Has anyone used a Polars dataframe with Seaborn to graph something? I've been working through a notebook on Kaggle that used Pandas, and I wanted to refactor it to Polars.
The dataframe I'm working with looks like this:
| PassengerID (i64) | Survived (i64) | Pclass (i64) | Name (str) | ... | Ticket (str) | Fare (f64) | Cabin (str) | Embarked (str) | Age (f64) |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 3 | your name here | ... | A/5 21171 | 7.25 | null | S | 24 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Kaggle has me making a histogram with the following code:
g = sns.FacetGrid(train_df, col='Survived')
g.map(plt.hist, 'Age', bins=20)
When I run these two lines I get the following error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/seaborn/axisgrid.py", line 678, in map
for (row_i, col_j, hue_k), data_ijk in self.facet_data():
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/seaborn/axisgrid.py", line 632, in facet_data
data_ijk = data[row & col & hue & self._not_na]
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/polars/internals/series/series.py", line 906, in __array_ufunc__
args.append(arg.view(ignore_nulls=True))
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/polars/internals/series/series.py", line 2680, in view
ptr_type = dtype_to_ctype(self.dtype)
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/polars/datatypes.py", line 550, in dtype_to_ctype
raise NotImplementedError(
NotImplementedError: Conversion of polars data type <class 'polars.datatypes.Boolean'> to C-type not implemented.
I don't have any boolean datatypes in my dataframe, so I'm not sure what to do about this error. Any ideas?
seaborn doesn't accept a polars dataframe as an input. You just have to use to_pandas()
so change g = sns.FacetGrid(train_df, col='Survived') to
g = sns.FacetGrid(train_df.to_pandas(), col='Survived')