JSON & Object Handling

from gluestick.pandas_utils import explode_json_to_cols

# Basic column explosion
df = explode_json_to_cols(df, 'array_column')

# With options
df = explode_json_to_cols(
    df,
    'array_column',
    drop=True,
    inplace=False
)

from gluestick.pandas_utils import explode_json_to_rows

# Basic explosion
df = explode_json_to_rows(df, 'array_column')

# With nesting control
df = explode_json_to_rows(
    df, 
    'array_column',
    drop=True,
    max_level=2
)

from gluestick.pandas_utils import json_tuple_to_cols

# Basic conversion with default settings
df = json_tuple_to_cols(df, 'json_column')

# Custom key-value mapping
config = {
    'cols': {'key_prop': 'CategoryName', 'value_prop': 'CategoryValue'},
    'look_up': {'key_prop': 'name', 'value_prop': 'value'}
}
df = json_tuple_to_cols(df, 'json_column', col_config=config)

from gluestick.pandas_utils import compress_rows_to_col

# Basic compression
df = compress_rows_to_col(df, 'line_items', 'invoice_id')

# With CSV storage
df = compress_rows_to_col(
    df,
    'line_items',
    'invoice_id',
    use_csv=True
)

from gluestick.pandas_utils import array_to_dict_reducer
from functools import reduce

# Create reducer
reducer = array_to_dict_reducer(
    key_prop='name',
    value_prop='value'
)

# Apply to array
result = reduce(reducer, array_data, {})

from gluestick.etl_utils import clean_obj_null_values

# Clean stringified object
cleaned = clean_obj_null_values(json_string)

import gluestick as gs
import pandas as pd

# Example with nested invoice data
def process_invoice_lines(df):
    # Clean nulls first
    df['line_items'] = df['line_items'].apply(gs.clean_obj_null_values)
    
    # Explode to rows
    df_lines = gs.explode_json_to_rows(df, 'line_items')
    
    # Process and transform
    # ... apply transformations ...
    
    # Compress back to column if needed
    df = gs.compress_rows_to_col(df_lines, 'line_items', 'invoice_id')
    
    return df