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

since strings data types have variable length, it is by default stored as object dtype. If you want to store them as string type, you can do something like this.

df['column'] = df['column'].astype('|S80') #where the max length is set at 80 bytes,

or alternatively

df['column'] = df['column'].astype('|S') # which will by default set the length to the max len it encounters

A new answer to reflect the most current practices: as of now (v1.2.4), neither astype('str') nor astype(str) work.

As per the documentation, a Series can be converted to the string datatype in the following ways:

df['id'] = df['id'].astype("string")

df['id'] = pandas.Series(df['id'], dtype="string")

df['id'] = pandas.Series(df['id'], dtype=pandas.StringDtype)

End to end example:

import pandas as pd

# Create a sample DataFrame
data = {
    'Name': ['John', 'Alice', 'Bob', 'John', 'Alice'],
    'Age': [25, 30, 35, 25, 30],
    'City': ['New York', 'London', 'Paris', 'New York', 'London'],
    'Salary': [50000, 60000, 70000, 50000, 60000],
    'Category': ['A', 'B', 'C', 'A', 'B']
}

df = pd.DataFrame(data)

# Print the DataFrame
print("Original DataFrame:")
print(df)
print("\nData types:")
print(df.dtypes)
cat_cols_ = None
# Apply the code to change data types
if not cat_cols_:
    # Get the columns with object data type
    object_columns = df.select_dtypes(include=['object']).columns.tolist()
    
    if len(object_columns) > 0:
        print(f"\nObject columns found, converting to string: {object_columns}")
        
        # Convert object columns to string type
        df[object_columns] = df[object_columns].astype('string')
    
    # Get the categorical columns (including string and category data types)
    cat_cols_ = df.select_dtypes(include=['category', 'string']).columns.tolist()

# Print the updated DataFrame and data types
print("\nUpdated DataFrame:")
print(df)
print("\nUpdated data types:")
print(df.dtypes)
print(f"\nCategorical columns (cat_cols_): {cat_cols_}")

Original DataFrame:
    Name  Age      City  Salary Category
0   John   25  New York   50000        A
1  Alice   30    London   60000        B
2    Bob   35     Paris   70000        C
3   John   25  New York   50000        A
4  Alice   30    London   60000        B

Data types:
Name        object
Age          int64
City        object
Salary       int64
Category    object
dtype: object

Object columns found, converting to string: ['Name', 'City', 'Category']

Updated DataFrame:
    Name  Age      City  Salary Category
0   John   25  New York   50000        A
1  Alice   30    London   60000        B
2    Bob   35     Paris   70000        C
3   John   25  New York   50000        A
4  Alice   30    London   60000        B

Updated data types:
Name        string[python]
Age                  int64
City        string[python]
Salary               int64
Category    string[python]
dtype: object

Categorical columns (cat_cols_): ['Name', 'City', 'Category']

One way to convert to string is to use astype:

Copytotal_rows['ColumnID'] = total_rows['ColumnID'].astype(str)

However, perhaps you are looking for the to_json function, which will convert keys to valid json (and therefore your keys to strings):

CopyIn [11]: df = pd.DataFrame([['A', 2], ['A', 4], ['B', 6]])

In [12]: df.to_json()
Out[12]: '{"0":{"0":"A","1":"A","2":"B"},"1":{"0":2,"1":4,"2":6}}'

In [13]: df[0].to_json()
Out[13]: '{"0":"A","1":"A","2":"B"}'

Note: you can pass in a buffer/file to save this to, along with some other options...