pandas dataframe convert column type to string or categorical

Question

How do I convert a single column of a pandas dataframe to type string? In the df of housing data below I need to convert zipcode to string so that when I run linear regressi

Answer 1

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)

From the docs:

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.

Information about pandas 1.0 can be found here:
https://pandas.pydata.org/pandas-docs/version/1.0.0/whatsnew/v1.0.0.html