### Install gluestick using pip

Source: https://github.com/hotgluexyz/gluestick/blob/master/README.md

This command installs the gluestick Python module using pip. Ensure you have Python and pip installed.

```shell
pip install gluestick
```

--------------------------------

### Parse JSON tuples to columns using json_tuple_to_cols in Python

Source: https://github.com/hotgluexyz/gluestick/wiki/json_tuple_to_cols

This example demonstrates how to use the json_tuple_to_cols function to parse JSON data within the 'Line.SalesItemLineDetail.ItemRef' and 'Line.SalesItemLineDetail.ItemAccountRef' columns. It specifies custom key-value mappings for the conversion.

```python
# Specify lookup keys
qb_lookup_keys = {'key_prop': 'name', 'value_prop': 'value'}

# Explode these into new columns
invoices = (invoices.pipe(gs.json_tuple_to_cols, 'Line.SalesItemLineDetail.ItemRef',
                  col_config={'cols': {'key_prop': 'Item', 'value_prop': 'Item Id'},
                              'look_up': qb_lookup_keys})
                  .pipe(gs.json_tuple_to_cols, 'Line.SalesItemLineDetail.ItemAccountRef',
                  col_config={'cols': {'key_prop': 'Item Ref', 'value_prop': 'Item Ref Id'},
                              'look_up': qb_lookup_keys}))
invoices[['Id', 'Item', 'Item Id', 'Item Ref', 'Item Ref Id']].head()
```

--------------------------------

### Use array_to_dict_reducer with explode_json_to_cols

Source: https://github.com/hotgluexyz/gluestick/wiki/array_to_dict_reducer

Example of using array_to_dict_reducer with the explode_json_to_cols function to process serialized JSON data in a DataFrame. It extracts specific key-value pairs from a JSON array stored in a column.

```python
# Grab the string value of entries
invoices = invoices.pipe(gs.explode_json_to_cols, 'CustomField', reducer=gs.array_to_dict_reducer('Name', 'StringValue'))
invoices[['Id', 'CustomField.Crew #']].head()
```

--------------------------------

### Define array_to_dict_reducer

Source: https://github.com/hotgluexyz/gluestick/wiki/array_to_dict_reducer

Defines the array_to_dict_reducer function, which takes key and value properties to create a dictionary from an array of JSON objects.

```python
def array_to_dict_reducer(key_prop, value_prop):
    """
    :param key_prop: property in dictionary for key
    :param value_prop: property in dictionary for value
    :return: a dictionary that has all the accumulated values
    """

```

--------------------------------

### Define explode_json_to_cols function

Source: https://github.com/hotgluexyz/gluestick/wiki/explode_json_to_cols

Defines the explode_json_to_cols function which takes a dataframe and a column name as input. It supports optional keyword arguments for customization, likely related to how the JSON array is processed.

```python
explode_json_to_cols(df, column_name, **kwargs):
"""
               :param df : the dataframe
               :param column_name: the column that has the JSON in it.
               :param reducer: a reducer that will convert the array of JSON into a panda series
               :return df - a new data frame with the JSON line expanded into columns and rows
"""
```

--------------------------------

### Python: Read CSV Folder with Converters and Index Columns

Source: https://github.com/hotgluexyz/gluestick/wiki/read_csv_folder

Reads CSV files from a specified folder, allowing for custom converters and index columns per entity. It returns a dictionary where keys are entity names and values are pandas DataFrames. This function is useful for processing Singer-tapped CSV data.

```python
read_csv_folder(path, converters={}, index_cols={})
    :param path: the folder directory
    :param converters: a dictionary with an array of converters that are passed to
    read_csv, the key of the dictionary is the name of the entity.
    :param index_cols: a dictionary with an array of
    index_cols, the key of the dictionary is the name of the entity.
    :return: a dict of pandas.DataFrames. the keys of which are the entity names
```

```python
entity_data = read_csv_folder(CSV_FOLDER_PATH, index_cols={'Invoice': 'DocNumber'},
                        converters={'Invoice': {'Line': ast.literal_eval, 'CustomField': ast.literal_eval,
                                                'Categories': ast.literal_eval}})
df = entity_data['Account']
```

--------------------------------

### Rename DataFrame columns using a dictionary mapping (Python)

Source: https://github.com/hotgluexyz/gluestick/wiki/rename

This Python code snippet demonstrates how to rename multiple columns in a Pandas DataFrame using the `rename` method with a dictionary. It maps old column names to new, more readable names. This is useful for cleaning and standardizing data.

```python
# Let's clean up the names of these columns
invoices = input_df.pipe(lambda x: x.rename(columns={'CustomerRef__value': 'CustomerId', 'CustomerRef__name': 'Customer',
                                              'MetaData__LastUpdatedTime': 'LastUpdated',
                                              'MetaData__CreateTime': 'CreatedOn', 'CurrencyRef__name': 'Currency',
                                              'CurrencyRef__value': 'CurrencyCode'}))
invoices.head()
```

--------------------------------

### Define explode_json_to_rows Function in Python

Source: https://github.com/hotgluexyz/gluestick/wiki/explode_json_to_rows

Defines the explode_json_to_rows function which takes a DataFrame, a column name containing JSON arrays, and an optional drop parameter. It processes the specified column to expand JSON objects into rows and columns.

```python
def explode_json_to_rows(df, column_name, drop=True, **kwargs):
    """
           :param df : the dataframe
           :param column_name: the column that has the JSON in it.
           :param drop: drop the source column in the result. Default is True
           df - a new data frame with the JSON line expanded into columns and rows
    """
```

--------------------------------

### Explode JSON array in DataFrame using gluestick

Source: https://github.com/hotgluexyz/gluestick/wiki/explode_json_to_cols

Demonstrates using the explode_json_to_cols function with a custom reducer to extract specific fields ('Name', 'StringValue') from a JSON array in the 'CustomField' column of a pandas DataFrame. The output is then displayed using head().

```python
invoices = invoices.pipe(gs.explode_json_to_cols, 'CustomField', reducer=gs.array_to_dict_reducer('Name', 'StringValue'))
invoices[['Id', 'CustomField.Crew #']].head()
```

--------------------------------

### Define json_tuple_to_cols function in Python

Source: https://github.com/hotgluexyz/gluestick/wiki/json_tuple_to_cols

This Python function takes a DataFrame, a column name containing JSON tuples, and an optional configuration for column mapping. It returns a modified DataFrame with the JSON tuples expanded into new columns.

```python
def json_tuple_to_cols(df, column_name, col_config={'cols': {'key_prop': 'Name', 'value_prop': 'Value'},
                                                    'look_up': {'key_prop': 'name', 'value_prop': 'value'}}):
    """
    :param df: the data frame
    :param column_name: column with the json tuple
    :param col_config: conversion config
    :return: a modified dataframe
    """

```

--------------------------------

### Explode JSON Array Column in Pandas DataFrame

Source: https://github.com/hotgluexyz/gluestick/wiki/explode_json_to_rows

Demonstrates how to use the explode_json_to_rows function from the gluestick library to flatten a JSON column in a Pandas DataFrame. It specifies the column to process and a maximum nesting level for the JSON objects.

```python
# Let's explode the Line column now
invoices = invoices.pipe(gs.explode_json_to_rows, "Line", max_level=1)
invoices.head()
```

--------------------------------

### Define rename function for DataFrame column renaming (Python)

Source: https://github.com/hotgluexyz/gluestick/wiki/rename

This Python function renames specified columns in a Pandas DataFrame. It takes the DataFrame and a dictionary of target columns as input and returns the modified DataFrame. Ensure the input DataFrame and column mapping are correctly formatted.

```python
def rename(df, target_columns):
       """
       :param df : dataframe
       :param target_columns: the columns to rename as a dictionary
       :returns df: a modified data frame
       """
```

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