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How to use _coerce_dtype method in pandera

Best Python code snippet using pandera_python

test_physt_histogram1d.py

Source:test_physt_histogram1d.py

...358        assert example.dtype == np.int32359        assert example.copy().dtype == np.int32360    def test_coerce(self):361        example = h1(values, dtype=np.int32)362        example._coerce_dtype(np.int64)363        assert example.dtype == np.int64364        example._coerce_dtype(np.float)365        assert example.dtype == np.float366        example._coerce_dtype(np.int32)367        assert example.dtype == np.float368    def test_update(self):369        example = h1(values)370        example.dtype = np.int16371        assert example.dtype == np.int16372        assert example.frequencies.dtype == np.int16373        example = h1(values, weights=[1, 2, 2.1, 3.2])374        with self.assertRaises(RuntimeError):375            example.dtype = np.int16376        example = h1(values, weights=[1, 2, 2, 3])377        example.dtype = np.int16378        assert example.dtype == np.int16379    def test_hist_arithmetic(self):380        example = h1(values, dtype=np.int32)...

FF_stats.py

Source:FF_stats.py

...28        self.na_lst = ['na','n/a','none','null','unk','nan']29        self.df = self.raw_df30        self.outfn = outfn31        self._clear_outfile()            32        self._coerce_dtype()33        34    def _clear_outfile(self):35        with open(self.outfn,'w') as self.outf:36            self.outf.write('*'*20+'   FracFocus raw stats   '+'*'*20+'\n')37    def _add_to_outfile(self,txt):38        with open(self.outfn,'a') as self.outf:39            self.outf.write(txt)40    def _get_filtered_raw(self):41        self.flags = pd.read_csv('./out/raw_filtered_guide.csv')42        return pd.merge(self.flags,self.raw_df,on='ingkey',how='left')43    def _coerce_dtype(self):44        """ some columns need to be coerced into their native data type - that45        is, pandas assigns them to the wrong type when importing the raw.  """46        bools = ['FederalWell','IndianWell','IngredientMSDS']47        for b in bools:48            self.df[b] = self.df[b].astype('bool')49        50    def _show_bool_col_stat(self):51        out = '{:>25}: {:>12} {:>12}\n'.format('Field Name','% not empty',52                                             '% True')53        out +='     --------------------------------------------------------------\n'54        cols = list(self.df.columns)55        tot = len(self.df)56        for col in cols:57            if self.df[col].dtype=='bool':...

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