Series of NumPy dtypes present in the DataFrame with index of column names.

        Returns
        -------
        Series
14739
def dtypes(self):
        
        return Series(np.array(list(self._gather_dtypes().values()), dtype=np.bytes_),
                      self.keys())
661761
def time_series_rdd_from_pandas_series_rdd(series_rdd):
    
    first = series_rdd.first()
    dt_index = irregular(first[1].index, series_rdd.ctx)
    return TimeSeriesRDD(dt_index, series_rdd.mapValues(lambda x: x.values))
1142082
def toPandas(self):
        
        assert pd is not None
        nindices = self.getNumIndices()
        headers = self.getHeaders()
        columns = {
            header: list(self.getColumn(header))
            for header in headers[nindices:]
        }
        index = zip(*[
            list(self.getColumn(header))
            for header in headers[:nindices]
        ])
        index = [key if len(key) > 1 else key[0] for key in index]
        if index == []:
            return pd.DataFrame(columns, index=None)
        else:
            return pd.DataFrame(columns, index=index)
996042
def global_idx_to_numeric_idx(self, axis, indices):
        
        assert axis in ["row", "col", "columns"]
        if axis == "row":
            return pandas.Index(
                pandas.Series(np.arange(len(self.index)), index=self.index)
                .loc[indices]
                .values
            )
        elif axis in ["col", "columns"]:
            return pandas.Index(
                pandas.Series(np.arange(len(self.columns)), index=self.columns)
                .loc[indices]
                .values
            )
479655
def elementTypeName(self):
        
        if self._ndFrame is None:
            return super(AbstractPandasNDFrameRti, self).elementTypeName
        else:
            try:
                return str(self._ndFrame.dtype) # Series
            except AttributeError:
                return '<structured>'
1170547
def compute_boxplot(self, series):
        
        from matplotlib.cbook import boxplot_stats
        series = series[series.notnull()]
        if len(series.values) == 0:
            return {}
        elif not is_numeric_dtype(series):
            return self.non_numeric_stats(series)
        stats = boxplot_stats(list(series.values))[0]
        stats['count'] = len(series.values)
        stats['fliers'] = "|".join(map(str, stats['fliers']))
        return stats
663657
def from_series(cls, series, offset=0):
        
        return cls(data=series.data, index=series.index, data_name=series.data_name, index_name=series.index_name,
                   sort=series.sort, offset=offset)
515923
def fromPandas(cls, df):
        
        assert pd is not None
        if isinstance(df, pd.Series):
            df = pd.DataFrame(df)
        else:
            assert isinstance(df, pd.DataFrame)
        keys = [
            key if isinstance(key, tuple) else (key,)
            for key in df.index.tolist()
        ]
        index = [
            ('index{}'.format(i), cindex)
            for i, cindex in enumerate(zip(*keys))
        ]
        columns = [
            (str(cname), df[cname].tolist())
            for cname in df.columns.tolist()
        ]
        return cls(index=index, columns=columns)
598080
def _ndarray_values(self) -> np.ndarray:
        
        if is_extension_array_dtype(self):
            return self.array._ndarray_values
        return self.values
771048
def get_sets_index(self):
        
        index = []
        for i, place in enumerate(self):
            index.append(np.ones(place.set_of_points.shape[0])*i)
        return np.concatenate(index).astype(int)
919261
def _retrieve_indices(cols):
        '''
        
        '''
        if isinstance(cols, int):
            return [cols]
        elif isinstance(cols, slice):
            start = cols.start if cols.start else 0
            stop = cols.stop
            step = cols.step if cols.step else 1
            return list(range(start, stop, step))
        elif isinstance(cols, list) and cols:
            if isinstance(cols[0], bool):
                return np.flatnonzero(np.asarray(cols))
            elif isinstance(cols[0], int):
                return cols
        else:
            raise TypeError('No valid column specifier. Only a scalar, list or slice of all'
                            'integers or a boolean mask are allowed.')
421535
def to_pandas(self):
        
        dataframe = self.get().to_pandas()
        assert type(dataframe) is pandas.DataFrame or type(dataframe) is pandas.Series

        return dataframe
1038171
def _col_type_set(self, col, df):
        
        type_set = set()
        if df[col].dtype == np.dtype(object):
            unindexed_col = list(df[col])
            for i in range(0, len(df[col])):
                if unindexed_col[i] == np.nan:
                    continue
                else:
                    type_set.add(type(unindexed_col[i]))
            return type_set
        else:
            type_set.add(df[col].dtype)
            return type_set
1051850
def _get_str_columns(sf):
    
    return [name for name in sf.column_names() if sf[name].dtype == str]
19697
def series_index(self, series):
        
        for idx, s in enumerate(self):
            if series is s:
                return idx
        raise ValueError('series not in chart data object')
1265661
def _df_dtypes(self, table_name, convert_dates):
        
        out = self._raw_table_dtypes[table_name]
        if convert_dates:
            out = out.copy()
            for date_column in self._datetime_int_cols[table_name]:
                out[date_column] = datetime64ns_dtype

        return out
773310
def init_dict(data, index, columns, dtype=None):
    
    if columns is not None:
        from pandas.core.series import Series
        arrays = Series(data, index=columns, dtype=object)
        data_names = arrays.index

        missing = arrays.isnull()
        if index is None:
            # GH10856
            # raise ValueError if only scalars in dict
            index = extract_index(arrays[~missing])
        else:
            index = ensure_index(index)

        # no obvious "empty" int column
        if missing.any() and not is_integer_dtype(dtype):
            if dtype is None or np.issubdtype(dtype, np.flexible):
                # GH#1783
                nan_dtype = object
            else:
                nan_dtype = dtype
            val = construct_1d_arraylike_from_scalar(np.nan, len(index),
                                                     nan_dtype)
            arrays.loc[missing] = [val] * missing.sum()

    else:
        keys = com.dict_keys_to_ordered_list(data)
        columns = data_names = Index(keys)
        # GH#24096 need copy to be deep for datetime64tz case
        # TODO: See if we can avoid these copies
        arrays = [data[k] if not is_datetime64tz_dtype(data[k]) else
                  data[k].copy(deep=True) for k in keys]
    return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)