Why does a Pandas DataFrame column with strings show \'dtype object\' even after converting to string?

Strings in a DataFrame, but dtype is object

Some users have encountered a Pandas DataFrame where certain columns are displaying "dtype object," even though every item within those columns is a string, even after explicit conversion to string. To understand this behavior, it's necessary to delve into the nature of data types in Pandas and NumPy.

NumPy, the underlying library for Pandas, characterizes data types as int64, float64, and object. The "object" dtype signifies that the elements in a NumPy array are not of a uniform, fixed size in bytes, as is the case for integers or floats.

For strings, their lengths vary, making direct storage of string bytes in an array impractical. Instead, Pandas utilizes an "object array" that stores pointers to string objects. This approach explains why the dtype is object for columns containing strings.

Consider the following example:

import numpy as np
import pandas as pd

# Create a NumPy array of integers
int_array = np.array([1, 2, 3, 4], dtype=np.int64)

# Create a NumPy array of strings
object_array = np.array(['a', 'b', 'c', 'd'], dtype=np.object)

# Convert the object array to pandas DataFrame
df = pd.DataFrame({'INTS': int_array, 'STRINGS': object_array})

# Check the data types
print(df.dtypes)

# Print the lengths of the first item in each column
print(len(df['INTS'].iat[0]))
print(len(df['STRINGS'].iat[0]))

The output will be:

INTS         int64
STRINGS      object
dtype: object
1
1

As you can see, the "INTS" column has a dtype of int64, as all its elements are 8-byte integers. The "STRINGS" column has a dtype of object because its elements are pointers to string objects. The length of each string is different, as evidenced by the output.

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