You need astype:

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

For converting to categorical:

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

Another solution is Categorical:

Copydf['zipcode'] = pd.Categorical(df.zipcode)

Sample with data:

Copyimport pandas as pd

df = pd.DataFrame({'zipcode': {17384: 98125, 2680: 98107, 722: 98005, 18754: 98109, 14554: 98155}, 'bathrooms': {17384: 1.5, 2680: 0.75, 722: 3.25, 18754: 1.0, 14554: 2.5}, 'sqft_lot': {17384: 1650, 2680: 3700, 722: 51836, 18754: 2640, 14554: 9603}, 'bedrooms': {17384: 2, 2680: 2, 722: 4, 18754: 2, 14554: 4}, 'sqft_living': {17384: 1430, 2680: 1440, 722: 4670, 18754: 1130, 14554: 3180}, 'floors': {17384: 3.0, 2680: 1.0, 722: 2.0, 18754: 1.0, 14554: 2.0}})

Copyprint (df)
       bathrooms  bedrooms  floors  sqft_living  sqft_lot  zipcode
722         3.25         4     2.0         4670     51836    98005
2680        0.75         2     1.0         1440      3700    98107
14554       2.50         4     2.0         3180      9603    98155
17384       1.50         2     3.0         1430      1650    98125
18754       1.00         2     1.0         1130      2640    98109

print (df.dtypes)
bathrooms      float64
bedrooms         int64
floors         float64
sqft_living      int64
sqft_lot         int64
zipcode          int64
dtype: object

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

print (df)
       bathrooms  bedrooms  floors  sqft_living  sqft_lot zipcode
722         3.25         4     2.0         4670     51836   98005
2680        0.75         2     1.0         1440      3700   98107
14554       2.50         4     2.0         3180      9603   98155
17384       1.50         2     3.0         1430      1650   98125
18754       1.00         2     1.0         1130      2640   98109

print (df.dtypes)
bathrooms       float64
bedrooms          int64
floors          float64
sqft_living       int64
sqft_lot          int64
zipcode        category
dtype: object

First, to convert a Categorical column to its numerical codes, you can do this easier with: dataframe['c'].cat.codes.
Further, it is possible to select automatically all columns with a certain dtype in a dataframe using select_dtypes. This way, you can apply above operation on multiple and automatically selected columns.

First making an example dataframe:

In [75]: df = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':list('abcab'),  'col3':list('ababb')})

In [76]: df['col2'] = df['col2'].astype('category')

In [77]: df['col3'] = df['col3'].astype('category')

In [78]: df.dtypes
Out[78]:
col1       int64
col2    category
col3    category
dtype: object

Then by using select_dtypes to select the columns, and then applying .cat.codes on each of these columns, you can get the following result:

In [80]: cat_columns = df.select_dtypes(['category']).columns

In [81]: cat_columns
Out[81]: Index([u'col2', u'col3'], dtype='object')

In [83]: df[cat_columns] = df[cat_columns].apply(lambda x: x.cat.codes)

In [84]: df
Out[84]:
   col1  col2  col3
0     1     0     0
1     2     1     1
2     3     2     0
3     4     0     1
4     5     1     1

Note:

1. NaN becomes -1
2. This method is fast because the relationship between code and category is readily available and do not need to be computed.

Use, the labels parameter to generate strings instead of pd.Intevals:

breaks = [-np.inf, .2, .4, np.inf]
test_cut = pd.cut(test,breaks, labels=pd.IntervalIndex.from_breaks(breaks).astype(str)) 

Try timings with this code.