The string accessor .str is an attribute of the Pandas Series class and provides functions to work with string data. If you try to use the .str accessor attribute on a column of a Pandas DataFrame or a Series and the values are not strings, you will raise the AttributeError: Can only use .str accessor with string values!
To solve this error, you can cast the values in the Series to string type using .astype(str) function before using .str. For example: df['column_name'] = df['column_name'].astype(str).str.replace('.', '').
This tutorial will go through the error in detail and how to solve it with code examples.
Table of contents
AttributeError: Can only use .str accessor with string values!
AttributeError occurs in a Python program when we try to access an attribute (method or property) that does not exist for a particular object. The part ‘Can only use .str accessor with string values‘ tells us that the str accessor is only suitable for a Series containing only string values. The data type of a column containing string values is object. We can show this by defining a DataFrame with different types of value in each column and using the DataFrame.dtypes attribute to get the dtype of each column.
df_check = pd.DataFrame({'float':[2.4],
'int':[5],
'datetime': [pd.Timestamp('20210310')],
'string': ['python']})
print(df_check.dtypes)
float float64 int int64 datetime datetime64[ns] string object dtype: object
We can see that the ‘string‘ column containing a string value has the object dtype. It is useful to check the dtype of the column before attempting to use the .str attribute.
Example
Let’s look at an example where we have a DataFrame containing pizzas and their prices.
import pandas as pd
# Create DataFrame
df = pd.DataFrame({'pizza':['margherita', 'pepperoni', 'four cheeses', 'hawaiian', 'parmigiana'],
'price':[7.99, 8.99, 10.99, 8.99, 11.99]})
print(df)
pizza price 0 margherita 7.99 1 pepperoni 8.99 2 four cheeses 10.99 3 hawaiian 8.99 4 parmigiana 11.99
We want to replace ‘.99‘ with ‘.00‘ to make the pizza prices cheaper. We will attempt to replace the numbers by calling str.replace on the column df['price']. Let’s look at the code:
# Replace substring
df['price'] = df['price'].str.replace('99','00')
AttributeError: Can only use .str accessor with string values!
The error occurs because the dtype of the Series is not object. We can verify the dtype of the different columns in the DataFrame by using the DataFrame.dtypes attribute
pizza object price float64 dtype: object
The price column is of type float64. The .str accessor is only suitable for string values.
Solution
We can cast the Series to string using the .astype() function to solve this error. Let’s look at the revised code:
import pandas as pd
# Create DataFrame
df = pd.DataFrame({'pizza':['margherita', 'pepperoni', 'four cheeses', 'hawaiian', 'parmigiana'],
'price':[7.99, 8.99, 10.99, 8.99, 11.99]})
# Replace substring
df['price'] = df['price'].astype(str).str.replace('99','00')
print(df)
print(df.dtypes)
pizza price 0 margherita 7.00 1 pepperoni 8.00 2 four cheeses 10.00 3 hawaiian 8.00 4 parmigiana 11.00 pizza object price object dtype: object
We can see that after using astype(str) the df['price'] column is object dtype. We are then able to use str.replace() to replace the ‘99‘ substring with ‘00‘.
Summary
Congratulations on reading to the end of this tutorial! The AttributeError: Can only use .str accessor with string values! occurs when you try to use the string accessor attribute .str , but the Series contains values that are not strings. You can solve this by casting the Series to string using astype(str).
For further reading on errors involving Pandas, go to the articles:
- How to Solve Python AttributeError: ‘str’ object has no attribute ‘contains’.
- How to Solve Python AttributeError: ‘Series’ object has no attribute ‘lower’.
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