Why is My DataFrame Column Showing \'Object\' Data Type After String Conversion?

DataFrame Columns Displaying "Object" Data Type Despite Explicit String Conversion

Problem:
Despite attempts to explicitly convert specified columns in a DataFrame to strings, they persist as dtype 'object'. Inspection of individual column values confirms they are indeed strings.

Int64Index: 56992 entries, 0 to 56991
Data columns (total 7 columns):
id            56992 non-null values
attr1         56992 non-null values
attr2         56992 non-null values
attr3         56992 non-null values
attr4         56992 non-null values
attr5         56992 non-null values
attr6         56992 non-null values
dtypes: int64(2), object(5)

Column 'attr2' remains as dtype 'object' despite conversion:

convert attr2 to string

Explanation:

Pandas uses dtype 'object' to describe columns that contain variable-length data types, such as strings. This differs from fixed-length data types like 'int64' and 'float64'. Internally, Pandas stores string data using pointers to string objects in an 'object' ndarray.

int64 array:
[1, 2, 3, 4]

object array:
[pointer to string 'John', pointer to string 'Mary', pointer to string 'Bob', pointer to string 'Alice']

The 'dtype object' does not imply that the objects within are not strings. Each string object still resides in memory and can be accessed via the pointers in the 'object' ndarray.

To ensure that Pandas recognizes columns as strings, ensure that all elements in those columns are consistent strings. Additionally, methods like .apply(str) or .astype('string') can be used to convert elements to strings.

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