pandas dataframe convert column type to string or categorical

You need astype:

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

For converting to categorical:

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

Another solution is Categorical:

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

Sample with data:

import pandas as pddf = 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}})

print (df)       bathrooms  bedrooms  floors  sqft_living  sqft_lot  zipcode722         3.25         4     2.0         4670     51836    980052680        0.75         2     1.0         1440      3700    9810714554       2.50         4     2.0         3180      9603    9815517384       1.50         2     3.0         1430      1650    9812518754       1.00         2     1.0         1130      2640    98109print (df.dtypes)bathrooms      float64bedrooms         int64floors         float64sqft_living      int64sqft_lot         int64zipcode          int64dtype: objectdf['zipcode'] = df.zipcode.astype('category')print (df)       bathrooms  bedrooms  floors  sqft_living  sqft_lot zipcode722         3.25         4     2.0         4670     51836   980052680        0.75         2     1.0         1440      3700   9810714554       2.50         4     2.0         3180      9603   9815517384       1.50         2     3.0         1430      1650   9812518754       1.00         2     1.0         1130      2640   98109print (df.dtypes)bathrooms       float64bedrooms          int64floors          float64sqft_living       int64sqft_lot          int64zipcode        categorydtype: 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      categorydtypes: category(1), object(1), string(1)

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

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

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

3. When reading code, the contents of an object dtype array is less clearthan 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

Prior answers focused on nominal data (e.g. unordered). If there is a reason to impose order for an ordinal variable, then one would use:

# Transform to categorydf['zipcode_category'] = df['zipcode_category'].astype('category')# Add ordered categorydf['zipcode_ordered'] = df['zipcode_category']# Setup the orderingdf.zipcode_ordered.cat.set_categories(    new_categories = [90211, 90210], ordered = True, inplace = True)# Output IDsdf['zipcode_ordered_id'] = df.zipcode_ordered.cat.codesprint(df)#  zipcode_category zipcode_ordered  zipcode_ordered_id#            90210           90210                   1#            90211           90211                   0

More details on setting ordered categories can be found at the pandas website:

https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#sorting-and-order