packages/vaex-core/vaex/dataframe.py
def describe(self, strings=True, virtual=True, selection=None):
        """Give a description of the DataFrame.

        >>> import vaex
        >>> df = vaex.example()[['x', 'y', 'z']]
        >>> df.describe()
                         x          y          z
        dtype      float64    float64    float64
        count       330000     330000     330000
        missing          0          0          0
        mean    -0.0671315 -0.0535899  0.0169582
        std        7.31746    7.78605    5.05521
        min       -128.294   -71.5524   -44.3342
        max        271.366    146.466    50.7185
        >>> df.describe(selection=df.x > 0)
                           x         y          z
        dtype        float64   float64    float64
        count         164060    164060     164060
        missing       165940    165940     165940
        mean         5.13572 -0.486786 -0.0868073
        std          5.18701   7.61621    5.02831
        min      1.51635e-05  -71.5524   -44.3342
        max          271.366   78.0724    40.2191

        :param bool strings: Describe string columns or not
        :param bool virtual: Describe virtual columns or not
        :param selection: Optional selection to use.
        :return: Pandas dataframe

        """
        import pandas as pd
        N = len(self)
        columns = {}
        for feature in self.get_column_names(strings=strings, virtual=virtual)[:]:
            dtype = str(self.dtype(feature)) if self.dtype(feature) != str else 'str'
            if self.dtype(feature) == str_type or self.dtype(feature).kind in ['S', 'U', 'O']:
                count = self.count(feature, selection=selection, delay=True)
                self.execute()
                count = count.get()
                columns[feature] = ((dtype, count, N-count, '--', '--', '--', '--'))
            else:
                count = self.count(feature, selection=selection, delay=True)
                mean = self.mean(feature, selection=selection, delay=True)
                std = self.std(feature, selection=selection, delay=True)
                minmax = self.minmax(feature, selection=selection, delay=True)
                self.execute()
                count, mean, std, minmax = count.get(), mean.get(), std.get(), minmax.get()
                count = int(count)
                columns[feature] = ((dtype, count, N-count, mean, std, minmax[0], minmax[1]))
        return pd.DataFrame(data=columns, index=['dtype', 'count', 'missing', 'mean', 'std', 'min', 'max'])
def describe(self, strings=True, virtual=True, selection=None):
        import pandas as pd
        N = len(self)
        columns = {}
        for feature in self.get_column_names(strings=strings, virtual=virtual)[:]:
            dtype = str(self.dtype(feature)) if self.dtype(feature) != str else 'str'
            if self.dtype(feature) == str_type or self.dtype(feature).kind in ['S', 'U', 'O']:
                count = self.count(feature, selection=selection, delay=True)
                self.execute()
                count = count.get()
                columns[feature] = ((dtype, count, N-count, '--', '--', '--', '--'))
            else:
                count = self.count(feature, selection=selection, delay=True)
                mean = self.mean(feature, selection=selection, delay=True)
                std = self.std(feature, selection=selection, delay=True)
                minmax = self.minmax(feature, selection=selection, delay=True)
                self.execute()
                count, mean, std, minmax = count.get(), mean.get(), std.get(), minmax.get()
                count = int(count)
                columns[feature] = ((dtype, count, N-count, mean, std, minmax[0], minmax[1]))
        return pd.DataFrame(data=columns, index=['dtype', 'count', 'missing', 'mean', 'std', 'min', 'max'])
def describe ( self , strings = True , virtual = True , selection = None ) : import pandas as pd N = len ( self ) columns = { } for feature in self . get_column_names ( strings = strings , virtual = virtual ) [ : ] : dtype = str ( self . dtype ( feature ) ) if self . dtype ( feature ) != str else 'str' if self . dtype ( feature ) == str_type or self . dtype ( feature ) . kind in [ 'S' , 'U' , 'O' ] : count = self . count ( feature , selection = selection , delay = True ) self . execute ( ) count = count . get ( ) columns [ feature ] = ( ( dtype , count , N - count , '--' , '--' , '--' , '--' ) ) else : count = self . count ( feature , selection = selection , delay = True ) mean = self . mean ( feature , selection = selection , delay = True ) std = self . std ( feature , selection = selection , delay = True ) minmax = self . minmax ( feature , selection = selection , delay = True ) self . execute ( ) count , mean , std , minmax = count . get ( ) , mean . get ( ) , std . get ( ) , minmax . get ( ) count = int ( count ) columns [ feature ] = ( ( dtype , count , N - count , mean , std , minmax [ 0 ] , minmax [ 1 ] ) ) return pd . DataFrame ( data = columns , index = [ 'dtype' , 'count' , 'missing' , 'mean' , 'std' , 'min' , 'max' ] )
Give a description of the DataFrame.

        >>> import vaex
        >>> df = vaex.example()[['x', 'y', 'z']]
        >>> df.describe()
                         x          y          z
        dtype      float64    float64    float64
        count       330000     330000     330000
        missing          0          0          0
        mean    -0.0671315 -0.0535899  0.0169582
        std        7.31746    7.78605    5.05521
        min       -128.294   -71.5524   -44.3342
        max        271.366    146.466    50.7185
        >>> df.describe(selection=df.x > 0)
                           x         y          z
        dtype        float64   float64    float64
        count         164060    164060     164060
        missing       165940    165940     165940
        mean         5.13572 -0.486786 -0.0868073
        std          5.18701   7.61621    5.02831
        min      1.51635e-05  -71.5524   -44.3342
        max          271.366   78.0724    40.2191

        :param bool strings: Describe string columns or not
        :param bool virtual: Describe virtual columns or not
        :param selection: Optional selection to use.
        :return: Pandas dataframe
Give a description of the DataFrame .