AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas

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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.

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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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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:

  1. You can use series to fix this error.

    dc_listings['price'].series.str.replace(',', '')
    

  1. And if series doesn’t work you can also alteratively use apply(str) as shown below:

    dc_listings['price'].apply(str).str.replace(',', '')
    

If price is a dtype float 64 then the data is not a string.
You can try dc_listings['price'].apply(function)


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