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:

total_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):

In [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...

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