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 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}})
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 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