No need for loops, Pandas can do it directly now, just pass a list of columns you want to convert and Pandas will convert them all.

cols = ['parks', 'playgrounds', 'sports', 'roading']
public[cols] = public[cols].astype('category')

df = pd.DataFrame({'a': ['a', 'b', 'c'], 'b': ['c', 'd', 'e']})

>>     a  b
>>  0  a  c
>>  1  b  d
>>  2  c  e

df.dtypes
>> a    object
>> b    object
>> dtype: object

df[df.columns] = df[df.columns].astype('category')
df.dtypes
>> a    category
>> b    category
>> dtype: object

With pandas >= 1.0 there is now a dedicated string datatype:

1) You can convert your column to this pandas string datatype using .astype('string'):

df['zipcode'] = df['zipcode'].astype('string')

2) This is different from using str which sets the pandas object datatype:

df['zipcode'] = df['zipcode'].astype(str)

3) For changing into categorical datatype use:

df['zipcode'] = df['zipcode'].astype('category')

You can see this difference in datatypes when you look at the info of the dataframe:

df = pd.DataFrame({
    'zipcode_str': [90210, 90211] ,
    'zipcode_string': [90210, 90211],
    'zipcode_category': [90210, 90211],
})

df['zipcode_str'] = df['zipcode_str'].astype(str)
df['zipcode_string'] = df['zipcode_str'].astype('string')
df['zipcode_category'] = df['zipcode_category'].astype('category')

df.info()

# you can see that the first column has dtype object
# while the second column has the new dtype string
# the third column has dtype category
 #   Column            Non-Null Count  Dtype   
---  ------            --------------  -----   
 0   zipcode_str       2 non-null      object  
 1   zipcode_string    2 non-null      string  
 2   zipcode_category  2 non-null      category
dtypes: category(1), object(1), string(1)

The 'string' extension type solves several issues with object-dtype NumPy arrays:

1. You can accidentally store a mixture of strings and non-strings in an object dtype array. A StringArray can only store strings.

2. object dtype breaks dtype-specific operations like DataFrame.select_dtypes(). There isn’t a clear way to select just text while excluding non-text, but still object-dtype columns.

3. When reading code, the contents of an object dtype array is less clear than string.
   

More info on working with the new string datatype can be found here: https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html