将列类型转换为字符串或分类

小开

To convert a column into a string type (that will be an object column per se in pandas), use astype:

df.zipcode = zipcode.astype(str)

If you want to get a Categorical column, you can pass the parameter 'category' to the function:

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

小开

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

小开

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:

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

小开

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


# Add ordered category
df['zipcode_ordered'] = df['zipcode_category']


# Setup the ordering
df.zipcode_ordered.cat.set_categories(
new_categories = [90211, 90210], ordered = True, inplace = True
)


# Output IDs
df['zipcode_ordered_id'] = df.zipcode_ordered.cat.codes
print(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