57        pending_hints: t.Optional[t.List[exp.Expression]] = None,
    58        output_expression_container: t.Optional[OutputExpressionContainer] = None,
    59        **kwargs,
    60    ):
    61        self.spark = spark
    62        self.expression = expression
    63        self.branch_id = branch_id or self.spark._random_branch_id
    64        self.sequence_id = sequence_id or self.spark._random_sequence_id
    65        self.last_op = last_op
    66        self.pending_hints = pending_hints or []
    67        self.output_expression_container = output_expression_container or exp.Select()

spark
expression
branch_id
sequence_id
last_op
pending_hints
output_expression_container
sparkSession
write
latest_cte_name: str
pending_join_hints
pending_partition_hints
columns: List[str]
na: DataFrameNaFunctions
def sql( self, dialect: Optional[<MagicMock id='140452570319408'>] = None, optimize: bool = True, **kwargs) -> List[str]: View Source

    301    def sql(
    302        self, dialect: t.Optional[DialectType] = None, optimize: bool = True, **kwargs
    303    ) -> t.List[str]:
    304        from sqlglot.dataframe.sql.session import SparkSession
    305
    306        if dialect and Dialect.get_or_raise(dialect)() != SparkSession().dialect:
    307            logger.warning(
    308                f"The recommended way of defining a dialect is by doing `SparkSession.builder.config('sqlframe.dialect', '{dialect}').getOrCreate()`. It is no longer needed then when calling `sql`. If you run into issues try updating your query to use this pattern."
    309            )
    310        df = self._resolve_pending_hints()
    311        select_expressions = df._get_select_expressions()
    312        output_expressions: t.List[t.Union[exp.Select, exp.Cache, exp.Drop]] = []
    313        replacement_mapping: t.Dict[exp.Identifier, exp.Identifier] = {}
    314        for expression_type, select_expression in select_expressions:
    315            select_expression = select_expression.transform(replace_id_value, replacement_mapping)
    316            if optimize:
    317                quote_identifiers(select_expression)
    318                select_expression = t.cast(
    319                    exp.Select, optimize_func(select_expression, dialect=SparkSession().dialect)
    320                )
    321            select_expression = df._replace_cte_names_with_hashes(select_expression)
    322            expression: t.Union[exp.Select, exp.Cache, exp.Drop]
    323            if expression_type == exp.Cache:
    324                cache_table_name = df._create_hash_from_expression(select_expression)
    325                cache_table = exp.to_table(cache_table_name)
    326                original_alias_name = select_expression.args["cte_alias_name"]
    327
    328                replacement_mapping[exp.to_identifier(original_alias_name)] = exp.to_identifier(  # type: ignore
    329                    cache_table_name
    330                )
    331                sqlglot.schema.add_table(
    332                    cache_table_name,
    333                    {
    334                        expression.alias_or_name: expression.type.sql(
    335                            dialect=SparkSession().dialect
    336                        )
    337                        for expression in select_expression.expressions
    338                    },
    339                    dialect=SparkSession().dialect,
    340                )
    341                cache_storage_level = select_expression.args["cache_storage_level"]
    342                options = [
    343                    exp.Literal.string("storageLevel"),
    344                    exp.Literal.string(cache_storage_level),
    345                ]
    346                expression = exp.Cache(
    347                    this=cache_table, expression=select_expression, lazy=True, options=options
    348                )
    349                # We will drop the "view" if it exists before running the cache table
    350                output_expressions.append(exp.Drop(this=cache_table, exists=True, kind="VIEW"))
    351            elif expression_type == exp.Create:
    352                expression = df.output_expression_container.copy()
    353                expression.set("expression", select_expression)
    354            elif expression_type == exp.Insert:
    355                expression = df.output_expression_container.copy()
    356                select_without_ctes = select_expression.copy()
    357                select_without_ctes.set("with", None)
    358                expression.set("expression", select_without_ctes)
    359                if select_expression.ctes:
    360                    expression.set("with", exp.With(expressions=select_expression.ctes))
    361            elif expression_type == exp.Select:
    362                expression = select_expression
    363            else:
    364                raise ValueError(f"Invalid expression type: {expression_type}")
    365            output_expressions.append(expression)
    366
    367        return [
    368            expression.sql(**{"dialect": SparkSession().dialect, **kwargs})
    369            for expression in output_expressions
    370        ]

def copy(self, **kwargs) -> DataFrame: View Source

    372    def copy(self, **kwargs) -> DataFrame:
    373        return DataFrame(**object_to_dict(self, **kwargs))

@operation(Operation.SELECT)
def select(self, *cols, **kwargs) -> DataFrame: View Source

    375    @operation(Operation.SELECT)
    376    def select(self, *cols, **kwargs) -> DataFrame:
    377        cols = self._ensure_and_normalize_cols(cols)
    378        kwargs["append"] = kwargs.get("append", False)
    379        if self.expression.args.get("joins"):
    380            ambiguous_cols = [
    381                col
    382                for col in cols
    383                if isinstance(col.column_expression, exp.Column) and not col.column_expression.table
    384            ]
    385            if ambiguous_cols:
    386                join_table_identifiers = [
    387                    x.this for x in get_tables_from_expression_with_join(self.expression)
    388                ]
    389                cte_names_in_join = [x.this for x in join_table_identifiers]
    390                # If we have columns that resolve to multiple CTE expressions then we want to use each CTE left-to-right
    391                # and therefore we allow multiple columns with the same name in the result. This matches the behavior
    392                # of Spark.
    393                resolved_column_position: t.Dict[Column, int] = {col: -1 for col in ambiguous_cols}
    394                for ambiguous_col in ambiguous_cols:
    395                    ctes_with_column = [
    396                        cte
    397                        for cte in self.expression.ctes
    398                        if cte.alias_or_name in cte_names_in_join
    399                        and ambiguous_col.alias_or_name in cte.this.named_selects
    400                    ]
    401                    # Check if there is a CTE with this column that we haven't used before. If so, use it. Otherwise,
    402                    # use the same CTE we used before
    403                    cte = seq_get(ctes_with_column, resolved_column_position[ambiguous_col] + 1)
    404                    if cte:
    405                        resolved_column_position[ambiguous_col] += 1
    406                    else:
    407                        cte = ctes_with_column[resolved_column_position[ambiguous_col]]
    408                    ambiguous_col.expression.set("table", cte.alias_or_name)
    409        return self.copy(
    410            expression=self.expression.select(*[x.expression for x in cols], **kwargs), **kwargs
    411        )

@operation(Operation.NO_OP)
def alias(self, name: str, **kwargs) -> DataFrame: View Source

    413    @operation(Operation.NO_OP)
    414    def alias(self, name: str, **kwargs) -> DataFrame:
    415        new_sequence_id = self.spark._random_sequence_id
    416        df = self.copy()
    417        for join_hint in df.pending_join_hints:
    418            for expression in join_hint.expressions:
    419                if expression.alias_or_name == self.sequence_id:
    420                    expression.set("this", Column.ensure_col(new_sequence_id).expression)
    421        df.spark._add_alias_to_mapping(name, new_sequence_id)
    422        return df._convert_leaf_to_cte(sequence_id=new_sequence_id)

@operation(Operation.WHERE)
def where( self, column: Union[Column, bool], **kwargs) -> DataFrame: View Source

    424    @operation(Operation.WHERE)
    425    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    426        col = self._ensure_and_normalize_col(column)
    427        return self.copy(expression=self.expression.where(col.expression))

@operation(Operation.WHERE)
def filter( self, column: Union[Column, bool], **kwargs) -> DataFrame: View Source

    424    @operation(Operation.WHERE)
    425    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    426        col = self._ensure_and_normalize_col(column)
    427        return self.copy(expression=self.expression.where(col.expression))

@operation(Operation.GROUP_BY)
def groupBy(self, *cols, **kwargs) -> GroupedData: View Source

    431    @operation(Operation.GROUP_BY)
    432    def groupBy(self, *cols, **kwargs) -> GroupedData:
    433        columns = self._ensure_and_normalize_cols(cols)
    434        return GroupedData(self, columns, self.last_op)

@operation(Operation.SELECT)
def agg(self, *exprs, **kwargs) -> DataFrame: View Source

    436    @operation(Operation.SELECT)
    437    def agg(self, *exprs, **kwargs) -> DataFrame:
    438        cols = self._ensure_and_normalize_cols(exprs)
    439        return self.groupBy().agg(*cols)

@operation(Operation.FROM)
def join( self, other_df: DataFrame, on: Union[str, List[str], Column, List[Column]], how: str = 'inner', **kwargs) -> DataFrame: View Source

    441    @operation(Operation.FROM)
    442    def join(
    443        self,
    444        other_df: DataFrame,
    445        on: t.Union[str, t.List[str], Column, t.List[Column]],
    446        how: str = "inner",
    447        **kwargs,
    448    ) -> DataFrame:
    449        other_df = other_df._convert_leaf_to_cte()
    450        join_columns = self._ensure_list_of_columns(on)
    451        # We will determine actual "join on" expression later so we don't provide it at first
    452        join_expression = self.expression.join(
    453            other_df.latest_cte_name, join_type=how.replace("_", " ")
    454        )
    455        join_expression = self._add_ctes_to_expression(join_expression, other_df.expression.ctes)
    456        self_columns = self._get_outer_select_columns(join_expression)
    457        other_columns = self._get_outer_select_columns(other_df)
    458        # Determines the join clause and select columns to be used passed on what type of columns were provided for
    459        # the join. The columns returned changes based on how the on expression is provided.
    460        if isinstance(join_columns[0].expression, exp.Column):
    461            """
    462            Unique characteristics of join on column names only:
    463            * The column names are put at the front of the select list
    464            * The column names are deduplicated across the entire select list and only the column names (other dups are allowed)
    465            """
    466            table_names = [
    467                table.alias_or_name
    468                for table in get_tables_from_expression_with_join(join_expression)
    469            ]
    470            potential_ctes = [
    471                cte
    472                for cte in join_expression.ctes
    473                if cte.alias_or_name in table_names
    474                and cte.alias_or_name != other_df.latest_cte_name
    475            ]
    476            # Determine the table to reference for the left side of the join by checking each of the left side
    477            # tables and see if they have the column being referenced.
    478            join_column_pairs = []
    479            for join_column in join_columns:
    480                num_matching_ctes = 0
    481                for cte in potential_ctes:
    482                    if join_column.alias_or_name in cte.this.named_selects:
    483                        left_column = join_column.copy().set_table_name(cte.alias_or_name)
    484                        right_column = join_column.copy().set_table_name(other_df.latest_cte_name)
    485                        join_column_pairs.append((left_column, right_column))
    486                        num_matching_ctes += 1
    487                if num_matching_ctes > 1:
    488                    raise ValueError(
    489                        f"Column {join_column.alias_or_name} is ambiguous. Please specify the table name."
    490                    )
    491                elif num_matching_ctes == 0:
    492                    raise ValueError(
    493                        f"Column {join_column.alias_or_name} does not exist in any of the tables."
    494                    )
    495            join_clause = functools.reduce(
    496                lambda x, y: x & y,
    497                [left_column == right_column for left_column, right_column in join_column_pairs],
    498            )
    499            join_column_names = [left_col.alias_or_name for left_col, _ in join_column_pairs]
    500            # To match spark behavior only the join clause gets deduplicated and it gets put in the front of the column list
    501            select_column_names = [
    502                column.alias_or_name
    503                if not isinstance(column.expression.this, exp.Star)
    504                else column.sql()
    505                for column in self_columns + other_columns
    506            ]
    507            select_column_names = [
    508                column_name
    509                for column_name in select_column_names
    510                if column_name not in join_column_names
    511            ]
    512            select_column_names = join_column_names + select_column_names
    513        else:
    514            """
    515            Unique characteristics of join on expressions:
    516            * There is no deduplication of the results.
    517            * The left join dataframe columns go first and right come after. No sort preference is given to join columns
    518            """
    519            join_columns = self._ensure_and_normalize_cols(join_columns, join_expression)
    520            if len(join_columns) > 1:
    521                join_columns = [functools.reduce(lambda x, y: x & y, join_columns)]
    522            join_clause = join_columns[0]
    523            select_column_names = [column.alias_or_name for column in self_columns + other_columns]
    524
    525        # Update the on expression with the actual join clause to replace the dummy one from before
    526        join_expression.args["joins"][-1].set("on", join_clause.expression)
    527        new_df = self.copy(expression=join_expression)
    528        new_df.pending_join_hints.extend(self.pending_join_hints)
    529        new_df.pending_hints.extend(other_df.pending_hints)
    530        new_df = new_df.select.__wrapped__(new_df, *select_column_names)
    531        return new_df

@operation(Operation.ORDER_BY)
def orderBy( self, *cols: Union[str, Column], ascending: Union[Any, List[Any], NoneType] = None) -> DataFrame: View Source

    533    @operation(Operation.ORDER_BY)
    534    def orderBy(
    535        self,
    536        *cols: t.Union[str, Column],
    537        ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
    538    ) -> DataFrame:
    539        """
    540        This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
    541        has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
    542        is unlikely to come up.
    543        """
    544        columns = self._ensure_and_normalize_cols(cols)
    545        pre_ordered_col_indexes = [
    546            x
    547            for x in [
    548                i if isinstance(col.expression, exp.Ordered) else None
    549                for i, col in enumerate(columns)
    550            ]
    551            if x is not None
    552        ]
    553        if ascending is None:
    554            ascending = [True] * len(columns)
    555        elif not isinstance(ascending, list):
    556            ascending = [ascending] * len(columns)
    557        ascending = [bool(x) for i, x in enumerate(ascending)]
    558        assert len(columns) == len(
    559            ascending
    560        ), "The length of items in ascending must equal the number of columns provided"
    561        col_and_ascending = list(zip(columns, ascending))
    562        order_by_columns = [
    563            exp.Ordered(this=col.expression, desc=not asc)
    564            if i not in pre_ordered_col_indexes
    565            else columns[i].column_expression
    566            for i, (col, asc) in enumerate(col_and_ascending)
    567        ]
    568        return self.copy(expression=self.expression.order_by(*order_by_columns))

This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this is unlikely to come up.

@operation(Operation.ORDER_BY)
def sort( self, *cols: Union[str, Column], ascending: Union[Any, List[Any], NoneType] = None) -> DataFrame: View Source

    533    @operation(Operation.ORDER_BY)
    534    def orderBy(
    535        self,
    536        *cols: t.Union[str, Column],
    537        ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
    538    ) -> DataFrame:
    539        """
    540        This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
    541        has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
    542        is unlikely to come up.
    543        """
    544        columns = self._ensure_and_normalize_cols(cols)
    545        pre_ordered_col_indexes = [
    546            x
    547            for x in [
    548                i if isinstance(col.expression, exp.Ordered) else None
    549                for i, col in enumerate(columns)
    550            ]
    551            if x is not None
    552        ]
    553        if ascending is None:
    554            ascending = [True] * len(columns)
    555        elif not isinstance(ascending, list):
    556            ascending = [ascending] * len(columns)
    557        ascending = [bool(x) for i, x in enumerate(ascending)]
    558        assert len(columns) == len(
    559            ascending
    560        ), "The length of items in ascending must equal the number of columns provided"
    561        col_and_ascending = list(zip(columns, ascending))
    562        order_by_columns = [
    563            exp.Ordered(this=col.expression, desc=not asc)
    564            if i not in pre_ordered_col_indexes
    565            else columns[i].column_expression
    566            for i, (col, asc) in enumerate(col_and_ascending)
    567        ]
    568        return self.copy(expression=self.expression.order_by(*order_by_columns))

This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this is unlikely to come up.

@operation(Operation.FROM)
def union( self, other: DataFrame) -> DataFrame: View Source

    572    @operation(Operation.FROM)
    573    def union(self, other: DataFrame) -> DataFrame:
    574        return self._set_operation(exp.Union, other, False)

@operation(Operation.FROM)
def unionAll( self, other: DataFrame) -> DataFrame: View Source

    572    @operation(Operation.FROM)
    573    def union(self, other: DataFrame) -> DataFrame:
    574        return self._set_operation(exp.Union, other, False)

@operation(Operation.FROM)
def unionByName( self, other: DataFrame, allowMissingColumns: bool = False): View Source

    578    @operation(Operation.FROM)
    579    def unionByName(self, other: DataFrame, allowMissingColumns: bool = False):
    580        l_columns = self.columns
    581        r_columns = other.columns
    582        if not allowMissingColumns:
    583            l_expressions = l_columns
    584            r_expressions = l_columns
    585        else:
    586            l_expressions = []
    587            r_expressions = []
    588            r_columns_unused = copy(r_columns)
    589            for l_column in l_columns:
    590                l_expressions.append(l_column)
    591                if l_column in r_columns:
    592                    r_expressions.append(l_column)
    593                    r_columns_unused.remove(l_column)
    594                else:
    595                    r_expressions.append(exp.alias_(exp.Null(), l_column, copy=False))
    596            for r_column in r_columns_unused:
    597                l_expressions.append(exp.alias_(exp.Null(), r_column, copy=False))
    598                r_expressions.append(r_column)
    599        r_df = (
    600            other.copy()._convert_leaf_to_cte().select(*self._ensure_list_of_columns(r_expressions))
    601        )
    602        l_df = self.copy()
    603        if allowMissingColumns:
    604            l_df = l_df._convert_leaf_to_cte().select(*self._ensure_list_of_columns(l_expressions))
    605        return l_df._set_operation(exp.Union, r_df, False)

@operation(Operation.FROM)
def intersect( self, other: DataFrame) -> DataFrame: View Source

    607    @operation(Operation.FROM)
    608    def intersect(self, other: DataFrame) -> DataFrame:
    609        return self._set_operation(exp.Intersect, other, True)

@operation(Operation.FROM)
def intersectAll( self, other: DataFrame) -> DataFrame: View Source

    611    @operation(Operation.FROM)
    612    def intersectAll(self, other: DataFrame) -> DataFrame:
    613        return self._set_operation(exp.Intersect, other, False)

@operation(Operation.FROM)
def exceptAll( self, other: DataFrame) -> DataFrame: View Source

    615    @operation(Operation.FROM)
    616    def exceptAll(self, other: DataFrame) -> DataFrame:
    617        return self._set_operation(exp.Except, other, False)

@operation(Operation.SELECT)
def distinct(self) -> DataFrame: View Source

    619    @operation(Operation.SELECT)
    620    def distinct(self) -> DataFrame:
    621        return self.copy(expression=self.expression.distinct())

@operation(Operation.SELECT)
def dropDuplicates(self, subset: Optional[List[str]] = None): View Source

    623    @operation(Operation.SELECT)
    624    def dropDuplicates(self, subset: t.Optional[t.List[str]] = None):
    625        if not subset:
    626            return self.distinct()
    627        column_names = ensure_list(subset)
    628        window = Window.partitionBy(*column_names).orderBy(*column_names)
    629        return (
    630            self.copy()
    631            .withColumn("row_num", F.row_number().over(window))
    632            .where(F.col("row_num") == F.lit(1))
    633            .drop("row_num")
    634        )

@operation(Operation.FROM)
def dropna( self, how: str = 'any', thresh: Optional[int] = None, subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> DataFrame: View Source

    636    @operation(Operation.FROM)
    637    def dropna(
    638        self,
    639        how: str = "any",
    640        thresh: t.Optional[int] = None,
    641        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    642    ) -> DataFrame:
    643        minimum_non_null = thresh or 0  # will be determined later if thresh is null
    644        new_df = self.copy()
    645        all_columns = self._get_outer_select_columns(new_df.expression)
    646        if subset:
    647            null_check_columns = self._ensure_and_normalize_cols(subset)
    648        else:
    649            null_check_columns = all_columns
    650        if thresh is None:
    651            minimum_num_nulls = 1 if how == "any" else len(null_check_columns)
    652        else:
    653            minimum_num_nulls = len(null_check_columns) - minimum_non_null + 1
    654        if minimum_num_nulls > len(null_check_columns):
    655            raise RuntimeError(
    656                f"The minimum num nulls for dropna must be less than or equal to the number of columns. "
    657                f"Minimum num nulls: {minimum_num_nulls}, Num Columns: {len(null_check_columns)}"
    658            )
    659        if_null_checks = [
    660            F.when(column.isNull(), F.lit(1)).otherwise(F.lit(0)) for column in null_check_columns
    661        ]
    662        nulls_added_together = functools.reduce(lambda x, y: x + y, if_null_checks)
    663        num_nulls = nulls_added_together.alias("num_nulls")
    664        new_df = new_df.select(num_nulls, append=True)
    665        filtered_df = new_df.where(F.col("num_nulls") < F.lit(minimum_num_nulls))
    666        final_df = filtered_df.select(*all_columns)
    667        return final_df

@operation(Operation.FROM)
def fillna( self, value: <MagicMock id='140452568654144'>, subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> DataFrame: View Source

    669    @operation(Operation.FROM)
    670    def fillna(
    671        self,
    672        value: t.Union[ColumnLiterals],
    673        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    674    ) -> DataFrame:
    675        """
    676        Functionality Difference: If you provide a value to replace a null and that type conflicts
    677        with the type of the column then PySpark will just ignore your replacement.
    678        This will try to cast them to be the same in some cases. So they won't always match.
    679        Best to not mix types so make sure replacement is the same type as the column
    680
    681        Possibility for improvement: Use `typeof` function to get the type of the column
    682        and check if it matches the type of the value provided. If not then make it null.
    683        """
    684        from sqlglot.dataframe.sql.functions import lit
    685
    686        values = None
    687        columns = None
    688        new_df = self.copy()
    689        all_columns = self._get_outer_select_columns(new_df.expression)
    690        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    691        if isinstance(value, dict):
    692            values = list(value.values())
    693            columns = self._ensure_and_normalize_cols(list(value))
    694        if not columns:
    695            columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    696        if not values:
    697            values = [value] * len(columns)
    698        value_columns = [lit(value) for value in values]
    699
    700        null_replacement_mapping = {
    701            column.alias_or_name: (
    702                F.when(column.isNull(), value).otherwise(column).alias(column.alias_or_name)
    703            )
    704            for column, value in zip(columns, value_columns)
    705        }
    706        null_replacement_mapping = {**all_column_mapping, **null_replacement_mapping}
    707        null_replacement_columns = [
    708            null_replacement_mapping[column.alias_or_name] for column in all_columns
    709        ]
    710        new_df = new_df.select(*null_replacement_columns)
    711        return new_df

Functionality Difference: If you provide a value to replace a null and that type conflicts with the type of the column then PySpark will just ignore your replacement. This will try to cast them to be the same in some cases. So they won't always match. Best to not mix types so make sure replacement is the same type as the column

Possibility for improvement: Use `typeof` function to get the type of the column and check if it matches the type of the value provided. If not then make it null.

@operation(Operation.FROM)
def replace( self, to_replace: Union[bool, int, float, str, List, Dict], value: Union[bool, int, float, str, List, NoneType] = None, subset: Union[Collection[<MagicMock id='140452569009312'>], <MagicMock id='140452569009312'>, NoneType] = None) -> DataFrame: View Source

    713    @operation(Operation.FROM)
    714    def replace(
    715        self,
    716        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    717        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    718        subset: t.Optional[t.Collection[ColumnOrName] | ColumnOrName] = None,
    719    ) -> DataFrame:
    720        from sqlglot.dataframe.sql.functions import lit
    721
    722        old_values = None
    723        new_df = self.copy()
    724        all_columns = self._get_outer_select_columns(new_df.expression)
    725        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    726
    727        columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    728        if isinstance(to_replace, dict):
    729            old_values = list(to_replace)
    730            new_values = list(to_replace.values())
    731        elif not old_values and isinstance(to_replace, list):
    732            assert isinstance(value, list), "value must be a list since the replacements are a list"
    733            assert len(to_replace) == len(
    734                value
    735            ), "the replacements and values must be the same length"
    736            old_values = to_replace
    737            new_values = value
    738        else:
    739            old_values = [to_replace] * len(columns)
    740            new_values = [value] * len(columns)
    741        old_values = [lit(value) for value in old_values]
    742        new_values = [lit(value) for value in new_values]
    743
    744        replacement_mapping = {}
    745        for column in columns:
    746            expression = Column(None)
    747            for i, (old_value, new_value) in enumerate(zip(old_values, new_values)):
    748                if i == 0:
    749                    expression = F.when(column == old_value, new_value)
    750                else:
    751                    expression = expression.when(column == old_value, new_value)  # type: ignore
    752            replacement_mapping[column.alias_or_name] = expression.otherwise(column).alias(
    753                column.expression.alias_or_name
    754            )
    755
    756        replacement_mapping = {**all_column_mapping, **replacement_mapping}
    757        replacement_columns = [replacement_mapping[column.alias_or_name] for column in all_columns]
    758        new_df = new_df.select(*replacement_columns)
    759        return new_df

@operation(Operation.SELECT)
def withColumn( self, colName: str, col: Column) -> DataFrame: View Source

    761    @operation(Operation.SELECT)
    762    def withColumn(self, colName: str, col: Column) -> DataFrame:
    763        col = self._ensure_and_normalize_col(col)
    764        existing_col_names = self.expression.named_selects
    765        existing_col_index = (
    766            existing_col_names.index(colName) if colName in existing_col_names else None
    767        )
    768        if existing_col_index:
    769            expression = self.expression.copy()
    770            expression.expressions[existing_col_index] = col.expression
    771            return self.copy(expression=expression)
    772        return self.copy().select(col.alias(colName), append=True)

@operation(Operation.SELECT)
def withColumnRenamed(self, existing: str, new: str): View Source

    774    @operation(Operation.SELECT)
    775    def withColumnRenamed(self, existing: str, new: str):
    776        expression = self.expression.copy()
    777        existing_columns = [
    778            expression
    779            for expression in expression.expressions
    780            if expression.alias_or_name == existing
    781        ]
    782        if not existing_columns:
    783            raise ValueError("Tried to rename a column that doesn't exist")
    784        for existing_column in existing_columns:
    785            if isinstance(existing_column, exp.Column):
    786                existing_column.replace(exp.alias_(existing_column, new))
    787            else:
    788                existing_column.set("alias", exp.to_identifier(new))
    789        return self.copy(expression=expression)

@operation(Operation.SELECT)
def drop( self, *cols: Union[str, Column]) -> DataFrame: View Source

    791    @operation(Operation.SELECT)
    792    def drop(self, *cols: t.Union[str, Column]) -> DataFrame:
    793        all_columns = self._get_outer_select_columns(self.expression)
    794        drop_cols = self._ensure_and_normalize_cols(cols)
    795        new_columns = [
    796            col
    797            for col in all_columns
    798            if col.alias_or_name not in [drop_column.alias_or_name for drop_column in drop_cols]
    799        ]
    800        return self.copy().select(*new_columns, append=False)

@operation(Operation.LIMIT)
def limit(self, num: int) -> DataFrame: View Source

    802    @operation(Operation.LIMIT)
    803    def limit(self, num: int) -> DataFrame:
    804        return self.copy(expression=self.expression.limit(num))

@operation(Operation.NO_OP)
def hint( self, name: str, *parameters: Union[str, int, NoneType]) -> DataFrame: View Source

    806    @operation(Operation.NO_OP)
    807    def hint(self, name: str, *parameters: t.Optional[t.Union[str, int]]) -> DataFrame:
    808        parameter_list = ensure_list(parameters)
    809        parameter_columns = (
    810            self._ensure_list_of_columns(parameter_list)
    811            if parameters
    812            else Column.ensure_cols([self.sequence_id])
    813        )
    814        return self._hint(name, parameter_columns)

@operation(Operation.NO_OP)
def repartition( self, numPartitions: Union[int, <MagicMock id='140452569096128'>], *cols: <MagicMock id='140452569223072'>) -> DataFrame: View Source

    816    @operation(Operation.NO_OP)
    817    def repartition(
    818        self, numPartitions: t.Union[int, ColumnOrName], *cols: ColumnOrName
    819    ) -> DataFrame:
    820        num_partition_cols = self._ensure_list_of_columns(numPartitions)
    821        columns = self._ensure_and_normalize_cols(cols)
    822        args = num_partition_cols + columns
    823        return self._hint("repartition", args)

@operation(Operation.NO_OP)
def coalesce(self, numPartitions: int) -> DataFrame: View Source

    825    @operation(Operation.NO_OP)
    826    def coalesce(self, numPartitions: int) -> DataFrame:
    827        num_partitions = Column.ensure_cols([numPartitions])
    828        return self._hint("coalesce", num_partitions)

@operation(Operation.NO_OP)
def cache(self) -> DataFrame: View Source

    830    @operation(Operation.NO_OP)
    831    def cache(self) -> DataFrame:
    832        return self._cache(storage_level="MEMORY_AND_DISK")

@operation(Operation.NO_OP)
def persist( self, storageLevel: str = 'MEMORY_AND_DISK_SER') -> DataFrame: View Source

    834    @operation(Operation.NO_OP)
    835    def persist(self, storageLevel: str = "MEMORY_AND_DISK_SER") -> DataFrame:
    836        """
    837        Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html
    838        """
    839        return self._cache(storageLevel)

Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html

class GroupedData: View Source

    14class GroupedData:
    15    def __init__(self, df: DataFrame, group_by_cols: t.List[Column], last_op: Operation):
    16        self._df = df.copy()
    17        self.spark = df.spark
    18        self.last_op = last_op
    19        self.group_by_cols = group_by_cols
    20
    21    def _get_function_applied_columns(
    22        self, func_name: str, cols: t.Tuple[str, ...]
    23    ) -> t.List[Column]:
    24        func_name = func_name.lower()
    25        return [getattr(F, func_name)(name).alias(f"{func_name}({name})") for name in cols]
    26
    27    @operation(Operation.SELECT)
    28    def agg(self, *exprs: t.Union[Column, t.Dict[str, str]]) -> DataFrame:
    29        columns = (
    30            [Column(f"{agg_func}({column_name})") for column_name, agg_func in exprs[0].items()]
    31            if isinstance(exprs[0], dict)
    32            else exprs
    33        )
    34        cols = self._df._ensure_and_normalize_cols(columns)
    35
    36        expression = self._df.expression.group_by(
    37            *[x.expression for x in self.group_by_cols]
    38        ).select(*[x.expression for x in self.group_by_cols + cols], append=False)
    39        return self._df.copy(expression=expression)
    40
    41    def count(self) -> DataFrame:
    42        return self.agg(F.count("*").alias("count"))
    43
    44    def mean(self, *cols: str) -> DataFrame:
    45        return self.avg(*cols)
    46
    47    def avg(self, *cols: str) -> DataFrame:
    48        return self.agg(*self._get_function_applied_columns("avg", cols))
    49
    50    def max(self, *cols: str) -> DataFrame:
    51        return self.agg(*self._get_function_applied_columns("max", cols))
    52
    53    def min(self, *cols: str) -> DataFrame:
    54        return self.agg(*self._get_function_applied_columns("min", cols))
    55
    56    def sum(self, *cols: str) -> DataFrame:
    57        return self.agg(*self._get_function_applied_columns("sum", cols))
    58
    59    def pivot(self, *cols: str) -> DataFrame:
    60        raise NotImplementedError("Sum distinct is not currently implemented")

GroupedData( df: DataFrame, group_by_cols: List[Column], last_op: sqlglot.dataframe.sql.operations.Operation) View Source

    15    def __init__(self, df: DataFrame, group_by_cols: t.List[Column], last_op: Operation):
    16        self._df = df.copy()
    17        self.spark = df.spark
    18        self.last_op = last_op
    19        self.group_by_cols = group_by_cols

spark
last_op
group_by_cols
@operation(Operation.SELECT)
def agg( self, *exprs: Union[Column, Dict[str, str]]) -> DataFrame: View Source

    27    @operation(Operation.SELECT)
    28    def agg(self, *exprs: t.Union[Column, t.Dict[str, str]]) -> DataFrame:
    29        columns = (
    30            [Column(f"{agg_func}({column_name})") for column_name, agg_func in exprs[0].items()]
    31            if isinstance(exprs[0], dict)
    32            else exprs
    33        )
    34        cols = self._df._ensure_and_normalize_cols(columns)
    35
    36        expression = self._df.expression.group_by(
    37            *[x.expression for x in self.group_by_cols]
    38        ).select(*[x.expression for x in self.group_by_cols + cols], append=False)
    39        return self._df.copy(expression=expression)

def count(self) -> DataFrame: View Source

    41    def count(self) -> DataFrame:
    42        return self.agg(F.count("*").alias("count"))

def mean(self, *cols: str) -> DataFrame: View Source

    44    def mean(self, *cols: str) -> DataFrame:
    45        return self.avg(*cols)

def avg(self, *cols: str) -> DataFrame: View Source

    47    def avg(self, *cols: str) -> DataFrame:
    48        return self.agg(*self._get_function_applied_columns("avg", cols))

def max(self, *cols: str) -> DataFrame: View Source

    50    def max(self, *cols: str) -> DataFrame:
    51        return self.agg(*self._get_function_applied_columns("max", cols))

def min(self, *cols: str) -> DataFrame: View Source

    53    def min(self, *cols: str) -> DataFrame:
    54        return self.agg(*self._get_function_applied_columns("min", cols))

def sum(self, *cols: str) -> DataFrame: View Source

    56    def sum(self, *cols: str) -> DataFrame:
    57        return self.agg(*self._get_function_applied_columns("sum", cols))

def pivot(self, *cols: str) -> DataFrame: View Source

    59    def pivot(self, *cols: str) -> DataFrame:
    60        raise NotImplementedError("Sum distinct is not currently implemented")

class Column: View Source

     16class Column:
     17    def __init__(self, expression: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]):
     18        from sqlglot.dataframe.sql.session import SparkSession
     19
     20        if isinstance(expression, Column):
     21            expression = expression.expression  # type: ignore
     22        elif expression is None or not isinstance(expression, (str, exp.Expression)):
     23            expression = self._lit(expression).expression  # type: ignore
     24        elif not isinstance(expression, exp.Column):
     25            expression = sqlglot.maybe_parse(expression, dialect=SparkSession().dialect).transform(
     26                SparkSession().dialect.normalize_identifier, copy=False
     27            )
     28        if expression is None:
     29            raise ValueError(f"Could not parse {expression}")
     30
     31        self.expression: exp.Expression = expression  # type: ignore
     32
     33    def __repr__(self):
     34        return repr(self.expression)
     35
     36    def __hash__(self):
     37        return hash(self.expression)
     38
     39    def __eq__(self, other: ColumnOrLiteral) -> Column:  # type: ignore
     40        return self.binary_op(exp.EQ, other)
     41
     42    def __ne__(self, other: ColumnOrLiteral) -> Column:  # type: ignore
     43        return self.binary_op(exp.NEQ, other)
     44
     45    def __gt__(self, other: ColumnOrLiteral) -> Column:
     46        return self.binary_op(exp.GT, other)
     47
     48    def __ge__(self, other: ColumnOrLiteral) -> Column:
     49        return self.binary_op(exp.GTE, other)
     50
     51    def __lt__(self, other: ColumnOrLiteral) -> Column:
     52        return self.binary_op(exp.LT, other)
     53
     54    def __le__(self, other: ColumnOrLiteral) -> Column:
     55        return self.binary_op(exp.LTE, other)
     56
     57    def __and__(self, other: ColumnOrLiteral) -> Column:
     58        return self.binary_op(exp.And, other)
     59
     60    def __or__(self, other: ColumnOrLiteral) -> Column:
     61        return self.binary_op(exp.Or, other)
     62
     63    def __mod__(self, other: ColumnOrLiteral) -> Column:
     64        return self.binary_op(exp.Mod, other)
     65
     66    def __add__(self, other: ColumnOrLiteral) -> Column:
     67        return self.binary_op(exp.Add, other)
     68
     69    def __sub__(self, other: ColumnOrLiteral) -> Column:
     70        return self.binary_op(exp.Sub, other)
     71
     72    def __mul__(self, other: ColumnOrLiteral) -> Column:
     73        return self.binary_op(exp.Mul, other)
     74
     75    def __truediv__(self, other: ColumnOrLiteral) -> Column:
     76        return self.binary_op(exp.Div, other)
     77
     78    def __div__(self, other: ColumnOrLiteral) -> Column:
     79        return self.binary_op(exp.Div, other)
     80
     81    def __neg__(self) -> Column:
     82        return self.unary_op(exp.Neg)
     83
     84    def __radd__(self, other: ColumnOrLiteral) -> Column:
     85        return self.inverse_binary_op(exp.Add, other)
     86
     87    def __rsub__(self, other: ColumnOrLiteral) -> Column:
     88        return self.inverse_binary_op(exp.Sub, other)
     89
     90    def __rmul__(self, other: ColumnOrLiteral) -> Column:
     91        return self.inverse_binary_op(exp.Mul, other)
     92
     93    def __rdiv__(self, other: ColumnOrLiteral) -> Column:
     94        return self.inverse_binary_op(exp.Div, other)
     95
     96    def __rtruediv__(self, other: ColumnOrLiteral) -> Column:
     97        return self.inverse_binary_op(exp.Div, other)
     98
     99    def __rmod__(self, other: ColumnOrLiteral) -> Column:
    100        return self.inverse_binary_op(exp.Mod, other)
    101
    102    def __pow__(self, power: ColumnOrLiteral, modulo=None):
    103        return Column(exp.Pow(this=self.expression, expression=Column(power).expression))
    104
    105    def __rpow__(self, power: ColumnOrLiteral):
    106        return Column(exp.Pow(this=Column(power).expression, expression=self.expression))
    107
    108    def __invert__(self):
    109        return self.unary_op(exp.Not)
    110
    111    def __rand__(self, other: ColumnOrLiteral) -> Column:
    112        return self.inverse_binary_op(exp.And, other)
    113
    114    def __ror__(self, other: ColumnOrLiteral) -> Column:
    115        return self.inverse_binary_op(exp.Or, other)
    116
    117    @classmethod
    118    def ensure_col(cls, value: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]) -> Column:
    119        return cls(value)
    120
    121    @classmethod
    122    def ensure_cols(cls, args: t.List[t.Union[ColumnOrLiteral, exp.Expression]]) -> t.List[Column]:
    123        return [cls.ensure_col(x) if not isinstance(x, Column) else x for x in args]
    124
    125    @classmethod
    126    def _lit(cls, value: ColumnOrLiteral) -> Column:
    127        if isinstance(value, dict):
    128            columns = [cls._lit(v).alias(k).expression for k, v in value.items()]
    129            return cls(exp.Struct(expressions=columns))
    130        return cls(exp.convert(value))
    131
    132    @classmethod
    133    def invoke_anonymous_function(
    134        cls, column: t.Optional[ColumnOrLiteral], func_name: str, *args: t.Optional[ColumnOrLiteral]
    135    ) -> Column:
    136        columns = [] if column is None else [cls.ensure_col(column)]
    137        column_args = [cls.ensure_col(arg) for arg in args]
    138        expressions = [x.expression for x in columns + column_args]
    139        new_expression = exp.Anonymous(this=func_name.upper(), expressions=expressions)
    140        return Column(new_expression)
    141
    142    @classmethod
    143    def invoke_expression_over_column(
    144        cls, column: t.Optional[ColumnOrLiteral], callable_expression: t.Callable, **kwargs
    145    ) -> Column:
    146        ensured_column = None if column is None else cls.ensure_col(column)
    147        ensure_expression_values = {
    148            k: [Column.ensure_col(x).expression for x in v]
    149            if is_iterable(v)
    150            else Column.ensure_col(v).expression
    151            for k, v in kwargs.items()
    152            if v is not None
    153        }
    154        new_expression = (
    155            callable_expression(**ensure_expression_values)
    156            if ensured_column is None
    157            else callable_expression(
    158                this=ensured_column.column_expression, **ensure_expression_values
    159            )
    160        )
    161        return Column(new_expression)
    162
    163    def binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    164        return Column(
    165            klass(this=self.column_expression, expression=Column(other).column_expression, **kwargs)
    166        )
    167
    168    def inverse_binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    169        return Column(
    170            klass(this=Column(other).column_expression, expression=self.column_expression, **kwargs)
    171        )
    172
    173    def unary_op(self, klass: t.Callable, **kwargs) -> Column:
    174        return Column(klass(this=self.column_expression, **kwargs))
    175
    176    @property
    177    def is_alias(self):
    178        return isinstance(self.expression, exp.Alias)
    179
    180    @property
    181    def is_column(self):
    182        return isinstance(self.expression, exp.Column)
    183
    184    @property
    185    def column_expression(self) -> t.Union[exp.Column, exp.Literal]:
    186        return self.expression.unalias()
    187
    188    @property
    189    def alias_or_name(self) -> str:
    190        return self.expression.alias_or_name
    191
    192    @classmethod
    193    def ensure_literal(cls, value) -> Column:
    194        from sqlglot.dataframe.sql.functions import lit
    195
    196        if isinstance(value, cls):
    197            value = value.expression
    198        if not isinstance(value, exp.Literal):
    199            return lit(value)
    200        return Column(value)
    201
    202    def copy(self) -> Column:
    203        return Column(self.expression.copy())
    204
    205    def set_table_name(self, table_name: str, copy=False) -> Column:
    206        expression = self.expression.copy() if copy else self.expression
    207        expression.set("table", exp.to_identifier(table_name))
    208        return Column(expression)
    209
    210    def sql(self, **kwargs) -> str:
    211        from sqlglot.dataframe.sql.session import SparkSession
    212
    213        return self.expression.sql(**{"dialect": SparkSession().dialect, **kwargs})
    214
    215    def alias(self, name: str) -> Column:
    216        from sqlglot.dataframe.sql.session import SparkSession
    217
    218        dialect = SparkSession().dialect
    219        alias: exp.Expression = sqlglot.maybe_parse(name, dialect=dialect)
    220        new_expression = exp.alias_(
    221            self.column_expression,
    222            alias.this if isinstance(alias, exp.Column) else name,
    223            dialect=dialect,
    224        )
    225        return Column(new_expression)
    226
    227    def asc(self) -> Column:
    228        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    229        return Column(new_expression)
    230
    231    def desc(self) -> Column:
    232        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    233        return Column(new_expression)
    234
    235    asc_nulls_first = asc
    236
    237    def asc_nulls_last(self) -> Column:
    238        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=False)
    239        return Column(new_expression)
    240
    241    def desc_nulls_first(self) -> Column:
    242        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=True)
    243        return Column(new_expression)
    244
    245    desc_nulls_last = desc
    246
    247    def when(self, condition: Column, value: t.Any) -> Column:
    248        from sqlglot.dataframe.sql.functions import when
    249
    250        column_with_if = when(condition, value)
    251        if not isinstance(self.expression, exp.Case):
    252            return column_with_if
    253        new_column = self.copy()
    254        new_column.expression.args["ifs"].extend(column_with_if.expression.args["ifs"])
    255        return new_column
    256
    257    def otherwise(self, value: t.Any) -> Column:
    258        from sqlglot.dataframe.sql.functions import lit
    259
    260        true_value = value if isinstance(value, Column) else lit(value)
    261        new_column = self.copy()
    262        new_column.expression.set("default", true_value.column_expression)
    263        return new_column
    264
    265    def isNull(self) -> Column:
    266        new_expression = exp.Is(this=self.column_expression, expression=exp.Null())
    267        return Column(new_expression)
    268
    269    def isNotNull(self) -> Column:
    270        new_expression = exp.Not(this=exp.Is(this=self.column_expression, expression=exp.Null()))
    271        return Column(new_expression)
    272
    273    def cast(self, dataType: t.Union[str, DataType]) -> Column:
    274        """
    275        Functionality Difference: PySpark cast accepts a datatype instance of the datatype class
    276        Sqlglot doesn't currently replicate this class so it only accepts a string
    277        """
    278        from sqlglot.dataframe.sql.session import SparkSession
    279
    280        if isinstance(dataType, DataType):
    281            dataType = dataType.simpleString()
    282        return Column(exp.cast(self.column_expression, dataType, dialect=SparkSession().dialect))
    283
    284    def startswith(self, value: t.Union[str, Column]) -> Column:
    285        value = self._lit(value) if not isinstance(value, Column) else value
    286        return self.invoke_anonymous_function(self, "STARTSWITH", value)
    287
    288    def endswith(self, value: t.Union[str, Column]) -> Column:
    289        value = self._lit(value) if not isinstance(value, Column) else value
    290        return self.invoke_anonymous_function(self, "ENDSWITH", value)
    291
    292    def rlike(self, regexp: str) -> Column:
    293        return self.invoke_expression_over_column(
    294            column=self, callable_expression=exp.RegexpLike, expression=self._lit(regexp).expression
    295        )
    296
    297    def like(self, other: str):
    298        return self.invoke_expression_over_column(
    299            self, exp.Like, expression=self._lit(other).expression
    300        )
    301
    302    def ilike(self, other: str):
    303        return self.invoke_expression_over_column(
    304            self, exp.ILike, expression=self._lit(other).expression
    305        )
    306
    307    def substr(self, startPos: t.Union[int, Column], length: t.Union[int, Column]) -> Column:
    308        startPos = self._lit(startPos) if not isinstance(startPos, Column) else startPos
    309        length = self._lit(length) if not isinstance(length, Column) else length
    310        return Column.invoke_expression_over_column(
    311            self, exp.Substring, start=startPos.expression, length=length.expression
    312        )
    313
    314    def isin(self, *cols: t.Union[ColumnOrLiteral, t.Iterable[ColumnOrLiteral]]):
    315        columns = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    316        expressions = [self._lit(x).expression for x in columns]
    317        return Column.invoke_expression_over_column(self, exp.In, expressions=expressions)  # type: ignore
    318
    319    def between(
    320        self,
    321        lowerBound: t.Union[ColumnOrLiteral],
    322        upperBound: t.Union[ColumnOrLiteral],
    323    ) -> Column:
    324        lower_bound_exp = (
    325            self._lit(lowerBound) if not isinstance(lowerBound, Column) else lowerBound
    326        )
    327        upper_bound_exp = (
    328            self._lit(upperBound) if not isinstance(upperBound, Column) else upperBound
    329        )
    330        return Column(
    331            exp.Between(
    332                this=self.column_expression,
    333                low=lower_bound_exp.expression,
    334                high=upper_bound_exp.expression,
    335            )
    336        )
    337
    338    def over(self, window: WindowSpec) -> Column:
    339        window_expression = window.expression.copy()
    340        window_expression.set("this", self.column_expression)
    341        return Column(window_expression)

Column( expression: Union[<MagicMock id='140452570822352'>, sqlglot.expressions.Expression, NoneType]) View Source

    17    def __init__(self, expression: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]):
    18        from sqlglot.dataframe.sql.session import SparkSession
    19
    20        if isinstance(expression, Column):
    21            expression = expression.expression  # type: ignore
    22        elif expression is None or not isinstance(expression, (str, exp.Expression)):
    23            expression = self._lit(expression).expression  # type: ignore
    24        elif not isinstance(expression, exp.Column):
    25            expression = sqlglot.maybe_parse(expression, dialect=SparkSession().dialect).transform(
    26                SparkSession().dialect.normalize_identifier, copy=False
    27            )
    28        if expression is None:
    29            raise ValueError(f"Could not parse {expression}")
    30
    31        self.expression: exp.Expression = expression  # type: ignore

expression: sqlglot.expressions.Expression
@classmethod
def ensure_col( cls, value: Union[<MagicMock id='140452569549264'>, sqlglot.expressions.Expression, NoneType]) -> Column: View Source

    117    @classmethod
    118    def ensure_col(cls, value: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]) -> Column:
    119        return cls(value)

@classmethod
def ensure_cols( cls, args: List[Union[<MagicMock id='140452569523600'>, sqlglot.expressions.Expression]]) -> List[Column]: View Source

    121    @classmethod
    122    def ensure_cols(cls, args: t.List[t.Union[ColumnOrLiteral, exp.Expression]]) -> t.List[Column]:
    123        return [cls.ensure_col(x) if not isinstance(x, Column) else x for x in args]

@classmethod
def invoke_anonymous_function( cls, column: Optional[<MagicMock id='140452569438608'>], func_name: str, *args: Optional[<MagicMock id='140452569350928'>]) -> Column: View Source

    132    @classmethod
    133    def invoke_anonymous_function(
    134        cls, column: t.Optional[ColumnOrLiteral], func_name: str, *args: t.Optional[ColumnOrLiteral]
    135    ) -> Column:
    136        columns = [] if column is None else [cls.ensure_col(column)]
    137        column_args = [cls.ensure_col(arg) for arg in args]
    138        expressions = [x.expression for x in columns + column_args]
    139        new_expression = exp.Anonymous(this=func_name.upper(), expressions=expressions)
    140        return Column(new_expression)

@classmethod
def invoke_expression_over_column( cls, column: Optional[<MagicMock id='140452569443312'>], callable_expression: Callable, **kwargs) -> Column: View Source

    142    @classmethod
    143    def invoke_expression_over_column(
    144        cls, column: t.Optional[ColumnOrLiteral], callable_expression: t.Callable, **kwargs
    145    ) -> Column:
    146        ensured_column = None if column is None else cls.ensure_col(column)
    147        ensure_expression_values = {
    148            k: [Column.ensure_col(x).expression for x in v]
    149            if is_iterable(v)
    150            else Column.ensure_col(v).expression
    151            for k, v in kwargs.items()
    152            if v is not None
    153        }
    154        new_expression = (
    155            callable_expression(**ensure_expression_values)
    156            if ensured_column is None
    157            else callable_expression(
    158                this=ensured_column.column_expression, **ensure_expression_values
    159            )
    160        )
    161        return Column(new_expression)

def binary_op( self, klass: Callable, other: <MagicMock id='140452567492128'>, **kwargs) -> Column: View Source

    163    def binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    164        return Column(
    165            klass(this=self.column_expression, expression=Column(other).column_expression, **kwargs)
    166        )

def inverse_binary_op( self, klass: Callable, other: <MagicMock id='140452567568912'>, **kwargs) -> Column: View Source

    168    def inverse_binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    169        return Column(
    170            klass(this=Column(other).column_expression, expression=self.column_expression, **kwargs)
    171        )

def unary_op(self, klass: Callable, **kwargs) -> Column: View Source

    173    def unary_op(self, klass: t.Callable, **kwargs) -> Column:
    174        return Column(klass(this=self.column_expression, **kwargs))

is_alias
is_column
column_expression: Union[sqlglot.expressions.Column, sqlglot.expressions.Literal]
alias_or_name: str
@classmethod
def ensure_literal(cls, value) -> Column: View Source

    192    @classmethod
    193    def ensure_literal(cls, value) -> Column:
    194        from sqlglot.dataframe.sql.functions import lit
    195
    196        if isinstance(value, cls):
    197            value = value.expression
    198        if not isinstance(value, exp.Literal):
    199            return lit(value)
    200        return Column(value)

def copy(self) -> Column: View Source

    202    def copy(self) -> Column:
    203        return Column(self.expression.copy())

def set_table_name(self, table_name: str, copy=False) -> Column: View Source

    205    def set_table_name(self, table_name: str, copy=False) -> Column:
    206        expression = self.expression.copy() if copy else self.expression
    207        expression.set("table", exp.to_identifier(table_name))
    208        return Column(expression)

def sql(self, **kwargs) -> str: View Source

    210    def sql(self, **kwargs) -> str:
    211        from sqlglot.dataframe.sql.session import SparkSession
    212
    213        return self.expression.sql(**{"dialect": SparkSession().dialect, **kwargs})

def alias(self, name: str) -> Column: View Source

    215    def alias(self, name: str) -> Column:
    216        from sqlglot.dataframe.sql.session import SparkSession
    217
    218        dialect = SparkSession().dialect
    219        alias: exp.Expression = sqlglot.maybe_parse(name, dialect=dialect)
    220        new_expression = exp.alias_(
    221            self.column_expression,
    222            alias.this if isinstance(alias, exp.Column) else name,
    223            dialect=dialect,
    224        )
    225        return Column(new_expression)

def asc(self) -> Column: View Source

    227    def asc(self) -> Column:
    228        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    229        return Column(new_expression)

def desc(self) -> Column: View Source

    231    def desc(self) -> Column:
    232        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    233        return Column(new_expression)

def asc_nulls_first(self) -> Column: View Source

    227    def asc(self) -> Column:
    228        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    229        return Column(new_expression)

def asc_nulls_last(self) -> Column: View Source

    237    def asc_nulls_last(self) -> Column:
    238        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=False)
    239        return Column(new_expression)

def desc_nulls_first(self) -> Column: View Source

    241    def desc_nulls_first(self) -> Column:
    242        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=True)
    243        return Column(new_expression)

def desc_nulls_last(self) -> Column: View Source

    231    def desc(self) -> Column:
    232        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    233        return Column(new_expression)

def when( self, condition: Column, value: Any) -> Column: View Source

    247    def when(self, condition: Column, value: t.Any) -> Column:
    248        from sqlglot.dataframe.sql.functions import when
    249
    250        column_with_if = when(condition, value)
    251        if not isinstance(self.expression, exp.Case):
    252            return column_with_if
    253        new_column = self.copy()
    254        new_column.expression.args["ifs"].extend(column_with_if.expression.args["ifs"])
    255        return new_column

def otherwise(self, value: Any) -> Column: View Source

    257    def otherwise(self, value: t.Any) -> Column:
    258        from sqlglot.dataframe.sql.functions import lit
    259
    260        true_value = value if isinstance(value, Column) else lit(value)
    261        new_column = self.copy()
    262        new_column.expression.set("default", true_value.column_expression)
    263        return new_column

def isNull(self) -> Column: View Source

    265    def isNull(self) -> Column:
    266        new_expression = exp.Is(this=self.column_expression, expression=exp.Null())
    267        return Column(new_expression)

def isNotNull(self) -> Column: View Source

    269    def isNotNull(self) -> Column:
    270        new_expression = exp.Not(this=exp.Is(this=self.column_expression, expression=exp.Null()))
    271        return Column(new_expression)

def cast( self, dataType: Union[str, sqlglot.dataframe.sql.types.DataType]) -> Column: View Source

    273    def cast(self, dataType: t.Union[str, DataType]) -> Column:
    274        """
    275        Functionality Difference: PySpark cast accepts a datatype instance of the datatype class
    276        Sqlglot doesn't currently replicate this class so it only accepts a string
    277        """
    278        from sqlglot.dataframe.sql.session import SparkSession
    279
    280        if isinstance(dataType, DataType):
    281            dataType = dataType.simpleString()
    282        return Column(exp.cast(self.column_expression, dataType, dialect=SparkSession().dialect))

Functionality Difference: PySpark cast accepts a datatype instance of the datatype class Sqlglot doesn't currently replicate this class so it only accepts a string

def startswith( self, value: Union[str, Column]) -> Column: View Source

    284    def startswith(self, value: t.Union[str, Column]) -> Column:
    285        value = self._lit(value) if not isinstance(value, Column) else value
    286        return self.invoke_anonymous_function(self, "STARTSWITH", value)

def endswith( self, value: Union[str, Column]) -> Column: View Source

    288    def endswith(self, value: t.Union[str, Column]) -> Column:
    289        value = self._lit(value) if not isinstance(value, Column) else value
    290        return self.invoke_anonymous_function(self, "ENDSWITH", value)

def rlike(self, regexp: str) -> Column: View Source

    292    def rlike(self, regexp: str) -> Column:
    293        return self.invoke_expression_over_column(
    294            column=self, callable_expression=exp.RegexpLike, expression=self._lit(regexp).expression
    295        )

def like(self, other: str): View Source

    297    def like(self, other: str):
    298        return self.invoke_expression_over_column(
    299            self, exp.Like, expression=self._lit(other).expression
    300        )

def ilike(self, other: str): View Source

    302    def ilike(self, other: str):
    303        return self.invoke_expression_over_column(
    304            self, exp.ILike, expression=self._lit(other).expression
    305        )

def substr( self, startPos: Union[int, Column], length: Union[int, Column]) -> Column: View Source

    307    def substr(self, startPos: t.Union[int, Column], length: t.Union[int, Column]) -> Column:
    308        startPos = self._lit(startPos) if not isinstance(startPos, Column) else startPos
    309        length = self._lit(length) if not isinstance(length, Column) else length
    310        return Column.invoke_expression_over_column(
    311            self, exp.Substring, start=startPos.expression, length=length.expression
    312        )

def isin( self, *cols: Union[<MagicMock id='140452567722752'>, Iterable[<MagicMock id='140452567722752'>]]): View Source

    314    def isin(self, *cols: t.Union[ColumnOrLiteral, t.Iterable[ColumnOrLiteral]]):
    315        columns = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    316        expressions = [self._lit(x).expression for x in columns]
    317        return Column.invoke_expression_over_column(self, exp.In, expressions=expressions)  # type: ignore

def between( self, lowerBound: <MagicMock id='140452567890480'>, upperBound: <MagicMock id='140452567945024'>) -> Column: View Source

    319    def between(
    320        self,
    321        lowerBound: t.Union[ColumnOrLiteral],
    322        upperBound: t.Union[ColumnOrLiteral],
    323    ) -> Column:
    324        lower_bound_exp = (
    325            self._lit(lowerBound) if not isinstance(lowerBound, Column) else lowerBound
    326        )
    327        upper_bound_exp = (
    328            self._lit(upperBound) if not isinstance(upperBound, Column) else upperBound
    329        )
    330        return Column(
    331            exp.Between(
    332                this=self.column_expression,
    333                low=lower_bound_exp.expression,
    334                high=upper_bound_exp.expression,
    335            )
    336        )

def over( self, window: <MagicMock id='140452567986240'>) -> Column: View Source

    338    def over(self, window: WindowSpec) -> Column:
    339        window_expression = window.expression.copy()
    340        window_expression.set("this", self.column_expression)
    341        return Column(window_expression)

class DataFrameNaFunctions: View Source

    842class DataFrameNaFunctions:
    843    def __init__(self, df: DataFrame):
    844        self.df = df
    845
    846    def drop(
    847        self,
    848        how: str = "any",
    849        thresh: t.Optional[int] = None,
    850        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    851    ) -> DataFrame:
    852        return self.df.dropna(how=how, thresh=thresh, subset=subset)
    853
    854    def fill(
    855        self,
    856        value: t.Union[int, bool, float, str, t.Dict[str, t.Any]],
    857        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    858    ) -> DataFrame:
    859        return self.df.fillna(value=value, subset=subset)
    860
    861    def replace(
    862        self,
    863        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    864        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    865        subset: t.Optional[t.Union[str, t.List[str]]] = None,
    866    ) -> DataFrame:
    867        return self.df.replace(to_replace=to_replace, value=value, subset=subset)

DataFrameNaFunctions(df: DataFrame) View Source

    843    def __init__(self, df: DataFrame):
    844        self.df = df

df
def drop( self, how: str = 'any', thresh: Optional[int] = None, subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> DataFrame: View Source

    846    def drop(
    847        self,
    848        how: str = "any",
    849        thresh: t.Optional[int] = None,
    850        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    851    ) -> DataFrame:
    852        return self.df.dropna(how=how, thresh=thresh, subset=subset)

def fill( self, value: Union[int, bool, float, str, Dict[str, Any]], subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> DataFrame: View Source

    854    def fill(
    855        self,
    856        value: t.Union[int, bool, float, str, t.Dict[str, t.Any]],
    857        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    858    ) -> DataFrame:
    859        return self.df.fillna(value=value, subset=subset)

def replace( self, to_replace: Union[bool, int, float, str, List, Dict], value: Union[bool, int, float, str, List, NoneType] = None, subset: Union[str, List[str], NoneType] = None) -> DataFrame: View Source

    861    def replace(
    862        self,
    863        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    864        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    865        subset: t.Optional[t.Union[str, t.List[str]]] = None,
    866    ) -> DataFrame:
    867        return self.df.replace(to_replace=to_replace, value=value, subset=subset)

class Window: View Source

    15class Window:
    16    _JAVA_MIN_LONG = -(1 << 63)  # -9223372036854775808
    17    _JAVA_MAX_LONG = (1 << 63) - 1  # 9223372036854775807
    18    _PRECEDING_THRESHOLD = max(-sys.maxsize, _JAVA_MIN_LONG)
    19    _FOLLOWING_THRESHOLD = min(sys.maxsize, _JAVA_MAX_LONG)
    20
    21    unboundedPreceding: int = _JAVA_MIN_LONG
    22
    23    unboundedFollowing: int = _JAVA_MAX_LONG
    24
    25    currentRow: int = 0
    26
    27    @classmethod
    28    def partitionBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    29        return WindowSpec().partitionBy(*cols)
    30
    31    @classmethod
    32    def orderBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    33        return WindowSpec().orderBy(*cols)
    34
    35    @classmethod
    36    def rowsBetween(cls, start: int, end: int) -> WindowSpec:
    37        return WindowSpec().rowsBetween(start, end)
    38
    39    @classmethod
    40    def rangeBetween(cls, start: int, end: int) -> WindowSpec:
    41        return WindowSpec().rangeBetween(start, end)

unboundedPreceding: int = -9223372036854775808
unboundedFollowing: int = 9223372036854775807
currentRow: int = 0
@classmethod
def partitionBy( cls, *cols: Union[<MagicMock id='140452568376960'>, List[<MagicMock id='140452568376960'>]]) -> WindowSpec: View Source

    27    @classmethod
    28    def partitionBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    29        return WindowSpec().partitionBy(*cols)

@classmethod
def orderBy( cls, *cols: Union[<MagicMock id='140452568432896'>, List[<MagicMock id='140452568432896'>]]) -> WindowSpec: View Source

    31    @classmethod
    32    def orderBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    33        return WindowSpec().orderBy(*cols)

@classmethod
def rowsBetween(cls, start: int, end: int) -> WindowSpec: View Source

    35    @classmethod
    36    def rowsBetween(cls, start: int, end: int) -> WindowSpec:
    37        return WindowSpec().rowsBetween(start, end)

@classmethod
def rangeBetween(cls, start: int, end: int) -> WindowSpec: View Source

    39    @classmethod
    40    def rangeBetween(cls, start: int, end: int) -> WindowSpec:
    41        return WindowSpec().rangeBetween(start, end)

class WindowSpec: View Source

     44class WindowSpec:
     45    def __init__(self, expression: exp.Expression = exp.Window()):
     46        self.expression = expression
     47
     48    def copy(self):
     49        return WindowSpec(self.expression.copy())
     50
     51    def sql(self, **kwargs) -> str:
     52        from sqlglot.dataframe.sql.session import SparkSession
     53
     54        return self.expression.sql(dialect=SparkSession().dialect, **kwargs)
     55
     56    def partitionBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
     57        from sqlglot.dataframe.sql.column import Column
     58
     59        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
     60        expressions = [Column.ensure_col(x).expression for x in cols]
     61        window_spec = self.copy()
     62        partition_by_expressions = window_spec.expression.args.get("partition_by", [])
     63        partition_by_expressions.extend(expressions)
     64        window_spec.expression.set("partition_by", partition_by_expressions)
     65        return window_spec
     66
     67    def orderBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
     68        from sqlglot.dataframe.sql.column import Column
     69
     70        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
     71        expressions = [Column.ensure_col(x).expression for x in cols]
     72        window_spec = self.copy()
     73        if window_spec.expression.args.get("order") is None:
     74            window_spec.expression.set("order", exp.Order(expressions=[]))
     75        order_by = window_spec.expression.args["order"].expressions
     76        order_by.extend(expressions)
     77        window_spec.expression.args["order"].set("expressions", order_by)
     78        return window_spec
     79
     80    def _calc_start_end(
     81        self, start: int, end: int
     82    ) -> t.Dict[str, t.Optional[t.Union[str, exp.Expression]]]:
     83        kwargs: t.Dict[str, t.Optional[t.Union[str, exp.Expression]]] = {
     84            "start_side": None,
     85            "end_side": None,
     86        }
     87        if start == Window.currentRow:
     88            kwargs["start"] = "CURRENT ROW"
     89        else:
     90            kwargs = {
     91                **kwargs,
     92                **{
     93                    "start_side": "PRECEDING",
     94                    "start": "UNBOUNDED"
     95                    if start <= Window.unboundedPreceding
     96                    else F.lit(start).expression,
     97                },
     98            }
     99        if end == Window.currentRow:
    100            kwargs["end"] = "CURRENT ROW"
    101        else:
    102            kwargs = {
    103                **kwargs,
    104                **{
    105                    "end_side": "FOLLOWING",
    106                    "end": "UNBOUNDED"
    107                    if end >= Window.unboundedFollowing
    108                    else F.lit(end).expression,
    109                },
    110            }
    111        return kwargs
    112
    113    def rowsBetween(self, start: int, end: int) -> WindowSpec:
    114        window_spec = self.copy()
    115        spec = self._calc_start_end(start, end)
    116        spec["kind"] = "ROWS"
    117        window_spec.expression.set(
    118            "spec",
    119            exp.WindowSpec(
    120                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    121            ),
    122        )
    123        return window_spec
    124
    125    def rangeBetween(self, start: int, end: int) -> WindowSpec:
    126        window_spec = self.copy()
    127        spec = self._calc_start_end(start, end)
    128        spec["kind"] = "RANGE"
    129        window_spec.expression.set(
    130            "spec",
    131            exp.WindowSpec(
    132                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    133            ),
    134        )
    135        return window_spec

WindowSpec(expression: sqlglot.expressions.Expression = (WINDOW )) View Source

    45    def __init__(self, expression: exp.Expression = exp.Window()):
    46        self.expression = expression

expression
def copy(self): View Source

    48    def copy(self):
    49        return WindowSpec(self.expression.copy())

def sql(self, **kwargs) -> str: View Source

    51    def sql(self, **kwargs) -> str:
    52        from sqlglot.dataframe.sql.session import SparkSession
    53
    54        return self.expression.sql(dialect=SparkSession().dialect, **kwargs)

def partitionBy( self, *cols: Union[<MagicMock id='140452568065808'>, List[<MagicMock id='140452568065808'>]]) -> WindowSpec: View Source

    56    def partitionBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    57        from sqlglot.dataframe.sql.column import Column
    58
    59        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    60        expressions = [Column.ensure_col(x).expression for x in cols]
    61        window_spec = self.copy()
    62        partition_by_expressions = window_spec.expression.args.get("partition_by", [])
    63        partition_by_expressions.extend(expressions)
    64        window_spec.expression.set("partition_by", partition_by_expressions)
    65        return window_spec

def orderBy( self, *cols: Union[<MagicMock id='140452568047552'>, List[<MagicMock id='140452568047552'>]]) -> WindowSpec: View Source

    67    def orderBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    68        from sqlglot.dataframe.sql.column import Column
    69
    70        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    71        expressions = [Column.ensure_col(x).expression for x in cols]
    72        window_spec = self.copy()
    73        if window_spec.expression.args.get("order") is None:
    74            window_spec.expression.set("order", exp.Order(expressions=[]))
    75        order_by = window_spec.expression.args["order"].expressions
    76        order_by.extend(expressions)
    77        window_spec.expression.args["order"].set("expressions", order_by)
    78        return window_spec

def rowsBetween(self, start: int, end: int) -> WindowSpec: View Source

    113    def rowsBetween(self, start: int, end: int) -> WindowSpec:
    114        window_spec = self.copy()
    115        spec = self._calc_start_end(start, end)
    116        spec["kind"] = "ROWS"
    117        window_spec.expression.set(
    118            "spec",
    119            exp.WindowSpec(
    120                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    121            ),
    122        )
    123        return window_spec

def rangeBetween(self, start: int, end: int) -> WindowSpec: View Source

    125    def rangeBetween(self, start: int, end: int) -> WindowSpec:
    126        window_spec = self.copy()
    127        spec = self._calc_start_end(start, end)
    128        spec["kind"] = "RANGE"
    129        window_spec.expression.set(
    130            "spec",
    131            exp.WindowSpec(
    132                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    133            ),
    134        )
    135        return window_spec

class DataFrameReader: View Source

    15class DataFrameReader:
    16    def __init__(self, spark: SparkSession):
    17        self.spark = spark
    18
    19    def table(self, tableName: str) -> DataFrame:
    20        from sqlglot.dataframe.sql.dataframe import DataFrame
    21        from sqlglot.dataframe.sql.session import SparkSession
    22
    23        sqlglot.schema.add_table(tableName, dialect=SparkSession().dialect)
    24
    25        return DataFrame(
    26            self.spark,
    27            exp.Select()
    28            .from_(
    29                exp.to_table(tableName, dialect=SparkSession().dialect).transform(
    30                    SparkSession().dialect.normalize_identifier
    31                )
    32            )
    33            .select(
    34                *(
    35                    column
    36                    for column in sqlglot.schema.column_names(
    37                        tableName, dialect=SparkSession().dialect
    38                    )
    39                )
    40            ),
    41        )

DataFrameReader(spark: SparkSession) View Source

    16    def __init__(self, spark: SparkSession):
    17        self.spark = spark

spark
def table(self, tableName: str) -> DataFrame: View Source

    19    def table(self, tableName: str) -> DataFrame:
    20        from sqlglot.dataframe.sql.dataframe import DataFrame
    21        from sqlglot.dataframe.sql.session import SparkSession
    22
    23        sqlglot.schema.add_table(tableName, dialect=SparkSession().dialect)
    24
    25        return DataFrame(
    26            self.spark,
    27            exp.Select()
    28            .from_(
    29                exp.to_table(tableName, dialect=SparkSession().dialect).transform(
    30                    SparkSession().dialect.normalize_identifier
    31                )
    32            )
    33            .select(
    34                *(
    35                    column
    36                    for column in sqlglot.schema.column_names(
    37                        tableName, dialect=SparkSession().dialect
    38                    )
    39                )
    40            ),
    41        )

class DataFrameWriter: View Source

     44class DataFrameWriter:
     45    def __init__(
     46        self,
     47        df: DataFrame,
     48        spark: t.Optional[SparkSession] = None,
     49        mode: t.Optional[str] = None,
     50        by_name: bool = False,
     51    ):
     52        self._df = df
     53        self._spark = spark or df.spark
     54        self._mode = mode
     55        self._by_name = by_name
     56
     57    def copy(self, **kwargs) -> DataFrameWriter:
     58        return DataFrameWriter(
     59            **{
     60                k[1:] if k.startswith("_") else k: v
     61                for k, v in object_to_dict(self, **kwargs).items()
     62            }
     63        )
     64
     65    def sql(self, **kwargs) -> t.List[str]:
     66        return self._df.sql(**kwargs)
     67
     68    def mode(self, saveMode: t.Optional[str]) -> DataFrameWriter:
     69        return self.copy(_mode=saveMode)
     70
     71    @property
     72    def byName(self):
     73        return self.copy(by_name=True)
     74
     75    def insertInto(self, tableName: str, overwrite: t.Optional[bool] = None) -> DataFrameWriter:
     76        from sqlglot.dataframe.sql.session import SparkSession
     77
     78        output_expression_container = exp.Insert(
     79            **{
     80                "this": exp.to_table(tableName),
     81                "overwrite": overwrite,
     82            }
     83        )
     84        df = self._df.copy(output_expression_container=output_expression_container)
     85        if self._by_name:
     86            columns = sqlglot.schema.column_names(
     87                tableName, only_visible=True, dialect=SparkSession().dialect
     88            )
     89            df = df._convert_leaf_to_cte().select(*columns)
     90
     91        return self.copy(_df=df)
     92
     93    def saveAsTable(self, name: str, format: t.Optional[str] = None, mode: t.Optional[str] = None):
     94        if format is not None:
     95            raise NotImplementedError("Providing Format in the save as table is not supported")
     96        exists, replace, mode = None, None, mode or str(self._mode)
     97        if mode == "append":
     98            return self.insertInto(name)
     99        if mode == "ignore":
    100            exists = True
    101        if mode == "overwrite":
    102            replace = True
    103        output_expression_container = exp.Create(
    104            this=exp.to_table(name),
    105            kind="TABLE",
    106            exists=exists,
    107            replace=replace,
    108        )
    109        return self.copy(_df=self._df.copy(output_expression_container=output_expression_container))

DataFrameWriter( df: DataFrame, spark: Optional[SparkSession] = None, mode: Optional[str] = None, by_name: bool = False) View Source

    45    def __init__(
    46        self,
    47        df: DataFrame,
    48        spark: t.Optional[SparkSession] = None,
    49        mode: t.Optional[str] = None,
    50        by_name: bool = False,
    51    ):
    52        self._df = df
    53        self._spark = spark or df.spark
    54        self._mode = mode
    55        self._by_name = by_name

def copy(self, **kwargs) -> DataFrameWriter: View Source

    57    def copy(self, **kwargs) -> DataFrameWriter:
    58        return DataFrameWriter(
    59            **{
    60                k[1:] if k.startswith("_") else k: v
    61                for k, v in object_to_dict(self, **kwargs).items()
    62            }
    63        )

def sql(self, **kwargs) -> List[str]: View Source

    65    def sql(self, **kwargs) -> t.List[str]:
    66        return self._df.sql(**kwargs)

def mode( self, saveMode: Optional[str]) -> DataFrameWriter: View Source

    68    def mode(self, saveMode: t.Optional[str]) -> DataFrameWriter:
    69        return self.copy(_mode=saveMode)

byName
def insertInto( self, tableName: str, overwrite: Optional[bool] = None) -> DataFrameWriter: View Source

    75    def insertInto(self, tableName: str, overwrite: t.Optional[bool] = None) -> DataFrameWriter:
    76        from sqlglot.dataframe.sql.session import SparkSession
    77
    78        output_expression_container = exp.Insert(
    79            **{
    80                "this": exp.to_table(tableName),
    81                "overwrite": overwrite,
    82            }
    83        )
    84        df = self._df.copy(output_expression_container=output_expression_container)
    85        if self._by_name:
    86            columns = sqlglot.schema.column_names(
    87                tableName, only_visible=True, dialect=SparkSession().dialect
    88            )
    89            df = df._convert_leaf_to_cte().select(*columns)
    90
    91        return self.copy(_df=df)

def saveAsTable( self, name: str, format: Optional[str] = None, mode: Optional[str] = None): View Source

     93    def saveAsTable(self, name: str, format: t.Optional[str] = None, mode: t.Optional[str] = None):
     94        if format is not None:
     95            raise NotImplementedError("Providing Format in the save as table is not supported")
     96        exists, replace, mode = None, None, mode or str(self._mode)
     97        if mode == "append":
     98            return self.insertInto(name)
     99        if mode == "ignore":
    100            exists = True
    101        if mode == "overwrite":
    102            replace = True
    103        output_expression_container = exp.Create(
    104            this=exp.to_table(name),
    105            kind="TABLE",
    106            exists=exists,
    107            replace=replace,
    108        )
    109        return self.copy(_df=self._df.copy(output_expression_container=output_expression_container))