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Str.replace method returns an attribute error.
dc_listings['price'].str.replace(',', '') AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas Here are the top 5 rows of my price column.
This stack overflow thread recommends to check if my column has NAN values but non of the values in my column are NAN. 
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The AttributeError: Can only use . str accessor with string values, which use np. object_ dtype in pandas occurs if you try to replace the values of string column, but in reality, it is of a different type. We can fix the issue by casting the column to a string before replacing the values in the column.
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) .
Moreover, having dtype as Object will make it less clear to work with just text and exclude the non-text values. With the new String dtype, the values are explicitly treated as strings.
The AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas occurs if you try to replace the values of string column, but in reality, it is of a different type. We can fix the issue by casting the column to a string before replacing the values in the column. Blogger, Traveller, Investor and Technologist.
The problem here is that when you read your txt file, in it is casting "c" as an integer and the .str accessor will not work with non-string dtypes, you can fix this problem a couple of ways: Cast the integer as a string in the print statement. Thanks for contributing an answer to Stack Overflow!
And yes df ['foo'] = df ['foo'].str.replace ('.', ',') worked with pandas. Can only use .str accessor with string values, which use np.object_ dtype in pandas
As the error states, you can only use .str with string columns, and you have a float64. There won't be any commas in a float, so what you have won't really do anything, but in general, you could cast it first:
dc_listings['price'].astype(str).str.replace... For example:
In [18]: df Out[18]: a b c d e 0 0.645821 0.152197 0.006956 0.600317 0.239679 1 0.865723 0.176842 0.226092 0.416990 0.290406 2 0.046243 0.931584 0.020109 0.374653 0.631048 3 0.544111 0.967388 0.526613 0.794931 0.066736 4 0.528742 0.670885 0.998077 0.293623 0.351879 In [19]: df['a'].astype(str).str.replace("5", " hi ") Out[19]: 0 0.64 hi 8208 hi hi 4779467 1 0.86 hi 7231174332336 2 0.04624337481411367 3 0. hi 44111244991 hi 194 4 0. hi 287421814241892 Name: a, dtype: object Two ways:
You can use series to fix this error.
dc_listings['price'].series.str.replace(',', '') And if series doesn't work you can also alteratively use apply(str) as shown below:
dc_listings['price'].apply(str).str.replace(',', '') If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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