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IO

toolbox_pyspark.io 🔗

Summary

The io module is used for reading and writing tables to/from directories.

SPARK_FORMATS module-attribute 🔗

SPARK_FORMATS = Literal[
    "parquet",
    "orc",
    "json",
    "csv",
    "text",
    "avro",
    "jdbc",
    "oracle",
    "mysql",
    "postgresql",
    "mssql",
    "db2",
    "delta",
    "xml",
    "excel",
    "hive",
    "mongodb",
    "cassandra",
    "elasticsearch",
]

The valid formats that can be used to read/write data in Spark.

PySpark's built-in data source formats:

  • parquet
  • orc
  • json
  • csv
  • text
  • avro

Database formats (with proper JDBC drivers):

  • jdbc
  • oracle
  • mysql
  • postgresql
  • mssql
  • db2

Other formats with additional dependencies:

  • delta (requires: io.delta:delta-core dependency and delata-spark package)
  • xml (requires: com.databricks:spark-xml dependency and spark-xml package)
  • excel (requires: com.crealytics:spark-excel dependency and spark-excel package)
  • hive (requires: Hive support)
  • mongodb (requires: org.mongodb.spark:mongo-spark-connector dependency and mongo-spark-connector package)
  • cassandra (requires: com.datastax.spark:spark-cassandra-connector dependency and spark-cassandra-connector package)
  • elasticsearch (requires: org.elasticsearch:elasticsearch-hadoop dependency and elasticsearch-hadoop package)

VALID_SPARK_FORMATS module-attribute 🔗

VALID_SPARK_FORMATS: str_tuple = get_args(SPARK_FORMATS)

The valid formats that can be used to read/write data in Spark.

PySpark's built-in data source formats:

  • parquet
  • orc
  • json
  • csv
  • text
  • avro

Database formats (with proper JDBC drivers):

  • jdbc
  • oracle
  • mysql
  • postgresql
  • mssql
  • db2

Other formats with additional dependencies:

  • delta (requires: io.delta:delta-core dependency and delata-spark package)
  • xml (requires: com.databricks:spark-xml dependency and spark-xml package)
  • excel (requires: com.crealytics:spark-excel dependency and spark-excel package)
  • hive (requires: Hive support)
  • mongodb (requires: org.mongodb.spark:mongo-spark-connector dependency and mongo-spark-connector package)
  • cassandra (requires: com.datastax.spark:spark-cassandra-connector dependency and spark-cassandra-connector package)
  • elasticsearch (requires: org.elasticsearch:elasticsearch-hadoop dependency and elasticsearch-hadoop package)

WRITE_MODES module-attribute 🔗

WRITE_MODES = Literal[
    "append",
    "overwrite",
    "ignore",
    "error",
    "errorifexists",
]

The valid modes you can use for writing data frames:

  • append
  • overwrite
  • ignore
  • error
  • errorifexists

VALID_WRITE_MODES module-attribute 🔗

VALID_WRITE_MODES: str_tuple = get_args(WRITE_MODES)

The valid modes you can use for writing data frames:

  • append
  • overwrite
  • ignore
  • error
  • errorifexists

read_from_path 🔗

read_from_path(
    spark_session: SparkSession,
    name: str,
    path: str,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    read_options: Optional[str_dict] = None,
) -> psDataFrame

Summary

Read an object from a given path in to memory as a pyspark dataframe.

Parameters:

Name Type Description Default
spark_session SparkSession

The Spark session to use for the reading.

 required
name str

The name of the table to read in.

 required
path str

The path from which it will be read.

 required
data_format Optional[SPARK_FORMATS]

The format of the object at location path.
Defaults to "delta".

 'parquet'
read_options Dict[str, str]

Any additional obtions to parse to the Spark reader.
Like, for example:

  • If the object is a CSV, you may want to define that it has a header row: {"header": "true"}.
  • If the object is a Delta table, you may want to query a specific version: {versionOf": "0"}.

For more info, check the pyspark docs: pyspark.sql.DataFrameReader.options.
Defaults to dict().

 None

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.

Returns:

Type Description
DataFrame

The loaded dataframe.
Examples
Set up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.io import read_from_path
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = pd.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": ["a", "b", "c", "d"],
...         "c": [1, 1, 1, 1],
...         "d": ["2", "2", "2", "2"],
...     }
... )
>>>
>>> # Write data
>>> df.to_csv("./test/table.csv")
>>> df.to_parquet("./test/table.parquet")

Check
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>>> import os
>>> print(os.listdir("./test"))
Terminal
["table.csv", "table.parquet"]

Example 1: Read CSV
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>>> df_csv = read_from_path(
...     name="table.csv",
...     path="./test",
...     spark_session=spark,
...     data_format="csv",
...     options={"header": "true"},
... )
>>>
>>> df_csv.show()
Terminal
+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | a | 1 | 2 |
| 2 | b | 1 | 2 |
| 3 | c | 1 | 2 |
| 4 | d | 1 | 2 |
+---+---+---+---+

Conclusion: Successfully read CSV.

Example 2: Read Parquet
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>>> df_parquet = read_from_path(
...     name="table.parquet",
...     path="./test",
...     spark_session=spark,
...     data_format="parquet",
... )
>>>
>>> df_parquet.show()
Terminal
+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | a | 1 | 2 |
| 2 | b | 1 | 2 |
| 3 | c | 1 | 2 |
| 4 | d | 1 | 2 |
+---+---+---+---+

Conclusion: Successfully read Parquet.

Example 3: Invalid Path
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>>> df_invalid_path = read_from_path(
...     name="invalid_table.csv",
...     path="./invalid_path",
...     spark_session=spark,
...     data_format="csv",
...     options={"header": "true"},
... )
Terminal
Py4JJavaError: An error occurred while calling o45.load.

Conclusion: Failed to read from invalid path.

Example 4: Invalid Format
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>>> df_invalid_format = read_from_path(
...     name="table.csv",
...     path="./test",
...     spark_session=spark,
...     data_format="invalid_format",
...     options={"header": "true"},
... )
Terminal
Py4JJavaError: An error occurred while calling o45.load.

Conclusion: Failed to read due to invalid format.

See Also
Source code in src/toolbox_pyspark/io.py 
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@typechecked
def read_from_path(
    spark_session: SparkSession,
    name: str,
    path: str,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    read_options: Optional[str_dict] = None,
) -> psDataFrame:
    """
    !!! note "Summary"
        Read an object from a given `path` in to memory as a `pyspark` dataframe.

    Params:
        spark_session (SparkSession):
            The Spark session to use for the reading.
        name (str):
            The name of the table to read in.
        path (str):
            The path from which it will be read.
        data_format (Optional[SPARK_FORMATS], optional):
            The format of the object at location `path`.<br>
            Defaults to `#!py "delta"`.
        read_options (Dict[str, str], optional):
            Any additional obtions to parse to the Spark reader.<br>
            Like, for example:<br>

            - If the object is a CSV, you may want to define that it has a header row: `#!py {"header": "true"}`.
            - If the object is a Delta table, you may want to query a specific version: `#!py {versionOf": "0"}`.

            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameReader.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameReader.options.html).<br>
            Defaults to `#!py dict()`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.

    Returns:
        (psDataFrame):
            The loaded dataframe.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.io import read_from_path
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = pd.DataFrame(
        ...     {
        ...         "a": [1, 2, 3, 4],
        ...         "b": ["a", "b", "c", "d"],
        ...         "c": [1, 1, 1, 1],
        ...         "d": ["2", "2", "2", "2"],
        ...     }
        ... )
        >>>
        >>> # Write data
        >>> df.to_csv("./test/table.csv")
        >>> df.to_parquet("./test/table.parquet")
        ```

        ```{.py .python linenums="1" title="Check"}
        >>> import os
        >>> print(os.listdir("./test"))
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        ["table.csv", "table.parquet"]
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Read CSV"}
        >>> df_csv = read_from_path(
        ...     name="table.csv",
        ...     path="./test",
        ...     spark_session=spark,
        ...     data_format="csv",
        ...     options={"header": "true"},
        ... )
        >>>
        >>> df_csv.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+---+---+
        | a | b | c | d |
        +---+---+---+---+
        | 1 | a | 1 | 2 |
        | 2 | b | 1 | 2 |
        | 3 | c | 1 | 2 |
        | 4 | d | 1 | 2 |
        +---+---+---+---+
        ```
        !!! success "Conclusion: Successfully read CSV."
        </div>

        ```{.py .python linenums="1" title="Example 2: Read Parquet"}
        >>> df_parquet = read_from_path(
        ...     name="table.parquet",
        ...     path="./test",
        ...     spark_session=spark,
        ...     data_format="parquet",
        ... )
        >>>
        >>> df_parquet.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+---+---+
        | a | b | c | d |
        +---+---+---+---+
        | 1 | a | 1 | 2 |
        | 2 | b | 1 | 2 |
        | 3 | c | 1 | 2 |
        | 4 | d | 1 | 2 |
        +---+---+---+---+
        ```
        !!! success "Conclusion: Successfully read Parquet."
        </div>

        ```{.py .python linenums="1" title="Example 3: Invalid Path"}
        >>> df_invalid_path = read_from_path(
        ...     name="invalid_table.csv",
        ...     path="./invalid_path",
        ...     spark_session=spark,
        ...     data_format="csv",
        ...     options={"header": "true"},
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Py4JJavaError: An error occurred while calling o45.load.
        ```
        !!! failure "Conclusion: Failed to read from invalid path."
        </div>

        ```{.py .python linenums="1" title="Example 4: Invalid Format"}
        >>> df_invalid_format = read_from_path(
        ...     name="table.csv",
        ...     path="./test",
        ...     spark_session=spark,
        ...     data_format="invalid_format",
        ...     options={"header": "true"},
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Py4JJavaError: An error occurred while calling o45.load.
        ```
        !!! failure "Conclusion: Failed to read due to invalid format."
        </div>

    ??? tip "See Also"
        - [`load_from_path`][toolbox_pyspark.io.load_from_path]
        - [`read`][toolbox_pyspark.io.read]
        - [`load`][toolbox_pyspark.io.load]
    """

    # Set default options ----
    read_options: str_dict = read_options or dict()
    data_format: str = data_format or "parquet"
    load_path: str = f"{path}{'/' if not path.endswith('/') else ''}{name}"

    # Initialise reader (including data format) ----
    reader: DataFrameReader = spark_session.read.format(data_format)

    # Add options (if exists) ----
    if read_options:
        reader.options(**read_options)

    # Load DataFrame ----
    return reader.load(load_path)

write_to_path 🔗

write_to_path(
    data_frame: psDataFrame,
    name: str,
    path: str,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    mode: Optional[WRITE_MODES] = None,
    write_options: Optional[str_dict] = None,
    partition_cols: Optional[str_collection] = None,
) -> None

Summary

For a given table, write it out to a specified path with name name and format format.

Parameters:

Name Type Description Default
data_frame DataFrame

The DataFrame to be written. Must be a valid pyspark DataFrame (pyspark.sql.DataFrame).

 required
name str

The name of the table where it will be written.

 required
path str

The path location for where to save the table.

 required
data_format Optional[SPARK_FORMATS]

The format that the table will be written to.
Defaults to "delta".

 'parquet'
mode Optional[WRITE_MODES]

The behaviour for when the data already exists.
For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.mode.
Defaults to None.

 None
write_options Dict[str, str]

Any additional settings to parse to the writer class.
Like, for example:

  • If you are writing to a Delta object, and wanted to overwrite the schema: {"overwriteSchema": "true"}.
  • If you"re writing to a CSV file, and wanted to specify the header row: {"header": "true"}.

For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.options.
Defaults to dict().

 None
partition_cols Optional[Union[str_collection, str]]

The column(s) that the table should partition by.
Defaults to None.

 None

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.

Returns:

Type Description
type(None)

Nothing is returned.
Note

You know that this function is successful if the table exists at the specified location, and there are no errors thrown.

Examples
Set up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.io import write_to_path
>>> from toolbox_pyspark.checks import table_exists
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "a": [1, 2, 3, 4],
...             "b": ["a", "b", "c", "d"],
...             "c": [1, 1, 1, 1],
...             "d": ["2", "2", "2", "2"],
...         }
...     )
... )

Check
1
>>> df.show()
Terminal
+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | a | 1 | 2 |
| 2 | b | 1 | 2 |
| 3 | c | 1 | 2 |
| 4 | d | 1 | 2 |
+---+---+---+---+

Example 1: Write to CSV
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>>> write_to_path(
...     data_frame=df,
...     name="df.csv",
...     path="./test",
...     data_format="csv",
...     mode="overwrite",
...     options={"header": "true"},
... )
>>>
>>> table_exists(
...     name="df.csv",
...     path="./test",
...     data_format="csv",
...     spark_session=df.sparkSession,
... )
Terminal
True

Conclusion: Successfully written to CSV.

Example 2: Write to Parquet
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>>> write_to_path(
...     data_frame=df,
...     name="df.parquet",
...     path="./test",
...     data_format="parquet",
...     mode="overwrite",
... )
>>>
>>> table_exists(
...     name="df.parquet",
...     path="./test",
...     data_format="parquet",
...     spark_session=df.sparkSession,
... )
Terminal
True

Conclusion: Successfully written to Parquet.

Example 3: Invalid Path
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>>> write_to_path(
...     data_frame=df,
...     name="df.csv",
...     path="./invalid_path",
...     data_format="csv",
...     mode="overwrite",
...     options={"header": "true"},
... )
Terminal
Py4JJavaError: An error occurred while calling o45.save.

Conclusion: Failed to write to invalid path.

Example 4: Invalid Format
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>>> write_to_path(
...     data_frame=df,
...     name="df.csv",
...     path="./test",
...     data_format="invalid_format",
...     mode="overwrite",
...     options={"header": "true"},
... )
Terminal
Py4JJavaError: An error occurred while calling o45.save.

Conclusion: Failed to write due to invalid format.

See Also
Source code in src/toolbox_pyspark/io.py 
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@typechecked
def write_to_path(
    data_frame: psDataFrame,
    name: str,
    path: str,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    mode: Optional[WRITE_MODES] = None,
    write_options: Optional[str_dict] = None,
    partition_cols: Optional[str_collection] = None,
) -> None:
    """
    !!! note "Summary"
        For a given `table`, write it out to a specified `path` with name `name` and format `format`.

    Params:
        data_frame (psDataFrame):
            The DataFrame to be written. Must be a valid `pyspark` DataFrame ([`pyspark.sql.DataFrame`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html)).
        name (str):
            The name of the table where it will be written.
        path (str):
            The path location for where to save the table.
        data_format (Optional[SPARK_FORMATS], optional):
            The format that the `table` will be written to.<br>
            Defaults to `#!py "delta"`.
        mode (Optional[WRITE_MODES], optional):
            The behaviour for when the data already exists.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.mode`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.mode.html).<br>
            Defaults to `#!py None`.
        write_options (Dict[str, str], optional):
            Any additional settings to parse to the writer class.<br>
            Like, for example:

            - If you are writing to a Delta object, and wanted to overwrite the schema: `#!py {"overwriteSchema": "true"}`.
            - If you"re writing to a CSV file, and wanted to specify the header row: `#!py {"header": "true"}`.

            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.options.html).<br>
            Defaults to `#!py dict()`.
        partition_cols (Optional[Union[str_collection, str]], optional):
            The column(s) that the table should partition by.<br>
            Defaults to `#!py None`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.

    Returns:
        (type(None)):
            Nothing is returned.

    ???+ tip "Note"
        You know that this function is successful if the table exists at the specified location, and there are no errors thrown.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.io import write_to_path
        >>> from toolbox_pyspark.checks import table_exists
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         {
        ...             "a": [1, 2, 3, 4],
        ...             "b": ["a", "b", "c", "d"],
        ...             "c": [1, 1, 1, 1],
        ...             "d": ["2", "2", "2", "2"],
        ...         }
        ...     )
        ... )
        ```

        ```{.py .python linenums="1" title="Check"}
        >>> df.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+---+---+
        | a | b | c | d |
        +---+---+---+---+
        | 1 | a | 1 | 2 |
        | 2 | b | 1 | 2 |
        | 3 | c | 1 | 2 |
        | 4 | d | 1 | 2 |
        +---+---+---+---+
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Write to CSV"}
        >>> write_to_path(
        ...     data_frame=df,
        ...     name="df.csv",
        ...     path="./test",
        ...     data_format="csv",
        ...     mode="overwrite",
        ...     options={"header": "true"},
        ... )
        >>>
        >>> table_exists(
        ...     name="df.csv",
        ...     path="./test",
        ...     data_format="csv",
        ...     spark_session=df.sparkSession,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully written to CSV."
        </div>

        ```{.py .python linenums="1" title="Example 2: Write to Parquet"}
        >>> write_to_path(
        ...     data_frame=df,
        ...     name="df.parquet",
        ...     path="./test",
        ...     data_format="parquet",
        ...     mode="overwrite",
        ... )
        >>>
        >>> table_exists(
        ...     name="df.parquet",
        ...     path="./test",
        ...     data_format="parquet",
        ...     spark_session=df.sparkSession,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully written to Parquet."
        </div>

        ```{.py .python linenums="1" title="Example 3: Invalid Path"}
        >>> write_to_path(
        ...     data_frame=df,
        ...     name="df.csv",
        ...     path="./invalid_path",
        ...     data_format="csv",
        ...     mode="overwrite",
        ...     options={"header": "true"},
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Py4JJavaError: An error occurred while calling o45.save.
        ```
        !!! failure "Conclusion: Failed to write to invalid path."
        </div>

        ```{.py .python linenums="1" title="Example 4: Invalid Format"}
        >>> write_to_path(
        ...     data_frame=df,
        ...     name="df.csv",
        ...     path="./test",
        ...     data_format="invalid_format",
        ...     mode="overwrite",
        ...     options={"header": "true"},
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Py4JJavaError: An error occurred while calling o45.save.
        ```
        !!! failure "Conclusion: Failed to write due to invalid format."
        </div>

    ??? tip "See Also"
        - [`save_to_path`][toolbox_pyspark.io.save_to_path]
        - [`write`][toolbox_pyspark.io.write]
        - [`save`][toolbox_pyspark.io.save]
    """

    # Set default options ----
    write_options: str_dict = write_options or dict()
    data_format: str = data_format or "parquet"
    write_path: str = f"{path}{'/' if not path.endswith('/') else ''}{name}"

    # Initialise writer (including data format) ----
    writer: DataFrameWriter = data_frame.write.mode(mode).format(data_format)

    # Add options (if exists) ----
    if write_options:
        writer.options(**write_options)

    # Add partition (if exists) ----
    if partition_cols is not None:
        partition_cols = [partition_cols] if is_type(partition_cols, str) else partition_cols
        writer = writer.partitionBy(list(partition_cols))

    # Write table ----
    writer.save(write_path)

transfer_by_path 🔗

transfer_by_path(
    spark_session: SparkSession,
    from_table_path: str,
    from_table_name: str,
    to_table_path: str,
    to_table_name: str,
    from_table_format: Optional[SPARK_FORMATS] = "parquet",
    from_table_options: Optional[str_dict] = None,
    to_table_format: Optional[SPARK_FORMATS] = "parquet",
    to_table_mode: Optional[WRITE_MODES] = None,
    to_table_options: Optional[str_dict] = None,
    to_table_partition_cols: Optional[
        str_collection
    ] = None,
) -> None

Summary

Copy a table from one location to another.

Details

This is a blind transfer. There is no validation, no alteration, no adjustments made at all. Simply read directly from one location and move immediately to another location straight away.

Parameters:

Name Type Description Default
spark_session SparkSession

The spark session to use for the transfer. Necessary in order to instantiate the reading process.

 required
from_table_path str

The path from which the table will be read.

 required
from_table_name str

The name of the table to be read.

 required
to_table_path str

The location where to save the table to.

 required
to_table_name str

The name of the table where it will be saved.

 required
from_table_format Optional[SPARK_FORMATS]

The format of the data at the reading location.

 'parquet'
to_table_format Optional[SPARK_FORMATS]

The format of the saved table.

 'parquet'
from_table_options Dict[str, str]

Any additional obtions to parse to the Spark reader.
For more info, check the pyspark docs: pyspark.sql.DataFrameReader.options.
Defaults to dict().

 None
to_table_mode Optional[WRITE_MODES]

The behaviour for when the data already exists.
For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.mode.
Defaults to None.

 None
to_table_options Dict[str, str]

Any additional settings to parse to the writer class.
For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.options.
Defaults to dict().

 None
to_table_partition_cols Optional[Union[str_collection, str]]

The column(s) that the table should partition by.
Defaults to None.

 None

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.

Returns:

Type Description
type(None)

Nothing is returned.
Note

You know that this function is successful if the table exists at the specified location, and there are no errors thrown.

Examples
Set up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.io import transfer_by_path
>>> from toolbox_pyspark.checks import table_exists
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = pd.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": ["a", "b", "c", "d"],
...         "c": [1, 1, 1 1],
...         "d": ["2", "2", "2", "2"],
...     }
... )
>>> df.to_csv("./test/table.csv")
>>> df.to_parquet("./test/table.parquet")

Check
1
2
>>> import os
>>> print(os.listdir("./test"))
Terminal
["table.csv", "table.parquet"]

Example 1: Transfer CSV
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>>> transfer_by_path(
...     spark_session=spark,
...     from_table_path="./test",
...     from_table_name="table.csv",
...     from_table_format="csv",
...     to_table_path="./other",
...     to_table_name="table.csv",
...     to_table_format="csv",
...     from_table_options={"header": "true"},
...     to_table_mode="overwrite",
...     to_table_options={"header": "true"},
... )
>>>
>>> table_exists(
...     name="df.csv",
...     path="./other",
...     data_format="csv",
...     spark_session=spark,
... )
Terminal
True

Conclusion: Successfully transferred CSV to CSV.

Example 2: Transfer Parquet
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>>> transfer_by_path(
...     spark_session=spark,
...     from_table_path="./test",
...     from_table_name="table.parquet",
...     from_table_format="parquet",
...     to_table_path="./other",
...     to_table_name="table.parquet",
...     to_table_format="parquet",
...     to_table_mode="overwrite",
...     to_table_options={"overwriteSchema": "true"},
... )
>>>
>>> table_exists(
...     name="df.parquet",
...     path="./other",
...     data_format="parquet",
...     spark_session=spark,
... )
Terminal
True

Conclusion: Successfully transferred Parquet to Parquet.

Example 3: Transfer CSV to Parquet
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>>> transfer_by_path(
...     spark_session=spark,
...     from_table_path="./test",
...     from_table_name="table.csv",
...     from_table_format="csv",
...     to_table_path="./other",
...     to_table_name="table.parquet",
...     to_table_format="parquet",
...     to_table_mode="overwrite",
...     to_table_options={"overwriteSchema": "true"},
... )
>>>
>>> table_exists(
...     name="df.parquet",
...     path="./other",
...     data_format="parquet",
...     spark_session=spark,
... )
Terminal
True

Conclusion: Successfully transferred CSV to Parquet.

Example 4: Invalid Source Path
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>>> transfer_by_path(
...     spark_session=spark,
...     from_table_path="./invalid_path",
...     from_table_name="table.csv",
...     from_table_format="csv",
...     to_table_path="./other",
...     to_table_name="table.csv",
...     to_table_format="csv",
...     from_table_options={"header": "true"},
...     to_table_mode="overwrite",
...     to_table_options={"header": "true"},
... )
Terminal
Py4JJavaError: An error occurred while calling o45.load.

Conclusion: Failed to transfer due to invalid source path.

Example 5: Invalid Target Format
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>>> transfer_by_path(
...     spark_session=spark,
...     from_table_path="./test",
...     from_table_name="table.csv",
...     from_table_format="csv",
...     to_table_path="./other",
...     to_table_name="table.csv",
...     to_table_format="invalid_format",
...     from_table_options={"header": "true"},
...     to_table_mode="overwrite",
...     to_table_options={"header": "true"},
... )
Terminal
Py4JJavaError: An error occurred while calling o45.save.

Conclusion: Failed to transfer due to invalid target format.

See Also
Source code in src/toolbox_pyspark/io.py 
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@typechecked
def transfer_by_path(
    spark_session: SparkSession,
    from_table_path: str,
    from_table_name: str,
    to_table_path: str,
    to_table_name: str,
    from_table_format: Optional[SPARK_FORMATS] = "parquet",
    from_table_options: Optional[str_dict] = None,
    to_table_format: Optional[SPARK_FORMATS] = "parquet",
    to_table_mode: Optional[WRITE_MODES] = None,
    to_table_options: Optional[str_dict] = None,
    to_table_partition_cols: Optional[str_collection] = None,
) -> None:
    """
    !!! note "Summary"
        Copy a table from one location to another.

    ???+ abstract "Details"
        This is a blind transfer. There is no validation, no alteration, no adjustments made at all. Simply read directly from one location and move immediately to another location straight away.

    Params:
        spark_session (SparkSession):
            The spark session to use for the transfer. Necessary in order to instantiate the reading process.
        from_table_path (str):
            The path from which the table will be read.
        from_table_name (str):
            The name of the table to be read.
        to_table_path (str):
            The location where to save the table to.
        to_table_name (str):
            The name of the table where it will be saved.
        from_table_format (Optional[SPARK_FORMATS], optional):
            The format of the data at the reading location.
        to_table_format (Optional[SPARK_FORMATS], optional):
            The format of the saved table.
        from_table_options (Dict[str, str], optional):
            Any additional obtions to parse to the Spark reader.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameReader.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameReader.options.html).<br>
            Defaults to `#! dict()`.
        to_table_mode (Optional[WRITE_MODES], optional):
            The behaviour for when the data already exists.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.mode`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.mode.html).<br>
            Defaults to `#!py None`.
        to_table_options (Dict[str, str], optional):
            Any additional settings to parse to the writer class.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.options.html).<br>
            Defaults to `#! dict()`.
        to_table_partition_cols (Optional[Union[str_collection, str]], optional):
            The column(s) that the table should partition by.<br>
            Defaults to `#!py None`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.

    Returns:
        (type(None)):
            Nothing is returned.

    ???+ tip "Note"
        You know that this function is successful if the table exists at the specified location, and there are no errors thrown.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.io import transfer_by_path
        >>> from toolbox_pyspark.checks import table_exists
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = pd.DataFrame(
        ...     {
        ...         "a": [1, 2, 3, 4],
        ...         "b": ["a", "b", "c", "d"],
        ...         "c": [1, 1, 1 1],
        ...         "d": ["2", "2", "2", "2"],
        ...     }
        ... )
        >>> df.to_csv("./test/table.csv")
        >>> df.to_parquet("./test/table.parquet")
        ```

        ```{.py .python linenums="1" title="Check"}
        >>> import os
        >>> print(os.listdir("./test"))
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        ["table.csv", "table.parquet"]
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Transfer CSV"}
        >>> transfer_by_path(
        ...     spark_session=spark,
        ...     from_table_path="./test",
        ...     from_table_name="table.csv",
        ...     from_table_format="csv",
        ...     to_table_path="./other",
        ...     to_table_name="table.csv",
        ...     to_table_format="csv",
        ...     from_table_options={"header": "true"},
        ...     to_table_mode="overwrite",
        ...     to_table_options={"header": "true"},
        ... )
        >>>
        >>> table_exists(
        ...     name="df.csv",
        ...     path="./other",
        ...     data_format="csv",
        ...     spark_session=spark,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully transferred CSV to CSV."
        </div>

        ```{.py .python linenums="1" title="Example 2: Transfer Parquet"}
        >>> transfer_by_path(
        ...     spark_session=spark,
        ...     from_table_path="./test",
        ...     from_table_name="table.parquet",
        ...     from_table_format="parquet",
        ...     to_table_path="./other",
        ...     to_table_name="table.parquet",
        ...     to_table_format="parquet",
        ...     to_table_mode="overwrite",
        ...     to_table_options={"overwriteSchema": "true"},
        ... )
        >>>
        >>> table_exists(
        ...     name="df.parquet",
        ...     path="./other",
        ...     data_format="parquet",
        ...     spark_session=spark,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully transferred Parquet to Parquet."
        </div>

        ```{.py .python linenums="1" title="Example 3: Transfer CSV to Parquet"}
        >>> transfer_by_path(
        ...     spark_session=spark,
        ...     from_table_path="./test",
        ...     from_table_name="table.csv",
        ...     from_table_format="csv",
        ...     to_table_path="./other",
        ...     to_table_name="table.parquet",
        ...     to_table_format="parquet",
        ...     to_table_mode="overwrite",
        ...     to_table_options={"overwriteSchema": "true"},
        ... )
        >>>
        >>> table_exists(
        ...     name="df.parquet",
        ...     path="./other",
        ...     data_format="parquet",
        ...     spark_session=spark,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully transferred CSV to Parquet."
        </div>

        ```{.py .python linenums="1" title="Example 4: Invalid Source Path"}
        >>> transfer_by_path(
        ...     spark_session=spark,
        ...     from_table_path="./invalid_path",
        ...     from_table_name="table.csv",
        ...     from_table_format="csv",
        ...     to_table_path="./other",
        ...     to_table_name="table.csv",
        ...     to_table_format="csv",
        ...     from_table_options={"header": "true"},
        ...     to_table_mode="overwrite",
        ...     to_table_options={"header": "true"},
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Py4JJavaError: An error occurred while calling o45.load.
        ```
        !!! failure "Conclusion: Failed to transfer due to invalid source path."
        </div>

        ```{.py .python linenums="1" title="Example 5: Invalid Target Format"}
        >>> transfer_by_path(
        ...     spark_session=spark,
        ...     from_table_path="./test",
        ...     from_table_name="table.csv",
        ...     from_table_format="csv",
        ...     to_table_path="./other",
        ...     to_table_name="table.csv",
        ...     to_table_format="invalid_format",
        ...     from_table_options={"header": "true"},
        ...     to_table_mode="overwrite",
        ...     to_table_options={"header": "true"},
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Py4JJavaError: An error occurred while calling o45.save.
        ```
        !!! failure "Conclusion: Failed to transfer due to invalid target format."
        </div>

    ??? tip "See Also"
        - [`transfer`][toolbox_pyspark.io.transfer]
    """

    # Read from source ----
    from_table: psDataFrame = read_from_path(
        name=from_table_name,
        path=from_table_path,
        spark_session=spark_session,
        data_format=from_table_format,
        read_options=from_table_options,
    )

    # Write to target ----
    write_to_path(
        data_frame=from_table,
        name=to_table_name,
        path=to_table_path,
        data_format=to_table_format,
        mode=to_table_mode,
        write_options=to_table_options,
        partition_cols=to_table_partition_cols,
    )

read_from_table 🔗

read_from_table(
    spark_session: SparkSession,
    name: str,
    schema: Optional[str] = None,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    read_options: Optional[str_dict] = None,
) -> psDataFrame

Summary

Read a table from a given schema and name into memory as a pyspark dataframe.

Details
  • If schema is None, then we would expect the name to contain both the schema and the table name in the same. Like: schema.name, for example production.orders.
  • Else, if schema is not None, then we would expect the schema to (quite logically) contain the name of the schema, and the name to contain the name of the table.

Parameters:

Name Type Description Default
spark_session SparkSession

The Spark session to use for the reading.

 required
name str

The name of the table to read in.

 required
schema Optional[str]

The schema of the table to read in.
Defaults to None.

 None
data_format Optional[SPARK_FORMATS]

The format of the table.
Defaults to "parquet".

 'parquet'
read_options Dict[str, str]

Any additional options to parse to the Spark reader.
For more info, check the pyspark docs: pyspark.sql.DataFrameReader.options.
Defaults to dict().

 None

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.
ValidationError

If name contains /, or is structured with three elements like: source.schema.table.

Returns:

Type Description
DataFrame

The loaded dataframe.
Examples
Set up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.io import read_from_table
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = pd.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": ["a", "b", "c", "d"],
...         "c": [1, 1, 1, 1],
...         "d": ["2", "2", "2", "2"],
...     }
... )
>>> df.to_parquet("./test/table.parquet")
>>> spark.read.parquet("./test/table.parquet").createOrReplaceTempView("test_table")

Example 1: Read Table
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>>> df_table = read_from_table(
...     name="test_table",
...     spark_session=spark,
...     data_format="parquet",
... )
>>>
>>> df_table.show()
Terminal
+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | a | 1 | 2 |
| 2 | b | 1 | 2 |
| 3 | c | 1 | 2 |
| 4 | d | 1 | 2 |
+---+---+---+---+

Conclusion: Successfully read table.

Example 2: Invalid table structure
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>>> df_table = read_from_table(
...     name="schema.test_table",
...     schema="source",
...     spark_session=spark,
...     data_format="parquet",
... )
Terminal
Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.

Conclusion: Failed to write to table due to invalid table structure.

See Also
Source code in src/toolbox_pyspark/io.py 
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@typechecked
def read_from_table(
    spark_session: SparkSession,
    name: str,
    schema: Optional[str] = None,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    read_options: Optional[str_dict] = None,
) -> psDataFrame:
    """
    !!! note "Summary"
        Read a table from a given `schema` and `name` into memory as a `pyspark` dataframe.

    ???+ abstract "Details"
        - If `schema` is `#!py None`, then we would expect the `name` to contain both the schema and the table name in the same. Like: `schema.name`, for example `production.orders`.
        - Else, if `schema` is not `#! None`, then we would expect the `schema` to (quite logically) contain the name of the schema, and the `name` to contain the name of the table.

    Params:
        spark_session (SparkSession):
            The Spark session to use for the reading.
        name (str):
            The name of the table to read in.
        schema (Optional[str], optional):
            The schema of the table to read in.<br>
            Defaults to `#!py None`.
        data_format (Optional[SPARK_FORMATS], optional):
            The format of the table.<br>
            Defaults to `#!py "parquet"`.
        read_options (Dict[str, str], optional):
            Any additional options to parse to the Spark reader.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameReader.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameReader.options.html).<br>
            Defaults to `#!py dict()`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.
        ValidationError:
            If `name` contains `/`, or is structured with three elements like: `source.schema.table`.

    Returns:
        (psDataFrame):
            The loaded dataframe.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.io import read_from_table
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = pd.DataFrame(
        ...     {
        ...         "a": [1, 2, 3, 4],
        ...         "b": ["a", "b", "c", "d"],
        ...         "c": [1, 1, 1, 1],
        ...         "d": ["2", "2", "2", "2"],
        ...     }
        ... )
        >>> df.to_parquet("./test/table.parquet")
        >>> spark.read.parquet("./test/table.parquet").createOrReplaceTempView("test_table")
        ```

        ```{.py .python linenums="1" title="Example 1: Read Table"}
        >>> df_table = read_from_table(
        ...     name="test_table",
        ...     spark_session=spark,
        ...     data_format="parquet",
        ... )
        >>>
        >>> df_table.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+---+---+
        | a | b | c | d |
        +---+---+---+---+
        | 1 | a | 1 | 2 |
        | 2 | b | 1 | 2 |
        | 3 | c | 1 | 2 |
        | 4 | d | 1 | 2 |
        +---+---+---+---+
        ```
        !!! success "Conclusion: Successfully read table."
        </div>

        ```{.py .python linenums="1" title="Example 2: Invalid table structure"}
        >>> df_table = read_from_table(
        ...     name="schema.test_table",
        ...     schema="source",
        ...     spark_session=spark,
        ...     data_format="parquet",
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.
        ```
        !!! failure "Conclusion: Failed to write to table due to invalid table structure."
        </div>

    ??? tip "See Also"
        - [`save_to_table`][toolbox_pyspark.io.save_to_table]
        - [`write`][toolbox_pyspark.io.write]
        - [`save`][toolbox_pyspark.io.save]
    """

    # Set default options ----
    data_format: str = data_format or "parquet"
    table: str = name if not schema else f"{schema}.{name}"

    # Validate that `table` is in the correct format ----
    _validate_table_name(table)

    # Initialise reader (including data format) ----
    reader: DataFrameReader = spark_session.read.format(data_format)

    # Add options (if exists) ----
    if read_options:
        reader.options(**read_options)

    # Load DataFrame ----
    return reader.table(table)

write_to_table 🔗

write_to_table(
    data_frame: psDataFrame,
    name: str,
    schema: Optional[str] = None,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    mode: Optional[WRITE_MODES] = None,
    write_options: Optional[str_dict] = None,
    partition_cols: Optional[str_collection] = None,
) -> None

Summary

For a given data_frame, write it out to a specified schema and name with format data_format.

Details
  • If schema is None, then we would expect the name to contain both the schema and the table name in the same. Like: schema.name, for example production.orders.
  • Else, if schema is not None, then we would expect the schema to (quite logically) contain the name of the schema, and the name to contain the name of the table.

Parameters:

Name Type Description Default
data_frame DataFrame

The DataFrame to be written. Must be a valid pyspark DataFrame (pyspark.sql.DataFrame).

 required
name str

The name of the table where it will be written.

 required
schema Optional[str]

The schema of the table where it will be written.
Defaults to None.

 None
data_format Optional[SPARK_FORMATS]

The format that the data_frame will be written to.
Defaults to "parquet".

 'parquet'
mode Optional[WRITE_MODES]

The behaviour for when the data already exists.
For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.mode.
Defaults to None.

 None
write_options Dict[str, str]

Any additional settings to parse to the writer class.
Like, for example:

  • If you are writing to a Delta object, and wanted to overwrite the schema: {"overwriteSchema": "true"}.
  • If you're writing to a CSV file, and wanted to specify the header row: {"header": "true"}.

For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.options.
Defaults to dict().

 None
partition_cols Optional[Union[str_collection, str]]

The column(s) that the table should partition by.
Defaults to None.

 None

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.
ValidationError

If name contains /, or is structured with three elements like: source.schema.table.

Returns:

Type Description
type(None)

Nothing is returned.
Note

You know that this function is successful if the table exists at the specified location, and there are no errors thrown.

Examples
Set up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.io import write_to_table
>>> from toolbox_pyspark.checks import table_exists
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "a": [1, 2, 3, 4],
...             "b": ["a", "b", "c", "d"],
...             "c": [1, 1, 1, 1],
...             "d": ["2", "2", "2", "2"],
...         }
...     )
... )

Check
1
>>> df.show()
Terminal
+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | a | 1 | 2 |
| 2 | b | 1 | 2 |
| 3 | c | 1 | 2 |
| 4 | d | 1 | 2 |
+---+---+---+---+

Example 1: Write to Table
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>>> write_to_table(
...     data_frame=df,
...     name="test_table",
...     schema="default",
...     data_format="parquet",
...     mode="overwrite",
... )
>>>
>>> table_exists(
...     name="test_table",
...     schema="default",
...     data_format="parquet",
...     spark_session=df.sparkSession,
... )
Terminal
True

Conclusion: Successfully written to table.

Example 2: Invalid table structure
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>>> write_to_table(
...     data_frame=df,
...     name="schema.test_table",
...     schema="source",
...     data_format="parquet",
...     mode="overwrite",
... )
Terminal
Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.

Conclusion: Failed to write to table due to invalid table structure.

See Also
Source code in src/toolbox_pyspark/io.py 
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@typechecked
def write_to_table(
    data_frame: psDataFrame,
    name: str,
    schema: Optional[str] = None,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    mode: Optional[WRITE_MODES] = None,
    write_options: Optional[str_dict] = None,
    partition_cols: Optional[str_collection] = None,
) -> None:
    """
    !!! note "Summary"
        For a given `data_frame`, write it out to a specified `schema` and `name` with format `data_format`.

    ???+ abstract "Details"
        - If `schema` is `#!py None`, then we would expect the `name` to contain both the schema and the table name in the same. Like: `schema.name`, for example `production.orders`.
        - Else, if `schema` is not `#! None`, then we would expect the `schema` to (quite logically) contain the name of the schema, and the `name` to contain the name of the table.

    Params:
        data_frame (psDataFrame):
            The DataFrame to be written. Must be a valid `pyspark` DataFrame ([`pyspark.sql.DataFrame`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html)).
        name (str):
            The name of the table where it will be written.
        schema (Optional[str], optional):
            The schema of the table where it will be written.<br>
            Defaults to `#!py None`.
        data_format (Optional[SPARK_FORMATS], optional):
            The format that the `data_frame` will be written to.<br>
            Defaults to `#!py "parquet"`.
        mode (Optional[WRITE_MODES], optional):
            The behaviour for when the data already exists.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.mode`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.mode.html).<br>
            Defaults to `#!py None`.
        write_options (Dict[str, str], optional):
            Any additional settings to parse to the writer class.<br>
            Like, for example:

            - If you are writing to a Delta object, and wanted to overwrite the schema: `#!py {"overwriteSchema": "true"}`.
            - If you're writing to a CSV file, and wanted to specify the header row: `#!py {"header": "true"}`.

            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.options.html).<br>
            Defaults to `#!py dict()`.
        partition_cols (Optional[Union[str_collection, str]], optional):
            The column(s) that the table should partition by.<br>
            Defaults to `#!py None`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.
        ValidationError:
            If `name` contains `/`, or is structured with three elements like: `source.schema.table`.

    Returns:
        (type(None)):
            Nothing is returned.

    ???+ tip "Note"
        You know that this function is successful if the table exists at the specified location, and there are no errors thrown.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.io import write_to_table
        >>> from toolbox_pyspark.checks import table_exists
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         {
        ...             "a": [1, 2, 3, 4],
        ...             "b": ["a", "b", "c", "d"],
        ...             "c": [1, 1, 1, 1],
        ...             "d": ["2", "2", "2", "2"],
        ...         }
        ...     )
        ... )
        ```

        ```{.py .python linenums="1" title="Check"}
        >>> df.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+---+---+
        | a | b | c | d |
        +---+---+---+---+
        | 1 | a | 1 | 2 |
        | 2 | b | 1 | 2 |
        | 3 | c | 1 | 2 |
        | 4 | d | 1 | 2 |
        +---+---+---+---+
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Write to Table"}
        >>> write_to_table(
        ...     data_frame=df,
        ...     name="test_table",
        ...     schema="default",
        ...     data_format="parquet",
        ...     mode="overwrite",
        ... )
        >>>
        >>> table_exists(
        ...     name="test_table",
        ...     schema="default",
        ...     data_format="parquet",
        ...     spark_session=df.sparkSession,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully written to table."
        </div>

        ```{.py .python linenums="1" title="Example 2: Invalid table structure"}
        >>> write_to_table(
        ...     data_frame=df,
        ...     name="schema.test_table",
        ...     schema="source",
        ...     data_format="parquet",
        ...     mode="overwrite",
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.
        ```
        !!! failure "Conclusion: Failed to write to table due to invalid table structure."
        </div>

    ??? tip "See Also"
        - [`save_to_table`][toolbox_pyspark.io.save_to_table]
        - [`write`][toolbox_pyspark.io.write]
        - [`save`][toolbox_pyspark.io.save]
    """

    # Set default options ----
    write_options: str_dict = write_options or dict()
    data_format: str = data_format or "parquet"
    table: str = name if not schema else f"{schema}.{name}"

    # Validate that `table` is in the correct format ----
    _validate_table_name(table)

    # Initialise writer (including data format) ----
    writer: DataFrameWriter = data_frame.write.mode(mode).format(data_format)

    # Add options (if exists) ----
    if write_options:
        writer.options(**write_options)

    # Add partition (if exists) ----
    if partition_cols is not None:
        partition_cols = [partition_cols] if is_type(partition_cols, str) else partition_cols
        writer = writer.partitionBy(list(partition_cols))

    # Write table ----
    writer.saveAsTable(table)

transfer_by_table 🔗

transfer_by_table(
    spark_session: SparkSession,
    from_table_name: str,
    to_table_name: str,
    from_table_schema: Optional[str] = None,
    from_table_format: Optional[SPARK_FORMATS] = "parquet",
    from_table_options: Optional[str_dict] = None,
    to_table_schema: Optional[str] = None,
    to_table_format: Optional[SPARK_FORMATS] = "parquet",
    to_table_mode: Optional[WRITE_MODES] = None,
    to_table_options: Optional[str_dict] = None,
    to_table_partition_cols: Optional[
        str_collection
    ] = None,
) -> None

Summary

Copy a table from one schema and name to another schema and name.

Details

This is a blind transfer. There is no validation, no alteration, no adjustments made at all. Simply read directly from one table and move immediately to another table straight away.

Parameters:

Name Type Description Default
spark_session SparkSession

The spark session to use for the transfer. Necessary in order to instantiate the reading process.

 required
from_table_name str

The name of the table to be read.

 required
to_table_name str

The name of the table where it will be saved.

 required
from_table_schema Optional[str]

The schema of the table to be read.
Defaults to None.

 None
from_table_format Optional[SPARK_FORMATS]

The format of the data at the reading location.
Defaults to "parquet".

 'parquet'
from_table_options Dict[str, str]

Any additional options to parse to the Spark reader.
For more info, check the pyspark docs: pyspark.sql.DataFrameReader.options.
Defaults to dict().

 None
to_table_schema Optional[str]

The schema of the table where it will be saved.
Defaults to None.

 None
to_table_format Optional[SPARK_FORMATS]

The format of the saved table.
Defaults to "parquet".

 'parquet'
to_table_mode Optional[WRITE_MODES]

The behaviour for when the data already exists.
For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.mode.
Defaults to None.

 None
to_table_options Dict[str, str]

Any additional settings to parse to the writer class.
For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.options.
Defaults to dict().

 None
to_table_partition_cols Optional[Union[str_collection, str]]

The column(s) that the table should partition by.
Defaults to None.

 None

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.

Returns:

Type Description
type(None)

Nothing is returned.
Note

You know that this function is successful if the table exists at the specified location, and there are no errors thrown.

Examples
Set up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.io import transfer_by_table
>>> from toolbox_pyspark.checks import table_exists
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = pd.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": ["a", "b", "c", "d"],
...         "c": [1, 1, 1, 1],
...         "d": ["2", "2", "2", "2"],
...     }
... )
>>> df.to_parquet("./test/table.parquet")
>>> spark.read.parquet("./test/table.parquet").createOrReplaceTempView("test_table")

Example 1: Transfer Table
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>>> transfer_by_table(
...     spark_session=spark,
...     from_table_name="test_table",
...     from_table_schema="default",
...     from_table_format="parquet",
...     to_table_name="new_table",
...     to_table_schema="default",
...     to_table_format="parquet",
...     to_table_mode="overwrite",
... )
>>>
>>> table_exists(
...     name="new_table",
...     schema="default",
...     data_format="parquet",
...     spark_session=spark,
... )
Terminal
True

Conclusion: Successfully transferred table.

Example 2: Invalid table structure
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>>> transfer_by_table(
...     spark_session=spark,
...     from_table_name="schema.test_table",
...     from_table_schema="source",
...     from_table_format="parquet",
...     to_table_name="new_table",
...     to_table_schema="default",
...     to_table_format="parquet",
...     to_table_mode="overwrite",
... )
Terminal
Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.

Conclusion: Failed to transfer table due to invalid table structure.

See Also
Source code in src/toolbox_pyspark/io.py 
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@typechecked
def transfer_by_table(
    spark_session: SparkSession,
    from_table_name: str,
    to_table_name: str,
    from_table_schema: Optional[str] = None,
    from_table_format: Optional[SPARK_FORMATS] = "parquet",
    from_table_options: Optional[str_dict] = None,
    to_table_schema: Optional[str] = None,
    to_table_format: Optional[SPARK_FORMATS] = "parquet",
    to_table_mode: Optional[WRITE_MODES] = None,
    to_table_options: Optional[str_dict] = None,
    to_table_partition_cols: Optional[str_collection] = None,
) -> None:
    """
    !!! note "Summary"
        Copy a table from one schema and name to another schema and name.

    ???+ abstract "Details"
        This is a blind transfer. There is no validation, no alteration, no adjustments made at all. Simply read directly from one table and move immediately to another table straight away.

    Params:
        spark_session (SparkSession):
            The spark session to use for the transfer. Necessary in order to instantiate the reading process.
        from_table_name (str):
            The name of the table to be read.
        to_table_name (str):
            The name of the table where it will be saved.
        from_table_schema (Optional[str], optional):
            The schema of the table to be read.<br>
            Defaults to `#!py None`.
        from_table_format (Optional[SPARK_FORMATS], optional):
            The format of the data at the reading location.<br>
            Defaults to `#!py "parquet"`.
        from_table_options (Dict[str, str], optional):
            Any additional options to parse to the Spark reader.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameReader.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameReader.options.html).<br>
            Defaults to `#!py dict()`.
        to_table_schema (Optional[str], optional):
            The schema of the table where it will be saved.<br>
            Defaults to `#!py None`.
        to_table_format (Optional[SPARK_FORMATS], optional):
            The format of the saved table.<br>
            Defaults to `#!py "parquet"`.
        to_table_mode (Optional[WRITE_MODES], optional):
            The behaviour for when the data already exists.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.mode`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.mode.html).<br>
            Defaults to `#!py None`.
        to_table_options (Dict[str, str], optional):
            Any additional settings to parse to the writer class.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.options.html).<br>
            Defaults to `#!py dict()`.
        to_table_partition_cols (Optional[Union[str_collection, str]], optional):
            The column(s) that the table should partition by.<br>
            Defaults to `#!py None`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.

    Returns:
        (type(None)):
            Nothing is returned.

    ???+ tip "Note"
        You know that this function is successful if the table exists at the specified location, and there are no errors thrown.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.io import transfer_by_table
        >>> from toolbox_pyspark.checks import table_exists
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = pd.DataFrame(
        ...     {
        ...         "a": [1, 2, 3, 4],
        ...         "b": ["a", "b", "c", "d"],
        ...         "c": [1, 1, 1, 1],
        ...         "d": ["2", "2", "2", "2"],
        ...     }
        ... )
        >>> df.to_parquet("./test/table.parquet")
        >>> spark.read.parquet("./test/table.parquet").createOrReplaceTempView("test_table")
        ```

        ```{.py .python linenums="1" title="Example 1: Transfer Table"}
        >>> transfer_by_table(
        ...     spark_session=spark,
        ...     from_table_name="test_table",
        ...     from_table_schema="default",
        ...     from_table_format="parquet",
        ...     to_table_name="new_table",
        ...     to_table_schema="default",
        ...     to_table_format="parquet",
        ...     to_table_mode="overwrite",
        ... )
        >>>
        >>> table_exists(
        ...     name="new_table",
        ...     schema="default",
        ...     data_format="parquet",
        ...     spark_session=spark,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully transferred table."
        </div>

        ```{.py .python linenums="1" title="Example 2: Invalid table structure"}
        >>> transfer_by_table(
        ...     spark_session=spark,
        ...     from_table_name="schema.test_table",
        ...     from_table_schema="source",
        ...     from_table_format="parquet",
        ...     to_table_name="new_table",
        ...     to_table_schema="default",
        ...     to_table_format="parquet",
        ...     to_table_mode="overwrite",
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.
        ```
        !!! failure "Conclusion: Failed to transfer table due to invalid table structure."
        </div>

    ??? tip "See Also"
        - [`transfer`][toolbox_pyspark.io.transfer]
    """

    # Read from source ----
    source_table: psDataFrame = read_from_table(
        name=from_table_name,
        schema=from_table_schema,
        spark_session=spark_session,
        data_format=from_table_format,
        read_options=from_table_options,
    )

    # Write to target ----
    write_to_table(
        data_frame=source_table,
        name=to_table_name,
        schema=to_table_schema,
        data_format=to_table_format,
        mode=to_table_mode,
        write_options=to_table_options,
        partition_cols=to_table_partition_cols,
    )

read 🔗

read(
    spark_session: SparkSession,
    name: str,
    method: Literal["table", "path"],
    path: Optional[str] = None,
    schema: Optional[str] = None,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    read_options: Optional[str_dict] = None,
) -> psDataFrame

Summary

Read a table or file from a given path or schema and name into memory as a pyspark dataframe.

Details

This function serves as a unified interface for reading data into a pyspark dataframe. Depending on the method parameter, it will either read from a file path or a table.

  • If method is "path", the function will use the read_from_path function to read the data from the specified path and name.
  • If method is "table", the function will use the read_from_table function to read the data from the specified schema and name.

Parameters:

Name Type Description Default
spark_session SparkSession

The Spark session to use for the reading.

 required
name str

The name of the table or file to read in.

 required
method Literal['table', 'path']

The method to use for reading the data. Either "table" or "path".

 required
path Optional[str]

The path from which the file will be read. Required if method is "path".
Defaults to None.

 None
schema Optional[str]

The schema of the table to read in. Required if method is "table".
Defaults to None.

 None
data_format Optional[SPARK_FORMATS]

The format of the data.
Defaults to "parquet".

 'parquet'
read_options Dict[str, str]

Any additional options to parse to the Spark reader.
For more info, check the pyspark docs: pyspark.sql.DataFrameReader.options.
Defaults to dict().

 None

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.
ValidationError

If name contains /, or is structured with three elements like: source.schema.table.

Returns:

Type Description
DataFrame

The loaded dataframe.
Examples
Set up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.io import read
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = pd.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": ["a", "b", "c", "d"],
...         "c": [1, 1, 1, 1],
...         "d": ["2", "2", "2", "2"],
...     }
... )
>>> df.to_csv("./test/table.csv")
>>> df.to_parquet("./test/table.parquet")
>>> spark.read.parquet("./test/table.parquet").createOrReplaceTempView("test_table")

Example 1: Read from Path
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>>> df_path = read(
...     spark_session=spark,
...     name="table.csv",
...     method="path",
...     path="./test",
...     data_format="csv",
...     read_options={"header": "true"},
... )
>>>
>>> df_path.show()
Terminal
+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | a | 1 | 2 |
| 2 | b | 1 | 2 |
| 3 | c | 1 | 2 |
| 4 | d | 1 | 2 |
+---+---+---+---+

Conclusion: Successfully read from path.

Example 2: Read from Table
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>>> df_table = read(
...     spark_session=spark,
...     name="test_table",
...     method="table",
...     schema="default",
...     data_format="parquet",
... )
>>>
>>> df_table.show()
Terminal
+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | a | 1 | 2 |
| 2 | b | 1 | 2 |
| 3 | c | 1 | 2 |
| 4 | d | 1 | 2 |
+---+---+---+---+

Conclusion: Successfully read from table.

Example 3: Invalid Path
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>>> df_invalid_path = read(
...     spark_session=spark,
...     name="invalid_table.csv",
...     method="path",
...     path="./invalid_path",
...     data_format="csv",
...     read_options={"header": "true"},
... )
Terminal
Py4JJavaError: An error occurred while calling o45.load.

Conclusion: Failed to read from invalid path.

Example 4: Invalid Table Structure
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>>> df_invalid_table = read(
...     spark_session=spark,
...     name="schema.invalid_table",
...     method="table",
...     schema="source",
...     data_format="parquet",
... )
Terminal
Invalid table. Should be in the format `schema.table`: `source.schema.invalid_table`.

Conclusion: Failed to read from invalid table structure.

See Also
Source code in src/toolbox_pyspark/io.py 
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@typechecked
def read(
    spark_session: SparkSession,
    name: str,
    method: Literal["table", "path"],
    path: Optional[str] = None,
    schema: Optional[str] = None,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    read_options: Optional[str_dict] = None,
) -> psDataFrame:
    """
    !!! note "Summary"
        Read a table or file from a given `path` or `schema` and `name` into memory as a `pyspark` dataframe.

    ???+ abstract "Details"
        This function serves as a unified interface for reading data into a `pyspark` dataframe. Depending on the `method` parameter, it will either read from a file path or a table.

        - If `method` is `#!py "path"`, the function will use the `read_from_path` function to read the data from the specified `path` and `name`.
        - If `method` is `#!py "table"`, the function will use the `read_from_table` function to read the data from the specified `schema` and `name`.

    Params:
        spark_session (SparkSession):
            The Spark session to use for the reading.
        name (str):
            The name of the table or file to read in.
        method (Literal["table", "path"]):
            The method to use for reading the data. Either `#!py "table"` or `#!py "path"`.
        path (Optional[str], optional):
            The path from which the file will be read. Required if `method` is `#!py "path"`.<br>
            Defaults to `#!py None`.
        schema (Optional[str], optional):
            The schema of the table to read in. Required if `method` is `#!py "table"`.<br>
            Defaults to `#!py None`.
        data_format (Optional[SPARK_FORMATS], optional):
            The format of the data.<br>
            Defaults to `#!py "parquet"`.
        read_options (Dict[str, str], optional):
            Any additional options to parse to the Spark reader.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameReader.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameReader.options.html).<br>
            Defaults to `#!py dict()`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.
        ValidationError:
            If `name` contains `/`, or is structured with three elements like: `source.schema.table`.

    Returns:
        (psDataFrame):
            The loaded dataframe.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.io import read
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = pd.DataFrame(
        ...     {
        ...         "a": [1, 2, 3, 4],
        ...         "b": ["a", "b", "c", "d"],
        ...         "c": [1, 1, 1, 1],
        ...         "d": ["2", "2", "2", "2"],
        ...     }
        ... )
        >>> df.to_csv("./test/table.csv")
        >>> df.to_parquet("./test/table.parquet")
        >>> spark.read.parquet("./test/table.parquet").createOrReplaceTempView("test_table")
        ```

        ```{.py .python linenums="1" title="Example 1: Read from Path"}
        >>> df_path = read(
        ...     spark_session=spark,
        ...     name="table.csv",
        ...     method="path",
        ...     path="./test",
        ...     data_format="csv",
        ...     read_options={"header": "true"},
        ... )
        >>>
        >>> df_path.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+---+---+
        | a | b | c | d |
        +---+---+---+---+
        | 1 | a | 1 | 2 |
        | 2 | b | 1 | 2 |
        | 3 | c | 1 | 2 |
        | 4 | d | 1 | 2 |
        +---+---+---+---+
        ```
        !!! success "Conclusion: Successfully read from path."
        </div>

        ```{.py .python linenums="1" title="Example 2: Read from Table"}
        >>> df_table = read(
        ...     spark_session=spark,
        ...     name="test_table",
        ...     method="table",
        ...     schema="default",
        ...     data_format="parquet",
        ... )
        >>>
        >>> df_table.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+---+---+
        | a | b | c | d |
        +---+---+---+---+
        | 1 | a | 1 | 2 |
        | 2 | b | 1 | 2 |
        | 3 | c | 1 | 2 |
        | 4 | d | 1 | 2 |
        +---+---+---+---+
        ```
        !!! success "Conclusion: Successfully read from table."
        </div>

        ```{.py .python linenums="1" title="Example 3: Invalid Path"}
        >>> df_invalid_path = read(
        ...     spark_session=spark,
        ...     name="invalid_table.csv",
        ...     method="path",
        ...     path="./invalid_path",
        ...     data_format="csv",
        ...     read_options={"header": "true"},
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Py4JJavaError: An error occurred while calling o45.load.
        ```
        !!! failure "Conclusion: Failed to read from invalid path."
        </div>

        ```{.py .python linenums="1" title="Example 4: Invalid Table Structure"}
        >>> df_invalid_table = read(
        ...     spark_session=spark,
        ...     name="schema.invalid_table",
        ...     method="table",
        ...     schema="source",
        ...     data_format="parquet",
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Invalid table. Should be in the format `schema.table`: `source.schema.invalid_table`.
        ```
        !!! failure "Conclusion: Failed to read from invalid table structure."
        </div>

    ??? tip "See Also"
        - [`read_from_path`][toolbox_pyspark.io.read_from_path]
        - [`read_from_table`][toolbox_pyspark.io.read_from_table]
        - [`load`][toolbox_pyspark.io.load]
    """

    if method == "table":
        return read_from_table(
            spark_session=spark_session,
            name=name,
            schema=schema,
            data_format=data_format,
            read_options=read_options,
        )
    if method == "path":
        return read_from_path(
            spark_session=spark_session,
            name=name,
            path=path,
            data_format=data_format,
            read_options=read_options,
        )

write 🔗

write(
    data_frame: psDataFrame,
    name: str,
    method: Literal["table", "path"],
    path: Optional[str] = None,
    schema: Optional[str] = None,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    mode: Optional[WRITE_MODES] = None,
    write_options: Optional[str_dict] = None,
    partition_cols: Optional[str_collection] = None,
) -> None

Summary

Write a dataframe to a specified path or schema and name with format data_format.

Details

This function serves as a unified interface for writing data from a pyspark dataframe. Depending on the method parameter, it will either write to a file path or a table.

  • If method is "path", the function will use the write_to_path function to write the data to the specified path and name.
  • If method is "table", the function will use the write_to_table function to write the data to the specified schema and name.

Parameters:

Name Type Description Default
data_frame DataFrame

The DataFrame to be written. Must be a valid pyspark DataFrame (pyspark.sql.DataFrame).

 required
name str

The name of the table or file where it will be written.

 required
method Literal['table', 'path']

The method to use for writing the data. Either "table" or "path".

 required
path Optional[str]

The path location for where to save the table. Required if method is "path".
Defaults to None.

 None
schema Optional[str]

The schema of the table where it will be written. Required if method is "table".
Defaults to None.

 None
data_format Optional[SPARK_FORMATS]

The format that the data_frame will be written to.
Defaults to "parquet".

 'parquet'
mode Optional[WRITE_MODES]

The behaviour for when the data already exists.
For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.mode.
Defaults to None.

 None
write_options Dict[str, str]

Any additional settings to parse to the writer class.
Like, for example:

  • If you are writing to a Delta object, and wanted to overwrite the schema: {"overwriteSchema": "true"}.
  • If you're writing to a CSV file, and wanted to specify the header row: {"header": "true"}.

For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.options.
Defaults to dict().

 None
partition_cols Optional[Union[str_collection, str]]

The column(s) that the table should partition by.
Defaults to None.

 None

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.
ValidationError

If name contains /, or is structured with three elements like: source.schema.table.

Returns:

Type Description
type(None)

Nothing is returned.
Note

You know that this function is successful if the table or file exists at the specified location, and there are no errors thrown.

Examples
Set up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.io import write
>>> from toolbox_pyspark.checks import table_exists
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "a": [1, 2, 3, 4],
...             "b": ["a", "b", "c", "d"],
...             "c": [1, 1, 1, 1],
...             "d": ["2", "2", "2", "2"],
...         }
...     )
... )

Check
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>>> df.show()
Terminal
+---+---+---+---+
| a | b | c | d |
+---+---+---+---+
| 1 | a | 1 | 2 |
| 2 | b | 1 | 2 |
| 3 | c | 1 | 2 |
| 4 | d | 1 | 2 |
+---+---+---+---+

Example 1: Write to Path
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>>> write(
...     data_frame=df,
...     name="df.csv",
...     method="path",
...     path="./test",
...     data_format="csv",
...     mode="overwrite",
...     write_options={"header": "true"},
... )
>>>
>>> table_exists(
...     name="df.csv",
...     path="./test",
...     data_format="csv",
...     spark_session=df.sparkSession,
... )
Terminal
True

Conclusion: Successfully written to path.

Example 2: Write to Table
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>>> write(
...     data_frame=df,
...     name="test_table",
...     method="table",
...     schema="default",
...     data_format="parquet",
...     mode="overwrite",
... )
>>>
>>> table_exists(
...     name="test_table",
...     schema="default",
...     data_format="parquet",
...     spark_session=df.sparkSession,
... )
Terminal
True

Conclusion: Successfully written to table.

Example 3: Invalid Path
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>>> write(
...     data_frame=df,
...     name="df.csv",
...     method="path",
...     path="./invalid_path",
...     data_format="csv",
...     mode="overwrite",
...     write_options={"header": "true"},
... )
Terminal
Py4JJavaError: An error occurred while calling o45.save.

Conclusion: Failed to write to invalid path.

Example 4: Invalid Table Structure
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>>> write(
...     data_frame=df,
...     name="schema.test_table",
...     method="table",
...     schema="source",
...     data_format="parquet",
...     mode="overwrite",
... )
Terminal
Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.

Conclusion: Failed to write to table due to invalid table structure.

See Also
Source code in src/toolbox_pyspark/io.py 
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@typechecked
def write(
    data_frame: psDataFrame,
    name: str,
    method: Literal["table", "path"],
    path: Optional[str] = None,
    schema: Optional[str] = None,
    data_format: Optional[SPARK_FORMATS] = "parquet",
    mode: Optional[WRITE_MODES] = None,
    write_options: Optional[str_dict] = None,
    partition_cols: Optional[str_collection] = None,
) -> None:
    """
    !!! note "Summary"
        Write a dataframe to a specified `path` or `schema` and `name` with format `data_format`.

    ???+ abstract "Details"
        This function serves as a unified interface for writing data from a `pyspark` dataframe. Depending on the `method` parameter, it will either write to a file path or a table.

        - If `method` is `#!py "path"`, the function will use the `write_to_path` function to write the data to the specified `path` and `name`.
        - If `method` is `#!py "table"`, the function will use the `write_to_table` function to write the data to the specified `schema` and `name`.

    Params:
        data_frame (psDataFrame):
            The DataFrame to be written. Must be a valid `pyspark` DataFrame ([`pyspark.sql.DataFrame`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html)).
        name (str):
            The name of the table or file where it will be written.
        method (Literal["table", "path"]):
            The method to use for writing the data. Either `#!py "table"` or `#!py "path"`.
        path (Optional[str], optional):
            The path location for where to save the table. Required if `method` is `#!py "path"`.<br>
            Defaults to `#!py None`.
        schema (Optional[str], optional):
            The schema of the table where it will be written. Required if `method` is `#!py "table"`.<br>
            Defaults to `#!py None`.
        data_format (Optional[SPARK_FORMATS], optional):
            The format that the `data_frame` will be written to.<br>
            Defaults to `#!py "parquet"`.
        mode (Optional[WRITE_MODES], optional):
            The behaviour for when the data already exists.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.mode`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.mode.html).<br>
            Defaults to `#!py None`.
        write_options (Dict[str, str], optional):
            Any additional settings to parse to the writer class.<br>
            Like, for example:

            - If you are writing to a Delta object, and wanted to overwrite the schema: `#!py {"overwriteSchema": "true"}`.
            - If you're writing to a CSV file, and wanted to specify the header row: `#!py {"header": "true"}`.

            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.options.html).<br>
            Defaults to `#!py dict()`.
        partition_cols (Optional[Union[str_collection, str]], optional):
            The column(s) that the table should partition by.<br>
            Defaults to `#!py None`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.
        ValidationError:
            If `name` contains `/`, or is structured with three elements like: `source.schema.table`.

    Returns:
        (type(None)):
            Nothing is returned.

    ???+ tip "Note"
        You know that this function is successful if the table or file exists at the specified location, and there are no errors thrown.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.io import write
        >>> from toolbox_pyspark.checks import table_exists
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         {
        ...             "a": [1, 2, 3, 4],
        ...             "b": ["a", "b", "c", "d"],
        ...             "c": [1, 1, 1, 1],
        ...             "d": ["2", "2", "2", "2"],
        ...         }
        ...     )
        ... )
        ```

        ```{.py .python linenums="1" title="Check"}
        >>> df.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+---+---+
        | a | b | c | d |
        +---+---+---+---+
        | 1 | a | 1 | 2 |
        | 2 | b | 1 | 2 |
        | 3 | c | 1 | 2 |
        | 4 | d | 1 | 2 |
        +---+---+---+---+
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Write to Path"}
        >>> write(
        ...     data_frame=df,
        ...     name="df.csv",
        ...     method="path",
        ...     path="./test",
        ...     data_format="csv",
        ...     mode="overwrite",
        ...     write_options={"header": "true"},
        ... )
        >>>
        >>> table_exists(
        ...     name="df.csv",
        ...     path="./test",
        ...     data_format="csv",
        ...     spark_session=df.sparkSession,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully written to path."
        </div>

        ```{.py .python linenums="1" title="Example 2: Write to Table"}
        >>> write(
        ...     data_frame=df,
        ...     name="test_table",
        ...     method="table",
        ...     schema="default",
        ...     data_format="parquet",
        ...     mode="overwrite",
        ... )
        >>>
        >>> table_exists(
        ...     name="test_table",
        ...     schema="default",
        ...     data_format="parquet",
        ...     spark_session=df.sparkSession,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully written to table."
        </div>

        ```{.py .python linenums="1" title="Example 3: Invalid Path"}
        >>> write(
        ...     data_frame=df,
        ...     name="df.csv",
        ...     method="path",
        ...     path="./invalid_path",
        ...     data_format="csv",
        ...     mode="overwrite",
        ...     write_options={"header": "true"},
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Py4JJavaError: An error occurred while calling o45.save.
        ```
        !!! failure "Conclusion: Failed to write to invalid path."
        </div>

        ```{.py .python linenums="1" title="Example 4: Invalid Table Structure"}
        >>> write(
        ...     data_frame=df,
        ...     name="schema.test_table",
        ...     method="table",
        ...     schema="source",
        ...     data_format="parquet",
        ...     mode="overwrite",
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.
        ```
        !!! failure "Conclusion: Failed to write to table due to invalid table structure."
        </div>

    ??? tip "See Also"
        - [`write_to_path`][toolbox_pyspark.io.write_to_path]
        - [`write_to_table`][toolbox_pyspark.io.write_to_table]
        - [`save`][toolbox_pyspark.io.save]
    """

    if method == "table":
        write_to_table(
            data_frame=data_frame,
            name=name,
            schema=schema,
            data_format=data_format,
            mode=mode,
            write_options=write_options,
            partition_cols=partition_cols,
        )
    if method == "path":
        write_to_path(
            data_frame=data_frame,
            name=name,
            path=path,
            data_format=data_format,
            mode=mode,
            write_options=write_options,
            partition_cols=partition_cols,
        )

transfer 🔗

transfer(
    spark_session: SparkSession,
    from_table_name: str,
    to_table_name: str,
    method: Literal["table", "path"],
    from_table_path: Optional[str] = None,
    from_table_schema: Optional[str] = None,
    from_table_format: Optional[SPARK_FORMATS] = "parquet",
    from_table_options: Optional[str_dict] = None,
    to_table_path: Optional[str] = None,
    to_table_schema: Optional[str] = None,
    to_table_format: Optional[SPARK_FORMATS] = "parquet",
    to_table_mode: Optional[WRITE_MODES] = None,
    to_table_options: Optional[str_dict] = None,
    to_partition_cols: Optional[str_collection] = None,
) -> None

Summary

Transfer a table or file from one location to another.

Details

This function serves as a unified interface for transferring data from one location to another. Depending on the method parameter, it will either transfer from a file path or a table.

  • If method is "path", the function will use the transfer_by_path function to transfer the data from the specified from_table_path and from_table_name to the specified to_table_path and to_table_name.
  • If method is "table", the function will use the transfer_by_table function to transfer the data from the specified from_table_schema and from_table_name to the specified to_table_schema and to_table_name.

Parameters:

Name Type Description Default
spark_session SparkSession

The Spark session to use for the transfer.

 required
from_table_name str

The name of the table or file to be transferred.

 required
to_table_name str

The name of the table or file where it will be transferred.

 required
method Literal['table', 'path']

The method to use for transferring the data. Either "table" or "path".

 required
from_table_path Optional[str]

The path from which the file will be transferred. Required if method is "path".
Defaults to None.

 None
from_table_schema Optional[str]

The schema of the table to be transferred. Required if method is "table".
Defaults to None.

 None
from_table_format Optional[SPARK_FORMATS]

The format of the data at the source location.
Defaults to "parquet".

 'parquet'
from_table_options Dict[str, str]

Any additional options to parse to the Spark reader.
For more info, check the pyspark docs: pyspark.sql.DataFrameReader.options.
Defaults to dict().

 None
to_table_path Optional[str]

The path location for where to save the table. Required if method is "path".
Defaults to None.

 None
to_table_schema Optional[str]

The schema of the table where it will be saved. Required if method is "table".
Defaults to None.

 None
to_table_format Optional[SPARK_FORMATS]

The format of the saved table.
Defaults to "parquet".

 'parquet'
to_table_mode Optional[WRITE_MODES]

The behaviour for when the data already exists.
For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.mode.
Defaults to None.

 None
to_table_options Dict[str, str]

Any additional settings to parse to the writer class.
For more info, check the pyspark docs: pyspark.sql.DataFrameWriter.options.
Defaults to dict().

 None
to_partition_cols Optional[Union[str_collection, str]]

The column(s) that the table should partition by.
Defaults to None.

 None

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.
ValidationError

If from_table_name or to_table_name contains /, or is structured with three elements like: source.schema.table.

Returns:

Type Description
type(None)

Nothing is returned.
Note

You know that this function is successful if the table or file exists at the specified location, and there are no errors thrown.

Examples
Set up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.io import transfer
>>> from toolbox_pyspark.checks import table_exists
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = pd.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": ["a", "b", "c", "d"],
...         "c": [1, 1, 1, 1],
...         "d": ["2", "2", "2", "2"],
...     }
... )
>>> df.to_csv("./test/table.csv")
>>> df.to_parquet("./test/table.parquet")
>>> spark.read.parquet("./test/table.parquet").createOrReplaceTempView("test_table")

Example 1: Transfer from Path
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>>> transfer(
...     spark_session=spark,
...     method="path",
...     from_table_name="table.csv",
...     from_table_path="./test",
...     from_table_format="csv",
...     from_table_options={"header": "true"},
...     to_table_name="new_table.csv",
...     to_table_path="./other",
...     to_table_format="csv",
...     to_table_mode="overwrite",
...     to_table_options={"header": "true"},
... )
>>>
>>> table_exists(
...     name="new_table.csv",
...     path="./other",
...     data_format="csv",
...     spark_session=spark,
... )
Terminal
True

Conclusion: Successfully transferred from path.

Example 2: Transfer from Table
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>>> transfer(
...     spark_session=spark,
...     method="table",
...     from_table_name="test_table",
...     from_table_schema="default",
...     from_table_format="parquet",
...     to_table_name="new_table",
...     to_table_schema="default",
...     to_table_format="parquet",
...     to_table_mode="overwrite",
... )
>>>
>>> table_exists(
...     name="new_table",
...     schema="default",
...     data_format="parquet",
...     spark_session=spark,
... )
Terminal
True

Conclusion: Successfully transferred from table.

Example 3: Invalid Path
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>>> transfer(
...     spark_session=spark,
...     method="path",
...     from_table_name="table.csv",
...     from_table_path="./invalid_path",
...     from_table_format="csv",
...     from_table_options={"header": "true"},
...     to_table_name="new_table.csv",
...     to_table_path="./other",
...     to_table_format="csv",
...     to_table_mode="overwrite",
...     to_table_options={"header": "true"},
... )
Terminal
Py4JJavaError: An error occurred while calling o45.load.

Conclusion: Failed to transfer from invalid path.

Example 4: Invalid Table Structure
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>>> transfer(
...     spark_session=spark,
...     method="table",
...     from_table_name="schema.test_table",
...     from_table_schema="source",
...     from_table_format="parquet",
...     to_table_name="new_table",
...     to_table_schema="default",
...     to_table_format="parquet",
...     to_table_mode="overwrite",
... )
Terminal
Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.

Conclusion: Failed to transfer from invalid table structure.

See Also
Source code in src/toolbox_pyspark/io.py 
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@typechecked
def transfer(
    spark_session: SparkSession,
    from_table_name: str,
    to_table_name: str,
    method: Literal["table", "path"],
    from_table_path: Optional[str] = None,
    from_table_schema: Optional[str] = None,
    from_table_format: Optional[SPARK_FORMATS] = "parquet",
    from_table_options: Optional[str_dict] = None,
    to_table_path: Optional[str] = None,
    to_table_schema: Optional[str] = None,
    to_table_format: Optional[SPARK_FORMATS] = "parquet",
    to_table_mode: Optional[WRITE_MODES] = None,
    to_table_options: Optional[str_dict] = None,
    to_partition_cols: Optional[str_collection] = None,
) -> None:
    """
    !!! note "Summary"
        Transfer a table or file from one location to another.

    ???+ abstract "Details"
        This function serves as a unified interface for transferring data from one location to another. Depending on the `method` parameter, it will either transfer from a file path or a table.

        - If `method` is `#!py "path"`, the function will use the `transfer_by_path` function to transfer the data from the specified `from_table_path` and `from_table_name` to the specified `to_table_path` and `to_table_name`.
        - If `method` is `#!py "table"`, the function will use the `transfer_by_table` function to transfer the data from the specified `from_table_schema` and `from_table_name` to the specified `to_table_schema` and `to_table_name`.

    Params:
        spark_session (SparkSession):
            The Spark session to use for the transfer.
        from_table_name (str):
            The name of the table or file to be transferred.
        to_table_name (str):
            The name of the table or file where it will be transferred.
        method (Literal["table", "path"]):
            The method to use for transferring the data. Either `#!py "table"` or `#!py "path"`.
        from_table_path (Optional[str], optional):
            The path from which the file will be transferred. Required if `method` is `#!py "path"`.<br>
            Defaults to `#!py None`.
        from_table_schema (Optional[str], optional):
            The schema of the table to be transferred. Required if `method` is `#!py "table"`.<br>
            Defaults to `#!py None`.
        from_table_format (Optional[SPARK_FORMATS], optional):
            The format of the data at the source location.<br>
            Defaults to `#!py "parquet"`.
        from_table_options (Dict[str, str], optional):
            Any additional options to parse to the Spark reader.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameReader.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameReader.options.html).<br>
            Defaults to `#!py dict()`.
        to_table_path (Optional[str], optional):
            The path location for where to save the table. Required if `method` is `#!py "path"`.<br>
            Defaults to `#!py None`.
        to_table_schema (Optional[str], optional):
            The schema of the table where it will be saved. Required if `method` is `#!py "table"`.<br>
            Defaults to `#!py None`.
        to_table_format (Optional[SPARK_FORMATS], optional):
            The format of the saved table.<br>
            Defaults to `#!py "parquet"`.
        to_table_mode (Optional[WRITE_MODES], optional):
            The behaviour for when the data already exists.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.mode`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.mode.html).<br>
            Defaults to `#!py None`.
        to_table_options (Dict[str, str], optional):
            Any additional settings to parse to the writer class.<br>
            For more info, check the `pyspark` docs: [`pyspark.sql.DataFrameWriter.options`](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.options.html).<br>
            Defaults to `#!py dict()`.
        to_partition_cols (Optional[Union[str_collection, str]], optional):
            The column(s) that the table should partition by.<br>
            Defaults to `#!py None`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.
        ValidationError:
            If `from_table_name` or `to_table_name` contains `/`, or is structured with three elements like: `source.schema.table`.

    Returns:
        (type(None)):
            Nothing is returned.

    ???+ tip "Note"
        You know that this function is successful if the table or file exists at the specified location, and there are no errors thrown.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.io import transfer
        >>> from toolbox_pyspark.checks import table_exists
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = pd.DataFrame(
        ...     {
        ...         "a": [1, 2, 3, 4],
        ...         "b": ["a", "b", "c", "d"],
        ...         "c": [1, 1, 1, 1],
        ...         "d": ["2", "2", "2", "2"],
        ...     }
        ... )
        >>> df.to_csv("./test/table.csv")
        >>> df.to_parquet("./test/table.parquet")
        >>> spark.read.parquet("./test/table.parquet").createOrReplaceTempView("test_table")
        ```

        ```{.py .python linenums="1" title="Example 1: Transfer from Path"}
        >>> transfer(
        ...     spark_session=spark,
        ...     method="path",
        ...     from_table_name="table.csv",
        ...     from_table_path="./test",
        ...     from_table_format="csv",
        ...     from_table_options={"header": "true"},
        ...     to_table_name="new_table.csv",
        ...     to_table_path="./other",
        ...     to_table_format="csv",
        ...     to_table_mode="overwrite",
        ...     to_table_options={"header": "true"},
        ... )
        >>>
        >>> table_exists(
        ...     name="new_table.csv",
        ...     path="./other",
        ...     data_format="csv",
        ...     spark_session=spark,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully transferred from path."
        </div>

        ```{.py .python linenums="1" title="Example 2: Transfer from Table"}
        >>> transfer(
        ...     spark_session=spark,
        ...     method="table",
        ...     from_table_name="test_table",
        ...     from_table_schema="default",
        ...     from_table_format="parquet",
        ...     to_table_name="new_table",
        ...     to_table_schema="default",
        ...     to_table_format="parquet",
        ...     to_table_mode="overwrite",
        ... )
        >>>
        >>> table_exists(
        ...     name="new_table",
        ...     schema="default",
        ...     data_format="parquet",
        ...     spark_session=spark,
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Successfully transferred from table."
        </div>

        ```{.py .python linenums="1" title="Example 3: Invalid Path"}
        >>> transfer(
        ...     spark_session=spark,
        ...     method="path",
        ...     from_table_name="table.csv",
        ...     from_table_path="./invalid_path",
        ...     from_table_format="csv",
        ...     from_table_options={"header": "true"},
        ...     to_table_name="new_table.csv",
        ...     to_table_path="./other",
        ...     to_table_format="csv",
        ...     to_table_mode="overwrite",
        ...     to_table_options={"header": "true"},
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Py4JJavaError: An error occurred while calling o45.load.
        ```
        !!! failure "Conclusion: Failed to transfer from invalid path."
        </div>

        ```{.py .python linenums="1" title="Example 4: Invalid Table Structure"}
        >>> transfer(
        ...     spark_session=spark,
        ...     method="table",
        ...     from_table_name="schema.test_table",
        ...     from_table_schema="source",
        ...     from_table_format="parquet",
        ...     to_table_name="new_table",
        ...     to_table_schema="default",
        ...     to_table_format="parquet",
        ...     to_table_mode="overwrite",
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        Invalid table. Should be in the format `schema.table`: `source.schema.test_table`.
        ```
        !!! failure "Conclusion: Failed to transfer from invalid table structure."
        </div>

    ??? tip "See Also"
        - [`transfer_by_path`][toolbox_pyspark.io.transfer_by_path]
        - [`transfer_by_table`][toolbox_pyspark.io.transfer_by_table]
    """

    if method == "table":
        transfer_by_table(
            spark_session=spark_session,
            from_table_name=from_table_name,
            to_table_name=to_table_name,
            from_table_schema=from_table_schema,
            from_table_format=from_table_format,
            from_table_options=from_table_options,
            to_table_schema=to_table_schema,
            to_table_format=to_table_format,
            to_table_mode=to_table_mode,
            to_table_options=to_table_options,
            to_table_partition_cols=to_partition_cols,
        )
    if method == "path":
        transfer_by_path(
            spark_session=spark_session,
            from_table_path=from_table_path,
            from_table_name=from_table_name,
            from_table_format=from_table_format,
            to_table_path=to_table_path,
            to_table_name=to_table_name,
            to_table_format=to_table_format,
            from_table_options=from_table_options,
            to_table_mode=to_table_mode,
            to_table_options=to_table_options,
            to_table_partition_cols=to_partition_cols,
        )

load_from_path module-attribute 🔗

load_from_path = read_from_path

save_to_path module-attribute 🔗

save_to_path = write_to_path

load_from_table module-attribute 🔗

load_from_table = read_from_table

save_to_table module-attribute 🔗

save_to_table = write_to_table

load module-attribute 🔗

load = read

save module-attribute 🔗

save = write