PySpark DataFrame When to use/ not to use Select - select

Question

Based on PySpark document:
A DataFrame is equivalent to a relational table in Spark SQL, and can be created using various functions in SQLContext
Meaning I can use Select for showing the value of a column, however, I saw sometimes these two equivalent codes are used instead:
# df is a sample DataFrame with column a
df.a
# or
df['a']
And sometimes when I use select I might get an error instead of them and vice versa sometimes I have to use Select.
For example, this is a DataFrame for finding a dog in a given picture problem:
joined_df.printSchema()
root
|-- folder: string (nullable = true)
|-- filename: string (nullable = true)
|-- width: string (nullable = true)
|-- height: string (nullable = true)
|-- dog_list: array (nullable = true)
| |-- element: string (containsNull = true)
If I want to select the dog details and show 10 rows, this code shows an error:
print(joined_df.dog_list.show(truncate=False))
Traceback (most recent call last):
 File "<stdin>", line 2, in <module>
    print(joined_df.dog_list.show(truncate=False))
TypeError: 'Column' object is not callable
And this is not:
print(joined_df.select('dog_list').show(truncate=False))
Question1: When I have to use Select and when I have to use df.a or df["a"]
Question2: what is the meaning of the error above? 'Column' object is not callable

Answer 1

df.col_name return a Column object but df.select("col_name") return another dataframe
see this for documentation
The key here is Those two methods are returning two different objects, that is why your print(joined_df.dog_list.show(truncate=False)) give you the error. Meaning that the Column object does not have this .show method but the dataframe does.
So when you call a function, function takes Column as input, you should use df.col_name, if you want to operate at dataframe level, you want to use df.select("col_name")