I get an PythonException: float() argument must be a string or a number, not 'NoneType' when attempting to save a DataFrame as a Delta Table. 

Here's the line of code that I am running:

```

df.write.format("delta").saveAsTable("schema1.df_table", mode="overwrite")

root

|-- ts: timestamp (nullable = true)

|-- _source: string (nullable = true)

|-- lat: decimal(9,6) (nullable = true)

|-- lng: decimal(9,6) (nullable = true)

|-- id: string (nullable = false)

|-- mm-yyyy: date (nullable = true)

|-- hid: string (nullable = true)

```

PythonException: An exception was thrown from the Python worker. Please see the stack trace below. 'TypeError: float() argument must be a string or a number, not 'NoneType'', from , line 3. Full traceback below: Traceback (most recent call last): File "", line 3, in TypeError: float() argument must be a string or a number, not 'NoneType'

How do we handle `None` in the spark DataFrame? I'd like to identify which rows / columns contain `None` and drop them?