TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe' while plotting a seaborn.regplot

Question

I'm trying to plot a regplot using seaborn and i'm not unable to plot it and facing TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe' .

My data has 731 rows and 16 column -

>>> bike_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 731 entries, 0 to 730
Data columns (total 16 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   instant     731 non-null    int64  
 1   dteday      731 non-null    object 
 2   season      731 non-null    int64  
 3   yr          731 non-null    int64  
 4   mnth        731 non-null    int64  
 5   holiday     731 non-null    int64  
 6   weekday     731 non-null    int64  
 7   workingday  731 non-null    int64  
 8   weathersit  731 non-null    int64  
 9   temp        731 non-null    float64
 10  atemp       731 non-null    float64
 11  hum         731 non-null    float64
 12  windspeed   731 non-null    float64
 13  casual      731 non-null    int64  
 14  registered  731 non-null    int64  
 15  cnt         731 non-null    int64  
dtypes: float64(4), int64(11), object(1)
memory usage: 88.6+ KB

Here is a snippet of the data And when i'm trying to plot regplot using seaborn -

>>> sns.regplot(x="casual", y="cnt", data=bike_df);

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-54-68533af96906> in <module>
----> 1 sns.regplot(x="casual", y="cnt", data=bike_df);

~\AppData\Local\Continuum\anaconda3\envs\rstudio\lib\site-packages\seaborn\regression.py in regplot(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)
    816     scatter_kws["marker"] = marker
    817     line_kws = {} if line_kws is None else copy.copy(line_kws)
--> 818     plotter.plot(ax, scatter_kws, line_kws)
    819     return ax
    820 

~\AppData\Local\Continuum\anaconda3\envs\rstudio\lib\site-packages\seaborn\regression.py in plot(self, ax, scatter_kws, line_kws)
    363 
    364         if self.fit_reg:
--> 365             self.lineplot(ax, line_kws)
    366 
    367         # Label the axes

~\AppData\Local\Continuum\anaconda3\envs\rstudio\lib\site-packages\seaborn\regression.py in lineplot(self, ax, kws)
    406         """Draw the model."""
    407         # Fit the regression model
--> 408         grid, yhat, err_bands = self.fit_regression(ax)
    409         edges = grid[0], grid[-1]
    410 

~\AppData\Local\Continuum\anaconda3\envs\rstudio\lib\site-packages\seaborn\regression.py in fit_regression(self, ax, x_range, grid)
    214             yhat, yhat_boots = self.fit_logx(grid)
    215         else:
--> 216             yhat, yhat_boots = self.fit_fast(grid)
    217 
    218         # Compute the confidence interval at each grid point

~\AppData\Local\Continuum\anaconda3\envs\rstudio\lib\site-packages\seaborn\regression.py in fit_fast(self, grid)
    239                                     n_boot=self.n_boot,
    240                                     units=self.units,
--> 241                                     seed=self.seed).T
    242         yhat_boots = grid.dot(beta_boots).T
    243         return yhat, yhat_boots

~\AppData\Local\Continuum\anaconda3\envs\rstudio\lib\site-packages\seaborn\algorithms.py in bootstrap(*args, **kwargs)
     83     for i in range(int(n_boot)):
     84         resampler = integers(0, n, n)
---> 85         sample = [a.take(resampler, axis=0) for a in args]
     86         boot_dist.append(f(*sample, **func_kwargs))
     87     return np.array(boot_dist)

~\AppData\Local\Continuum\anaconda3\envs\rstudio\lib\site-packages\seaborn\algorithms.py in <listcomp>(.0)
     83     for i in range(int(n_boot)):
     84         resampler = integers(0, n, n)
---> 85         sample = [a.take(resampler, axis=0) for a in args]
     86         boot_dist.append(f(*sample, **func_kwargs))
     87     return np.array(boot_dist)

TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'

I tried changing the datatypes using dtypes for all the rows like below -

>>> bike_df['cnt'] = bike_df['cnt'].astype(np.int32)

but this did not help and got the same error again while plotting.

Any suggestions are appreciated.

Thanks in advance.

Answer 1

Update: this bug is solved in Seaborn version 0.10.1 (April 2020).

I encountered the same problem. It is issue 1950 at Seaborn's github. Related to running a 32-bit version of numpy. It will be solved in the next release.

To work around the problem, I changed line 84 of my local version of Seaborn's algorithm.py:

resampler = integers(0, n, n, dtype=np.int_)

This happened with:

• numpy version: 1.18.1

• seaborn version: 0.10.0

Answer 2

I had this issue from my machine too!!

I've tried modifying the Seaborn's algorithm.py code as mentioned as JohanC mentioned, but It didn't work...

The I realized that my python version was 32-bit, so I installed a newer python 64-bit version and run the same code.

The version I downloaded and installed was 64-bit (3.8.2) of this link.

That made my python run the script without problems!!