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