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

Note that since pandas 0.23.0 you no longer apply to convert multiple columns to categorical data types. Now you can simply do df[to_convert].astype('category') instead (where to_convert is a set of columns as defined in the question).

First, let's create a mcve to play with:

import pandas as pd
import numpy as np

In [1]: categorical_array = np.random.choice(['Var1','Var2','Var3'],
                                             size=(5,3), p=[0.25,0.5,0.25])
        df = pd.DataFrame(categorical_array,
               columns=map(lambda x:chr(97+x), range(categorical_array.shape[1])))
        # Add another column that isn't categorical but float
        df['d'] = np.random.rand(len(df))
        print(df)

Out[1]:
      a     b     c         d
0  Var3  Var3  Var3  0.953153
1  Var1  Var2  Var1  0.924896
2  Var2  Var2  Var2  0.273205
3  Var2  Var1  Var3  0.459676
4  Var2  Var1  Var1  0.114358

Now we can use pd.get_dummies to encode the first three columns.

Note that I'm using the drop_firstparameter because N-1 dummies are sufficient to fully describe N possibilities (eg: if a_Var2 and a_Var3 are 0, then it's a_Var1). Also, I'm specifically specifying the columns but I don't have to as it will be columns with dtype either object or categorical (more below).

In [2]: df_encoded = pd.get_dummies(df, columns=['a','b', 'c'], drop_first=True)
        print(df_encoded]
Out[2]:
          d  a_Var2  a_Var3  b_Var2  b_Var3  c_Var2  c_Var3
0  0.953153       0       1       0       1       0       1
1  0.924896       0       0       1       0       0       0
2  0.273205       1       0       1       0       1       0
3  0.459676       1       0       0       0       0       1
4  0.114358       1       0       0       0       0       0

In your specific application, you'll have to provide a list of column that are Categorical, or you'll have to infer which columns are Categorical.

Best case scenario your dataframe already has these columns with a dtype=category and you can pass columns=df.columns[df.dtypes == 'category'] to get_dummies.

Otherwise I suggest setting the dtype of all other columns as appropriate (hint: pd.to_numeric, pd.to_datetime, etc) and you'll be left with columns that have an object dtype and these should be your categorical columns.

The pd.get_dummies parameter columns defaults as follows:

columns : list-like, default None
    Column names in the DataFrame to be encoded.
    If `columns` is None then all the columns with
    `object` or `category` dtype will be converted.