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

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.

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(',', '')
   

---

2. 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)