Metadata-Version: 2.1
Name: pyspark_nested_functions
Version: 0.1.4
Summary: Utility functions to manipulate nested structures using pyspark
Home-page: https://github.com/golosegor/pyspark-nested-fields-functions
Author: Brend Braeckmans <brend@distilleddata.be>, Egor Goloshchapov <golosegor@gmail.com>
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.7, <3.12
Description-Content-Type: text/markdown
License-File: LICENSE

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### Nested fields transformation for pyspark

## Motivation

Applying transformations for nested structures in spark is tricky.
Assuming we have JSON data with highly nested structure:

```json
[
  {
    "data": {
      "city": {
        "addresses": [
          {
            "id": "my-id"
          },
          {
            "id": "my-id2"
          }
        ]
      }
    }
  }
]
```

To hash nested "id" field you need to write following spark code

```python
import pyspark.sql.functions as F

hashed = df.withColumn("data",
                       (F.col("data")
                        .withField("city", F.col("data.city")
                                   .withField("addresses", F.transform("data.city.addresses",
                                                                       lambda c: c.withField("id",
                                                                                             F.sha2(c.getField("id"),
                                                                                                    256)))))))
```

With the library the code above could be simplified to

```python
import pyspark.sql.functions as F
from pyspark.sql.types import StringType
from nestedfunctions.functions.terminal_operations import apply_terminal_operation

hashed = apply_terminal_operation(df, "data.city.addresses.id", lambda c, t: F.sha2(c.cast(StringType()), 256))
```

Instead of dealing of nested transformation functions you could specify terminal operation as 'lambda' and field
hierarchy in flat format and library will generate spark codebase for you.

## Install

To install the current release

```
$ pip install pyspark-nested-functions
```

## Available functions

#### Add nested field

Adding a nested field called new_column_name based on a lambda function working on the column_to_process nested field.
Fields column_to_process and new_column_name need have the same parent or be at the root!

```python
from nestedfunctions.functions.add_nested_field import add_nested_field
from pyspark.sql.functions import when
processed = add_nested_field(
      df,
      column_to_process="payload.array.booleanField",
      new_column_name="payload.filingPacket.booleanFieldAsString",
      f=lambda column: when(column, "Y").when(~column, "N").otherwise(""),
  )
```

#### Date Format

Format a nested date field from to current_date_format to target_date_format.

```python
from nestedfunctions.functions.date_format import format_date
date_formatted_df = format_date(
      df,
      field="customDimensions.value",
      current_date_format="y-d-M",
      target_date_format="y-MM"
  )
```

#### Drop

Recursively drop fields on any nested level (including child)

```python
from nestedfunctions.functions.drop import drop

dropped_df = drop(df, field="root_level.children1.children2")
```

#### Duplicate

Duplicate the nested field column_to_duplicate as duplicated_column_name.
Fields column_to_duplicate and duplicated_column_name need have the same parent or be at the root!

```python
from nestedfunctions.functions.duplicate import duplicate
duplicated_df = duplicate(
      df,
      column_to_duplicate="payload.lineItems.comments",
      duplicated_column_name="payload.lineItems.commentsDuplicate"
  )
# Usage details not available in the provided workspace
```

#### Expr

Add or overwrite a nested field based on an expression.

```python
from nestedfunctions.functions.expr import expr
field = "emails.unverified"
processed = expr(df, field=field, expr=f"transform({field}, x -> (upper(x)))")
```

#### Field Rename

Rename all the fields based on any rename function.

```python
from nestedfunctions.functions.field_rename import rename
def capitalize_field_name(field_name: str, suffix: str) -> str:
  return field_name.upper()
renamed_df = rename(df, rename_func=capitalize_field_name())
```

#### Fillna

This function mimics the vanilla pyspark fillna functionality with added support for filling nested fields.
The use of the input parameters value and subset is exactly the same as for the vanilla pyspark implementation.

```python
from nestedfunctions.functions.fillna import fillna
# Fill all null boolean fields with False
filled_df = fillna(df, value=False)
# Fill nested field with value
filled_df = fillna(df, subset="payload.lineItems.availability.stores.availableQuantity", value=0)
# To fill array which is null specify list of values
filled_df = fillna(df, value={"payload.comments" : ["Automatically triggered stock check"]})
# To fill elements of array that are null specify single value
filled_df = fillna(df, value={"payload.comments" : "Empty comment"})
```

### Flattener

Return flattened representation of the data frame.

```python
from nestedfunctions.spark_schema.utility import SparkSchemaUtility

flatten_schema = SparkSchemaUtility().flatten_schema(df.schema)
# flatten_schema = ["root-element",
#                   "root-element-array-primitive",
#                   "root-element-array-of-structs.d1.d2",
#                   "nested-structure.n1",
#                   "nested-structure.d1.d2"]
```

#### Hash

Replace a nested field by its SHA-2 hash value.
By default the number of bits in the output hash value will be 256 but a different value can be set.

```python
from nestedfunctions.functions.hash import hash_field
hashed_df = hash_field(df, "data.city.addresses.id", num_bits=256)
```

### Nullify

Making field null on any nested level

```python
from nestedfunctions.functions.nullify import nullify

nullified_df = nullify(df, field="creditCard.id")
```

#### Redact

Replace a field by the default value of its data type.
The default value of a data type is typically its min or max value.

```python
from nestedfunctions.functions.redact import redact
redacted_df = redact(df, field="customDimensions.metabolicsConditions")
```

#### Whitelist

Preserving all fields listed in parameters. All other fields will be dropped

```python
from nestedfunctions.functions.whitelist import whitelist

whitelisted_df = whitelist(df, ["addresses.postalCode", "creditCard"]) 
```

## License

[Apache License 2.0](LICENSE)
