# 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            (
     87                F.col(name).cast(data_type).alias(name).expression
     88                if data_type is not None
     89                else F.col(name).expression
     90            )
     91            for name, data_type in column_mapping.items()
     92        ]
     93
     94        select_kwargs = {
     95            "expressions": sel_columns,
     96            "from": exp.From(
     97                this=exp.Values(
     98                    expressions=data_expressions,
     99                    alias=exp.TableAlias(
    100                        this=exp.to_identifier(self._auto_incrementing_name),
    101                        columns=[exp.to_identifier(col_name) for col_name in column_mapping],
    102                    ),
    103                ),
    104            ),
    105        }
    106
    107        sel_expression = exp.Select(**select_kwargs)
    108        return DataFrame(self, sel_expression)
    109
    110    def sql(self, sqlQuery: str) -> DataFrame:
    111        expression = sqlglot.parse_one(sqlQuery, read=self.dialect)
    112        if isinstance(expression, exp.Select):
    113            df = DataFrame(self, expression)
    114            df = df._convert_leaf_to_cte()
    115        elif isinstance(expression, (exp.Create, exp.Insert)):
    116            select_expression = expression.expression.copy()
    117            if isinstance(expression, exp.Insert):
    118                select_expression.set("with", expression.args.get("with"))
    119                expression.set("with", None)
    120            del expression.args["expression"]
    121            df = DataFrame(self, select_expression, output_expression_container=expression)  # type: ignore
    122            df = df._convert_leaf_to_cte()
    123        else:
    124            raise ValueError(
    125                "Unknown expression type provided in the SQL. Please create an issue with the SQL."
    126            )
    127        return df
    128
    129    @property
    130    def _auto_incrementing_name(self) -> str:
    131        name = f"a{self.incrementing_id}"
    132        self.incrementing_id += 1
    133        return name
    134
    135    @property
    136    def _random_branch_id(self) -> str:
    137        id = self._random_id
    138        self.known_branch_ids.add(id)
    139        return id
    140
    141    @property
    142    def _random_sequence_id(self):
    143        id = self._random_id
    144        self.known_sequence_ids.add(id)
    145        return id
    146
    147    @property
    148    def _random_id(self) -> str:
    149        id = "r" + uuid.uuid4().hex
    150        self.known_ids.add(id)
    151        return id
    152
    153    @property
    154    def _join_hint_names(self) -> t.Set[str]:
    155        return {"BROADCAST", "MERGE", "SHUFFLE_HASH", "SHUFFLE_REPLICATE_NL"}
    156
    157    def _add_alias_to_mapping(self, name: str, sequence_id: str):
    158        self.name_to_sequence_id_mapping[name].append(sequence_id)
    159
    160    class Builder:
    161        SQLFRAME_DIALECT_KEY = "sqlframe.dialect"
    162
    163        def __init__(self):
    164            self.dialect = "spark"
    165
    166        def __getattr__(self, item) -> SparkSession.Builder:
    167            return self
    168
    169        def __call__(self, *args, **kwargs):
    170            return self
    171
    172        def config(
    173            self,
    174            key: t.Optional[str] = None,
    175            value: t.Optional[t.Any] = None,
    176            *,
    177            map: t.Optional[t.Dict[str, t.Any]] = None,
    178            **kwargs: t.Any,
    179        ) -> SparkSession.Builder:
    180            if key == self.SQLFRAME_DIALECT_KEY:
    181                self.dialect = value
    182            elif map and self.SQLFRAME_DIALECT_KEY in map:
    183                self.dialect = map[self.SQLFRAME_DIALECT_KEY]
    184            return self
    185
    186        def getOrCreate(self) -> SparkSession:
    187            spark = SparkSession()
    188            spark.dialect = Dialect.get_or_raise(self.dialect)
    189            return spark
    190
    191    @classproperty
    192    def builder(cls) -> Builder:
    193        return cls.Builder()

DEFAULT_DIALECT = 'spark'
read: DataFrameReader View Source

    39    @property
    40    def read(self) -> DataFrameReader:
    41        return DataFrameReader(self)

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='139688854402048'>], List[<MagicMock id='139688854402048'>], Tuple]], schema: Optional[<MagicMock id='139688855867008'>] = 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            (
     87                F.col(name).cast(data_type).alias(name).expression
     88                if data_type is not None
     89                else F.col(name).expression
     90            )
     91            for name, data_type in column_mapping.items()
     92        ]
     93
     94        select_kwargs = {
     95            "expressions": sel_columns,
     96            "from": exp.From(
     97                this=exp.Values(
     98                    expressions=data_expressions,
     99                    alias=exp.TableAlias(
    100                        this=exp.to_identifier(self._auto_incrementing_name),
    101                        columns=[exp.to_identifier(col_name) for col_name in column_mapping],
    102                    ),
    103                ),
    104            ),
    105        }
    106
    107        sel_expression = exp.Select(**select_kwargs)
    108        return DataFrame(self, sel_expression)

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

    110    def sql(self, sqlQuery: str) -> DataFrame:
    111        expression = sqlglot.parse_one(sqlQuery, read=self.dialect)
    112        if isinstance(expression, exp.Select):
    113            df = DataFrame(self, expression)
    114            df = df._convert_leaf_to_cte()
    115        elif isinstance(expression, (exp.Create, exp.Insert)):
    116            select_expression = expression.expression.copy()
    117            if isinstance(expression, exp.Insert):
    118                select_expression.set("with", expression.args.get("with"))
    119                expression.set("with", None)
    120            del expression.args["expression"]
    121            df = DataFrame(self, select_expression, output_expression_container=expression)  # type: ignore
    122            df = df._convert_leaf_to_cte()
    123        else:
    124            raise ValueError(
    125                "Unknown expression type provided in the SQL. Please create an issue with the SQL."
    126            )
    127        return df

builder: SparkSession.Builder View Source

    191    @classproperty
    192    def builder(cls) -> Builder:
    193        return cls.Builder()

class SparkSession.Builder: View Source

    160    class Builder:
    161        SQLFRAME_DIALECT_KEY = "sqlframe.dialect"
    162
    163        def __init__(self):
    164            self.dialect = "spark"
    165
    166        def __getattr__(self, item) -> SparkSession.Builder:
    167            return self
    168
    169        def __call__(self, *args, **kwargs):
    170            return self
    171
    172        def config(
    173            self,
    174            key: t.Optional[str] = None,
    175            value: t.Optional[t.Any] = None,
    176            *,
    177            map: t.Optional[t.Dict[str, t.Any]] = None,
    178            **kwargs: t.Any,
    179        ) -> SparkSession.Builder:
    180            if key == self.SQLFRAME_DIALECT_KEY:
    181                self.dialect = value
    182            elif map and self.SQLFRAME_DIALECT_KEY in map:
    183                self.dialect = map[self.SQLFRAME_DIALECT_KEY]
    184            return self
    185
    186        def getOrCreate(self) -> SparkSession:
    187            spark = SparkSession()
    188            spark.dialect = Dialect.get_or_raise(self.dialect)
    189            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

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

def getOrCreate(self) -> SparkSession: View Source

    186        def getOrCreate(self) -> SparkSession:
    187            spark = SparkSession()
    188            spark.dialect = Dialect.get_or_raise(self.dialect)
    189            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(self, dialect: DialectType = None, optimize: bool = True, **kwargs) -> t.List[str]:
    302        from sqlglot.dataframe.sql.session import SparkSession
    303
    304        dialect = Dialect.get_or_raise(dialect or SparkSession().dialect)
    305
    306        df = self._resolve_pending_hints()
    307        select_expressions = df._get_select_expressions()
    308        output_expressions: t.List[t.Union[exp.Select, exp.Cache, exp.Drop]] = []
    309        replacement_mapping: t.Dict[exp.Identifier, exp.Identifier] = {}
    310
    311        for expression_type, select_expression in select_expressions:
    312            select_expression = select_expression.transform(replace_id_value, replacement_mapping)
    313            if optimize:
    314                quote_identifiers(select_expression, dialect=dialect)
    315                select_expression = t.cast(
    316                    exp.Select, optimize_func(select_expression, dialect=dialect)
    317                )
    318
    319            select_expression = df._replace_cte_names_with_hashes(select_expression)
    320
    321            expression: t.Union[exp.Select, exp.Cache, exp.Drop]
    322            if expression_type == exp.Cache:
    323                cache_table_name = df._create_hash_from_expression(select_expression)
    324                cache_table = exp.to_table(cache_table_name)
    325                original_alias_name = select_expression.args["cte_alias_name"]
    326
    327                replacement_mapping[exp.to_identifier(original_alias_name)] = exp.to_identifier(  # type: ignore
    328                    cache_table_name
    329                )
    330                sqlglot.schema.add_table(
    331                    cache_table_name,
    332                    {
    333                        expression.alias_or_name: expression.type.sql(dialect=dialect)
    334                        for expression in select_expression.expressions
    335                    },
    336                    dialect=dialect,
    337                )
    338
    339                cache_storage_level = select_expression.args["cache_storage_level"]
    340                options = [
    341                    exp.Literal.string("storageLevel"),
    342                    exp.Literal.string(cache_storage_level),
    343                ]
    344                expression = exp.Cache(
    345                    this=cache_table, expression=select_expression, lazy=True, options=options
    346                )
    347
    348                # We will drop the "view" if it exists before running the cache table
    349                output_expressions.append(exp.Drop(this=cache_table, exists=True, kind="VIEW"))
    350            elif expression_type == exp.Create:
    351                expression = df.output_expression_container.copy()
    352                expression.set("expression", select_expression)
    353            elif expression_type == exp.Insert:
    354                expression = df.output_expression_container.copy()
    355                select_without_ctes = select_expression.copy()
    356                select_without_ctes.set("with", None)
    357                expression.set("expression", select_without_ctes)
    358
    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
    366            output_expressions.append(expression)
    367
    368        return [expression.sql(dialect=dialect, **kwargs) for expression in output_expressions]
    369
    370    def copy(self, **kwargs) -> DataFrame:
    371        return DataFrame(**object_to_dict(self, **kwargs))
    372
    373    @operation(Operation.SELECT)
    374    def select(self, *cols, **kwargs) -> DataFrame:
    375        cols = self._ensure_and_normalize_cols(cols)
    376        kwargs["append"] = kwargs.get("append", False)
    377        if self.expression.args.get("joins"):
    378            ambiguous_cols = [
    379                col
    380                for col in cols
    381                if isinstance(col.column_expression, exp.Column) and not col.column_expression.table
    382            ]
    383            if ambiguous_cols:
    384                join_table_identifiers = [
    385                    x.this for x in get_tables_from_expression_with_join(self.expression)
    386                ]
    387                cte_names_in_join = [x.this for x in join_table_identifiers]
    388                # If we have columns that resolve to multiple CTE expressions then we want to use each CTE left-to-right
    389                # and therefore we allow multiple columns with the same name in the result. This matches the behavior
    390                # of Spark.
    391                resolved_column_position: t.Dict[Column, int] = {col: -1 for col in ambiguous_cols}
    392                for ambiguous_col in ambiguous_cols:
    393                    ctes_with_column = [
    394                        cte
    395                        for cte in self.expression.ctes
    396                        if cte.alias_or_name in cte_names_in_join
    397                        and ambiguous_col.alias_or_name in cte.this.named_selects
    398                    ]
    399                    # Check if there is a CTE with this column that we haven't used before. If so, use it. Otherwise,
    400                    # use the same CTE we used before
    401                    cte = seq_get(ctes_with_column, resolved_column_position[ambiguous_col] + 1)
    402                    if cte:
    403                        resolved_column_position[ambiguous_col] += 1
    404                    else:
    405                        cte = ctes_with_column[resolved_column_position[ambiguous_col]]
    406                    ambiguous_col.expression.set("table", cte.alias_or_name)
    407        return self.copy(
    408            expression=self.expression.select(*[x.expression for x in cols], **kwargs), **kwargs
    409        )
    410
    411    @operation(Operation.NO_OP)
    412    def alias(self, name: str, **kwargs) -> DataFrame:
    413        new_sequence_id = self.spark._random_sequence_id
    414        df = self.copy()
    415        for join_hint in df.pending_join_hints:
    416            for expression in join_hint.expressions:
    417                if expression.alias_or_name == self.sequence_id:
    418                    expression.set("this", Column.ensure_col(new_sequence_id).expression)
    419        df.spark._add_alias_to_mapping(name, new_sequence_id)
    420        return df._convert_leaf_to_cte(sequence_id=new_sequence_id)
    421
    422    @operation(Operation.WHERE)
    423    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    424        col = self._ensure_and_normalize_col(column)
    425        return self.copy(expression=self.expression.where(col.expression))
    426
    427    filter = where
    428
    429    @operation(Operation.GROUP_BY)
    430    def groupBy(self, *cols, **kwargs) -> GroupedData:
    431        columns = self._ensure_and_normalize_cols(cols)
    432        return GroupedData(self, columns, self.last_op)
    433
    434    @operation(Operation.SELECT)
    435    def agg(self, *exprs, **kwargs) -> DataFrame:
    436        cols = self._ensure_and_normalize_cols(exprs)
    437        return self.groupBy().agg(*cols)
    438
    439    @operation(Operation.FROM)
    440    def join(
    441        self,
    442        other_df: DataFrame,
    443        on: t.Union[str, t.List[str], Column, t.List[Column]],
    444        how: str = "inner",
    445        **kwargs,
    446    ) -> DataFrame:
    447        other_df = other_df._convert_leaf_to_cte()
    448        join_columns = self._ensure_list_of_columns(on)
    449        # We will determine actual "join on" expression later so we don't provide it at first
    450        join_expression = self.expression.join(
    451            other_df.latest_cte_name, join_type=how.replace("_", " ")
    452        )
    453        join_expression = self._add_ctes_to_expression(join_expression, other_df.expression.ctes)
    454        self_columns = self._get_outer_select_columns(join_expression)
    455        other_columns = self._get_outer_select_columns(other_df)
    456        # Determines the join clause and select columns to be used passed on what type of columns were provided for
    457        # the join. The columns returned changes based on how the on expression is provided.
    458        if isinstance(join_columns[0].expression, exp.Column):
    459            """
    460            Unique characteristics of join on column names only:
    461            * The column names are put at the front of the select list
    462            * The column names are deduplicated across the entire select list and only the column names (other dups are allowed)
    463            """
    464            table_names = [
    465                table.alias_or_name
    466                for table in get_tables_from_expression_with_join(join_expression)
    467            ]
    468            potential_ctes = [
    469                cte
    470                for cte in join_expression.ctes
    471                if cte.alias_or_name in table_names
    472                and cte.alias_or_name != other_df.latest_cte_name
    473            ]
    474            # Determine the table to reference for the left side of the join by checking each of the left side
    475            # tables and see if they have the column being referenced.
    476            join_column_pairs = []
    477            for join_column in join_columns:
    478                num_matching_ctes = 0
    479                for cte in potential_ctes:
    480                    if join_column.alias_or_name in cte.this.named_selects:
    481                        left_column = join_column.copy().set_table_name(cte.alias_or_name)
    482                        right_column = join_column.copy().set_table_name(other_df.latest_cte_name)
    483                        join_column_pairs.append((left_column, right_column))
    484                        num_matching_ctes += 1
    485                if num_matching_ctes > 1:
    486                    raise ValueError(
    487                        f"Column {join_column.alias_or_name} is ambiguous. Please specify the table name."
    488                    )
    489                elif num_matching_ctes == 0:
    490                    raise ValueError(
    491                        f"Column {join_column.alias_or_name} does not exist in any of the tables."
    492                    )
    493            join_clause = functools.reduce(
    494                lambda x, y: x & y,
    495                [left_column == right_column for left_column, right_column in join_column_pairs],
    496            )
    497            join_column_names = [left_col.alias_or_name for left_col, _ in join_column_pairs]
    498            # To match spark behavior only the join clause gets deduplicated and it gets put in the front of the column list
    499            select_column_names = [
    500                (
    501                    column.alias_or_name
    502                    if not isinstance(column.expression.this, exp.Star)
    503                    else column.sql()
    504                )
    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            i for i, col in enumerate(columns) if isinstance(col.expression, exp.Ordered)
    547        ]
    548        if ascending is None:
    549            ascending = [True] * len(columns)
    550        elif not isinstance(ascending, list):
    551            ascending = [ascending] * len(columns)
    552        ascending = [bool(x) for i, x in enumerate(ascending)]
    553        assert len(columns) == len(
    554            ascending
    555        ), "The length of items in ascending must equal the number of columns provided"
    556        col_and_ascending = list(zip(columns, ascending))
    557        order_by_columns = [
    558            (
    559                exp.Ordered(this=col.expression, desc=not asc)
    560                if i not in pre_ordered_col_indexes
    561                else columns[i].column_expression
    562            )
    563            for i, (col, asc) in enumerate(col_and_ascending)
    564        ]
    565        return self.copy(expression=self.expression.order_by(*order_by_columns))
    566
    567    sort = orderBy
    568
    569    @operation(Operation.FROM)
    570    def union(self, other: DataFrame) -> DataFrame:
    571        return self._set_operation(exp.Union, other, False)
    572
    573    unionAll = union
    574
    575    @operation(Operation.FROM)
    576    def unionByName(self, other: DataFrame, allowMissingColumns: bool = False):
    577        l_columns = self.columns
    578        r_columns = other.columns
    579        if not allowMissingColumns:
    580            l_expressions = l_columns
    581            r_expressions = l_columns
    582        else:
    583            l_expressions = []
    584            r_expressions = []
    585            r_columns_unused = copy(r_columns)
    586            for l_column in l_columns:
    587                l_expressions.append(l_column)
    588                if l_column in r_columns:
    589                    r_expressions.append(l_column)
    590                    r_columns_unused.remove(l_column)
    591                else:
    592                    r_expressions.append(exp.alias_(exp.Null(), l_column, copy=False))
    593            for r_column in r_columns_unused:
    594                l_expressions.append(exp.alias_(exp.Null(), r_column, copy=False))
    595                r_expressions.append(r_column)
    596        r_df = (
    597            other.copy()._convert_leaf_to_cte().select(*self._ensure_list_of_columns(r_expressions))
    598        )
    599        l_df = self.copy()
    600        if allowMissingColumns:
    601            l_df = l_df._convert_leaf_to_cte().select(*self._ensure_list_of_columns(l_expressions))
    602        return l_df._set_operation(exp.Union, r_df, False)
    603
    604    @operation(Operation.FROM)
    605    def intersect(self, other: DataFrame) -> DataFrame:
    606        return self._set_operation(exp.Intersect, other, True)
    607
    608    @operation(Operation.FROM)
    609    def intersectAll(self, other: DataFrame) -> DataFrame:
    610        return self._set_operation(exp.Intersect, other, False)
    611
    612    @operation(Operation.FROM)
    613    def exceptAll(self, other: DataFrame) -> DataFrame:
    614        return self._set_operation(exp.Except, other, False)
    615
    616    @operation(Operation.SELECT)
    617    def distinct(self) -> DataFrame:
    618        return self.copy(expression=self.expression.distinct())
    619
    620    @operation(Operation.SELECT)
    621    def dropDuplicates(self, subset: t.Optional[t.List[str]] = None):
    622        if not subset:
    623            return self.distinct()
    624        column_names = ensure_list(subset)
    625        window = Window.partitionBy(*column_names).orderBy(*column_names)
    626        return (
    627            self.copy()
    628            .withColumn("row_num", F.row_number().over(window))
    629            .where(F.col("row_num") == F.lit(1))
    630            .drop("row_num")
    631        )
    632
    633    @operation(Operation.FROM)
    634    def dropna(
    635        self,
    636        how: str = "any",
    637        thresh: t.Optional[int] = None,
    638        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    639    ) -> DataFrame:
    640        minimum_non_null = thresh or 0  # will be determined later if thresh is null
    641        new_df = self.copy()
    642        all_columns = self._get_outer_select_columns(new_df.expression)
    643        if subset:
    644            null_check_columns = self._ensure_and_normalize_cols(subset)
    645        else:
    646            null_check_columns = all_columns
    647        if thresh is None:
    648            minimum_num_nulls = 1 if how == "any" else len(null_check_columns)
    649        else:
    650            minimum_num_nulls = len(null_check_columns) - minimum_non_null + 1
    651        if minimum_num_nulls > len(null_check_columns):
    652            raise RuntimeError(
    653                f"The minimum num nulls for dropna must be less than or equal to the number of columns. "
    654                f"Minimum num nulls: {minimum_num_nulls}, Num Columns: {len(null_check_columns)}"
    655            )
    656        if_null_checks = [
    657            F.when(column.isNull(), F.lit(1)).otherwise(F.lit(0)) for column in null_check_columns
    658        ]
    659        nulls_added_together = functools.reduce(lambda x, y: x + y, if_null_checks)
    660        num_nulls = nulls_added_together.alias("num_nulls")
    661        new_df = new_df.select(num_nulls, append=True)
    662        filtered_df = new_df.where(F.col("num_nulls") < F.lit(minimum_num_nulls))
    663        final_df = filtered_df.select(*all_columns)
    664        return final_df
    665
    666    @operation(Operation.FROM)
    667    def fillna(
    668        self,
    669        value: t.Union[ColumnLiterals],
    670        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    671    ) -> DataFrame:
    672        """
    673        Functionality Difference: If you provide a value to replace a null and that type conflicts
    674        with the type of the column then PySpark will just ignore your replacement.
    675        This will try to cast them to be the same in some cases. So they won't always match.
    676        Best to not mix types so make sure replacement is the same type as the column
    677
    678        Possibility for improvement: Use `typeof` function to get the type of the column
    679        and check if it matches the type of the value provided. If not then make it null.
    680        """
    681        from sqlglot.dataframe.sql.functions import lit
    682
    683        values = None
    684        columns = None
    685        new_df = self.copy()
    686        all_columns = self._get_outer_select_columns(new_df.expression)
    687        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    688        if isinstance(value, dict):
    689            values = list(value.values())
    690            columns = self._ensure_and_normalize_cols(list(value))
    691        if not columns:
    692            columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    693        if not values:
    694            values = [value] * len(columns)
    695        value_columns = [lit(value) for value in values]
    696
    697        null_replacement_mapping = {
    698            column.alias_or_name: (
    699                F.when(column.isNull(), value).otherwise(column).alias(column.alias_or_name)
    700            )
    701            for column, value in zip(columns, value_columns)
    702        }
    703        null_replacement_mapping = {**all_column_mapping, **null_replacement_mapping}
    704        null_replacement_columns = [
    705            null_replacement_mapping[column.alias_or_name] for column in all_columns
    706        ]
    707        new_df = new_df.select(*null_replacement_columns)
    708        return new_df
    709
    710    @operation(Operation.FROM)
    711    def replace(
    712        self,
    713        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    714        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    715        subset: t.Optional[t.Collection[ColumnOrName] | ColumnOrName] = None,
    716    ) -> DataFrame:
    717        from sqlglot.dataframe.sql.functions import lit
    718
    719        old_values = None
    720        new_df = self.copy()
    721        all_columns = self._get_outer_select_columns(new_df.expression)
    722        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    723
    724        columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    725        if isinstance(to_replace, dict):
    726            old_values = list(to_replace)
    727            new_values = list(to_replace.values())
    728        elif not old_values and isinstance(to_replace, list):
    729            assert isinstance(value, list), "value must be a list since the replacements are a list"
    730            assert len(to_replace) == len(
    731                value
    732            ), "the replacements and values must be the same length"
    733            old_values = to_replace
    734            new_values = value
    735        else:
    736            old_values = [to_replace] * len(columns)
    737            new_values = [value] * len(columns)
    738        old_values = [lit(value) for value in old_values]
    739        new_values = [lit(value) for value in new_values]
    740
    741        replacement_mapping = {}
    742        for column in columns:
    743            expression = Column(None)
    744            for i, (old_value, new_value) in enumerate(zip(old_values, new_values)):
    745                if i == 0:
    746                    expression = F.when(column == old_value, new_value)
    747                else:
    748                    expression = expression.when(column == old_value, new_value)  # type: ignore
    749            replacement_mapping[column.alias_or_name] = expression.otherwise(column).alias(
    750                column.expression.alias_or_name
    751            )
    752
    753        replacement_mapping = {**all_column_mapping, **replacement_mapping}
    754        replacement_columns = [replacement_mapping[column.alias_or_name] for column in all_columns]
    755        new_df = new_df.select(*replacement_columns)
    756        return new_df
    757
    758    @operation(Operation.SELECT)
    759    def withColumn(self, colName: str, col: Column) -> DataFrame:
    760        col = self._ensure_and_normalize_col(col)
    761        existing_col_names = self.expression.named_selects
    762        existing_col_index = (
    763            existing_col_names.index(colName) if colName in existing_col_names else None
    764        )
    765        if existing_col_index:
    766            expression = self.expression.copy()
    767            expression.expressions[existing_col_index] = col.expression
    768            return self.copy(expression=expression)
    769        return self.copy().select(col.alias(colName), append=True)
    770
    771    @operation(Operation.SELECT)
    772    def withColumnRenamed(self, existing: str, new: str):
    773        expression = self.expression.copy()
    774        existing_columns = [
    775            expression
    776            for expression in expression.expressions
    777            if expression.alias_or_name == existing
    778        ]
    779        if not existing_columns:
    780            raise ValueError("Tried to rename a column that doesn't exist")
    781        for existing_column in existing_columns:
    782            if isinstance(existing_column, exp.Column):
    783                existing_column.replace(exp.alias_(existing_column, new))
    784            else:
    785                existing_column.set("alias", exp.to_identifier(new))
    786        return self.copy(expression=expression)
    787
    788    @operation(Operation.SELECT)
    789    def drop(self, *cols: t.Union[str, Column]) -> DataFrame:
    790        all_columns = self._get_outer_select_columns(self.expression)
    791        drop_cols = self._ensure_and_normalize_cols(cols)
    792        new_columns = [
    793            col
    794            for col in all_columns
    795            if col.alias_or_name not in [drop_column.alias_or_name for drop_column in drop_cols]
    796        ]
    797        return self.copy().select(*new_columns, append=False)
    798
    799    @operation(Operation.LIMIT)
    800    def limit(self, num: int) -> DataFrame:
    801        return self.copy(expression=self.expression.limit(num))
    802
    803    @operation(Operation.NO_OP)
    804    def hint(self, name: str, *parameters: t.Optional[t.Union[str, int]]) -> DataFrame:
    805        parameter_list = ensure_list(parameters)
    806        parameter_columns = (
    807            self._ensure_list_of_columns(parameter_list)
    808            if parameters
    809            else Column.ensure_cols([self.sequence_id])
    810        )
    811        return self._hint(name, parameter_columns)
    812
    813    @operation(Operation.NO_OP)
    814    def repartition(
    815        self, numPartitions: t.Union[int, ColumnOrName], *cols: ColumnOrName
    816    ) -> DataFrame:
    817        num_partition_cols = self._ensure_list_of_columns(numPartitions)
    818        columns = self._ensure_and_normalize_cols(cols)
    819        args = num_partition_cols + columns
    820        return self._hint("repartition", args)
    821
    822    @operation(Operation.NO_OP)
    823    def coalesce(self, numPartitions: int) -> DataFrame:
    824        num_partitions = Column.ensure_cols([numPartitions])
    825        return self._hint("coalesce", num_partitions)
    826
    827    @operation(Operation.NO_OP)
    828    def cache(self) -> DataFrame:
    829        return self._cache(storage_level="MEMORY_AND_DISK")
    830
    831    @operation(Operation.NO_OP)
    832    def persist(self, storageLevel: str = "MEMORY_AND_DISK_SER") -> DataFrame:
    833        """
    834        Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html
    835        """
    836        return self._cache(storageLevel)

DataFrame( spark: <MagicMock id='139688858878976'>, 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='139688859097632'>] = 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 View Source

    79    @property
    80    def sparkSession(self):
    81        return self.spark

write View Source

    83    @property
    84    def write(self):
    85        return DataFrameWriter(self)

latest_cte_name: str View Source

    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

pending_join_hints View Source

    101    @property
    102    def pending_join_hints(self):
    103        return [hint for hint in self.pending_hints if isinstance(hint, exp.JoinHint)]

pending_partition_hints View Source

    105    @property
    106    def pending_partition_hints(self):
    107        return [hint for hint in self.pending_hints if isinstance(hint, exp.Anonymous)]

columns: List[str] View Source

    109    @property
    110    def columns(self) -> t.List[str]:
    111        return self.expression.named_selects

na: DataFrameNaFunctions View Source

    113    @property
    114    def na(self) -> DataFrameNaFunctions:
    115        return DataFrameNaFunctions(self)

def sql( self, dialect: <MagicMock id='139688853981648'> = None, optimize: bool = True, **kwargs) -> List[str]: View Source

    301    def sql(self, dialect: DialectType = None, optimize: bool = True, **kwargs) -> t.List[str]:
    302        from sqlglot.dataframe.sql.session import SparkSession
    303
    304        dialect = Dialect.get_or_raise(dialect or SparkSession().dialect)
    305
    306        df = self._resolve_pending_hints()
    307        select_expressions = df._get_select_expressions()
    308        output_expressions: t.List[t.Union[exp.Select, exp.Cache, exp.Drop]] = []
    309        replacement_mapping: t.Dict[exp.Identifier, exp.Identifier] = {}
    310
    311        for expression_type, select_expression in select_expressions:
    312            select_expression = select_expression.transform(replace_id_value, replacement_mapping)
    313            if optimize:
    314                quote_identifiers(select_expression, dialect=dialect)
    315                select_expression = t.cast(
    316                    exp.Select, optimize_func(select_expression, dialect=dialect)
    317                )
    318
    319            select_expression = df._replace_cte_names_with_hashes(select_expression)
    320
    321            expression: t.Union[exp.Select, exp.Cache, exp.Drop]
    322            if expression_type == exp.Cache:
    323                cache_table_name = df._create_hash_from_expression(select_expression)
    324                cache_table = exp.to_table(cache_table_name)
    325                original_alias_name = select_expression.args["cte_alias_name"]
    326
    327                replacement_mapping[exp.to_identifier(original_alias_name)] = exp.to_identifier(  # type: ignore
    328                    cache_table_name
    329                )
    330                sqlglot.schema.add_table(
    331                    cache_table_name,
    332                    {
    333                        expression.alias_or_name: expression.type.sql(dialect=dialect)
    334                        for expression in select_expression.expressions
    335                    },
    336                    dialect=dialect,
    337                )
    338
    339                cache_storage_level = select_expression.args["cache_storage_level"]
    340                options = [
    341                    exp.Literal.string("storageLevel"),
    342                    exp.Literal.string(cache_storage_level),
    343                ]
    344                expression = exp.Cache(
    345                    this=cache_table, expression=select_expression, lazy=True, options=options
    346                )
    347
    348                # We will drop the "view" if it exists before running the cache table
    349                output_expressions.append(exp.Drop(this=cache_table, exists=True, kind="VIEW"))
    350            elif expression_type == exp.Create:
    351                expression = df.output_expression_container.copy()
    352                expression.set("expression", select_expression)
    353            elif expression_type == exp.Insert:
    354                expression = df.output_expression_container.copy()
    355                select_without_ctes = select_expression.copy()
    356                select_without_ctes.set("with", None)
    357                expression.set("expression", select_without_ctes)
    358
    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
    366            output_expressions.append(expression)
    367
    368        return [expression.sql(dialect=dialect, **kwargs) for expression in output_expressions]

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

    370    def copy(self, **kwargs) -> DataFrame:
    371        return DataFrame(**object_to_dict(self, **kwargs))

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

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

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

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

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

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

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

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

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

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

    434    @operation(Operation.SELECT)
    435    def agg(self, *exprs, **kwargs) -> DataFrame:
    436        cols = self._ensure_and_normalize_cols(exprs)
    437        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

    439    @operation(Operation.FROM)
    440    def join(
    441        self,
    442        other_df: DataFrame,
    443        on: t.Union[str, t.List[str], Column, t.List[Column]],
    444        how: str = "inner",
    445        **kwargs,
    446    ) -> DataFrame:
    447        other_df = other_df._convert_leaf_to_cte()
    448        join_columns = self._ensure_list_of_columns(on)
    449        # We will determine actual "join on" expression later so we don't provide it at first
    450        join_expression = self.expression.join(
    451            other_df.latest_cte_name, join_type=how.replace("_", " ")
    452        )
    453        join_expression = self._add_ctes_to_expression(join_expression, other_df.expression.ctes)
    454        self_columns = self._get_outer_select_columns(join_expression)
    455        other_columns = self._get_outer_select_columns(other_df)
    456        # Determines the join clause and select columns to be used passed on what type of columns were provided for
    457        # the join. The columns returned changes based on how the on expression is provided.
    458        if isinstance(join_columns[0].expression, exp.Column):
    459            """
    460            Unique characteristics of join on column names only:
    461            * The column names are put at the front of the select list
    462            * The column names are deduplicated across the entire select list and only the column names (other dups are allowed)
    463            """
    464            table_names = [
    465                table.alias_or_name
    466                for table in get_tables_from_expression_with_join(join_expression)
    467            ]
    468            potential_ctes = [
    469                cte
    470                for cte in join_expression.ctes
    471                if cte.alias_or_name in table_names
    472                and cte.alias_or_name != other_df.latest_cte_name
    473            ]
    474            # Determine the table to reference for the left side of the join by checking each of the left side
    475            # tables and see if they have the column being referenced.
    476            join_column_pairs = []
    477            for join_column in join_columns:
    478                num_matching_ctes = 0
    479                for cte in potential_ctes:
    480                    if join_column.alias_or_name in cte.this.named_selects:
    481                        left_column = join_column.copy().set_table_name(cte.alias_or_name)
    482                        right_column = join_column.copy().set_table_name(other_df.latest_cte_name)
    483                        join_column_pairs.append((left_column, right_column))
    484                        num_matching_ctes += 1
    485                if num_matching_ctes > 1:
    486                    raise ValueError(
    487                        f"Column {join_column.alias_or_name} is ambiguous. Please specify the table name."
    488                    )
    489                elif num_matching_ctes == 0:
    490                    raise ValueError(
    491                        f"Column {join_column.alias_or_name} does not exist in any of the tables."
    492                    )
    493            join_clause = functools.reduce(
    494                lambda x, y: x & y,
    495                [left_column == right_column for left_column, right_column in join_column_pairs],
    496            )
    497            join_column_names = [left_col.alias_or_name for left_col, _ in join_column_pairs]
    498            # To match spark behavior only the join clause gets deduplicated and it gets put in the front of the column list
    499            select_column_names = [
    500                (
    501                    column.alias_or_name
    502                    if not isinstance(column.expression.this, exp.Star)
    503                    else column.sql()
    504                )
    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            i for i, col in enumerate(columns) if isinstance(col.expression, exp.Ordered)
    547        ]
    548        if ascending is None:
    549            ascending = [True] * len(columns)
    550        elif not isinstance(ascending, list):
    551            ascending = [ascending] * len(columns)
    552        ascending = [bool(x) for i, x in enumerate(ascending)]
    553        assert len(columns) == len(
    554            ascending
    555        ), "The length of items in ascending must equal the number of columns provided"
    556        col_and_ascending = list(zip(columns, ascending))
    557        order_by_columns = [
    558            (
    559                exp.Ordered(this=col.expression, desc=not asc)
    560                if i not in pre_ordered_col_indexes
    561                else columns[i].column_expression
    562            )
    563            for i, (col, asc) in enumerate(col_and_ascending)
    564        ]
    565        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            i for i, col in enumerate(columns) if isinstance(col.expression, exp.Ordered)
    547        ]
    548        if ascending is None:
    549            ascending = [True] * len(columns)
    550        elif not isinstance(ascending, list):
    551            ascending = [ascending] * len(columns)
    552        ascending = [bool(x) for i, x in enumerate(ascending)]
    553        assert len(columns) == len(
    554            ascending
    555        ), "The length of items in ascending must equal the number of columns provided"
    556        col_and_ascending = list(zip(columns, ascending))
    557        order_by_columns = [
    558            (
    559                exp.Ordered(this=col.expression, desc=not asc)
    560                if i not in pre_ordered_col_indexes
    561                else columns[i].column_expression
    562            )
    563            for i, (col, asc) in enumerate(col_and_ascending)
    564        ]
    565        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

    569    @operation(Operation.FROM)
    570    def union(self, other: DataFrame) -> DataFrame:
    571        return self._set_operation(exp.Union, other, False)

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

    569    @operation(Operation.FROM)
    570    def union(self, other: DataFrame) -> DataFrame:
    571        return self._set_operation(exp.Union, other, False)

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

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

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

    604    @operation(Operation.FROM)
    605    def intersect(self, other: DataFrame) -> DataFrame:
    606        return self._set_operation(exp.Intersect, other, True)

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

    608    @operation(Operation.FROM)
    609    def intersectAll(self, other: DataFrame) -> DataFrame:
    610        return self._set_operation(exp.Intersect, other, False)

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

    612    @operation(Operation.FROM)
    613    def exceptAll(self, other: DataFrame) -> DataFrame:
    614        return self._set_operation(exp.Except, other, False)

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

    616    @operation(Operation.SELECT)
    617    def distinct(self) -> DataFrame:
    618        return self.copy(expression=self.expression.distinct())

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

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

@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

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

@operation(Operation.FROM)
def fillna( self, value: <MagicMock id='139688854309184'>, subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> DataFrame: View Source

    666    @operation(Operation.FROM)
    667    def fillna(
    668        self,
    669        value: t.Union[ColumnLiterals],
    670        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    671    ) -> DataFrame:
    672        """
    673        Functionality Difference: If you provide a value to replace a null and that type conflicts
    674        with the type of the column then PySpark will just ignore your replacement.
    675        This will try to cast them to be the same in some cases. So they won't always match.
    676        Best to not mix types so make sure replacement is the same type as the column
    677
    678        Possibility for improvement: Use `typeof` function to get the type of the column
    679        and check if it matches the type of the value provided. If not then make it null.
    680        """
    681        from sqlglot.dataframe.sql.functions import lit
    682
    683        values = None
    684        columns = None
    685        new_df = self.copy()
    686        all_columns = self._get_outer_select_columns(new_df.expression)
    687        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    688        if isinstance(value, dict):
    689            values = list(value.values())
    690            columns = self._ensure_and_normalize_cols(list(value))
    691        if not columns:
    692            columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    693        if not values:
    694            values = [value] * len(columns)
    695        value_columns = [lit(value) for value in values]
    696
    697        null_replacement_mapping = {
    698            column.alias_or_name: (
    699                F.when(column.isNull(), value).otherwise(column).alias(column.alias_or_name)
    700            )
    701            for column, value in zip(columns, value_columns)
    702        }
    703        null_replacement_mapping = {**all_column_mapping, **null_replacement_mapping}
    704        null_replacement_columns = [
    705            null_replacement_mapping[column.alias_or_name] for column in all_columns
    706        ]
    707        new_df = new_df.select(*null_replacement_columns)
    708        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='139688854920384'>], <MagicMock id='139688854920384'>, NoneType] = None) -> DataFrame: View Source

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

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

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

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

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

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

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

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

    799    @operation(Operation.LIMIT)
    800    def limit(self, num: int) -> DataFrame:
    801        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

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

@operation(Operation.NO_OP)
def repartition( self, numPartitions: Union[int, <MagicMock id='139688854920384'>], *cols: <MagicMock id='139688854920384'>) -> DataFrame: View Source

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

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

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

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

    827    @operation(Operation.NO_OP)
    828    def cache(self) -> DataFrame:
    829        return self._cache(storage_level="MEMORY_AND_DISK")

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

    831    @operation(Operation.NO_OP)
    832    def persist(self, storageLevel: str = "MEMORY_AND_DISK_SER") -> DataFrame:
    833        """
    834        Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html
    835        """
    836        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: (
    149                [Column.ensure_col(x).expression for x in v]
    150                if is_iterable(v)
    151                else Column.ensure_col(v).expression
    152            )
    153            for k, v in kwargs.items()
    154            if v is not None
    155        }
    156        new_expression = (
    157            callable_expression(**ensure_expression_values)
    158            if ensured_column is None
    159            else callable_expression(
    160                this=ensured_column.column_expression, **ensure_expression_values
    161            )
    162        )
    163        return Column(new_expression)
    164
    165    def binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    166        return Column(
    167            klass(this=self.column_expression, expression=Column(other).column_expression, **kwargs)
    168        )
    169
    170    def inverse_binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    171        return Column(
    172            klass(this=Column(other).column_expression, expression=self.column_expression, **kwargs)
    173        )
    174
    175    def unary_op(self, klass: t.Callable, **kwargs) -> Column:
    176        return Column(klass(this=self.column_expression, **kwargs))
    177
    178    @property
    179    def is_alias(self):
    180        return isinstance(self.expression, exp.Alias)
    181
    182    @property
    183    def is_column(self):
    184        return isinstance(self.expression, exp.Column)
    185
    186    @property
    187    def column_expression(self) -> t.Union[exp.Column, exp.Literal]:
    188        return self.expression.unalias()
    189
    190    @property
    191    def alias_or_name(self) -> str:
    192        return self.expression.alias_or_name
    193
    194    @classmethod
    195    def ensure_literal(cls, value) -> Column:
    196        from sqlglot.dataframe.sql.functions import lit
    197
    198        if isinstance(value, cls):
    199            value = value.expression
    200        if not isinstance(value, exp.Literal):
    201            return lit(value)
    202        return Column(value)
    203
    204    def copy(self) -> Column:
    205        return Column(self.expression.copy())
    206
    207    def set_table_name(self, table_name: str, copy=False) -> Column:
    208        expression = self.expression.copy() if copy else self.expression
    209        expression.set("table", exp.to_identifier(table_name))
    210        return Column(expression)
    211
    212    def sql(self, **kwargs) -> str:
    213        from sqlglot.dataframe.sql.session import SparkSession
    214
    215        return self.expression.sql(**{"dialect": SparkSession().dialect, **kwargs})
    216
    217    def alias(self, name: str) -> Column:
    218        from sqlglot.dataframe.sql.session import SparkSession
    219
    220        dialect = SparkSession().dialect
    221        alias: exp.Expression = sqlglot.maybe_parse(name, dialect=dialect)
    222        new_expression = exp.alias_(
    223            self.column_expression,
    224            alias.this if isinstance(alias, exp.Column) else name,
    225            dialect=dialect,
    226        )
    227        return Column(new_expression)
    228
    229    def asc(self) -> Column:
    230        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    231        return Column(new_expression)
    232
    233    def desc(self) -> Column:
    234        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    235        return Column(new_expression)
    236
    237    asc_nulls_first = asc
    238
    239    def asc_nulls_last(self) -> Column:
    240        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=False)
    241        return Column(new_expression)
    242
    243    def desc_nulls_first(self) -> Column:
    244        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=True)
    245        return Column(new_expression)
    246
    247    desc_nulls_last = desc
    248
    249    def when(self, condition: Column, value: t.Any) -> Column:
    250        from sqlglot.dataframe.sql.functions import when
    251
    252        column_with_if = when(condition, value)
    253        if not isinstance(self.expression, exp.Case):
    254            return column_with_if
    255        new_column = self.copy()
    256        new_column.expression.args["ifs"].extend(column_with_if.expression.args["ifs"])
    257        return new_column
    258
    259    def otherwise(self, value: t.Any) -> Column:
    260        from sqlglot.dataframe.sql.functions import lit
    261
    262        true_value = value if isinstance(value, Column) else lit(value)
    263        new_column = self.copy()
    264        new_column.expression.set("default", true_value.column_expression)
    265        return new_column
    266
    267    def isNull(self) -> Column:
    268        new_expression = exp.Is(this=self.column_expression, expression=exp.Null())
    269        return Column(new_expression)
    270
    271    def isNotNull(self) -> Column:
    272        new_expression = exp.Not(this=exp.Is(this=self.column_expression, expression=exp.Null()))
    273        return Column(new_expression)
    274
    275    def cast(self, dataType: t.Union[str, DataType]) -> Column:
    276        """
    277        Functionality Difference: PySpark cast accepts a datatype instance of the datatype class
    278        Sqlglot doesn't currently replicate this class so it only accepts a string
    279        """
    280        from sqlglot.dataframe.sql.session import SparkSession
    281
    282        if isinstance(dataType, DataType):
    283            dataType = dataType.simpleString()
    284        return Column(exp.cast(self.column_expression, dataType, dialect=SparkSession().dialect))
    285
    286    def startswith(self, value: t.Union[str, Column]) -> Column:
    287        value = self._lit(value) if not isinstance(value, Column) else value
    288        return self.invoke_anonymous_function(self, "STARTSWITH", value)
    289
    290    def endswith(self, value: t.Union[str, Column]) -> Column:
    291        value = self._lit(value) if not isinstance(value, Column) else value
    292        return self.invoke_anonymous_function(self, "ENDSWITH", value)
    293
    294    def rlike(self, regexp: str) -> Column:
    295        return self.invoke_expression_over_column(
    296            column=self, callable_expression=exp.RegexpLike, expression=self._lit(regexp).expression
    297        )
    298
    299    def like(self, other: str):
    300        return self.invoke_expression_over_column(
    301            self, exp.Like, expression=self._lit(other).expression
    302        )
    303
    304    def ilike(self, other: str):
    305        return self.invoke_expression_over_column(
    306            self, exp.ILike, expression=self._lit(other).expression
    307        )
    308
    309    def substr(self, startPos: t.Union[int, Column], length: t.Union[int, Column]) -> Column:
    310        startPos = self._lit(startPos) if not isinstance(startPos, Column) else startPos
    311        length = self._lit(length) if not isinstance(length, Column) else length
    312        return Column.invoke_expression_over_column(
    313            self, exp.Substring, start=startPos.expression, length=length.expression
    314        )
    315
    316    def isin(self, *cols: t.Union[ColumnOrLiteral, t.Iterable[ColumnOrLiteral]]):
    317        columns = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    318        expressions = [self._lit(x).expression for x in columns]
    319        return Column.invoke_expression_over_column(self, exp.In, expressions=expressions)  # type: ignore
    320
    321    def between(
    322        self,
    323        lowerBound: t.Union[ColumnOrLiteral],
    324        upperBound: t.Union[ColumnOrLiteral],
    325    ) -> Column:
    326        lower_bound_exp = (
    327            self._lit(lowerBound) if not isinstance(lowerBound, Column) else lowerBound
    328        )
    329        upper_bound_exp = (
    330            self._lit(upperBound) if not isinstance(upperBound, Column) else upperBound
    331        )
    332        return Column(
    333            exp.Between(
    334                this=self.column_expression,
    335                low=lower_bound_exp.expression,
    336                high=upper_bound_exp.expression,
    337            )
    338        )
    339
    340    def over(self, window: WindowSpec) -> Column:
    341        window_expression = window.expression.copy()
    342        window_expression.set("this", self.column_expression)
    343        return Column(window_expression)

Column( expression: Union[<MagicMock id='139688856903568'>, 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='139688856903568'>, 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='139688856903568'>, 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='139688856903568'>], func_name: str, *args: Optional[<MagicMock id='139688856903568'>]) -> 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='139688856903568'>], 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: (
    149                [Column.ensure_col(x).expression for x in v]
    150                if is_iterable(v)
    151                else Column.ensure_col(v).expression
    152            )
    153            for k, v in kwargs.items()
    154            if v is not None
    155        }
    156        new_expression = (
    157            callable_expression(**ensure_expression_values)
    158            if ensured_column is None
    159            else callable_expression(
    160                this=ensured_column.column_expression, **ensure_expression_values
    161            )
    162        )
    163        return Column(new_expression)

def binary_op( self, klass: Callable, other: <MagicMock id='139688856903568'>, **kwargs) -> Column: View Source

    165    def binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    166        return Column(
    167            klass(this=self.column_expression, expression=Column(other).column_expression, **kwargs)
    168        )

def inverse_binary_op( self, klass: Callable, other: <MagicMock id='139688856903568'>, **kwargs) -> Column: View Source

    170    def inverse_binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    171        return Column(
    172            klass(this=Column(other).column_expression, expression=self.column_expression, **kwargs)
    173        )

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

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

is_alias View Source

    178    @property
    179    def is_alias(self):
    180        return isinstance(self.expression, exp.Alias)

is_column View Source

    182    @property
    183    def is_column(self):
    184        return isinstance(self.expression, exp.Column)

column_expression: Union[sqlglot.expressions.Column, sqlglot.expressions.Literal] View Source

    186    @property
    187    def column_expression(self) -> t.Union[exp.Column, exp.Literal]:
    188        return self.expression.unalias()

alias_or_name: str View Source

    190    @property
    191    def alias_or_name(self) -> str:
    192        return self.expression.alias_or_name

@classmethod
def ensure_literal(cls, value) -> Column: View Source

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

def copy(self) -> Column: View Source

    204    def copy(self) -> Column:
    205        return Column(self.expression.copy())

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

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

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

    212    def sql(self, **kwargs) -> str:
    213        from sqlglot.dataframe.sql.session import SparkSession
    214
    215        return self.expression.sql(**{"dialect": SparkSession().dialect, **kwargs})

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

    217    def alias(self, name: str) -> Column:
    218        from sqlglot.dataframe.sql.session import SparkSession
    219
    220        dialect = SparkSession().dialect
    221        alias: exp.Expression = sqlglot.maybe_parse(name, dialect=dialect)
    222        new_expression = exp.alias_(
    223            self.column_expression,
    224            alias.this if isinstance(alias, exp.Column) else name,
    225            dialect=dialect,
    226        )
    227        return Column(new_expression)

def asc(self) -> Column: View Source

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

def desc(self) -> Column: View Source

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

def asc_nulls_first(self) -> Column: View Source

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

def asc_nulls_last(self) -> Column: View Source

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

def desc_nulls_first(self) -> Column: View Source

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

def desc_nulls_last(self) -> Column: View Source

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

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

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

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

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

def isNull(self) -> Column: View Source

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

def isNotNull(self) -> Column: View Source

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

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

    275    def cast(self, dataType: t.Union[str, DataType]) -> Column:
    276        """
    277        Functionality Difference: PySpark cast accepts a datatype instance of the datatype class
    278        Sqlglot doesn't currently replicate this class so it only accepts a string
    279        """
    280        from sqlglot.dataframe.sql.session import SparkSession
    281
    282        if isinstance(dataType, DataType):
    283            dataType = dataType.simpleString()
    284        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

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

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

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

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

    294    def rlike(self, regexp: str) -> Column:
    295        return self.invoke_expression_over_column(
    296            column=self, callable_expression=exp.RegexpLike, expression=self._lit(regexp).expression
    297        )

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

    299    def like(self, other: str):
    300        return self.invoke_expression_over_column(
    301            self, exp.Like, expression=self._lit(other).expression
    302        )

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

    304    def ilike(self, other: str):
    305        return self.invoke_expression_over_column(
    306            self, exp.ILike, expression=self._lit(other).expression
    307        )

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

    309    def substr(self, startPos: t.Union[int, Column], length: t.Union[int, Column]) -> Column:
    310        startPos = self._lit(startPos) if not isinstance(startPos, Column) else startPos
    311        length = self._lit(length) if not isinstance(length, Column) else length
    312        return Column.invoke_expression_over_column(
    313            self, exp.Substring, start=startPos.expression, length=length.expression
    314        )

def isin( self, *cols: Union[<MagicMock id='139688856903568'>, Iterable[<MagicMock id='139688856903568'>]]): View Source

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

def between( self, lowerBound: <MagicMock id='139688856903568'>, upperBound: <MagicMock id='139688856903568'>) -> Column: View Source

    321    def between(
    322        self,
    323        lowerBound: t.Union[ColumnOrLiteral],
    324        upperBound: t.Union[ColumnOrLiteral],
    325    ) -> Column:
    326        lower_bound_exp = (
    327            self._lit(lowerBound) if not isinstance(lowerBound, Column) else lowerBound
    328        )
    329        upper_bound_exp = (
    330            self._lit(upperBound) if not isinstance(upperBound, Column) else upperBound
    331        )
    332        return Column(
    333            exp.Between(
    334                this=self.column_expression,
    335                low=lower_bound_exp.expression,
    336                high=upper_bound_exp.expression,
    337            )
    338        )

def over( self, window: <MagicMock id='139688853198576'>) -> Column: View Source

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

class DataFrameNaFunctions: View Source

    839class DataFrameNaFunctions:
    840    def __init__(self, df: DataFrame):
    841        self.df = df
    842
    843    def drop(
    844        self,
    845        how: str = "any",
    846        thresh: t.Optional[int] = None,
    847        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    848    ) -> DataFrame:
    849        return self.df.dropna(how=how, thresh=thresh, subset=subset)
    850
    851    def fill(
    852        self,
    853        value: t.Union[int, bool, float, str, t.Dict[str, t.Any]],
    854        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    855    ) -> DataFrame:
    856        return self.df.fillna(value=value, subset=subset)
    857
    858    def replace(
    859        self,
    860        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    861        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    862        subset: t.Optional[t.Union[str, t.List[str]]] = None,
    863    ) -> DataFrame:
    864        return self.df.replace(to_replace=to_replace, value=value, subset=subset)

DataFrameNaFunctions(df: DataFrame) View Source

    840    def __init__(self, df: DataFrame):
    841        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

    843    def drop(
    844        self,
    845        how: str = "any",
    846        thresh: t.Optional[int] = None,
    847        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    848    ) -> DataFrame:
    849        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

    851    def fill(
    852        self,
    853        value: t.Union[int, bool, float, str, t.Dict[str, t.Any]],
    854        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    855    ) -> DataFrame:
    856        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

    858    def replace(
    859        self,
    860        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    861        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    862        subset: t.Optional[t.Union[str, t.List[str]]] = None,
    863    ) -> DataFrame:
    864        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='139688853906400'>, List[<MagicMock id='139688853906400'>]]) -> 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='139688853906400'>, List[<MagicMock id='139688853906400'>]]) -> 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": (
     95                        "UNBOUNDED"
     96                        if start <= Window.unboundedPreceding
     97                        else F.lit(start).expression
     98                    ),
     99                },
    100            }
    101        if end == Window.currentRow:
    102            kwargs["end"] = "CURRENT ROW"
    103        else:
    104            kwargs = {
    105                **kwargs,
    106                **{
    107                    "end_side": "FOLLOWING",
    108                    "end": (
    109                        "UNBOUNDED" if end >= Window.unboundedFollowing else F.lit(end).expression
    110                    ),
    111                },
    112            }
    113        return kwargs
    114
    115    def rowsBetween(self, start: int, end: int) -> WindowSpec:
    116        window_spec = self.copy()
    117        spec = self._calc_start_end(start, end)
    118        spec["kind"] = "ROWS"
    119        window_spec.expression.set(
    120            "spec",
    121            exp.WindowSpec(
    122                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    123            ),
    124        )
    125        return window_spec
    126
    127    def rangeBetween(self, start: int, end: int) -> WindowSpec:
    128        window_spec = self.copy()
    129        spec = self._calc_start_end(start, end)
    130        spec["kind"] = "RANGE"
    131        window_spec.expression.set(
    132            "spec",
    133            exp.WindowSpec(
    134                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    135            ),
    136        )
    137        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='139688853906400'>, List[<MagicMock id='139688853906400'>]]) -> 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='139688853906400'>, List[<MagicMock id='139688853906400'>]]) -> 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

    115    def rowsBetween(self, start: int, end: int) -> WindowSpec:
    116        window_spec = self.copy()
    117        spec = self._calc_start_end(start, end)
    118        spec["kind"] = "ROWS"
    119        window_spec.expression.set(
    120            "spec",
    121            exp.WindowSpec(
    122                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    123            ),
    124        )
    125        return window_spec

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

    127    def rangeBetween(self, start: int, end: int) -> WindowSpec:
    128        window_spec = self.copy()
    129        spec = self._calc_start_end(start, end)
    130        spec["kind"] = "RANGE"
    131        window_spec.expression.set(
    132            "spec",
    133            exp.WindowSpec(
    134                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    135            ),
    136        )
    137        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 View Source

    71    @property
    72    def byName(self):
    73        return self.copy(by_name=True)

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))