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Formatting

toolbox_pyspark.formatting 🔗

Summary

The formatting module provides functions for formatting and displaying.

format_numbers 🔗

format_numbers(dataframe: psDataFrame) -> psDataFrame

Summary

Format numbers in a Spark DataFrame.

Details

This function formats numbers in a Spark DataFrame. It formats integers to have no decimal places and floats to have two decimal places. The function is useful for displaying intermediary tables in a more readable format. It will replace all numeric columns to string.

Parameters:

Name Type Description Default
dataframe DataFrame

The Spark DataFrame to format.

 required

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 formatted Spark DataFrame.
Examples

Set Up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.formatting import format_numbers
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "a": [1, 2, 3, 4],
...             "b": ["a", "b", "c", "d"],
...             "c": [1.0, 2.0, 3.0, 4.0],
...             "d": [1.1, 2.2, 3.3, 4.4],
...             "e": [1000, 10000, 100000, 1000000],
...             "f": [1111.11, 22222.22, 333333.33, 4444444.44],
...         }
...     )
... )
>>>
>>> # Check
>>> df.show()
Terminal
+---+---+-----+-----+---------+------------+
| a | b |   c |   d |       e |          f |
+---+---+-----+-----+---------+------------+
| 1 | a | 1.0 | 1.1 |    1000 |    1111.11 |
| 2 | b | 2.0 | 2.2 |   10000 |   22222.22 |
| 3 | c | 3.0 | 3.3 |  100000 |  333333.33 |
| 4 | d | 4.0 | 4.4 | 1000000 | 4444444.44 |
+---+---+-----+-----+---------+------------+

Example 1: Format Numbers by function
1
>>> format_numbers(df).show()
Terminal
+---+---+-----+-----+-----------+--------------+
| a | b |   c |   d |         e |            f |
+---+---+-----+-----+-----------+--------------+
| 1 | a | 1.0 | 1.1 |     1,000 |     1,111.11 |
| 2 | b | 2.0 | 2.2 |    10,000 |    22,222.22 |
| 3 | c | 3.0 | 3.3 |   100,000 |   333,333.33 |
| 4 | d | 4.0 | 4.4 | 1,000,000 | 4,444,444.44 |
+---+---+-----+-----+-----------+--------------+

Conclusion: Successfully formatted dataframe.

Example 2: Format Numbers by method
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>>> df.transform(format_numbers).show()
Terminal
+---+---+-----+-----+-----------+--------------+
| a | b |   c |   d |         e |            f |
+---+---+-----+-----+-----------+--------------+
| 1 | a | 1.0 | 1.1 |     1,000 |     1,111.11 |
| 2 | b | 2.0 | 2.2 |    10,000 |    22,222.22 |
| 3 | c | 3.0 | 3.3 |   100,000 |   333,333.33 |
| 4 | d | 4.0 | 4.4 | 1,000,000 | 4,444,444.44 |
+---+---+-----+-----+-----------+--------------+

Conclusion: Successfully formatted dataframe.

Source code in src/toolbox_pyspark/formatting.py 
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@typechecked
def format_numbers(dataframe: psDataFrame) -> psDataFrame:
    """
    !!! note "Summary"
        Format numbers in a Spark DataFrame.

    ??? abstract "Details"
        This function formats numbers in a Spark DataFrame. It formats integers to have no decimal places and floats to have two decimal places. The function is useful for displaying intermediary tables in a more readable format. It will replace all numeric columns to string.

    Params:
        dataframe (psDataFrame):
            The Spark DataFrame to format.

    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 formatted Spark DataFrame.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set Up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.formatting import format_numbers
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         {
        ...             "a": [1, 2, 3, 4],
        ...             "b": ["a", "b", "c", "d"],
        ...             "c": [1.0, 2.0, 3.0, 4.0],
        ...             "d": [1.1, 2.2, 3.3, 4.4],
        ...             "e": [1000, 10000, 100000, 1000000],
        ...             "f": [1111.11, 22222.22, 333333.33, 4444444.44],
        ...         }
        ...     )
        ... )
        >>>
        >>> # Check
        >>> df.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+---------+------------+
        | a | b |   c |   d |       e |          f |
        +---+---+-----+-----+---------+------------+
        | 1 | a | 1.0 | 1.1 |    1000 |    1111.11 |
        | 2 | b | 2.0 | 2.2 |   10000 |   22222.22 |
        | 3 | c | 3.0 | 3.3 |  100000 |  333333.33 |
        | 4 | d | 4.0 | 4.4 | 1000000 | 4444444.44 |
        +---+---+-----+-----+---------+------------+
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Format Numbers by function"}
        >>> format_numbers(df).show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+-----------+--------------+
        | a | b |   c |   d |         e |            f |
        +---+---+-----+-----+-----------+--------------+
        | 1 | a | 1.0 | 1.1 |     1,000 |     1,111.11 |
        | 2 | b | 2.0 | 2.2 |    10,000 |    22,222.22 |
        | 3 | c | 3.0 | 3.3 |   100,000 |   333,333.33 |
        | 4 | d | 4.0 | 4.4 | 1,000,000 | 4,444,444.44 |
        +---+---+-----+-----+-----------+--------------+
        ```
        !!! success "Conclusion: Successfully formatted dataframe."
        </div>

        ```{.py .python linenums="1" title="Example 2: Format Numbers by method"}
        >>> df.transform(format_numbers).show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+-----------+--------------+
        | a | b |   c |   d |         e |            f |
        +---+---+-----+-----+-----------+--------------+
        | 1 | a | 1.0 | 1.1 |     1,000 |     1,111.11 |
        | 2 | b | 2.0 | 2.2 |    10,000 |    22,222.22 |
        | 3 | c | 3.0 | 3.3 |   100,000 |   333,333.33 |
        | 4 | d | 4.0 | 4.4 | 1,000,000 | 4,444,444.44 |
        +---+---+-----+-----+-----------+--------------+
        ```
        !!! success "Conclusion: Successfully formatted dataframe."
        </div>
    """
    for col, typ in dataframe.dtypes:
        if typ in ("int", "tinyint", "smallint", "bigint"):
            dataframe = dataframe.withColumn(col, F.format_number(col, 0))
        elif typ in ("float", "double"):
            dataframe = dataframe.withColumn(col, F.format_number(col, 2))
    return dataframe

display_intermediary_table 🔗

display_intermediary_table(
    dataframe: psDataFrame,
    reformat_numbers: bool = True,
    num_rows: int = 20,
) -> psDataFrame

Summary

Display an intermediary Spark DataFrame.

Details

This function displays an intermediary Spark DataFrame. The function is useful for displaying intermediary tables in a more readable format. Optionally, it can format numbers in the DataFrame to make it more readable.

Parameters:

Name Type Description Default
dataframe DataFrame

The Spark DataFrame to display.

 required
reformat_numbers bool

Whether to format numbers in the DataFrame. Default is True.

 True
num_rows int

The number of rows to display. Default is 20.

 20

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 original Spark DataFrame.
Examples

Set Up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.formatting import display_intermediary_table
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "a": [1, 2, 3, 4],
...             "b": ["a", "b", "c", "d"],
...             "c": [1.0, 2.0, 3.0, 4.0],
...             "d": [1.1, 2.2, 3.3, 4.4],
...         }
...     )
... )
>>>
>>> # Check
>>> df.show()
Terminal
+---+---+-----+-----+
| a | b |   c |   d |
+---+---+-----+-----+
| 1 | a | 1.0 | 1.1 |
| 2 | b | 2.0 | 2.2 |
| 3 | c | 3.0 | 3.3 |
| 4 | d | 4.0 | 4.4 |
+---+---+-----+-----+

Example 1: Display intermediary table with no subsequent formatting
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>>> (
...     df
...     .transform(display_intermediary_table, reformat_numbers=False, num_rows=2)
...     .show()
... )
Terminal
+---+---+-----+-----+
| a | b |   c |   d |
+---+---+-----+-----+
| 1 | a | 1.0 | 1.1 |
| 2 | b | 2.0 | 2.2 |
+---+---+-----+-----+
Terminal
+---+---+-----+-----+
| a | b |   c |   d |
+---+---+-----+-----+
| 1 | a | 1.0 | 1.1 |
| 2 | b | 2.0 | 2.2 |
| 3 | c | 3.0 | 3.3 |
| 4 | d | 4.0 | 4.4 |
+---+---+-----+-----+

Conclusion: Successfully displayed intermediary table with no subsequent formatting.

Example 2: Display intermediary table with subsequent formatting
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>>> (
...     df
...     .transform(display_intermediary_table, reformat_numbers=True)
...     .withColumn("c", F.expr("c * 2"))
...     .show()
... )
Terminal
+---+---+-----+-----+
| a | b |   c |   d |
+---+---+-----+-----+
| 1 | a | 1.0 | 1.1 |
| 2 | b | 2.0 | 2.2 |
| 3 | c | 3.0 | 3.3 |
| 4 | d | 4.0 | 4.4 |
+---+---+-----+-----+
Terminal
+---+---+-----+-----+
| a | b |   c |   d |
+---+---+-----+-----+
| 1 | a | 2.0 | 1.1 |
| 2 | b | 4.0 | 2.2 |
| 3 | c | 6.0 | 3.3 |
| 4 | d | 8.0 | 4.4 |
+---+---+-----+-----+

Conclusion: Successfully displayed intermediary table with subsequent formatting.

Source code in src/toolbox_pyspark/formatting.py 
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@typechecked
def display_intermediary_table(
    dataframe: psDataFrame, reformat_numbers: bool = True, num_rows: int = 20
) -> psDataFrame:
    """
    !!! note "Summary"
        Display an intermediary Spark DataFrame.

    ???+ abstract "Details"
        This function displays an intermediary Spark DataFrame. The function is useful for displaying intermediary tables in a more readable format. Optionally, it can format numbers in the DataFrame to make it more readable.

    Params:
        dataframe (psDataFrame):
            The Spark DataFrame to display.
        reformat_numbers (bool):
            Whether to format numbers in the DataFrame. Default is `True`.
        num_rows (int):
            The number of rows to display. Default is `20`.

    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 original Spark DataFrame.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set Up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.formatting import display_intermediary_table
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         {
        ...             "a": [1, 2, 3, 4],
        ...             "b": ["a", "b", "c", "d"],
        ...             "c": [1.0, 2.0, 3.0, 4.0],
        ...             "d": [1.1, 2.2, 3.3, 4.4],
        ...         }
        ...     )
        ... )
        >>>
        >>> # Check
        >>> df.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+
        | a | b |   c |   d |
        +---+---+-----+-----+
        | 1 | a | 1.0 | 1.1 |
        | 2 | b | 2.0 | 2.2 |
        | 3 | c | 3.0 | 3.3 |
        | 4 | d | 4.0 | 4.4 |
        +---+---+-----+-----+
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Display intermediary table with no subsequent formatting"}
        >>> (
        ...     df
        ...     .transform(display_intermediary_table, reformat_numbers=False, num_rows=2)
        ...     .show()
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+
        | a | b |   c |   d |
        +---+---+-----+-----+
        | 1 | a | 1.0 | 1.1 |
        | 2 | b | 2.0 | 2.2 |
        +---+---+-----+-----+
        ```
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+
        | a | b |   c |   d |
        +---+---+-----+-----+
        | 1 | a | 1.0 | 1.1 |
        | 2 | b | 2.0 | 2.2 |
        | 3 | c | 3.0 | 3.3 |
        | 4 | d | 4.0 | 4.4 |
        +---+---+-----+-----+
        ```
        !!! success "Conclusion: Successfully displayed intermediary table with no subsequent formatting."
        </div>

        ```{.py .python linenums="1" title="Example 2: Display intermediary table with subsequent formatting"}
        >>> (
        ...     df
        ...     .transform(display_intermediary_table, reformat_numbers=True)
        ...     .withColumn("c", F.expr("c * 2"))
        ...     .show()
        ... )
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+
        | a | b |   c |   d |
        +---+---+-----+-----+
        | 1 | a | 1.0 | 1.1 |
        | 2 | b | 2.0 | 2.2 |
        | 3 | c | 3.0 | 3.3 |
        | 4 | d | 4.0 | 4.4 |
        +---+---+-----+-----+
        ```
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+
        | a | b |   c |   d |
        +---+---+-----+-----+
        | 1 | a | 2.0 | 1.1 |
        | 2 | b | 4.0 | 2.2 |
        | 3 | c | 6.0 | 3.3 |
        | 4 | d | 8.0 | 4.4 |
        +---+---+-----+-----+
        ```
        !!! success "Conclusion: Successfully displayed intermediary table with subsequent formatting."
        </div>
    """
    if reformat_numbers:
        dataframe.transform(format_numbers).show(n=num_rows, truncate=False)
    else:
        dataframe.show(n=num_rows, truncate=False)
    return dataframe

display_intermediary_schema 🔗

display_intermediary_schema(
    dataframe: psDataFrame,
) -> psDataFrame

Summary

Display the schema of an intermediary Spark DataFrame.

Details

This function displays the schema of an intermediary Spark DataFrame. The function is useful for displaying intermediary tables in a more readable format.

Parameters:

Name Type Description Default
dataframe DataFrame

The Spark DataFrame to display.

 required

Returns:

Type Description
DataFrame

The original Spark DataFrame.
Examples

Set Up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.formatting import display_intermediary_schema
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "a": [1, 2, 3, 4],
...             "b": ["a", "b", "c", "d"],
...             "c": [1.0, 2.0, 3.0, 4.0],
...             "d": [1.1, 2.2, 3.3, 4.4],
...         }
...     )
... )
>>>
>>> # Check
>>> df.show()
>>> df.printSchema()
Terminal
+---+---+-----+-----+
| a | b |   c |   d |
+---+---+-----+-----+
| 1 | a | 1.0 | 1.1 |
| 2 | b | 2.0 | 2.2 |
| 3 | c | 3.0 | 3.3 |
| 4 | d | 4.0 | 4.4 |
+---+---+-----+-----+
Terminal
root
|-- a: long (nullable = true)
|-- b: string (nullable = true)
|-- c: double (nullable = true)
|-- d: double (nullable = true)

Example 1: Display intermediary schema
1
>>> df.transform(display_intermediary_schema).show()
Terminal
root
|-- a: long (nullable = true)
|-- b: string (nullable = true)
|-- c: double (nullable = true)
|-- d: double (nullable = true)
Terminal
+---+---+-----+-----+
| a | b |   c |   d |
+---+---+-----+-----+
| 1 | a | 1.0 | 1.1 |
| 2 | b | 2.0 | 2.2 |
| 3 | c | 3.0 | 3.3 |
| 4 | d | 4.0 | 4.4 |
+---+---+-----+-----+

Conclusion: Successfully displayed intermediary schema.

Example 2: Display intermediary schema with subsequent formatting
1
>>> df.transform(display_intermediary_schema).withColumn("e", F.expr("c * 2")).show()
Terminal
root
|-- a: long (nullable = true)
|-- b: string (nullable = true)
|-- c: double (nullable = true)
|-- d: double (nullable = true)
Terminal
+---+---+-----+-----+---+
| a | b |   c |   d | e |
+---+---+-----+-----+---+
| 1 | a | 1.0 | 1.1 | 2 |
| 2 | b | 2.0 | 2.2 | 4 |
| 3 | c | 3.0 | 3.3 | 6 |
| 4 | d | 4.0 | 4.4 | 8 |
+---+---+-----+-----+---+

Conclusion: Successfully displayed intermediary schema.

Source code in src/toolbox_pyspark/formatting.py 
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def display_intermediary_schema(dataframe: psDataFrame) -> psDataFrame:
    """
    !!! note "Summary"
        Display the schema of an intermediary Spark DataFrame.

    ??? abstract "Details"
        This function displays the schema of an intermediary Spark DataFrame. The function is useful for displaying intermediary tables in a more readable format.

    Params:
        dataframe (psDataFrame):
            The Spark DataFrame to display.

    Returns:
        (psDataFrame):
            The original Spark DataFrame.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set Up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.formatting import display_intermediary_schema
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         {
        ...             "a": [1, 2, 3, 4],
        ...             "b": ["a", "b", "c", "d"],
        ...             "c": [1.0, 2.0, 3.0, 4.0],
        ...             "d": [1.1, 2.2, 3.3, 4.4],
        ...         }
        ...     )
        ... )
        >>>
        >>> # Check
        >>> df.show()
        >>> df.printSchema()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+
        | a | b |   c |   d |
        +---+---+-----+-----+
        | 1 | a | 1.0 | 1.1 |
        | 2 | b | 2.0 | 2.2 |
        | 3 | c | 3.0 | 3.3 |
        | 4 | d | 4.0 | 4.4 |
        +---+---+-----+-----+
        ```
        ```{.txt .text title="Terminal"}
        root
        |-- a: long (nullable = true)
        |-- b: string (nullable = true)
        |-- c: double (nullable = true)
        |-- d: double (nullable = true)
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Display intermediary schema"}
        >>> df.transform(display_intermediary_schema).show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        root
        |-- a: long (nullable = true)
        |-- b: string (nullable = true)
        |-- c: double (nullable = true)
        |-- d: double (nullable = true)
        ```
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+
        | a | b |   c |   d |
        +---+---+-----+-----+
        | 1 | a | 1.0 | 1.1 |
        | 2 | b | 2.0 | 2.2 |
        | 3 | c | 3.0 | 3.3 |
        | 4 | d | 4.0 | 4.4 |
        +---+---+-----+-----+
        ```
        !!! success "Conclusion: Successfully displayed intermediary schema."
        </div>

        ```{.py .python linenums="1" title="Example 2: Display intermediary schema with subsequent formatting"}
        >>> df.transform(display_intermediary_schema).withColumn("e", F.expr("c * 2")).show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        root
        |-- a: long (nullable = true)
        |-- b: string (nullable = true)
        |-- c: double (nullable = true)
        |-- d: double (nullable = true)
        ```
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+---+
        | a | b |   c |   d | e |
        +---+---+-----+-----+---+
        | 1 | a | 1.0 | 1.1 | 2 |
        | 2 | b | 2.0 | 2.2 | 4 |
        | 3 | c | 3.0 | 3.3 | 6 |
        | 4 | d | 4.0 | 4.4 | 8 |
        +---+---+-----+-----+---+
        ```
        !!! success "Conclusion: Successfully displayed intermediary schema."
        </div>
    """
    dataframe.printSchema()
    return dataframe

display_intermediary_columns 🔗

display_intermediary_columns(
    dataframe: psDataFrame,
) -> psDataFrame

Summary

Display the columns of an intermediary Spark DataFrame.

Details

This function displays the columns of an intermediary Spark DataFrame. The function is useful for displaying intermediary tables in a more readable format.

Parameters:

Name Type Description Default
dataframe DataFrame

The Spark DataFrame to display.

 required

Returns:

Type Description
DataFrame

The original Spark DataFrame.
Examples

Set Up
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>>> # Imports
>>> import pandas as pd
>>> from pyspark.sql import SparkSession
>>> from toolbox_pyspark.formatting import display_intermediary_columns
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "a": [1, 2, 3, 4],
...             "b": ["a", "b", "c", "d"],
...             "c": [1.0, 2.0, 3.0, 4.0],
...             "d": [1.1, 2.2, 3.3, 4.4],
...         }
...     )
... )
>>>
>>> # Check
>>> df.show()
Terminal
+---+---+-----+-----+
| a | b |   c |   d |
+---+---+-----+-----+
| 1 | a | 1.0 | 1.1 |
| 2 | b | 2.0 | 2.2 |
| 3 | c | 3.0 | 3.3 |
| 4 | d | 4.0 | 4.4 |
+---+---+-----+-----+

Example 1: Display intermediary columns
1
>>> df.transform(display_intermediary_columns).show()
Terminal
['a', 'b', 'c', 'd']
Terminal
+---+---+-----+-----+
| a | b |   c |   d |
+---+---+-----+-----+
| 1 | a | 1.0 | 1.1 |
| 2 | b | 2.0 | 2.2 |
| 3 | c | 3.0 | 3.3 |
| 4 | d | 4.0 | 4.4 |
+---+---+-----+-----+
!!! success "Conclusion: Successfully displayed intermediary columns.

Example 2: Display intermediary columns with subsequent formatting
1
>>> df.transform(display_intermediary_columns).withColumn("e", F.expr("c * 2")).show()
Terminal
['a', 'b', 'c', 'd']
Terminal
+---+---+-----+-----+---+
| a | b |   c |   d | e |
+---+---+-----+-----+---+
| 1 | a | 1.0 | 1.1 | 2 |
| 2 | b | 2.0 | 2.2 | 4 |
| 3 | c | 3.0 | 3.3 | 6 |
| 4 | d | 4.0 | 4.4 | 8 |
+---+---+-----+-----+---+

Source code in src/toolbox_pyspark/formatting.py 
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def display_intermediary_columns(dataframe: psDataFrame) -> psDataFrame:
    """
    !!! note "Summary"
        Display the columns of an intermediary Spark DataFrame.

    ??? abstract "Details"
        This function displays the columns of an intermediary Spark DataFrame. The function is useful for displaying intermediary tables in a more readable format.

    Params:
        dataframe (psDataFrame):
            The Spark DataFrame to display.

    Returns:
        (psDataFrame):
            The original Spark DataFrame.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set Up"}
        >>> # Imports
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> from toolbox_pyspark.formatting import display_intermediary_columns
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         {
        ...             "a": [1, 2, 3, 4],
        ...             "b": ["a", "b", "c", "d"],
        ...             "c": [1.0, 2.0, 3.0, 4.0],
        ...             "d": [1.1, 2.2, 3.3, 4.4],
        ...         }
        ...     )
        ... )
        >>>
        >>> # Check
        >>> df.show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+
        | a | b |   c |   d |
        +---+---+-----+-----+
        | 1 | a | 1.0 | 1.1 |
        | 2 | b | 2.0 | 2.2 |
        | 3 | c | 3.0 | 3.3 |
        | 4 | d | 4.0 | 4.4 |
        +---+---+-----+-----+
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Display intermediary columns"}
        >>> df.transform(display_intermediary_columns).show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        ['a', 'b', 'c', 'd']
        ```
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+
        | a | b |   c |   d |
        +---+---+-----+-----+
        | 1 | a | 1.0 | 1.1 |
        | 2 | b | 2.0 | 2.2 |
        | 3 | c | 3.0 | 3.3 |
        | 4 | d | 4.0 | 4.4 |
        +---+---+-----+-----+
        ```
        !!! success "Conclusion: Successfully displayed intermediary columns.
        </div>

        ```{.py .python linenums="1" title="Example 2: Display intermediary columns with subsequent formatting"}
        >>> df.transform(display_intermediary_columns).withColumn("e", F.expr("c * 2")).show()
        ```
        <div class="result" markdown>
        ```{.txt .text title="Terminal"}
        ['a', 'b', 'c', 'd']
        ```
        ```{.txt .text title="Terminal"}
        +---+---+-----+-----+---+
        | a | b |   c |   d | e |
        +---+---+-----+-----+---+
        | 1 | a | 1.0 | 1.1 | 2 |
        | 2 | b | 2.0 | 2.2 | 4 |
        | 3 | c | 3.0 | 3.3 | 6 |
        | 4 | d | 4.0 | 4.4 | 8 |
        +---+---+-----+-----+---+
        ```
    """
    print(dataframe.columns)
    return dataframe